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4,900 | Mechanism of piR-1245/PIWI-like protein-2 regulating Janus kinase-2/signal transducer and activator of transcription-3/vascular endothelial growth factor signaling pathway in retinal neovascularization | Inhibiting retinal neovascularization is the optimal strategy for the treatment of retina-related diseases, but there is currently no effective treatment for retinal neovascularization. P-element-induced wimpy testis (PIWI)-interacting RNA (piRNA) is a type of small non-coding RNA implicated in a variety of diseases. In this study, we found that the expression of piR-1245 and the interacting protein PIWIL2 were remarkably increased in human retinal endothelial cells cultured in a hypoxic environment, and cell apoptosis, migration, tube formation and proliferation were remarkably enhanced in these cells. Knocking down piR-1245 inhibited the above phenomena. After intervention by a p-JAK2 activator, piR-1245 decreased the expression of hypoxia inducible factor-1α and vascular endothelial growth factor through the JAK2/STAT3 pathway. For in vivo analysis, 7-day-old newborn mice were raised in 75 ± 2% hyperoxia for 5 days and then piR-1245 in the retina was knocked down. In these mice, the number of newly formed vessels in the retina was decreased, the expressions of inflammation-related proteins were reduced, the number of apoptotic cells in the retina was decreased, the JAK2/STAT3 pathway was inhibited, and the expressions of hypoxia inducible factor-1α and vascular endothelial growth factor were decreased. Injection of the JAK2 inhibitor JAK2/TYK2-IN-1 into the vitreous cavity inhibited retinal neovascularization in mice and reduced expression of hypoxia inducible factor-1α and vascular endothelial growth factor. These findings suggest that piR-1245 activates the JAK2/STAT3 pathway, regulates the expression of hypoxia inducible factor-1α and vascular endothelial growth factor, and promotes retinal neovascularization. Therefore, piR-1245 may be a new therapeutic target for retinal neovascularization. |
4,901 | Geometrically programmed self-limited assembly of tubules using DNA origami colloids | Self-assembly is one of the most promising strategies for making functional materials at the nanoscale, yet new design principles for making self-limiting architectures, rather than spatially unlimited periodic lattice structures, are needed. To address this challenge, we explore the tradeoffs between addressable assembly and self-closing assembly of a specific class of self-limiting structures: cylindrical tubules. We make triangular subunits using DNA origami that have specific, valence-limited interactions and designed binding angles, and we study their assembly into tubules that have a self-limited width that is much larger than the size of an individual subunit. In the simplest case, the tubules are assembled from a single component by geometrically programming the dihedral angles between neighboring subunits. We show that the tubules can reach many micrometers in length and that their average width can be prescribed through the dihedral angles. We find that there is a distribution in the width and the chirality of the tubules, which we rationalize by developing a model that considers the finite bending rigidity of the assembled structure as well as the mechanism of self-closure. Finally, we demonstrate that the distributions of tubules can be further sculpted by increasing the number of subunit species, thereby increasing the assembly complexity, and demonstrate that using two subunit species successfully reduces the number of available end states by half. These results help to shed light on the roles of assembly complexity and geometry in self-limited assembly and could be extended to other self-limiting architectures, such as shells, toroids, or triply periodic frameworks. |
4,902 | The Effectiveness of an Oral Fixed-Dose Combination of Netupitant and Palonosetron (NEPA) in Patients With Multiple Risk Factors for Chemotherapy-Induced Nausea and Vomiting: A Multicenter, Observational Indian Study | Background Female gender, young age, first chemotherapy cycle, and low alcohol intake have all been linked to an increased risk of nausea and vomiting from chemotherapy. We intended to see if netupitant and palonosetron (NEPA) could prevent chemotherapy-induced nausea and vomiting (CINV) in patients with risk factors such as age, gender, chemotherapy cycle number, and alcohol consumption history. Methods In this retrospective study, chemotherapy-naïve patients who were prescribed netupitant (300 mg) and palonosetron (0.50 mg) (NEPA) before the first cycle of chemotherapy were analyzed for overall, acute, and delayed phases of complete response (CR), complete protection (CP), and control. Results In the acute phase (AP), delayed phase (DP), and overall phase (OP), complete response was 88.23%, 86.27%, and 86.27%, respectively; complete protection was 80.39%, 78.43%, and 76.47%, respectively; and complete control was 76.47%, 72.54%, and 70.58%, respectively, in the whole population (i.e., 51 patients). Complete response, protection, and control in the overall phase were achieved by 86.27%, 72.72%, and 68.18% of patients who received the highly emetogenic chemotherapy (HEC) regimen (i.e., 44 patients), respectively. Conclusion NEPA provided a consistent magnitude of benefit for patients who are at high risk, receiving HEC and moderately emetogenic chemotherapy (MEC), and having good control in the acute, delayed, and overall phases of CINV. |
4,903 | A Generic Interface Enabling Combinations of State-of-the-Art Path Planning and Tracking Algorithms | In the development of Level 4 automated driving functions, very specific, but diverse, requirements with respect to the operational design domain have to be considered. In order to accelerate this development, it is advantageous to combine dedicated state-of-the-art software components, as building blocks in modular automated driving function architectures, instead of developing special solutions from scratch. However, e.g., in local motion planning and control, the combination of components is still limited in practice, due to necessary interface alignments, which might yield sub-optimal solutions and additional development overhead. The application of generic interfaces, which manage the data transfer between the software components, has the potential to avoid these drawbacks and hence, to further boost this development approach. This publication contributes such a generic interface concept between the local path planning and path tracking systems. The crucial point is a generalization of the lateral tracking error computation, based on an introduced error classification. It substantiates the integration of an internal reference path representation into the interface, to resolve the component interdependencies. The resulting, proposed interface enables arbitrary combinations of components from a comprehensive set of state-of-the-art path planning and tracking algorithms. Two interface implementations are finally applied in an exemplary automated driving function assembly task. |
4,904 | Positive and Negative Label Propagations | This paper extends the state-of-the-art label propagation (LP) framework in the propagation of negative labels. More specifically, the state-of-the-art LP methods propagate information of the form "thesample i should be assigned the label k." The proposed method extends the state-of-the-art framework by considering additional information of the form "the sample i should not be assigned the label k." A theoretical analysis is presented in order to include negative LP in the problem formulation. Moreover, a method for selecting the negative labels in cases when they are not inherent from the data structure is presented. Furthermore, the incorporation of negative label information in two multigraph LP methods is presented. Finally, a discussion on the proposed algorithm extension to out of sample data, as well as scalability issues, is presented. Experimental results in various scenarios showed that the incorporation of negative label information increases, in all cases, the classification accuracy of the state of the art. |
4,905 | QML-Based Joint Diagonalization of Positive-Definite Hermitian Matrices | In this paper, a new algorithm for approximate joint diagonalization (AJD) of positive-definite Hermitian matrices is presented. The AJD matrix, which is assumed to be square non-unitary, is derived via minimization of a quasi-maximum likelihood (QML) objective function. This objective function coincides asymptotically with the maximum likelihood (ML) objective function, hence enabling the proposed algorithm to asymptotically approach the ML estimation performance. In the proposed method, the rows of the AJD matrix are obtained independently, in an iterative manner. This feature enables direct estimation of full row-rank rectangular AJD sub-matrices. Under some mild assumptions, convergence of the proposed algorithm is asymptotically guarantied, such that the error norm corresponding to each row of the AJD matrix reduces significantly after the first iteration, and the convergence is almost Q-super linear. This property results rapid convergence, which leads to low computational load in the proposed method. The performance of the proposed algorithm is evaluated and compared to other state-of-the-art algorithms for AJD and its practical use is demonstrated in the blind source separation and blind source extraction problems. The results imply that under the assumptions of high signal-to-noise ratio and large amount of matrices, the proposed algorithm is computationally efficient with performance similar to state-of-the-art algorithms for AJD. |
4,906 | Fungal assisted bio-treatment of environmental pollutants with comprehensive emphasis on noxious heavy metals: Recent updates | In the present time of speedy developments and industrialization, heavy metals are being uncovered in aquatic environment and soil via refining, electroplating, processing, mining, metallurgical activities, dyeing and other several metallic and metal based industrial and synthetic activities. Heavy metals like lead (Pb), mercury (Hg), cadmium (Cd), arsenic (As), Zinc (Zn), Cobalt (Co), Iron (Fe), and many other are considered as seriously noxious and toxic for the aquatic environment, human, and other aquatic lives and have damaging influences. Such heavy metals, which are very tough to be degraded, can be managed by reducing their potential through various processes like removal, precipitation, oxidation-reduction, bio-sorption, recovery, bioaccumulation, bio-mineralization etc. Microbes are known as talented bio-agents for the heavy metals detoxification process and fungi are one of the cherished bio-sources that show noteworthy aptitude of heavy metal sorption and metal tolerance. Thus, the main objective of the authors was to come with a comprehensive review having methodological insights on the novel and recent results in the field of mycoremediation of heavy metals. This review significantly assesses the potential talent of fungi in heavy metal detoxification and thus, in environmental restoration. Many reported works, methodologies and mechanistic sights have been evaluated to explore the fungal-assisted heavy metal remediation. Herein, a compact and effectual discussion on the recent mycoremediation studies of organic pollutants like dyes, petroleum, pesticides, insecticides, herbicides, and pharmaceutical wastes have also been presented. |
4,907 | Globefish-Inspired Balloon Catheter with Intelligent Microneedle Coating for Endovascular Drug Delivery | Balloon catheters exhibit important values in treating cardiovascular diseases, while their functions are still under improvements. Here, inspired by the thorn-hiding and deflating-inflating characteristics of globefish, intelligent balloon catheters decorated with invisible microneedles are presented for endovascular drug delivery to inhibit postintervention restenosis (PIRS). These microneedle balloon catheters (MNBCs) fabricated by dipping and rolling-assisted template replication contain three coating layers of sandwiched drug-carrying microneedles and black phosphorus (BP)-carrying gelatin. During the emplacement, the microneedles of MNBCs are hidden under the outermost gelatin protective layer, allowing smooth movements inside the blood vessel. After reaching the destination, the embedded BP converts near infrared (NIR) into heat, increases local temperature, and melts the gelatin layer, enabling the exposure and vascular penetration of the microneedles. Besides, as the innermost gelatin also melts, the microneedles can detach from the balloon catheter and be left inside the blood vessel for continuous drug release. Based on advantages of responsiveness, penetration capacity, and biosafety, it is demonstrated that the MNBCs behave satisfactorily in delivering rapamycin to inhibit abdominal aorta restenosis in rats. All these features indicate that these MNBCs are promising medical devices for clinical applications. |
4,908 | Hydrogen storage in clathrate hydrates: Current state of the art and future directions | Hydrogen is looked upon as the next generation clean energy carrier, search for an efficient material and method for storing hydrogen has been pursued relentlessly. Improving hydrogen storage capacity to meet DOE targets has been challenging and research efforts are continuously put forth to achieve the set targets and to make hydrogen storage a commercially realizable process. This review comprehensively summarizes the state of the art experimental work conducted on the storage of hydrogen as hydrogen clathrates both at the molecular level and macroscopic level. It identifies future directions and challenges for this exciting area of research. Hydrogen storage capacities of different clathrate structures - sI, sII, sH, sVI and semi clathrates have been compiled and presented. In addition, promising new approaches for increasing hydrogen storage capacity have been described. Future directions for achieving increased hydrogen storage and process scale up have been outlined. Despite few limitations in storing hydrogen in the form of clathrates, this domain receives prominent attention due to more environmental-friendly method of synthesis, easy recovery of molecular hydrogen with minimum energy requirement, and improved safety of the process. (C) 2014 Elsevier Ltd. All rights reserved. |
4,909 | Design, Synthesis, and Fungicidal Activity of Novel Plant Elicitors Based on a Diversity-Oriented Synthesis Strategy | The novel plant elicitors, 3-benzyl-5-[1-(2-oxo-4-phenyl-1-oxaspiro[4.5]dec-3-en-3-yl)ethylidene]-2-aminoimidazolin-4-one derivatives, were designed based on the diversity-oriented synthesis strategy and synthesized in four steps via the Knoevenagel condensation reaction as the key step. They were characterized by 1H NMR, 13C NMR, HR-ESI-MS, and X-ray diffraction. The position of the C═N bond of Z- and E-configuration compounds was determined by X-ray diffraction. The in vivo fungicidal activity evaluation revealed that most of these compounds exhibited remarkable activities (100%) against Pseudoperonospora cubensis at 400 μg/mL, among which compound 8e still exhibited excellent protective activity with a 50% inhibition rate at 0.1 μg/mL. Because the in vitro effect on tested phytopathogens was poor, the mechanism to induce the immune responses and reinforce the resistance of cucumber against Botrytis cinerea was studied. The results indicated that the compound 8e-mediated defense response against B. cinerea was based on the accumulation of pathogenesis-related proteins and cell wall reinforcement by callose deposition. Quantitative analysis of salicylic acid (SA) and jasmonic acid (JA) and the increased expression of induced resistance-related genes and the defense-associated phenylalanine ammonia lyase revealed that the immune response triggered by compound 8e was highly associated with the SA signaling pathway. Significant upregulation of JA-related genes Cs-AOS2 indicated that the JA signaling pathway was also influenced. It was also shown that the plants treated with compound 8e promoted primary root elongation, which resulted in enhanced plant growth. Most importantly, these compounds have completely new structures compared with the traditional plant elicitors. Further research of 8e-mediated plant disease resistance might have a great influence on the development of plant elicitors. |
4,910 | Boosting 3D Adversarial Attacks With Attacking on Frequency | Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks in the image domain. Recently, 3D adversarial attacks, especially adversarial attacks on point clouds, have elicited mounting interest. However, adversarial point clouds obtained by previous methods show weak transferability and are easy to defend. To address these problems, in this paper we propose a strong point cloud attack method named AOF which pays more attention to the low-frequency component of point clouds. We combine the losses from point cloud and its low-frequency component to craft adversarial samples and focus on the low-frequency component of point cloud in the process of optimization. Extensive experiments validate that AOF can improve the transferability significantly compared to state-of-the-art (SOTA) attacks, and is more robust to state-of-the-art 3D defense methods. Otherwise, compared to adversarial point clouds generated by other adversarial attack methods, adversarial point clouds obtained by AOF contain more deformation than outlier. Code is available at: https://github.com/code-roamer/AOF. |
4,911 | Direct Intermode Selection for H.264 Video Coding Using Phase Correlation | The H.264 video coding standard exhibits higher performance compared to the other existing standards such as H. 263, MPEG-X. This improved performance is achieved mainly due to the multiple-mode motion estimation and compensation. Recent research tried to reduce the computational time using the predictive motion estimation, early zero motion vector detection, fast motion estimation, and fast mode decision, etc. These approaches reduce the computational time substantially, at the expense of degrading image quality and/or increase bitrates to a certain extent. In this paper, we use phase correlation to capture the motion information between the current and reference blocks and then devise an algorithm for direct motion estimation mode prediction, without excessive motion estimation. A bigger amount of computational time is reduced by the direct mode decision and exploitation of available motion vector information from phase correlation. The experimental results show that the proposed scheme outperforms the existing relevant fast algorithms, in terms of both operating efficiency and video coding quality. To be more specific, 82 similar to 92% of encoding time is saved compared to the exhaustive mode selection (against 58 similar to 74% in the relevant state-of-the-art), and this is achieved without jeopardizing image quality (in fact, there is some improvement over the exhaustive mode selection at mid to high bit rates) and for a wide range of videos and bitrates (another advantages over the relevant state-of-the-art). |
4,912 | A General Differentiable Mesh Renderer for Image-Based 3D Reasoning | Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental step called rasterization, which prevents rendering to be differentiable. Unlike the state-of-the-art differentiable renderers (Kato et al. 2018 and Loper 2018), which only approximate the rendering gradient in the backpropagation, we propose a natually differentiable rendering framework that is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervisions to mesh vertices and their attributes from various forms of image representations. The key to our framework is a novel formulation that views rendering as an aggregation function that fuses the probabilistic contributions of all mesh triangles with respect to the rendered pixels. Such formulation enables our framework to flow gradients to the occluded and distant vertices, which cannot be achieved by the previous state-of-the-arts. We show that by using the proposed renderer, one can achieve significant improvement in 3D unsupervised single-view reconstruction both qualitatively and quantitatively. Experiments also demonstrate that our approach can handle the challenging tasks in image-based shape fitting, which remain nontrivial to existing differentiable renders. |
4,913 | Derivative-Based Steganographic Distortion and its Non-additive Extensions for Audio | Steganography is the art of covert communication, which aims to hide the secret messages into cover medium while achieving high undetectability. To this end, the framework of minimal distortion embedding is widely adopted for adaptive steganography, where a well-designed distortion function is significant. In this paper, inspired by the phenomenon that the modification of audio samples with the low amplitude will be easily detected, a novel distortion is presented for audio steganography. Taking the fragility of the low amplitude audio samples into account, the proposed distortion is inversely proportional to the amplitude. Furthermore, in order to resist the strong steganalysis, the derivative filter is utilized for acquiring the residual of audio, which plays an important role in distortion definition. The experimental results show that the proposed distortion outperforms the state-of-the-art methods defending strong steganalytic methods. To take a step forward, considering the mutual impact caused by embedding modification, the non-additive extensions of the proposed methods are put forward. The extending experiments show that in most cases, the proposed non-additive extensions can achieve higher level of security than the original methods. |
4,914 | Joint Superresolution and Rectification for Solar Cell Inspection | Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multiframe superresolution (MFSR) to a sequence of low-resolution measurements. In addition, the rectification and removal of lens distortion simplifies subsequent analysis. Therefore, we propose to fuse this preprocessing with standard MFSR algorithms. This is advantageous, because we omit a separate processing step, the motion estimation becomes more stable and the spacing of high-resolution pixels on the rectified module image becomes uniform w.r.t. the module plane, regardless of perspective distortion. We present a comprehensive user study showing that MFSR is beneficial for defect recognition by human experts and that the proposed method performs better than the state-of-the-art. Furthermore, we apply automated crack segmentation and show that the proposed method performs 3x better than bicubic upsampling and 2x better than the state-of-the-art for automated inspection. |
4,915 | Anatomical Attention Guided Deep Networks for ROI Segmentation of Brain MR Images | Brain region-of-interest (ROI) segmentation based on structural magnetic resonance imaging (MRI) scans is an essential step for many computer-aid medical image analysis applications. Due to low intensity contrast around ROI boundary and large inter-subject variance, it has been remaining a challenging task to effectively segment brain ROIs from structural MR images. Even though several deep learning methods for brain MR image segmentation have been developed, most of them do not incorporate shape priors to take advantage of the regularity of brain structures, thus leading to sub-optimal performance. To address this issue, we propose an anatomical attention guided deep learning framework for brain ROI segmentation of structural MR images, containing two subnetworks. The first one is a segmentation subnetwork, used to simultaneously extract discriminative image representation and segment ROIs for each input MR image. The second one is an anatomical attention subnetwork, designed to capture the anatomical structure information of the brain from a set of labeled atlases. To utilize the anatomical attention knowledge learned from atlases, we develop an anatomical gate architecture to fuse feature maps derived from a set of atlas label maps and those from the to-be-segmented image for brain ROI segmentation. In this way, the anatomical prior learned from atlases can be explicitly employed to guide the segmentation process for performance improvement. Within this framework, we develop two anatomical attention guided segmentation models, denoted as anatomical gated fully convolutional network (AG-FCN) and anatomical gated U-Net (AG-UNet), respectively. Experimental results on both ADNI and LONI-LPBA40 datasets suggest that the proposed AG-FCN and AG-UNet methods achieve superior performance in ROI segmentation of brain MR images, compared with several state-of-the-art methods. |
4,916 | MATEC: A lightweight neural network for online encrypted traffic classification | Increased awareness of privacy protection has led to a surge in the volume of encrypted traffic, which creates a heavy burden for efficient network management (e.g. quality-of-service guarantees). The opacity of encrypted traffic essentially requires high computational overheads to make traffic classification, which is even worse when encrypted traffic surges. However, existing deep learning approaches sacrifice the efficiency to obtain high-precision classification results, which are no longer suitable for scenarios with large volumes of encrypted traffic. In this paper, a lightweight and online approach implemented as MATEC is proposed. The way we optimize the classification process follows the "Maximizing the reuse of thin modules"design principle. The multi-head attention and the convolutional network are adopted in the thin module. Attributed to the one-step interaction of all packets and the parallel computing of the multi-head attention mechanism, a key advantage of our model is that the number of parameters and running time are significantly reduced. In addition, the effectiveness and efficiency of convolutional networks have been proved in traffic classification. Comparisons to the existing state-of-the-art models on three typical datasets demonstrate that the proposed MATEC model has higher accuracy and running efficiency. In addition, the number of parameters is reduced to 1.8% of the state-of-the-art models and the training time halves. |
4,917 | Desertification assessment in China: An overview | Desertification, land degradation in arid, semi-arid, and dry sub-humid regions, is a global environmental problem. Accurate assessment of the status, change, and trend of desertification will be instrumental in developing global actions to prevent and eradicate the problem. As one of the most seriously affected countries, China has made great efforts to combat desertification. Although improvements have been made in some areas, degradation continues to expand and intensify throughout the entire country. Further land degradation assessments, such as assessments made by the Chinese Committee for Implementing UN Convention to Combat Desertification (CCICCD), will be necessary to ensure successful decision-making, to combat increasing desertification, and to implement Western strategies. This paper overviews the state-of-the-art desertification assessments on both the national and local levels. Also, two major problems facing the assessment of degradation-the uncertainty of baseline assessments and indictor systems and the misuse of remotely sensed data sources-are presented along with suggestions for possible solutions to these problems. (c) 2005 Published by Elsevier Ltd. |
4,918 | Functional characterization of secologanin synthase-like homologous genes suggests their involvement in the biosynthesis of diverse metabolites in the secoiridoid biosynthetic pathway of Camptotheca acuminata Decne | The assignment of functions based on homology has recently been challenged by the frequent discovery of functional divergence among homologous gene family members of enzymes involved in plant secondary metabolism. Secologanin synthase (SLS) is the key CYP450 enzyme that acts critically in the biosynthesis of Strychnos alkaloid scaffold. In this study, to fully elucidate the mechanism that underlies metabolic variation, the CYP450 paralogs that participate in oxidative transformation of the secoiridoid pathway were functionally characterized by combining multitiered strategies of metabolite profiling, phylogenetic analyses, biochemistry assays and reverse genetics techniques. Five CaSLSs-like homologous genes were mined and isolated from an integrative multi-omics database of Camptotheca acuminata. Protein sequences, structural comparisons, and phylogenetic analyses confirmed that CaSLS1-2 and CaSLS4-5 were grouped into the SLS clade, and only CaSLS3 clustered into the 7DLH clade. Five homologs, including two previously identified enzymatic genes, were thus designated as CaSLAS1, CaSLAS2, Ca7DLH, CaSLS4 and CaSLS5. Enzymatic assays of the recombinant proteins in yeast showed that CaSLAS1 and CaSLAS2 displayed multi-catalytic activities of SLS, secologanic acid synthase (SLAS) and secoxyloganin synthase (SXS). Additionally, the reactions of CaSLASs enzymes generated stereospecific isomers of secoiridoid products, and a new product of secoxyloganin was observed. CaSLS5, a third SLS enzyme isoform that catalyzes the formation of secologanin, was reported for the first time. However, CaSXS enzymatic activities in vitro had little physiological impact on the biosynthesis of camptothecin (CPT) in Camptotheca acuminata. The primary and secondary roles of CaSLSs-like genes in secoiridoid metabolism were confirmed by virus-induced gene silencing (VIGS) in plant leaves. Efficient silencing and transcriptional downregulation of CaSLAS2, compared with the CaSLAS1 homologs, resulted in a greater reduction of the accumulation of CPT within silenced plants, and CaSLS5 had barely any effect on the contents of metabolites in planta. Thus, CaSLAS2, rather than CaSLAS1, appeared to function as a major participant in the biosynthesis of CPT, and there were redundant functions in the CaSLSs-like enzymes. Consistent with such roles, CaSLAS2 was ubiquitously expressed at very high levels in Camptotheca tissues, and CaSLAS2 was specifically expressed in young leaves. In contrast, CaSLS5 was poorly expressed in every tissue tested. Our findings demonstrate that homologs that belong to the CYP72 gene family are functionally diverse and exhibit divergence and thereby uncover an expanding group of enzymatic genes that determine the chemo-diversity of the iridoid pathway. |
4,919 | Faber: A Hardware/SoftWare Toolchain for Image Registration | Image registration is a well-defined computation paradigm widely applied to align one or more images to a target image. This paradigm, which builds upon three main components, is particularly compute-intensive and represents many image processing pipelines' bottlenecks. State-of-the-art solutions leverage hardware acceleration to speed up image registration, but they are usually limited to implementing a single component. We present Faber, an open-source HW/SW CAD toolchain tailored to image registration. The Faber toolchain comprises HW/SW highly-tunable registration components, supports users with different expertise in building custom pipelines, and automates the design process. In this direction, Faber provides both default settings for entry-level users and latency and resource models to guide HW experts in customizing the different components. Finally, Faber achieves from 1.5x to 54x in speedup and from 2x to 177x in energy efficiency against state-of-the-art tools on a Xeon Gold. |
4,920 | Chx10+V2a interneurons in spinal motor regulation and spinal cord injury | Chx10-expressing V2a (Chx10+V2a) spinal interneurons play a large role in the excitatory drive of motoneurons. Chemogenetic ablation studies have demonstrated the essential nature of Chx10+V2a interneurons in the regulation of locomotor initiation, maintenance, alternation, speed, and rhythmicity. The role of Chx10+V2a interneurons in locomotion and autonomic nervous system regulation is thought to be robust, but their precise role in spinal motor regulation and spinal cord injury have not been fully explored. The present paper reviews the origin, characteristics, and functional roles of Chx10+V2a interneurons with an emphasis on their involvement in the pathogenesis of spinal cord injury. The diverse functional properties of these cells have only been substantiated by and are due in large part to their integration in a variety of diverse spinal circuits. Chx10+V2a interneurons play an integral role in conferring locomotion, which integrates various corticospinal, mechanosensory, and interneuron pathways. Moreover, accumulating evidence suggests that Chx10+V2a interneurons also play an important role in rhythmic patterning maintenance, left-right alternation of central pattern generation, and locomotor pattern generation in higher order mammals, likely conferring complex locomotion. Consequently, the latest research has focused on postinjury transplantation and noninvasive stimulation of Chx10+V2a interneurons as a therapeutic strategy, particularly in spinal cord injury. Finally, we review the latest preclinical study advances in laboratory derivation and stimulation/transplantation of these cells as a strategy for the treatment of spinal cord injury. The evidence supports that the Chx10+V2a interneurons act as a new therapeutic target for spinal cord injury. Future optimization strategies should focus on the viability, maturity, and functional integration of Chx10+V2a interneurons transplanted in spinal cord injury foci. |
4,921 | Improved sufficient condition of l(1-2)-minimisation for robust signal recovery | By means of the restricted isometry property of order k and the restricted orthogonality property of order (k,k), this Letter mainly establishes an improved sufficient condition for a recently emerged $\ell _{1-2}$l1-2-minimisation to guarantee the robust signal recovery. The obtained condition is proved to be much better than the state-of-the-art ones for almost all parameters k. |
4,922 | Noise Limits of CMOS Current Interfaces for Biosensors: A Review | Current sensing readout is one of the most frequent techniques used in biosensing due to the charge-transfer phenomena occurring at solid-liquid interfaces. The development of novel nanodevices for biosensing determines new challenges for electronic interface design based on current sensing, especially when compact and efficient arrays need to be organized, such as in recent trends of rapid label-free electronic detection of DNA synthesis. This paper will review the basic noise limitations of current sensing interfaces with particular emphasis on integrated CMOS technology. Starting from the basic theory, the paper presents, investigates and compares charge-sensitive amplifier architectures used in both continuous-time and discrete-time approaches, along with their design trade-offs involving noise floor, sensitivity to stray capacitance and bandwidth. The ultimate goal of this review is providing analog designers with helpful design rules and analytical tools. Also, in order to present a comprehensive overview of the state-of-the-art, the most relevant papers recently appeared in the literature about this topic are discussed and compared. |
4,923 | A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity | Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analysis into a single spatial scale. In addition, most methods focus on the pairwise relationships between the ROIs and ignore high-order associations between subjects. In this letter, we propose a mutual multi-scale triplet graph convolutional network (MMTGCN) to analyze functional and structural connectivity for brain disorder diagnosis. We first employ several templates with different scales of ROI parcellation to construct coarse-to-fine brain connectivity networks for each subject. Then, a triplet GCN (TGCN) module is developed to learn functional/structural representations of brain connectivity networks at each scale, with the triplet relationship among subjects explicitly incorporated into the learning process. Finally, we propose a template mutual learning strategy to train different scale TGCNs collaboratively for disease classification. Experimental results on 1,160 subjects from three datasets with fMRI or dMRI data demonstrate that our MMTGCN outperforms several state-of-the-art methods in identifying three types of brain disorders. |
4,924 | A light-weight stereo matching network based on multi-scale features fusion and robust disparity refinement | In recent years, convolutional-neural-network based stereo matching methods have achieved significant gains compared to conventional methods in terms of both speed and accuracy. Current state-of-the-art disparity estimation algorithms require many parameters and large amounts of computational resources and are not suited for applications on edge devices. In this paper, an end-to-end light-weight network (LWNet) for fast stereo matching is proposed, which consists of an efficient backbone with multi-scale feature fusion for feature extraction, a 3D U-Net aggregation architecture for disparity computation, and color guidance in a 2D convolutional neural network (CNN) for disparity refinement. MobileNetV2 is adopted as an efficient backbone in feature extraction. The channel attention module is applied to improve the representational capacity of features and multi-resolution information is adaptively incorporated into the cost volume via cross-scale connections. Further, a left-right consistency check and color guidance refinement are introduced and a robust disparity refinement network is designed with skip connections and dilated convolutions to capture global context information and improve disparity estimation accuracy with little computational cost and memory space. Extensive experiments on Scene Flow, KITTI 2015, and KITTI 2012 demonstrate that the proposed LWNet achieves competitive accuracy and speed when compared with state-of-the-art stereo matching methods. |
4,925 | Few-Shot Image and Sentence Matching via Aligned Cross-Modal Memory | Image and sentence matching has attracted much attention recently, and many effective methods have been proposed to deal with it. But even the current state-of-the-arts still cannot well associate those challenging pairs of images and sentences containing few-shot content in their regions and words. In fact, such a few-shot matching problem is seldom studied and has become a bottleneck for further performance improvement in real-world applications. In this work, we formulate this challenging problem as few-shot image and sentence matching, and accordingly propose an Aligned Cross-Modal Memory (ACMM) model to deal with it. The model can not only softly align few-shot regions and words in a weakly-supervised manner, but also persistently store and update cross-modal prototypical representations of few-shot classes as references, without using any groundtruth region-word correspondence. The model can also adaptively balance the relative importance between few-shot and common content in the image and sentence, which leads to better measurement of overall similarity. We perform extensive experiments in terms of both few-shot and conventional image and sentence matching, and demonstrate the effectiveness of the proposed model by achieving the state-of-the-art results on two public benchmark datasets. |
4,926 | Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimization for Multi-Modal Cardiac Image Segmentation | Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despite capturing similar anatomical structures. It is mainly because the data distribution between the two domains is different. Moreover, creating annotation for every new modality is a tedious and time-consuming task, which also suffers from high inter- and intra- observer variability. Unsupervised domain adaptation (UDA) methods intend to reduce the gap between source and target domains by leveraging source domain labelled data to generate labels for the target domain. However, current state-of-the-art (SOTA) UDA methods demonstrate degraded performance when there is insufficient data in source and target domains. In this paper, we present a novel UDA method for multi-modal cardiac image segmentation. The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces. The paper introduces an end-to-end framework that integrates: a) entropy minimization, b) output feature space alignment and c) a novel point-cloud shape adaptation based on the latent features learned by the segmentation model. We validated our method on two cardiac datasets by adapting from the annotated source domain, bSSFP-MRI (balanced Steady-State Free Procession-MRI), to the unannotated target domain, LGE-MRI (Late-gadolinium enhance-MRI), for the multi-sequence dataset; and from MRI (source) to CT (target) for the cross-modality dataset. The results highlighted that by enforcing adversarial learning in different parts of the network, the proposed method delivered promising performance, compared to other SOTA methods. |
4,927 | Development and application of a multiplex qPCR assay for the detection of duck circovirus, duck Tembusu virus, Muscovy duck reovirus, and new duck reovirus | A multiplex qPCR assay was developed to simultaneously detect duck circovirus (DuCV), duck Tembusu virus (DTMUV), Muscovy duck reovirus (MDRV), and novel duck reovirus (NDRV), but it did not amplify other viruses, including duck virus enteritis (DVE), infectious bursal disease virus (IBDV), avian reovirus (ARV), H5 avian influenza virus (H5 AIV), H7 avian influenza virus (H7 AIV), H9 avian influenza virus (H9 AIV), Newcastle disease virus (NDV), and Muscovy duck parvovirus (MDPV), and the detection limit for DuCV, DTMUV, MDRV, and NDRV was 1.51 × 101 copies/μL. The intra- and interassay coefficients of variation were less than 1.54% in the repeatability test with standard plasmid concentrations of 1.51 × 107, 1.51 × 105, and 1.51 × 103 copies/μL. The developed multiple qPCR assay was used to examine 404 clinical samples to verify its practicability. The positivity rates for DuCV, DTMUV, MDRV, and NDRV were 26.0%, 9.9%, 4.0%, and 4.7%, respectively, and the mixed infection rates for DuCV + DTMUV, DuCV + MDRV, DuCV + NDRV, MDRV + NDRV, DTMUV + MDRV, and DTMUV + NDRV were 2.7%, 1.2%, 1.2%, 1.0%, 0.5%, and 0.7%, respectively. |
4,928 | Climate change and higher education: Assessing factors that affect curriculum requirements | Pervasive misinformation about climate change might be reduced if colleges were to include the topic within general education curriculum. This paper analyzes the general education (or "core") curriculum in the top 100 universities and liberal-arts colleges in the U.S. to assess the proportion of core courses that highlight climate change or climate science. The probability that a student takes at least one climate change course via the core curriculum is estimated at 0.17 across all schools. The probability is higher at research universities than at liberal arts colleges, in core programs that have more science and social science courses, and at public universities in states with a Democrat-controlled legislature than in states with a Republican-controlled or split legislature. Drawing on cases of best practices in the U.S. identified from the data set, the authors discuss strategies that could ensure a higher likelihood that the core curriculum includes education on climate science and climate change. The study advances the broader research literature on sustainability in higher education programs by bringing it into conversation with research on the college core curriculum and by focusing both on the specific issue of climate-change education. (C) 2017 Elsevier Ltd. All rights reserved. |
4,929 | HEVC-based lossless intra coding for efficient still image compression | Latest advancements in capture and display technologies demand better compression techniques for the storage and transmission of still images and video. High efficiency video coding (HEVC) is the latest video compression standard developed by the joint collaborative team on video coding (JCTVC) with this objective. Although the main design goal of HEVC is the compression of high resolution video, its performance in still image compression is at par with state-of-the-art still image compression standards. This work explores the possibility of incorporating the efficient intra prediction techniques employed in HEVC into the compression of high resolution still images. In the lossless coding mode of HEVC, sample- based angular intra prediction (SAP) methods have shown better prediction accuracy compared to the conventional block-based prediction (BP). In this paper, we propose an improved sample-based angular intra prediction (ISAP), which enhances the accuracy of the highly crucial intra prediction within HEVC. The experimental results show that ISAP in lossless compression of still images outclasses archival tools, state-of-the-art image compression standards and other HEVC-based lossless image compression codecs. |
4,930 | Analytical strategies in neurotransmitter measurements: A mini literature review | Neurotransmitters (NTs) are endogenous, polar, low-molecular-weight compounds that play multiple pivotal roles in the central nervous system. NTs are involved in communicating information, responding to stress, regulating motor coordination, and allowing interneuronal communication in living organisms. It is essential to determine the distribution of NTs in brain regions to better understand drug dependence and abuse, neurological disorders, psychological disorders, and aging. Monitoring NT levels is also important in diagnosing and avoiding serious illnesses. We here review chromatography-based analytical techniques, including pretreatment methods (e.g., microdialysis and solid-phase microextraction), as well as detection strategies (e.g., MS and electrochemistry), focusing on developments in these techniques over the past 5 years. We then highlight recent advances in electrochemical and fluorescence imaging methods in vivo and the disadvantages and advantages of such technologies, including high spatiotemporal resolution, polymer specificity, and high sensitivity. Finally, we summarize and compare the complementary advantages of chromatography-based analytical techniques and biosensors and discuss trends in the development of NT detection technologies. |
4,931 | Palaeolithic ceramic technology: The artistic origins and impacts of a technological innovation | This paper analyses the assemblages of Upper Palaeolithic ceramic figurines and figurine fragments from Czech Republic ("Pavlovian") and Croatia, which are some of the first iterations of this material and technological innovation in Europe. Using chaine operatoire methodology, this paper compares both the technologies and gestures involved in the manufacture of these artefacts as well as the impact of these new materials on art and society in each context. These analyses reveal how the introduction of this innovative material and the associated technologies used to make ceramic art proved to be an important catalyst for more experimentation and play in the production of art, which led to innovations in artistic expression. Furthermore, this research highlights the need to study Palaeolithic ceramic artefacts using quantitative and nuanced analytical methodologies that move beyond the traditional focus on the most iconographically-striking Palaeolithic art. (C) 2016 Elsevier Ltd and INQUA. All rights reserved. |
4,932 | Folding Features and Dynamics of 3D Genome Architecture in Plant Fungal Pathogens | The folding and dynamics of three-dimensional (3D) genome organization are fundamental for eukaryotes executing genome functions but have been largely unexplored in nonmodel fungi. Using high-throughput sequencing coupled with chromosome conformation capture (Hi-C) data, we generated two chromosome-level assemblies for Puccinia striiformis f. sp. tritici, a fungus causing stripe rust disease on wheat, for studying 3D genome architectures of plant pathogenic fungi. The chromatin organization of the fungus followed a combination of the fractal globule model and the equilibrium globule model. Surprisingly, chromosome compartmentalization was not detected. Dynamics of 3D genome organization during two developmental stages of P. striiformis f. sp. tritici indicated that regulation of gene activities might be independent of the changes of genome organization. In addition, chromatin conformation conservation was found to be independent of genome sequence synteny conservation among different fungi. These results highlighted the distinct folding principles of fungal 3D genomes. Our findings should be an important step toward a holistic understanding of the principles and functions of genome architecture across different eukaryotic kingdoms. IMPORTANCE Previously, our understanding of 3D genome architecture has mainly come from model mammals, insects, and plants. However, the organization and regulatory functions of 3D genomes in fungi are largely unknown. In this study, we comprehensively investigated P. striiformis f. sp. tritici, a plant fungal pathogen, and revealed distinct features of the 3D genome, comparing it with the universal folding feature of 3D genomes in higher eukaryotic organisms. We further suggested that there might be distinct regulatory mechanisms of gene expression that are independent of chromatin organization changes during the developmental stages of this rust fungus. Moreover, we showed that the evolutionary pattern of 3D genomes in this fungus is also different from the cases in mammalian genomes. In addition, the genome assembly pipeline and the generated two chromosome-level genomes will be valuable resources. These results highlighted the unexplored distinct features of 3D genome organization in fungi. Therefore, our study provided complementary knowledge to holistically understand the organization and functions of 3D genomes across different eukaryotes. |
4,933 | 'NEW URBANITY' AND CONTEMPORARY FORMS OF PUBLIC ART NOTES ON CITIZEN FIREFIGHTER (K. HUNTER) | In the course of global economic restructuring, 'new urbanity' has become a key concept within German-speaking urban studies. Despite a certain ambiguity, the concept often conveys a positive image of the traditional European city and its supposed urban qualities. The present paper aims to challenge this image by drawing upon contemporary forms of public art. Taking Kenny Hunters sculpture Citizen Firefighter as a case study, alternative concepts Of urbanity are explored. Using qualitative interviews with the artist, the relationship between artistic and academic imaginations of urban life will be discussed. In this way a more balanced image of the traditional European city can be achieved. |
4,934 | Erythrocyte fatty acid profiles and plasma homocysteine, folate and vitamin B6 and B12 in recurrent depression: Implications for co-morbidity with cardiovascular disease | Oxidative stress induced interactions between fatty acid (FA) and one-carbon metabolism may be involved in co-occurrence of major depressive disorder (MDD) and cardiovascular disease (CVD), which have been scarcely studied together. In 137 recurrent MDD-patients vs. 73 age- and sex-matched healthy controls, we simultaneously measured key components of one-carbon metabolism in plasma (homocysteine, folate, vitamins B6 and B12), and of FA-metabolism in red blood cell membranes [main polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and arachidonic acid (AA) and structural FA-indices (chain length, unsaturation, peroxidation)]. Results show significant positive associations of folate with EPA, DHA, and the peroxidation index, which were similar in patients and controls. After correction for confounders, these associations were lost except for EPA. Associations between B-vitamins and FA-parameters were non-significant, but also similar in patients and controls. Homocysteine and DHA were significantly less negatively associated in patients than in controls. In conclusion, these data indicate similarities but also differences in associations between parameters of one-carbon and FA-metabolism in recurrent MDD patients vs. controls, which may reflect differences in handling of oxidative stress. Further research should test the consequences of these differences, particularly the premature development of CVD in MDD. |
4,935 | Helicobacter pylori-Negative Gastric Mucosa-Associated Lymphoid Tissue Lymphoma Presenting as Massive Gastrointestinal Bleed | This case reports a patient that represents the minority of patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma who do not have underlying Helicobacter pylori gastritis. Gastric MALT lymphoma is a type of primary gastric lymphoma (PGL), which are extremely rare gastric malignancies characterized by proliferation of B-cells and infiltration of lymphoid tissue leading to destruction of gastric glands. Development of gastric MALT lymphoma is associated with H. pylori gastritis. Patients typically present with a wide range of symptoms including but not limited to epigastric pain, weight loss, gastrointestinal bleeding and gastric wall perforation. Gastric MALT lymphoma presenting as a massive gastrointestinal bleed is quite rare and only a few cases have been documented. Our case demonstrates that it is important to recognize that acute presentations of this disease may also occur. |
4,936 | Viral Suppression and Resistance in a Cohort of Perinatally-HIV Infected (PHIV plus ) Pregnant Women | Our objective was to describe viral suppression and antiretroviral (ARV) resistance mutations in an ongoing cohort of perinatally-infected HIV+ (PHIV+) pregnant women. Descriptive analysis was performed using SPSS 18.0. From 2011 to 2014, we followed 22 PHIV+ pregnant women. Median age at prenatal entry was 19 years (Interquartile range (IQR) 17.6-21.0); 86% had an AIDS diagnosis; 81% had disclosed their HIV status to partner 11. The median age at HIV diagnosis was 8.3 y (IQR 4.0-13.6), the median age at sexual debut was 16 years (IQR 14-18). At the time of prenatal care initiation, four (18%) were on their first antiretroviral treatment (ART), eight (36%) in their second regimen and nine (41%) in their third regimen or beyond, and one had no data. Seventeen of 22 (77%) had HIV-viral load (VL) > 50 copies/mL at prenatal care entry, 16 had a genotyping exam performed. Seventeen of 22 PHIV+ had VL results near delivery: 7/17 (41%) had VL < 50 copies/mL. Among those who had genotyping at prenatal entry, 11/16 (69%) had mutations associated with ARV resistance. The most frequent major mutations were K103N, M184V, T215, M41L, D67N at reverse transcriptase gene and M46, I54V and V82A at protease gene. No vertical transmissions occurred. Management of pregnancy among PHIV+ is challenging. Individualized ART are needed to achieve viral suppression in a highly ART-exposed subpopulation. |
4,937 | A Fully-Parallel Turbo Decoding Algorithm | This paper proposes a novel alternative to the Logarithmic Bahl-Cocke-Jelinek-Raviv (Log-BCJR) algorithm for turbo decoding, yielding significantly improved processing throughput and latency. While the Log-BCJR processes turbo-encoded bits in a serial forwards-backwards manner, the proposed algorithm operates in a fully-parallel manner, processing all bits in both components of the turbo code at the same time. The proposed algorithm is compatible with all turbo codes, including those of the LTE and WiMAX standards. These standardized codes employ odd-even interleavers, facilitating a novel technique for reducing the complexity of the proposed algorithm by 50%. More specifically, odd-even interleavers allow the proposed algorithm to alternate between processing the odd-indexed bits of the first component code at the same time as the even-indexed bits of the second component, and vice-versa. Furthermore, the proposed fully-parallel algorithm is shown to converge to the same error correction performance as the state-of-the-art turbo decoding algorithm. Owing to its significantly increased parallelism, the proposed algorithm facilitates throughputs and latencies that are up to 6.86 times superior to those of the state-of-the art algorithm, when employed for the LTE and WiMAX turbo codes. However, this is achieved at the cost of a moderately increased computational complexity and resource requirement. |
4,938 | Research on garment pattern design based on fractal graphics | This paper firstly analyzes the basic principle of generating fractal art graphics and the artistic features of graphics and then uses scientific visualization method to innovate and improve the theoretical model used in this paper. The generation principle and graphic characteristics of fractal graphics of complex dynamic system and L-system are mainly analyzed, and two kinds of art graphicsflower art graphics and geometric art graphicshave been developed. On this basis, the generated artistic figures are designed for the second time and then applied to the design of clothing patterns. By using MATLAB programming software to generate art graphics conforming to a specific style, combined with image processing software Photoshop to process and redesign the generated graphics, these art graphics can assist the design of clothing printing patterns and make patterns applicable for clothing fabrics. Finally, the fractal pattern theory is applied to silk scarves design and clothing fabric design through digital printing technology, which can fully reflect the practicability and superiority of clothing pattern design based on the fractal theory. Based on the experimental result, it shows that it is completely feasible to design clothing fabric printing patterns based on fractal theory, and the unusual artistic patterns designed have very considerable practical value. In addition, this method encourages creativity in the garment pattern design process and accelerates new design generation. |
4,939 | Relational Fusion Networks: Graph Convolutional Networks for Road Networks | The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road networks. We introduce the Relational Fusion Network (RFN), a novel type of Graph Convolutional Network (GCN) designed specifically for road networks. In particular, we propose methods that outperform state-of-the-art GCN architectures by up to 21-40% on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs may fail to effectively leverage road network structure and may not generalize well to other road networks. |
4,940 | Geochemical accumulation and source tracing of heavy metals in arable soils from a black shale catchment, southwestern China | Heavy metal enrichment in soils has been linked to the weathering of lithologies with high geochemical backgrounds, such as black shale. Therefore, this study conducted a typical sampling of surface soils in a black shale catchment in southwestern China to characterize the accumulation and sources of the heavy metals As, Cd, Co, Cr, Cu, Ni, Pb, Zn, Mo and Tl. Elevated concentrations of most heavy metals in the soils underlain by black shale are determined to exceed the regional soil background values, even the risk screening values, especially for Mo, As and Cd. Sequential extraction analysis, together with previous results, reveals that most heavy metals in soils are mainly bound in the residual fraction (> 65 %) as a result of the fixation of stable aluminosilicates (e.g., clay minerals). In contrast, Cd mainly occurs in relatively labile proportions as exchangeable (24.42 %), carbonate (24.48 %) and Fe/Mn oxide fractions (26.60 %) due to the non-specific adsorption of soil colloids and the precipitation of carbonates and Fe/Mn oxides. Pb isotopic tracing and APCS/MLR receptor model suggest that heavy metals in the urban surface soils (SG1) have a mixed source of black shale weathering, vehicle exhaust and agricultural input, while heavy metals in the rural surface soils (SG2) are a geogenic source of black shale weathering. Overall, this study provides new insights into contamination management, land use planning and health risk assessment in regions with high geochemical backgrounds. |
4,941 | SAW Tags With Enhanced Penetration Depth for Buried Assets Identification (Aug. 2020) | To identify buried assets, this article presents a novel design of surface acoustic wave (SAW) tag to enhance its underground penetration capacity. A penetration depth model for a SAW tag buried in materials is established to investigate the depth of detection and guide the design of the SAW tag. Multipulse position with all reflectors in two groups (MPP-ART) coding scheme is proposed to reduce the insertion loss of the SAW chip. A novel embedded monopole microstrip tag antenna is investigated with a stable frequency performance when embedded in diverse types of materials. Furthermore, a linear frequency modulation pulse compression transceiver based on software defined radio is implemented to enhance the penetration depth. The insertion loss of the SAW chip at 920 MHz with the MPP-ART coding scheme, is reduced to 26.58 dB. Through experiments, the penetration depth of the SAW radio frequency identification prototype has reached 0.75 m in river water, 0.8 m in tap water, 1.5 m in wet sand, and 3 m in dry sand, respectively. The experimental results demonstrate that the SAW tag can work in various materials with good penetration depth, relatively immune to electromagnetic interference with adjacent metallic objects, making it suitable for the identification of buried assets for industrial applications. |
4,942 | TM-generation model: a template-based method for automatically solving mathematical word problems | In this study, we propose a novel model called template-based multitask generation (TM-generation) that can improve the problem-solving accuracy of mathematical word problem-solving task. In automatic mathematical word problem-solving task, a machine learning model should deduce an answer to a given problem by acquiring implied numeric information. To build a robust model that can sufficiently utilize numeric information to solve various mathematical word problems, such a model should address two challenges: (1) filling in missing world knowledge required to solve the given mathematical word problem, and (2) understanding the implied relationship between numbers and variables. To address these two challenges, we propose template-based multitask generation (TM-generation). To address challenge (1), we utilize the state-of-the-art language models called ELECTRA. To address challenge (2), we propose an operator identification layer that models the relationship between numbers and variables. Our experimental results show that using the MAWPS and Math23k datasets, state-of-the-art performance was achieved: 85.2% in MAWPS and 85.3% in Math23k. |
4,943 | Low-Latency Software Polar Decoders | Polar codes are a new class of capacity-achieving error-correcting codes with low encoding and decoding complexity. Their low-complexity decoding algorithms rendering them attractive for use in software-defined radio applications where computational resources are limited. In this work, we present low-latency software polar decoders that exploit modern processor capabilities. We show how adapting the algorithm at various levels can lead to significant improvements in latency and throughput, yielding polar decoders that are suitable for high-performance software-defined radio applications on modern desktop processors and embedded-platform processors. These proposed decoders have an order of magnitude lower latency and memory footprint compared to state-of-the-art decoders, while maintaining comparable throughput. In addition, we present strategies and results for implementing polar decoders on graphical processing units. Finally, we show that the energy efficiency of the proposed decoders is comparable to state-of-the-art software polar decoders. |
4,944 | ASSESSMENT OF THE RISK FOR DEVELOPING A SECOND MALIGNANCY FROM SCATTERED AND SECONDARY RADIATION IN RADIATION THERAPY | With the average age of radiation therapy patients decreasing and the advent of more complex treatment options comes the concern that the incidences of radiation-induced cancer might increase in the future. The carcinogenic effects of radiation are not well understood for the entire dose range experienced in radiation therapy. Longer epidemiologic studies are needed to improve current risk models and reduce uncertainties of current risk model parameters. On the other hand, risk estimations are needed today to judge the risks versus benefits of modern radiation therapy techniques. This paper describes the current state-of-the-art in risk modeling for radiation-induced malignancies in radiation therapy, distinguishing between two volumes: first, the organs within the main radiation field receiving low or intermediate doses (typically between 0.1 and 50 Gy); and second, the organs far away from the treatment volume receiving low doses mainly due to scattered and secondary radiation (typically below 0.1 Gy). The dosimetry as well as the risk model formalisms are outlined. Furthermore, example calculations and results are presented for intensity-modulated photon therapy versus proton therapy. Health Phys. 103(5):652-661; 2012 |
4,945 | Parameterized Strain Estimation for Vascular Ultrasound Elastography With Sparse Representation | Ultrasound vascular strain imaging has shown its potential to interrogate the motion of the vessel wall induced by the cardiac pulsation for predicting plaque instability. In this study, a sparse model strain estimator (SMSE) is proposed to reconstruct a dense strain field at a high resolution, with no spatial derivatives, and a high computation efficiency. This sparse model utilizes the highly-compacted property of discrete cosine transform (DCT) coefficients, thereby allowing to parameterize displacement and strain fields with truncated DCT coefficients. The derivation of affine strain components (axial and lateral strains and shears) was reformulated into solving truncated DCT coefficients and then reconstructed with them. Moreover, an analytical solution was derived to reduce estimation time. With simulations, the SMSE reduced estimation errors by up to 50% compared with the state-of-the-art window-based Lagrangian speckle model estimator (LSME). The SMSE was also proven to be more robust than the LSME against global and local noise. For in vitro and in vivo tests, residual strains assessing cumulated errors with the SMSE were 2 to 3 times lower than with the LSME. Regarding computation efficiency, the processing time of the SMSE was reduced by 4 to 25 times compared with the LSME, according to simulations, in vitro and in vivo results. Finally, phantom studies demonstrated the enhanced spatial resolution of the proposed SMSE algorithm against LSME. |
4,946 | Impact of adherence to hydroxyurea on health outcomes among patients with sickle cell disease | Although new pharmaceutical therapy options have recently become available, hydroxyurea is still the most commonly used and affordable treatment option for sickle cell disease (SCD). This study aimed to update the evidence on hydroxyurea adherence and its association with clinical and economic outcomes among individuals with SCD. This retrospective study used Texas Medicaid claims data from 09/2011-08/2016. Individuals were included if they had ≥1 inpatient or ≥2 outpatient SCD diagnoses, had ≥1 hydroxyurea prescription, were 2-63 years of age, and were continuously enrolled in Texas Medicaid between 6 months before and 1 year after the first hydroxyurea prescription fill date (index date). Hydroxyurea adherence (Medication Possession Ratio; MPR), vaso-occlusive crisis (VOC)-related outcomes, healthcare utilization and expenditures (SCD-related and all-cause) during the 1 year following the index date were measured. Bivariate and multivariable analyses were used to address the study objectives. Among 1035 included individuals (age: 18.8 ± 12.5 years, female: 52.1%), 20.9% were adherent to hydroxyurea (defined as MPR≥0.8). After adjustment for demographic and clinical characteristics, compared to being non-adherent, adhering to hydroxyurea was significantly associated with: a lower risk (Odds Ratio [OR] = 0.480, p = .0007) and hazard rate (Hazard Ratio [HR] = 0.748, p = .0005) of a VOC event, fewer VOC events (Incidence Rate Ratio [IRR] = 0.767, p = .0009), fewer VOC-related hospital days (IRR = 0.593, p = .0003), fewer all-cause and SCD-related hospitalizations (IRR = 0.712, p = .0008; IRR = 0.707, p = .0008, respectively) and emergency department visits (IRR = 0.768, p = .0037; IRR = 0.746, p = .0041, respectively), and lower SCD-related total healthcare expenditures (IRR = 0.796, p = .0266). Efforts to increase adherence to hydroxyurea could improve clinical and economic outcomes among individuals with SCD. |
4,947 | On the design and operation of primary settling tanks in state of the art wastewater treatment and water resources recovery | In state of the art wastewater treatment, primary settling tanks (PSTs) are considered as an integral part of the biological wastewater and sludge treatment process, as well as of the biogas and electric energy production. Consequently they strongly influence the efficiency of the entire wastewater treatment plant. However, in the last decades the inner physical processes of PSTs, largely determining their efficiency, have been poorly addressed. In common practice PSTs are still solely designed and operated based on the surface overflow rate and the hydraulic retention time (HRT) as a black box. The paper shows the results of a comprehensive investigation programme, including 16 PSTs. Their removal efficiency and inner physical processes (like the settling process of primary sludge), internal flow structures within PSTs and their impact on performance were investigated. The results show that: (1) the removal rates of PSTs are generally often underestimated in current design guidelines, (2) the removal rate of different PSTs shows a strongly fluctuating pattern even in the same range of the HRT, and (3) inlet design of PSTs becomes highly relevant in the removal efficiency at rather high surface overflow rates, above 5 m/h, which is the upper design limit of PSTs for dry weather load. |
4,948 | SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases | Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of state-of-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19. |
4,949 | Residue buildup predictive modeling for stencil cleaning profile decision-making using recurrent neural network | This research proposes a novel framework to control the stencil cleaning profile selection in the stencil printing process (SPP). The SPP is a major contributor to yield loss in surface mount technology (SMT). Enhancement in SPP performance is critical to improving the printed circuit board (PCB) assembly line. The selection of a solvent-based or a dry-based cleaning profile is challenging, but the choice determines the effectiveness and efficiency of the stencil cleaning operation. The amount of residue buildup under the stencil is the main criterion used to decide the appropriate cleaning profile in SPP. In this research, a multi-dimensional temporal recurrent neural network (RNN) approach is used to accurately predict the amount of residue buildup on the underneath surface of the stencil in real-time. Specifically, the long short-term memory (LSTM) architecture is trained using actual residue buildup data. The proposed LSTM prediction model is compared with other state-of-the-art regression models such as multilayer perceptron (MLP) and ensemble learning models. Experimental results show the proposed LSTM model outperforms the state-of-the-art regression models and accurately predicts the stencil status. The proposed research aids decision-makers in the SPP line to select the appropriate stencil cleaning profile adaptively and in real-time. As a result, the overall SPP performance is improved. |
4,950 | Benchmarking Optimization-Based Energy Disaggregation Algorithms | Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED. |
4,951 | Reflective Tomography Lidar Image Reconstruction for Long Distance Non-Cooperative Target | In the long-distance space target detection, the technique of laser reflection tomography (LRT) shows great power and attracts more attention for further study and real use. However, space targets are often non-cooperative, and normally a 360 degrees complete view of reflection projections cannot be obtained. Therefore, this article firstly introduces an improved LRT system design with more advanced laser equipment for long-distance non-cooperative detection to ensure the high quality of the lidar beam and the lidar projection data. Then, the LRT image reconstruction is proposed and focused on the laser image reconstruction method utilizing the total variation (TV) minimization approach based on the sparse algebraic reconstruction technique (ART) model, in order to reconstruct the laser image in a sparse or incomplete view of projections. At last, comparative experiments with the system are performed to validate the advantages of this method with the LRT system. In both near and far field experiments, the effectiveness and superiority of the proposed method are verified for different types of projection data through comparison to typical methods. |
4,952 | Artificial Reef Detection Method for Multibeam Sonar Imagery Based on Convolutional Neural Networks | Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods. |
4,953 | Amplified Gliosis and Interferon-Associated Inflammation in the Aging Brain following Diffuse Traumatic Brain Injury | Traumatic brain injury (TBI) is associated with chronic psychiatric complications and increased risk for development of neurodegenerative pathology. Aged individuals account for most TBI-related hospitalizations and deaths. Nonetheless, neurobiological mechanisms that underlie worsened functional outcomes after TBI in the elderly remain unclear. Therefore, this study aimed to identify pathways that govern differential responses to TBI with age. Here, adult (2 months of age) and aged (16-18 months of age) male C57BL/6 mice were subjected to diffuse brain injury (midline fluid percussion), and cognition, gliosis, and neuroinflammation were determined 7 or 30 d postinjury (dpi). Cognitive impairment was evident 7 dpi, independent of age. There was enhanced morphologic restructuring of microglia and astrocytes 7 dpi in the cortex and hippocampus of aged mice compared with adults. Transcriptional analysis revealed robust age-dependent amplification of cytokine/chemokine, complement, innate immune, and interferon-associated inflammatory gene expression in the cortex 7 dpi. Ingenuity pathway analysis of the transcriptional data showed that type I interferon (IFN) signaling was significantly enhanced in the aged brain after TBI compared with adults. Age prolonged inflammatory signaling and microgliosis 30 dpi with an increased presence of rod microglia. Based on these results, a STING (stimulator of interferon genes) agonist, DMXAA, was used to determine whether augmenting IFN signaling worsened cortical inflammation and gliosis after TBI. DMXAA-treated Adult-TBI mice showed comparable expression of myriad genes that were overexpressed in the cortex of Aged-TBI mice, including Irf7, Clec7a, Cxcl10, and Ccl5 Overall, diffuse TBI promoted amplified IFN signaling in aged mice, resulting in extended inflammation and gliosis.SIGNIFICANCE STATEMENT Elderly individuals are at higher risk of complications following traumatic brain injury (TBI). Individuals >70 years old have the highest rates of TBI-related hospitalization, neurodegenerative pathology, and death. Although inflammation has been linked with poor outcomes in aging, the specific biological pathways driving worsened outcomes after TBI in aging remain undefined. In this study, we identify amplified interferon-associated inflammation and gliosis in aged mice following TBI that was associated with persistent inflammatory gene expression and microglial morphologic diversity 30 dpi. STING (stimulator of interferon genes) agonist DMXAA was used to demonstrate a causal link between augmented interferon signaling and worsened neuroinflammation after TBI. Therefore, interferon signaling may represent a therapeutic target to reduce inflammation-associated complications following TBI. |
4,954 | Deep Learning Extraction of the Temperature-Dependent Parameters of Bulk Defects | Bulk defects in silicon solar cells are a key contributor to loss of efficiency. To detect and identify those defects, temperature- and injection-dependent lifetime spectroscopy is usually used, and the defect parameters are traditionally extracted using fitting methods to the Shockley-Read-Hall recombination statistics. In this study, we propose a deep learning-based extraction technique that is based on an alternative representation of the lifetime curves: lifetime mapping in the temperature and minority carrier concentration space. The deep learning approach successfully predicts all the defect parameters while addressing one of the main limitations of the traditional approach of locating the defect in the energy spectrum, which usually outputs two possible solutions. Furthermore, the approach is applied to temperature-dependent defect parameters where the traditional approach is not applicable, achieving satisfying levels of prediction of the defect parameters. Image representation and deep learning have the potential to bolster solar cell characterization techniques by extracting more insights from the characterization data. |
4,955 | Robotics Inspired Renewable Energy Developments: Prospective Opportunities and Challenges | The domain of Robotics is a good partner of renewable energy and is becoming critical to the sustainability and survival of the energy industry. The multi-disciplinary nature of robots offers precision, repeatability, reliability, productivity and intelligence, thus rendering their services in diversified tasks ranging from manufacturing, assembling, and installation to inspection and maintenance of renewable resources. This paper explores applications of real robots in four feasible renewable energy domains; solar, wind, hydro, and biological setups. In each case, existing state-of-the-art innovative robotic systems are investigated that have the potential to create a difference in the corresponding renewable sector in terms of reduced set-up time, lesser cost, improved quality, enhanced productivity and exceptional competitiveness in the global market. Instrumental opportunities and challenges of robot deployment in the renewable sector are also discussed with a brief case study of Saudi Arabia. It is expected that the wider dissemination of the instrumental role of robotics in renewable energy will contribute to further developments and stimulate more collaborations and partnerships between professionals of robotics and energy communities. |
4,956 | Visuo-tactile heading perception | Self-motion through an environment induces various sensory signals, i.e., visual, vestibular, auditory, or tactile. Numerous studies have investigated the role of visual and vestibular stimulation for the perception of self-motion direction (heading). Here, we investigated the rarely considered interaction of visual and tactile stimuli in heading perception. Participants were presented optic flow simulating forward self-motion across a horizontal ground plane (visual), airflow toward the participants' forehead (tactile), or both. In separate blocks of trials, participants indicated perceived heading from unimodal visual or tactile or bimodal sensory signals. In bimodal trials, presented headings were either spatially congruent or incongruent with a maximum offset between visual and tactile heading of 30°. To investigate the reference frame in which visuo-tactile heading is encoded, we varied head and eye orientation during presentation of the stimuli. Visual and tactile stimuli were designed to achieve comparable precision of heading reports between modalities. Nevertheless, in bimodal trials heading perception was dominated by the visual stimulus. A change of head orientation had no significant effect on perceived heading, whereas, surprisingly, a change in eye orientation affected tactile heading perception. Overall, we conclude that tactile flow is more important to heading perception than previously thought.NEW & NOTEWORTHY We investigated heading perception from visual-only (optic flow), tactile-only (tactile flow), or bimodal self-motion stimuli in different conditions varying in head and eye position. Overall, heading perception was body or world centered and non-Bayes optimal and revealed a centripetal bias. Although being visually dominated, tactile flow revealed a significant influence during bimodal heading perception. |
4,957 | Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection | Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e., varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train convolutional neural networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many such methods are supplemented by spatial or morphological processing. Using a set of canonical cell nuclei shapes, prepared with the help of a domain expert, we develop a new approach that we call shape priors (SPs) with CNNs (SPs-CNN). We further extend the network to introduce an SP layer and then allowing it to become trainable (i.e., optimizable). We call this network as tunable SP-CNN (TSP-CNN). In summary, we present new network structures that can incorporate "expected behavior" of nucleus shapes via two components: learnable layers that perform the nucleus detection and a fixed processing part that guides the learning with prior information. Analytically, we formulate two new regularization terms that are targeted at: 1) learning the shapes and 2) reducing false positives while simultaneously encouraging detection inside the cell nucleus boundary. Experimental results on two challenging datasets reveal that the proposed SP-CNN and TSP-CNN can outperform the state-of-the-art alternatives. |
4,958 | A critical assessment of the potential and limitations of physicochemical analysis to advance knowledge on Levantine rock art | This paper offers an updated review of the variety of physicochemical analysis applied so far to Levantine rock art (Spain) to characterize the composition of the pigments, as well as the substrate and/or the natural coating covering these particular prehistoric paintings. This paper is part of a broader special issue evaluating the real contribution of scientific approaches to rock art research, assessing how they have improved our understanding of this particular heritage and the new research questions they open. In this context, and with a focus on Levantine rock art, our aim is to explore: 1. The guiding principles behind the different sorts of analysis conducted and published so far; 2. The non-invasive and invasive techniques applied to answer the research questions raised, and 3. If the result published as yet have met the expectations of rock art researchers. We also reflect on the potential, the limitations and the future developments of this sort of studies, as well as on the ethics and desirable protocols of applying invasive techniques to this UNESCO World Heritage listed archaeological remain. While the focus is Levantine rock art, the discussions raised by this paper and the experiences reported in relation to the various techniques used are of global interest, especially when dealing with open-air rock art. |
4,959 | DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks | In the past decade deep neural networks (DNNs) have shown state-of-the-art performance on a wide range of complex machine learning tasks. Many of these results have been achieved while growing the size of DNNs, creating a demand for efficient compression and transmission of them. In this work we present DeepCABAC, a universal compression algorithm for DNNs that is based on applying Context-based Adaptive Binary Arithmetic Coder (CABAC) to the DNN parameters. CABAC was originally designed for the H.264/AVC video coding standard and became the state-of-the-art for the lossless compression part of video compression. DeepCABAC applies a novel quantization scheme that minimizes a rate-distortion function while simultaneously taking the impact of quantization to the DNN performance into account. Experimental results show that DeepCABAC consistently attains higher compression rates than previously proposed coding techniques for DNN compression. For instance, it is able to compress the VGG16 ImageNet model by x63.6 with no loss of accuracy, thus being able to represent the entire network with merely 9 MB. The source code for encoding and decoding can be found at https://github.com/fraunhoferhhi/DeepCABAC. |
4,960 | FARNet: Fragmented affinity reasoning network of text instances for arbitrary shape text detection | Arbitrary shape text detection is a challenging task in scene text recognition. Driven by deep learning and large-scale data sets, the detection method based on connected component (CC) has increasingly gained popularity. However, there are still problems of unclear separation of text instances and incorrect component links. Thus, in this paper, the authors propose a novel component connection method, that is, Fragmented Affinity Reasoning Network of Text Instances (FARNet), for arbitrary shape text detection. The network consists of a Weighted Feature Fusion Pyramid Network (WFFPN), Text Fragments Subgraph (TFS), and Dense Graph Attention Network (DGAT), which can be trained end-to-end. The WFFPN is used to generate text fragments, TFS and DGAT jointly construct an affinity reasoning network. Since the neighbouring boundaries between text instances may blend them into a single instance, the core idea is to use the WFFPN to divide the text instance into a series of rectangular fragments, the affinity reasoning network infers the affinity between fragments and then links them to rebuild text instances. Extensive experiments on seven challenging datasets (ICDAR2015, MSRA-TD500, Totaltext, CTW-1500, ICDAR 2019MLT, ICDAR2019 ArT, and DAST-1500) demonstrate that the proposed text detector achieves state-of-the-art performance in both on polygon datasets and quadrilateral datasets. The code is available at . |
4,961 | Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition | In this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The technique decomposes the input image into the four frequency subbands by using DWT and estimates the singular value matrix of the low-low subband image, and, then, it reconstructs the enhanced image by applying inverse DWT. The technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as brightness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques. |
4,962 | Mapping of PET-measured aerosol deposition: a comparison study | Three-dimensional positron emission tomography (PET) can be used to assess the spatial distribution of inhaled aerosols, and with lung models of airway parameters this data can be converted into aerosol deposition information for each airway generation. Two alternative methods for extracting this generational data, the analytical and the arithmetic reconstruction technique (ART), are investigated in conjunction with two different airway branching models, monopodial and dichotomous, to determine pulmonary deposition in a canine model. All solutions revealed two regions with high deposition: the first 10 generations past the trachea (1-10) and the deepest lung generations (18-23). Post-imaging autoradiographic images were in agreement with the deposition pattern in the larger airway generations (1-10). In comparing the different techniques, the dichotomous lung model yielded large variance in mapped activity but similar concentrations because of variation in generation volumes, while ART showed a greater ability to discern small differences in deposition patterns between subjects. (C) 2005 Published by Elsevier Ltd. |
4,963 | Safety, Pharmacokinetics, and Pharmacodynamics of CC-90001 (BMS-986360), a c-Jun N-terminal Kinase Inhibitor, in Phase 1 Studies in Healthy Participants | CC-90001 selectively inhibits c-Jun N-terminal kinase (JNK), a stress-activated protein implicated in fibrosis. In 3 phase 1 trials evaluating CC-90001 pharmacokinetics, pharmacodynamics, and safety, healthy adults (N = 184) received oral CC-90001 in a single dose (10-720 mg) or multiple doses (30-480 mg once daily for 7-18 days) or placebo. CC-90001 was rapidly absorbed (median time to maximum concentration, 1-4 hours) and eliminated with a mean terminal elimination half-life of 12-28 hours. Steady state was reached on day 5, with a mean accumulation ratio of 1.5- to 2-fold following daily dosing. Exposure was similar in fed versus fasted participants and in Japanese versus non-Japanese participants. CC-90001 demonstrated dose- and exposure-dependent inhibition of JNK as determined by histopathological analysis of c-Jun phosphorylation in ultraviolet-irradiated skin. The most common treatment-emergent adverse events were nausea and headache; all were mild or moderate in intensity. Based on exposure-response analysis using high-quality electrocardiogram data, no clinically relevant QT prolongation liability for CC-90001 was observed. Overall, single- and multiple-dose CC-90001 were generally safe and well tolerated at the tested doses and demonstrated JNK pathway engagement. These results support further clinical evaluation of CC-90001. |
4,964 | Detection and localization of partial audio matches in various application scenarios | In this paper, we describe various application scenarios for archive management, broadcast/stream analysis, media search and media forensics which require the detection and accurate localization of unknown partial audio matches within items and datasets. We explain why they cannot be addressed with state-of-the-art matching approaches based on fingerprinting, and propose a new partial matching algorithm which can satisfy the relevant requirements. We propose two distinct requirement sets and hence two variants / settings for our proposed approach: One focusing on lower time granularity and hence lower computational complexity, to be able to deal with large datasets, and one focusing on fine-grain analysis for small datasets and individual items. Both variants are tested using distinct evaluation sets and methodologies and compared with a popular audio matching algorithm, thereby demonstrating that the proposed algorithm achieves convincing performance for the relevant application scenarios beyond the current state-of-the-art. |
4,965 | Effects of ultrasound pretreatment on functional property, antioxidant activity, and digestibility of soy protein isolate nanofibrils | Nanofibrils, an effective method to modulate the functional properties of proteins, can be promoted by ultrasound pretreatment. This study investigated the effect of ultrasound pretreatment on the structure, functional property, antioxidant activity and digestibility of soy protein isolate (SPI) nanofibrils. The results showed that high amplitude ultrasound had a significant effect on structure of SPI nanofibrils. SPI nanofibrils pretreated by 80% amplitude ultrasound showed a blueshift of the amide II band in Fourier transform infrared spectroscopy (FTIR), resulted in more tryptophan residues being buried and increased the crystallinity. Low amplitude ultrasound (20%) pretreatment significantly improved the solubility, emulsifying activity index (EAI) and water absorption capacity (WAC) of SPI nanofibrils, but 80% amplitude ultrasound pretreatment of SPI nanofibrils reduced emulsifying stability index (ESI). High amplitude ultrasound (60% and 80%) pretreatment of SPI nanofibrils improved the foaming capacity and foaming stability and decreased denaturation temperature. DPPH radical scavenging activity of SPI nanofibrils were significantly improved by ultrasound pretreatment. 20% amplitude ultrasound pretreatment improved DPPH, ABTS radical scavenging activity and ferric reducing antioxidant power of SPI nanofibrils. The digestion rate of 80% amplitude ultrasound-pretreated nanofibrils were consistently higher, and SPI nanofibrils pretreated by ultrasound were more fragmented and shorter after simulating gastrointestinal digestion. This study would expand the application of food-grade protein nanofibrils in the food industry. |
4,966 | Indoor air quality evaluation of two museums in a subtropical climate conditions | Indoor air quality can affect and influence the state of works of art exhibited inside museums. Museums, usually suffer badly from the generally higher levels of air pollution and the control of such contamination requires continual evaluation and the strict monitoring, using by using the most appropriate materials. The main aim of this paper was to characterize, analyze and determine indoor air pollutants in Cypriot-Archeological and Byzantine museum. Experimental measurements have been carried out concerning the measurements of CO, CO2, PM1, PM2.5, PM7, PM10, Relative Humidity (RH), O-3, NO, NO2, SO2, Benzene (BEN), Tulane (TOL), Ethylene (ETH), Xylene (XYL), NH3, H2S, for a period of 12 months. Considering the safety of exhibits in both museums and from the final results, the quality of air unfortunately cannot be described as satisfactory. The findings from this study are useful to policy makers in order to develop a new strategy plan for indoor air quality and especially for museums. (C) 2015 Elsevier Ltd. All rights reserved. |
4,967 | Dating Palaeolithic cave art: Why U-Th is the way to go | The chronology of European Upper Palaeolithic cave art is poorly known. Three chronometric techniques are commonly applicable: AMS C-14, TL and U-Th, and in recent years the efficacy of each has been the subject of considerable debate. We review here the use of the U-Th technique to date the formation of calcites that can be shown to have stratigraphic relationships to cave art. We focus particularly on two recent critiques of the method. By using specific examples from our own work using this method in Spain, we demonstrate how these critiques are highly flawed and hence misleading, and we argue that the U-Th dating of calcites is currently the most reliable of available chronometric techniques for dating cave art. Crown Copyright (C) 2015 Published by Elsevier Ltd. All rights reserved. |
4,968 | Blame it on her 'baby brain'? Investigating the contents of social stereotypes about pregnant women's warmth and competence | The Stereotype Content Model proposes that social stereotypes broadly exist along two dimensions: warmth and competence. This framework has been used to investigate the contents of stereotypes of gendered groups in a range of contexts. However, it has not been extensively applied to perceptions of pregnant women. This is important, given how pregnant women are typically framed by society to have 'baby brain' or reduced competence. Therefore, we investigated the contents of social stereotypes of pregnant women. In Study 1, participants (N = 590) rated a target group (pregnant women) and thirteen other comparison groups on perceptions of warmth (compassion, empathy and comfort) and competence (mathematics ability, logic and memory). Pregnant women were generally stereotyped to have low competence and high warmth, relative to other groups. Study 2 (N = 54) then descriptively investigated the wider contents of stereotypes related to pregnant women, new mothers, men and women using a trait generation task. Generated traits were coded within the dimensions of warmth and competence. This showed, again, that pregnant women were assigned traits related to warmth and poor competence. Taken together, these studies confirmed that perceptions of low competence and 'baby brain' in pregnancy is broadly held by a non-pregnant sample. |
4,969 | Observations on aesthetic and structural changes induced in Polish historic objects by microorganisms | During a long career at the Institute for the Study, Restoration and Conservation of Cultural Property, Nicholas Copernicus University, the author has observed that most of the objects reaching the conservation laboratories have suffered deterioration caused by microorganisms. This deterioration is due to the exposure of these historical objects at some stage of their long existence to conditions moist enough to be conductive to microbial growth. The combination of sufficient moisture and the nutrients in the materials from which historic art objects are formed provides the conditions that allow deteriorative microorganisms to survive, develop and cause damage to the objects over extended periods of time. Examples of deterioration in various museum and library items in Poland are discussed. (C) 2003 Elsevier Ltd. All rights reserved. |
4,970 | Task-based parameter isolation for foreground segmentation without catastrophic forgetting using multi-scale region and edges fusion network | Foreground segmentation of moving objects is widely used in different computer vision applications; however, existing deep learning-based methods generally suffer from overall degraded F-measure performance. The two main sources that degrade the F-measure are under-segmentation and catastrophic forgetting. Under segmentation is the problem of misdetecting objects' fine details. The catastrophic forgetting problem occurs when training on a large number of video sequences that leads to forgetting information learned from early video sequences. This paper proposes a novel multi-scale region and edges fusion network with task-based parameter isolation (REFNet-TBPI) to overcome these two problems. The proposed method consists of a novel multi-scale region and edges fusion network (REFNet) to capture the moving objects' boundary details by extracting regions and boundary edges of each object at different feature scales and fusing them to produce high-detailed segmented objects. REFNet is trained using a novel continual learning technique called task based parameter isolation (TBPI) to overcome the catastrophic forgetting problem. The proposed method (REFNet-TBPI) is extensively evaluated on three benchmarks, namely CDnet2014, DAVIS2016, and SegTrack. By comparing REFNet-TBPI with current state-of-the-art methods, the proposed method outperforms the best reported state-of-the-art by 4.4% on average. (c) 2021 Elsevier B.V. All rights reserved. |
4,971 | Effects of PM2.5 on Third Grade Students' Proficiency in Math and English Language Arts | Fine particulate air pollution is harmful to children in myriad ways. While evidence is mounting that chronic exposures are associated with reduced academic proficiency, no research has examined the frequency of peak exposures. It is also unknown if pollution exposures influence academic proficiency to the same degree in all schools or if the level of children's social disadvantage in schools modifies the effects, such that some schools' academic proficiency levels are more sensitive to exposures. We address these gaps by examining the percentage of third grade students who tested below the grade level in math and English language arts (ELA) in Salt Lake County, Utah primary schools (n = 156), where fine particulate pollution is a serious health threat. More frequent peak exposures were associated with reduced math and ELA proficiency, as was greater school disadvantage. High frequency peak exposures were more strongly linked to lower math proficiency in more advantaged schools. Findings highlight the need for policies to reduce the number of days with peak air pollution. |
4,972 | Human papillomavirus vaccinations at recommended ages: How a middle school-based educational and vaccination program increased uptake in the Rio Grande Valley | Human papillomavirus (HPV) vaccination is recommended for U.S. adolescents at ages 11-12 requiring two or three doses depending on if the vaccine series started before age 15. The objective was to compare HPV vaccination rates among medically underserved, economically disadvantaged, students in rural middle school districts (Rio Grande Valley [RGV], Texas) by age of initiation (≤ age 11 years vs. age 12 years and older). This quasi-experimental study included 1,766 students (884 females; 882 males) who received at least one HPV vaccine dose through our school-based vaccination program between 08/2016-06/2022. Summary statistics were stratified by age at initiation and gender. The overall HPV up-to-date (UTD) rate was 59.7% (95% Confidence Interval: 57.4-62.0%). The median age at HPV UTD (range) was 12 years (9-19) and median interval between HPV vaccine doses (range) was 316 days (150-2,855). Most students received the HPV vaccine bundled with other vaccinations (72.4%, 1,279/1,766). There was a higher HPV UTD rate among students who initiated the HPV vaccine on or before age 11 than those who initiated on or after age 12 (73.6% versus 45.1%, respectively). The median age of HPV UTD was age 12 for those initiating on or before 11 years versus age 13 for those initiating on or after 12 years of age. Initiating the HPV vaccine at age ≤11 years increased completion of the HPV vaccine series. Improving HPV vaccine coverage and introduction of pan-gender vaccination programs will significantly decrease HPV-related diseases in the RGV. |
4,973 | First molecular detection of Equine Herpesvirus type 3 (EHV-3) in Chile | Equine coital rash (ECE) is a highly contagious benign infection that induces lesions on external genitals, and it is caused by the equine herpesvirus type 3 (EHV-3). Although the disease is globally distributed, its presence in Chile has not been documented from a genetic point of view. Here, we performed polymerase chain reaction screenings for EHV-3 in lesions of external genitals in four horses belonging to a riding station at Bulnes, Ñuble Region, Chile. We sequenced a fragment of the glycoprotein G (gG) gene from three horses with clinical signs of ECE. The sequences were identical between them and 99.7% similar to a haplotype of EHV-3 detected in Brazil, and phylogenetically related with homologue from Japan, Russia and Brazil. Our results show the presence of EHV-3 for the first time in horses with ECE in Chile. |
4,974 | Establishment and Evaluation of a New Meteorological Observation-Based Grid Model for Estimating Zenith Wet Delay in Ground-Based Global Navigation Satellite System (GNSS) | With the availability to high-accuracy a priori zenith wet delay (ZWD) data, the positioning efficiency of the precise point positioning (PPP) processing can be effectively improved, including accelerating the convergence time and improving the positioning precision, in ground-based Global Navigation Satellite System (GNSS) technology. Considering the limitations existing in the state-of-the-art ZWD models, this paper established and evaluated a new in-situ meteorological observation-based grid model for estimating ZWD named GridZWD using the radiosonde data and the European Centre for Medium-Range Weather Forecasts (ECWMF) data. The results show that ZWD has a strong correlation with the meteorological parameter water vapor pressure in continental and high-latitude regions. The root of mean square error (RMS) of 24.6 mm and 36.0 mm are achievable by the GridZWD model when evaluated with the ECWMF data and the radiosonde data, respectively. An accuracy improvement of approximately 10%similar to 30% compared with the state-of-the-art models (e.g., the Saastamoinen, Hopfield and GPT2w models) can be found for the new built model. |
4,975 | Neuro-epithelial-ILC2 crosstalk in barrier tissues | Group 2 innate lymphoid cells (ILC2s) contribute to the maintenance of mammalian barrier tissue homeostasis. We review how ILC2s integrate epithelial signals and neurogenic components to preserve the tissue microenvironment and modulate inflammation. The epithelium that overlies barrier tissues, including the skin, lungs, and gut, generates epithelial cytokines that elicit ILC2 activation. Sympathetic, parasympathetic, sensory, and enteric fibers release neural signals to modulate ILC2 functions. We also highlight recent findings suggesting neuro-epithelial-ILC2 crosstalk and its implications in immunity, inflammation and resolution, tissue repair, and restoring homeostasis. We further discuss the pathogenic effects of disturbed ILC2-centered neuro-epithelial-immune cell interactions and putative areas for therapeutic targeting. |
4,976 | A 4 mu W/Ch Analog Front-End Module With Moderate Inversion and Power-Scalable Sampling Operation for 3-D Neural Microsystems | We report an analog front-end prototype designed in 0.25 mu m CMOS process for hybrid integration into 3-D neural recording microsystems. For scaling towards massive parallel neural recording, the prototype has investigated some critical circuit challenges in power, area, interface, and modularity. We achieved extremely low power consumption of 4 mu W/channel, optimized energy efficiency using moderate inversion in low-noise amplifiers (K of 5.98 X 10(8) or NEF of 2.9), and minimized asynchronous interface (only 2 per 16 channels) for command and data capturing. We also implemented adaptable operations including programmable-gain amplification, power-scalable sampling (up to 50 kS/s/channel), wide configuration range (9-bit) for programmable gain and bandwidth, and 5-bit site selection capability (selecting 16 out of 128 sites). The implemented front-end module has achieved a reduction in noise-energy-area product by a factor of 5-25 times as compared to the state-of-the-art analog front-end approaches reported to date. |
4,977 | 2- and 3-Fluoro-3-deazaneplanocins, 2-fluoro-3-deazaaristeromycins, and 3-methyl-3-deazaneplanocin: Synthesis and antiviral properties | The 3-deaza analogs of the naturally occurring adenine-based carbocyclic nucleosides aristeromycin and neplanocin possess biological properties that have not been optimized. In that direction, this paper reports the strategic placement of a fluorine atom at the C-2 and C-3 positions and a methyl at the C-3 site of the 3-deazaadenine ring of the aforementioned compounds. The synthesis and S-adenosylhomocysteine hydrolase inhibitory and antiviral properties of these targets are described. Some, but not all, compounds in this series showed significant activity toward herpes, arena, bunya, flavi, and orthomyxoviruses. |
4,978 | An attention-based deep learning model for multiple pedestrian attributes recognition | The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects are captured from varying distances, under multiple poses, with partial occlusion. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with two-fold contributions: 1) considering the strong semantic correlation between the different full-body attributes, we propose a multi-task deep model that uses an element-wise multiplication layer to extract more comprehensive feature representations. In practice, this layer serves as a filter to remove irrelevant background features, and is particularly important to handle complex, cluttered data; and 2) we introduce a weighted-sum term to the loss function that not only relativizes the contribution of each task but also is crucial for performance improvement in multiple-attribute inference settings. Our experiments were performed on two well-known datasets (RAP and PETA) and point for the superiority of the proposed method with respect to the state-of-the-art. The code is available at https://github.com/EhsanYaghoubi/MAN-PAR-. (C) 2020 Elsevier B.V. All rights reserved. |
4,979 | Microfluidic nanodevices for drug sensing and screening applications | The outbreak of pandemics (e.g., severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 in 2019), influenza A viruses (H1N1 in 2009), etc.), and worldwide spike in the aging population have created unprecedented urgency for developing new drugs to improve disease treatment. As a result, extensive efforts have been made to design novel techniques for efficient drug monitoring and screening, which form the backbone of drug development. Compared to traditional techniques, microfluidics-based platforms have emerged as promising alternatives for high-throughput drug screening due to their inherent miniaturization characteristics, low sample consumption, integration, and compatibility with diverse analytical strategies. Moreover, the microfluidic-based models utilizing human cells to produce in-vitro biomimetics of the human body pave new ways to predict more accurate drug effects in humans. This review provides a comprehensive summary of different microfluidics-based drug sensing and screening strategies and briefly discusses their advantages. Most importantly, an in-depth outlook of the commonly used detection techniques integrated with microfluidic chips for highly sensitive drug screening is provided. Then, the influence of critical parameters such as sensing materials and microfluidic platform geometries on screening performance is summarized. This review also outlines the recent applications of microfluidic approaches for screening therapeutic and illicit drugs. Moreover, the current challenges and the future perspective of this research field is elaborately highlighted, which we believe will contribute immensely towards significant achievements in all aspects of drug development. |
4,980 | The educational buildings of Pius IV: variations upon a building type in urban monuments | Pope Pius IV (1559-1565) was a passionate patron of letters and the arts, seeking to enlist them in his reform of the Catholic Church. He commissioned numerous architectural projects in order to house and represent adequately various offices and institutions. In Pius's mind, architecture was an urbanistic art; his buildings demonstrated the reform and resurgence of the Church in the setting in which the faithful lived out their lives. Among his many endeavors, Pius sought to promote higher education. To support these efforts, Pius commissioned three buildings for educational institutions: the Archiginnasio for the University of Bologna, the Sapienza for the University in Rome, and the Collegio Borromeo for a recently founded college of the same name in Pavia. While these buildings share a common typology, they took advantage of the range of expression allowed for the type. The differences reflected the nature of the institution, allowing the buildings to be urban proclamations about the college or university. Pius's buildings were important examples of the type. Examining these three seminal buildings allows us to understand how this important yet understudied patron employed architecture to serve his reforms, without demanding standard forms or requiring a uniform architectural language. (c) 2004 Elsevier B.V. All rights reserved. |
4,981 | Pic2PolyArt: Transforming a photograph into polygon-based geometric art | Geometric art, an artwork that is structured by geometric shapes, was first made popular by the introduction of Cubism paintings by Pablo Picasso and Georges Braque in the early-20th-century. With the recent advancement in digital imaging technology coupled with the rising popularity of social media such as Instagram, automatic geometric abstraction that can automatically transform a photograph into a piece of geometric art starts to gain research attention. Several state-of-the-art works have been proposed. Notably, despite the fact that Cubism artworks by renown artists illustrate the importance of the main subject in a painting to be recognizable and should be given more detailed representation, most of the state-of-the-art abstraction algorithms are not subject-focused. In this paper, we present Pic2PolyArt, a unified subject-focused geometric abstraction framework that can support both triangle-based and polygon-based abstraction. Given an input photograph, our proposed algorithm first identifies the main subject and important features of an image with a combination of saliency, edge, and face detection techniques. It then generates a set of seed points that are used by Delaunay Triangulation and Voronoi Tessellation to generate triangle-based and polygon-based geometric abstraction respectively. Results from qualitative evaluation on benchmark dataset and empirical user studies demonstrate the effectiveness of our proposed abstraction framework in generating pleasant geometric abstraction from photographs and provide insightful knowledge on users preference with regards to the type of polygons and the level of abstraction used to represent the resulting geometric art. |
4,982 | Brief bursts of infrasound may improve cognitive function--an fMRI study | At present, infrasound (sound frequency < 20 Hz; IS) is being controversially discussed as a potential mediator of several adverse bodily as well as psychological effects. However, it remains unclear, if and in what way IS influences cognition. Here, we conducted an fMRI experiment, in which 13 healthy participants were exposed to IS, while cognitive performance was assessed in an n-back working memory paradigm. During the task, short sinusoidal tone bursts of 12 Hz were administered monaurally with sound pressure levels that had been determined individually in a categorical loudness scaling session prior to the fMRI experiment. We found that task execution was associated with a significant activation of the prefrontal and the parietal cortex, as well as the striatum and the cerebellum, indicating the recruitment of a cognitive control network. Reverse contrast analysis (n-back with tone vs. n-back without tone) revealed a significant activation of the bilateral primary auditory cortex (Brodmann areas 41, 42). Surprisingly, we also found a strong, yet non-significant trend for an improvement of task performance during IS exposure. There was no correlation between performance and brain activity measures in tone and no-tone condition with sum scores of depression-, anxiety-, and personality factor assessment scales (BDI, STAIX1/X2, BFI-S). Although exerting a pronounced effect on cortical brain activity, we obtained no evidence for an impairment of cognition due to brief bursts of IS. On the contrary, potential improvement of working memory function introduces an entirely new aspect to the debate on IS-related effects. |
4,983 | Tumor microenvironment: barrier or opportunity towards effective cancer therapy | Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME's individual components and their interplay at higher resolution. Deeper understanding of the immune cell's diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME. |
4,984 | A rare homozygous missense GDF2 (BMP9) mutation causing PAH in siblings: Does BMP10 status contribute? | Pulmonary arterial hypertension (PAH) is a disease characterized by pathological remodeling of the pulmonary vasculature causing elevated pulmonary artery pressures and ultimately, right ventricular failure from chronic pressure overload. Heterozygous pathogenic GDF2 (encoding bone morphogenetic protein 9 (BMP9)) variants account for some (>1%) adult PAH cases. Only three pediatric PAH cases, harboring homozygous or compound heterozygous variants, are reported to date. Ultra-rare pathogenic GDF2 variants are reported in hereditary hemorrhagic telangiectasia and overlapping disorders characterized by telangiectasias and arteriovenous malformations (AVMs). Here, we present two siblings with PAH homozygous for a GDF2 mutation that impairs BMP9 proprotein processing and reduces growth factor domain availability. We confirm an absence of measurable plasma BMP9 whereas BMP10 levels are detectable and serum-dependent endothelial BMP activity is evident. This contrasts with the absence of activity which we reported in two children with homozygous pathogenic GDF2 nonsense variants, one with PAH and one with pulmonary AVMs, both with telangiectasias, suggesting loss of BMP10 and endothelial BMP activity in the latter may precipitate telangiectasia development. An absence of phenotype in related heterozygous GDF2 variant carriers suggests incomplete penetrance in PAH and AVM-related diseases, indicating that additional somatic and/or genetic modifiers may be necessary for disease precipitation. |
4,985 | Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking | Bounding box estimation by overlap maximization has improved the state of the art of visual tracking significantly, yet the improvement in robustness and accuracy is restricted by the limited reference information, i.e., the initial target. In this paper, we present DCOM, a novel bounding box estimation method for visual tracking, based on distribution calibration and overlap maximization. We assume every dimension in the modulation vector follows a Gaussian distribution, so that the mean and the variance can borrow from those of similar targets in large-scale training datasets. As such, sufficient and reliable reference information can be obtained from the calibrated distribution, leading to a more robust and accurate target estimation. Additionally, an updating strategy for the modulation vector is proposed to adapt the variation of the target object. Our method can be built on top of off-the-shelf networks without finetuning and extra parameters. It yields state-of-the-art performance on three popular benchmarks, including GOT-10k, LaSOT, and NfS while running at around 40 FPS, confirming its effectiveness and efficiency. |
4,986 | Adoption of analytical technologies for verification of authenticity of halal foods - a review | Halal authentication has become essential in the food industry to ensure food is free from any prohibited ingredients according to Islamic law. Diversification of food origin and adulteration issues have raised concerns among Muslim consumers. Therefore, verification of food constituents and their quality is paramount. From conventional methods based on physical and chemical properties, various diagnostic methods have emerged relying on protein or DNA measurements. Protein-based methods that have been used in halal detection including electrophoresis, chromatographic-based methods, molecular spectroscopy and immunoassays. Polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP) are DNA-based techniques that possess better accuracy and sensitivity. Biosensors are miniatured devices that operate by converting biochemical signals into a measurable quantity. CRISPR-Cas is one of the latest novel emerging nucleic acid detection tools in halal food analysis as well as quantification of stable isotopes method for identification of animal species. Within this context, this review provides an overview of the various techniques in halal detection along with their advantages and limitations. The future trend and growth of detection technologies are also discussed in this review. |
4,987 | EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art | Many Internet of Things (IoT) technologies have been used in applications for money flow, logistics flow, people flow, interactive art design, and so on. To manage these increasing disparate devices and connectivity options, ETSI has specified end-to-end machine-to-machine (M2M) system architecture for IoT applications. Based on this architecture, we develop an IoT EasyConnect system to manage IoT devices. In our approach, an IoT device is characterized by its "features" (e.g., temperature, vibration, and display) that are manipulated by the network applications. If a network application handles the individual device features independently, then we can write a software module for each device feature, and the network application can be simply constructed by including these brick-like device feature modules. Based on the concept of device feature, brick-like software modules can provide simple and efficient mechanism to develop IoT device applications and interactions. |
4,988 | Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model | Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images. Comprehensive semantic representations of radiological findings would enable a wide range of secondary use applications to support diagnosis, triage, outcomes prediction, and clinical research. In this paper, we present a new corpus of radiology reports annotated with clinical findings. Our annotation schema captures detailed representations of pathologic findings that are observable on imaging ("lesions") and other types of clinical problems ("medical problems"). The schema used an event-based representation to capture fine-grained details, including assertion, anatomy, characteristics, size, and count. Our gold standard corpus contained a total of 500 annotated computed tomography (CT) reports. We extracted triggers and argument entities using two state-of-the-art deep learning architectures, including BERT. We then predicted the linkages between trigger and argument entities (referred to as argument roles) using a BERT-based relation extraction model. We achieved the best extraction performance using a BERT model pre-trained on 3 million radiology reports from our institution: 90.9-93.4% F1 for finding triggers and 72.0-85.6% F1 for argument roles. To assess model generalizability, we used an external validation set randomly sampled from the MIMIC Chest X-ray (MIMIC-CXR) database. The extraction performance on this validation set was 95.6% for finding triggers and 79.1-89.7% for argument roles, demonstrating that the model generalized well to the cross-institutional data with a different imaging modality. We extracted the finding events from all the radiology reports in the MIMIC-CXR database and provided the extractions to the research community. |
4,989 | Mixed Populations and Co-Infection: Pseudomonas aeruginosa and Staphylococcus aureus | The human pathogens Pseudomonas aeruginosa and Staphylococcus aureus are frequently co-isolated from chronic wounds or cystic fibrosis patient airways. Clinical studies analysing the impact of co-infection on patient clinical outcomes lead to contradictory results. However, laboratory approaches suggest that the two pathogens co-colonize the same infection niches and form a mixed-species biofilm, therefore favouring their resistance to antibiotics and immune response. In parallel, many recent studies have focused on the different interactions between the two bacterial species. It has long been recognized that P. aeruginosa usually outcompetes S. aureus, and the molecular mechanisms involved in this state of bacterial competition are now well understood. However, several recent studies show that interactions between P. aeruginosa and S. aureus can be diverse and evolve over time. Thus, many CF isolates of P. aeruginosa and S. aureus can coexist and develop cooperative behaviours. In this chapter, we will provide an overview of the current knowledge on the mixed populations of P. aeruginosa and S. aureus, from their mechanisms of establishment to their impacts on bacterial physiology and clinical outcomes. |
4,990 | Autoregression and Structured Low-Rank Modeling of Sinogram Neighborhoods | Sinograms are commonly used to represent the raw data from tomographic imaging experiments. Although it is already well-known that sinograms posess some amount of redundancy, in this work, we present novel theory suggesting that sinograms will often possess substantial additional redundancies that have not been explicitly exploited by previous methods. Specifically, we derive that sinograms will often satisfy multiple simple data-dependent autoregression relationships. This kind of autoregressive structure enables missing/degraded sinogram samples to be linearly predicted using a simple shift-invariant linear combination of neighboring samples. Our theory also further implies that if sinogram samples are assembled into a structured Hankel/Toeplitz matrix, then the matrix will be expected to have low-rank characteristics. As a result, sinogram restoration problems can be formulated as structured low-rank matrix recovery problems. Illustrations of this approach are provided using several different (real and simulated) X-ray imaging datasets, including comparisons against a state-of-the-art deep learning approach. Results suggest that structured low-rank matrix methods for sinogram recovery can have comparable performance to state-of-the-art approaches. Although our evaluation focuses on competitive comparisons against other approaches, we believe that autoregressive constraints are actually complementary to existing approaches with strong potential synergies. |
4,991 | Improving Semi-Supervised Learning for Audio Classification with FixMatch | Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement. |
4,992 | RESEARCH ON THE ART OF ENVIRONMENTAL EDUCATION | In this paper, the authors explore preliminarily 'effectiveness environmental education method' through the teaching practice using the search literature, questionnaire interviews, teaching practice and other methods. The whole text is divided into four parts: The first part analyses the important significance of offering environment art education course in university stage against Commercial pop culture. The second part based on interviews with teachers and students in some schools in Suzhou is about the current situation of the effective environment education of environment art education in high school. In the third part, the authors put forward the practical method of improving the effectiveness of the teaching of environment art education, namely, the special teaching method. In this part, the authors try to explore the use of special teaching method, to stimulate students' interest in learning, to carry out reasonable and scientific recombination, the teaching focus and difficult to extract, form a special study to guide students through active research and participation, to break through the key and difficult to understand, so as to effectively improve classroom teaching effect. |
4,993 | Impact of Safe Water Programs on Water Treatment Practices of People Living with Human Immunodeficiency Virus, Ethiopia, 2008 | Household water chlorination has been shown to reduce diarrhea incidence among people living with Human Immunodeficiency Virus (PLHIV). Some HIV programs in Ethiopia previously provided a socially marketed chlorination product (brand name WuhaAgar) to prevent diarrhea. To evaluate the program, we compared WuhaAgar use and water treatment practices between 795 clients from 20 antiretroviral therapy (ART) clinics and 795 community members matched by age, sex, and neighborhood. Overall, 19% of study participants reported water treatment with WuhaAgar. Being an ART clinic client was associated with reported treatment of drinking water (matched odds ratios (mOR): 3.8, 95% confidence interval (CI): 2.9-5.0), reported current water treatment with WuhaAgar (mOR: 5.5, 95% CI 3.9-7.7), and bottles of WuhaAgar observed in the home (mOR: 8.8, 95% CI 5.4-14.3). Being an ART clinic client was also associated with reported diarrhea among respondents (mOR: 4.8, 95% CI 2.9-7.9) and household members (mOR:2.8, 95% CI: 1.9-4.2) in the two weeks preceding the survey. Results suggest that promoting and distributing water chlorination products in ART clinics was effective in increasing access to and use of water treatment products among PLHIV. The positive association between ART clinic attendees and diarrhea likely resulted from the immunocompromised status of ART clinic clients. |
4,994 | A study on the effect of the internal exposure to Po-210 on the excretion of urinary proteins in rats | This study was designed to assess the feasibility of a noninvasive urine specimen for the detection of proteins as indicators of internal exposure to ionizing radiation. Three groups of rats (five in each group) were intravenously injected with 1601 +/- A 376, 10,846 +/- A 591 and 48,467 +/- A 2812 Bq of Po-210 in citrate form. A sham-exposed control group of five rats was intravenously injected with sterile physiological saline. Daily urine samples were collected over 4 days following injection. Purification and pre-concentration of urinary proteins were carried out by ultrafiltration using a 3000 Da molecular weight cutoff membrane filter. The concentration of common urinary proteins, namely albumin, alpha-1-acid glycoprotein, immunoglobulins IgA and IgG, was measured by an enzyme-linked immunosorbent assay. Urinary excretion of albumin decreased dose-dependently (p < 0.05) 96 h post-injection relative to the control group. In contrast, no statistically significant effects were observed for other proteins tested. The dose-dependent decrease in urinary excretion of albumin observed in this study underscores the need for further research, which may lead to the discovery of new biomarkers that would reflect the changes in the primary target organs for deposition of Po-210. |
4,995 | Edge-Computing-Enabled Smart Cities: A Comprehensive Survey | Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency-related issues, its deployment engenders new challenges. In this article, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing-enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions. |
4,996 | Semantic Image Segmentation with Contextual Hierarchical Models | Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500). |
4,997 | Real-time tracking of humans and visualization of their future footsteps in public indoor environments An intelligent interactive system for public entertainment | In this work, an interactive entertainment system which employs multiple-human tracking from a single camera is presented. The proposed system robustly tracks people in an indoor environment and displays their predicted future footsteps in front of them in real-time. The system is composed of a video camera, a computer and a projector. There are three main modules: tracking, analysis and visualization. The tracking module extracts people as moving blobs by using an adaptive background subtraction algorithm. Then, the location and orientation of their next footsteps are predicted. The future footsteps are visualized by a high-paced continuous display of foot images in the predicted location to simulate the natural stepping of a person. To evaluate the performance, the proposed system was exhibited during a public art exhibition in an airport. People showed surprise, excitement, curiosity. They tried to control the display of the footsteps by making various movements. |
4,998 | A novel family of IC-based similarity measures with a detailed experimental survey on WordNet | This paper introduces a novel family of ontology-based similarity measures based on the Information Content (IC) theory, a detailed state of the art, a large experimental survey into ontology-based similarity measures on WordNet, and a new comparison between intrinsic and corpus-based IC models. Our experiments are based on our implementation of a large set of similarity measures, intrinsic and corpus-based IC models, which are evaluated on two known datasets and three different WordNet versions. The new measures are called weighted Jiang-Conrath distance (wJ&Cdist) and similarity (wJ&Csim), cosine-normalized Jiang-Conrath similarity (cosJ&Csim) and cosine-normalized weighted Jiang-Conrath similarity (coswl&Csim). Two of our similarity measures outperform the state-of-the-art measures on the RG65 dataset, and one of them obtains the third overall score on all the datasets and evaluated WordNet versions. The cosine-normalized similarity measures are a non-linear normalization of the classic Jiang-Conrath (J&C) distance and the new wJ&C distance. On the other hand, the wJ&C distance is a generalization of the classic J&C distance which is based on the length of the shortest path between concepts within an IC-based weighted graph. Our measures are based on two not previously considered notions: (1) a generalization of the classic J&C distance to any type of taxonomy, based on an IC-based weighted graph derived from the conditional probabilities between child and parent concepts, and (2) a non-linear normalization function that converts the ontology-based semantic distances into similarity functions. Finally, the corpus-based IC models based on the Resnik method obtain rivaling results as regards the state-of-the-art intrinsic IC models, when they are used with some unexplored WordNet-based frequency files. Therefore, this latter fact allows us to reconsider some previous conclusions about the outperformance of the intrinsic IC models over the corpus-based ones. (C) 2015 Elsevier Ltd. All rights reserved. |
4,999 | Robust long-term correlation tracking with multiple models | To address the challenge of repetitive target appearance variation and frequent occlusion, existing visual tracking methods either handle corrupted samples or correct the appearance model. In this study, the authors propose a novel framework that successfully combines these two strategies. In their method, the base tracker is an improved discriminative correlation filter-based tracker, in which an independent classifier is employed to alleviate the problem of corrupted samples; the best model is selected for improvement from a group of models, which they call a 'model colony'. The model colony is composed of models updated via different processes. The correlation output and the peak-to-sidelobe ratio are used to evaluate each model in the model colony. In addition, they propose a novel criterion called the maximum-to-others ratio for superior model selection. Experiments on 80 challenging sequences show that their tracker outperforms state-of-the-art trackers. In addition, experimental results demonstrate that their formulation significantly improves the performance of their base tracker. |
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