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2,400 | Worms and gills, plates and spines: the evolutionary origins and incredible disparity of deuterostomes revealed by fossils, genes, and development | Deuterostomes are the major division of animal life which includes sea stars, acorn worms, and humans, among a wide variety of ecologically and morphologically disparate taxa. However, their early evolution is poorly understood, due in part to their disparity, which makes identifying commonalities difficult, as well as their relatively poor early fossil record. Here, we review the available morphological, palaeontological, developmental, and molecular data to establish a framework for exploring the origins of this important and enigmatic group. Recent fossil discoveries strongly support a vermiform ancestor to the group Hemichordata, and a fusiform active swimmer as ancestor to Chordata. The diverse and anatomically bewildering variety of forms among the early echinoderms show evidence of both bilateral and radial symmetry. We consider four characteristics most critical for understanding the form and function of the last common ancestor to Deuterostomia: Hox gene expression patterns, larval morphology, the capacity for biomineralization, and the morphology of the pharyngeal region. We posit a deuterostome last common ancestor with a similar antero-posterior gene regulatory system to that found in modern acorn worms and cephalochordates, a simple planktonic larval form, which was later elaborated in the ambulacrarian lineage, the ability to secrete calcium minerals in a limited fashion, and a pharyngeal respiratory region composed of simple pores. This animal was likely to be motile in adult form, as opposed to the sessile origins that have been historically suggested. Recent debates regarding deuterostome monophyly as well as the wide array of deuterostome-affiliated problematica further suggest the possibility that those features were not only present in the last common ancestor of Deuterostomia, but potentially in the ur-bilaterian. The morphology and development of the early deuterostomes, therefore, underpin some of the most significant questions in the study of metazoan evolution. |
2,401 | Going Deeper With Contextual CNN for Hyperspectral Image Classification | In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatiospectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets. |
2,402 | Accelerating Edit-Distance Sequence Alignment on GPU Using the Wavefront Algorithm | Sequence alignment remains a fundamental problem with practical applications ranging from pattern recognition to computational biology. Traditional algorithms based on dynamic programming are hard to parallelize, require significant amounts of memory, and fail to scale for large inputs. This work presents eWFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute the exact edit-distance sequence alignment based on the wavefront alignment algorithm (WFA). This approach exploits the similarities between the input sequences to accelerate the alignment process while requiring less memory than other algorithms. Our implementation takes full advantage of the massive parallel capabilities of modern GPUs to accelerate the alignment process. In addition, we propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast shared memory of a GPU. Our results show that our GPU implementation outperforms by 3-9 x the baseline edit-distance WFA implementation running on a 20 core machine. As a result, eWFA-GPU is up to 265 times faster than state-of-the-art CPU implementation, and up to 56 times faster than state-of-the-art GPU implementations. |
2,403 | Student Team Achievement Division as a tool for peer assisted co-operative learning in neuroanatomy | Student Team Achievement Division (STAD) is a co-operative learning approach premised on a group learning activity that emphazises learning as a social exchange of knowledge between students, in which each student is accountable for his or her own learning and is also encouraged to assist others in achieving their goals. It promotes the cognitive, psychomotor, and emotional growth of students involved in the team. By random sequencing, 60 participants were allocated to interventional group (n=30) and control group (n=30). The participants of the interventional group were subjected to STAD strategy and participants of the control group were instructed to do a conventional self learning on the ventricles of brain. The outcomes were statistically analysed. It was found that the performance of the students is far better with STAD approach than conventional self learning. Our study has shown that Students team Achievement Division can be used as an effective tool for Peer assisted Co-operative Learning in Anatomy. Further studies can be done to investigate the contribution of STAD to teaching other disciplines of Anatomy and other basic medical sciences. |
2,404 | Impact of internal structural design on quality and nutritional properties of 3D printed food products during post-printing: a critical review | 3D food printing (3DFP) provides an excellent opportunity to deposit layers of multiple food materials to create unique complex structures of products with more engaging visuals, specific textures, and customized nutritional properties. Many printed products require post-printing processing which can result in sensory variance, texture changes, and even nutritional modification. Hence it is necessary to implement the design of the complex internal structure to ensure the desired quality of the printed products following post-printing. 3-D printing of various types of food products, for example, chocolate, cheese, meat, vegetables, fruits, fish, eggs, cereal-based products, and so on, has been examined with regard to post-printing requirements. This review aims to summarize the current work on the latest developments in 3DFP technology concerning the internal structure design of 3D printed products and its effect on quality during post-printing. The quality parameters include: textural, physical, morphological, and dimensional characteristics as well as nutritional properties. Furthermore, post-printing modifications such as 4D are also analyzed. |
2,405 | Fun with food - A parent-child community cooking intervention reduces parental fear and increases children's perceived competence | Cooking is being promoted as a preventative strategy for numerous health outcomes. However, there has been a reported decline in opportunities for children to learn in the home environment due to parental barriers such as time and concerns around children conducting certain skills. Therefore, this study aimed to understand the impact of a parent-child community cooking intervention on children's perceived cooking competence and interest in cooking and parental perceptions around including children in cooking. 'Fun with Food' was a four-week parent-child cooking intervention based on Experiential Learning Theory and designed by Home Economists. A mixed-method approach was undertaken to understand the effectiveness of the community-based intervention. Parents completed pre and post focus group discussions that were analyzed using Thematic Analysis. Pre and post surveys were used to investigate children's perceived cooking competence and analyzed with paired-samples t-tests and Cohen's d. Children's perceived cooking competence significantly increased after the intervention (P < 0.001, effect size -0.92). Parental fears around children performing certain skills, such as chopping and cutting, were reduced. Additionally, both children and parents found it an enjoyable experience, and appreciated the time spent together, which may be an indicator for positive wellbeing. Parents reported that children have been more actively involved in cooking since the intervention. Further, parents felt strongly that children should be cooking from as young as possible and that Home Economics should be introduced in primary school and made compulsory for older students in secondary school. The parent-child format for cooking has shown to be effective for increasing children's perceived cooking competence and reducing parental fears, highlighting it as a promising method for future interventions. |
2,406 | Prehistoric landmarks in contrasted territories: Rock art of the Libyan Desert massifs, Egypt | In the Libyan Desert, the Gilf el-Kebir and Jebel el-'Uweinat are two large rock formations located in the extreme South-West of Egypt, at the edge of the Libyan and Sudanese borders. A hundred and twenty kilometers from each other, they are surrounded by plains and sandy formations, punctuated by a few smaller massifs. Although they are of different ages and geological formations, the two great massifs both offered interesting and complementary refuges for prehistoric groups who used rock shelters, cliffs and boulders for engraving and painting. The existence of a multitude of styles and techniques allow to detect striking parallels between the rock art record of the two regions, providing a dynamic view of the regionalization of rock art and of how these territories were conceived and occupied by semi-nomadic groups during the Holocene optimum period (8000-3500 BCE). Paintings from the Gilf el-Kebir show very close stylistic affinities with representations identified in the Jebel el-'Uweinat. But the fact that they remain a minority in the overall rock art record from both areas tends to evidence that, contrary to what has been hypothesized before, migrations between the Gilf el-Kebir and the Jebel el-'Uweinat were not systematic. This paper also highlights a possible increase in the contacts and migrations between the two massifs after the adoption of pastoralist lifestyles. The repartition of rock art and the evolution through times of the parallels offers interesting insights into land use strategies of both hunter-gatherers and herd keepers in such contrasted environments, and into what can be called symbolic territories. (c) 2017 Published by Elsevier Ltd. |
2,407 | Study on the desulfurization performance of calcium-based desulfurizer and NaHCO3 desulfurizer | The commonly used calcium desulfurizers have low desulfurization efficiency. NaHCO3 desulfurizers can meet the requirements of desulfurization efficiency, but the high price and the difficulty in handling desulfurization products make dry flue desulfurization technology quite difficult to realize the large-scale application. Preliminary research found a new calcium desulfurizer, to understand its performance, comparing investigation into the desulfurization performance of different calcium desulfurizer and NaHCO3 desulfurizer. The results showed that with the high-performance calcium desulfurizer, conventional NaHCO3 desulfurizer, and ultrafine NaHCO3 desulfurizer, the operating time with 100% desulfurization efficiency is 25,200, 21,600, and 6000 s, when the flue temperature of 373.15-573.15 K, the "break-through" temperature is 533.15, 473.15, and 373.15 K, expand the use range of desulfurizer flue gas temperature. Regarding the desulfurizer per unit mass, the production costs of ultrafine NaHCO3 desulfurizer are 5.36 times higher than calcium desulfurizer. Compared with NaHCO3 desulfurizer, high-performance calcium desulfurizer is characterized by several advantages, including high desulfurization efficiency, wider applicable temperatures, and low preparation cost, allowing for significant development potential in flue gas desulfurization. |
2,408 | Automatic Transcription of Flamenco Singing From Polyphonic Music Recordings | Automatic note-level transcription is considered one of the most challenging tasks in music information retrieval. The specific case of flamenco singing transcription poses a particular challenge due to its complex melodic progressions, intonation inaccuracies, the use of a high degree of ornamentation, and the presence of guitar accompaniment. In this study, we explore the limitations of existing state of the art transcription systems for the case of flamenco singing and propose a specific solution for this genre: We first extract the predominant melody and apply a novel contour filtering process to eliminate segments of the pitch contour which originate from the guitar accompaniment. We formulate a set of onset detection functions based on volume and pitch characteristics to segment the resulting vocal pitch contour into discrete note events. A quantised pitch label is assigned to each note event by combining global pitch class probabilities with local pitch contour statistics. The proposed system outperforms state of the art singing transcription systems with respect to voicing accuracy, onset detection, and overall performance when evaluated on flamenco singing datasets. |
2,409 | Circadian preference in young adults: Associations with sleep and mental health outcomes from a national survey of Norwegian university students | Individual preferred timing of sleep and activity patterns, known as circadian preference, ranges from definitely morning types to definitely evening types. Being an evening type has been linked to adverse sleep and mental health outcomes. This study aimed to explore the associations between circadian preference and self-reported sleep, depression, anxiety, quality of life, loneliness, and self-harm/suicidal thoughts. Data stem from a national survey of students in higher education in Norway (the SHoT-study). All 169,572 students in Norway were invited to participate, and 59,554 students (66.5% women) accepted (response rate = 35.1%). Circadian preference was associated with sleep and mental health outcomes in a dose-response manner. For both genders, being an evening type (either definitely evening or more evening than morning) was associated with an increase in age-adjusted relative risk (RR-adjusted; range = 1.44 to 2.52 vs. 1.15 to 1.90, respectively) across all outcomes compared with definitely morning types. Overall, the present study provides further evidence that evening circadian preference is associated with adverse sleep and mental health outcomes in young adults. As such, future efforts to improve sleep and mental health in young adults should consider their circadian preferences. |
2,410 | Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks | Recently, functional brain network has been used for the classification of brain disorders, such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods either ignore the non-imaging information associated with the subjects and the relationship between the subjects, or cannot identify and analyze disease-related local brain regions and biomarkers, leading to inaccurate classification results. This paper proposes a local-to-global graph neural network (LG-GNN) to address this issue. A local ROI-GNN is designed to learn feature embeddings of local brain regions and identify biomarkers, and a global Subject-GNN is then established to learn the relationship between the subjects with the embeddings generated by the local ROI-GNN and the non-imaging information. The local ROI-GNN contains a self-attention based pooling module to preserve the embeddings most important for the classification. The global Subject-GNN contains an adaptive weight aggregation block to generate the multi-scale feature embedding corresponding to each subject. The proposed LG-GNN is thoroughly validated using two public datasets for ASD and AD classification. The experimental results demonstrated that it achieves the state-of-the-art performance in terms of various evaluation metrics. |
2,411 | Simultaneous Truth and Performance Level Estimation Through Fusion of Probabilistic Segmentations | Recent research has demonstrated that improved image segmentation can be achieved by multiple template fusion utilizing both label and intensity information. However, intensity weighted fusion approaches use local intensity similarity as a surrogate measure of local template quality for predicting target segmentation and do not seek to characterize template performance. This limits both the usefulness and accuracy of these techniques. Our work here was motivated by the observation that the local intensity similarity is a poor surrogate measure for direct comparison of the template image with the true image target segmentation. Although the true image target segmentation is not available, a high quality estimate can be inferred, and this in turn allows a principled estimate to be made of the local quality of each template at contributing to the target segmentation. We developed a fusion algorithm that uses probabilistic segmentations of the target image to simultaneously infer a reference standard segmentation of the target image and the local quality of each probabilistic segmentation. The concept of comparing templates to a hidden reference standard segmentation enables accurate assessments of the contribution of each template to inferring the target image segmentation to be made, and in practice leads to excellent target image segmentation. We have used the new algorithm for the multiple-template-based segmentation and parcellation of magnetic resonance images of the brain. Intensity and label map images of each one of the aligned templates are used to train a local Gaussian mixture model based classifier. Then, each classifier is used to compute the probabilistic segmentations of the target image. Finally, the generated probabilistic segmentations are fused together using the new fusion algorithm to obtain the segmentation of the target image. We evaluated our method in comparison to other state-of-the-art segmentation methods. We demonstrated that our new fusion algorithm has higher segmentation performance than these methods. |
2,412 | FHENet: Lightweight Feature Hierarchical Exploration Network for Real-Time Rail Surface Defect Inspection in RGB-D Images | In recent years, computer vision systems have been increasingly applied to rail defect inspection. Rail defects should be identified quickly and accurately to ensure safe, stable, and fast train operations and thereby reduce the incidence of accidents and economic losses. As most existing methods focus on accuracy, they cannot be deployed on mobile devices with limited computational resources. To solve this problem, we propose a lightweight feature hierarchical exploration network (FHENet) for real-time rail surface defect inspection. First, boundary textures of rail defects are acquired based on the maximum function and maximum pooling in a novel boundary extraction module (BEM), which improves boundary prediction while avoiding heavy computations. Second, a novel cross-modality exploration module (CEM) complements prominent regions through basic operations to avoid complex inference while providing high detection performance. Third, a novel multifeature integration module (MIM) optimizes representative feature regions by using simple operations to avoid complex computations. Results from extensive experiments demonstrate the superiority of the proposed FHENet to 14 state-of-the-art methods. Regarding efficiency, FHENet outperforms the state-of-the-art methods with only 5.26 M parameters and a processing speed of 60.33 frames/s. The FHENet code and results are available at https://github.com/hjklearn/Rail-Defect-Detection. |
2,413 | Rock art imagery as a proxy for Holocene environmental change: A view from Shuwaymis, NW Saudi Arabia | The animal species depicted in the rock art of Shuwaymis, Saudi Arabia, provide a record of Holocene climatic changes, as seen by the engravers. Of 1903 animal engravings, 1514 contained sufficient detail to allow identification with confidence. In addition, the stratigraphy of the engravings and the depiction of domesticates provide a broad chronological framework that allows a division into images created during the Holocene humid phase and animals represented after the onset of desert conditions. Despite the large sample size, only 16 animal species could be identified, which represents an extraordinarily narrow species spectrum. Comparison with the scarce faunal record of the Arabian Peninsula shows that all larger animals that are thought to have been present in the area were also depicted in the rock art. The contemporaneous presence of at least four large carnivores during the Holocene humid phase suggests that prey animals were abundant, and that the landscape consisted of a mosaic of habitats, potentially with thicker vegetation along the water courses of the wadis and more open vegetation in the landscape around them. Community Earth System Models (COSMOS) climate simulations show that Shuwaymis was at the northern edge of the African Summer Monsoon rainfall regime. It is therefore possible that Shuwaymis was ecologically connected with southwestern Arabia, and that an arid barrier remained in place to the north, restricting the dispersal of Levantine species into Arabia. |
2,414 | De-smokeGCN: Generative Cooperative Networks for Joint Surgical Smoke Detection and Removal | Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this article, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images. |
2,415 | Skin dose contamination conversion coefficients. Benchmark with three simulation codes | Handling of radioactive material by operators can lead to contamination at the surface of the skin in case of an accident. The quantification of the dose received by the skin due to a contamination scenario is performed by means of dedicated dose coefficients as it is the case for other radiation protection dose quantities described in the literature. However, most available coefficients do not match realistic scenarios according to state-of-the-art of science and technology. Therefore, this work deals with dedicated dose conversion factors for skin contamination. Since there is an increasing demand on dose coefficients in general, these specific coefficients can be used for various calculations in radiation protection. In this work a method to evaluate such coefficients for the skin contamination dose related to photons, electrons, positrons, alpha and neutron particles is proposed. The coefficients are generated using Monte-Carlo simulations with three well established calculation codes (FLUKA, MCNP, and GEANT4). The results of the various codes are compared against each other for benchmarking purposes. The new dose coefficients allow the computation of the skin received dose, in the case of skin contamination scenario of an individual, taking into account the decay radiation of the radionuclides of interest. To benchmark the quantity derived here, comparisons of radionuclide contamination doses to the skin using the VARSKIN code available in the literature are performed with the results of this work. |
2,416 | Can economic growth and carbon emissions reduction be owned: evidence from the convergence of digital services and manufacturing in China | Under the background of the deep convergence of China's digital services and manufacturing, it is of great significance to investigate the effects of the convergence of digital services and manufacturing on economic growth and carbon emissions reduction to the application of digital technology in the whole world. This paper constructs a simultaneous equation model and uses three-stage least squares to estimate the effect and mechanism of industrial convergence on economic growth and carbon emissions. The results show that (i) industrial convergence improves the change of total factor productivity (TFP) and the change of technical efficiency, and the reduction of carbon emissions is the main factor driving the growth of TFP and technical efficiency; (ii) industrial convergence and carbon emissions show a significant U-shaped relationship; (iii) the heterogeneity analysis shows that the convergence of capital-intensive, technology-intensive and labor-intensive manufacturing with digital services will help to improve the growth of TFP, it can inhibit carbon emissions first and then promote it. Therefore, the government should take targeted measures to promote industrial convergence of digital services and manufacturing according to the economic development and industry characteristics, so as to give full play to its positive role in economic growth and emissions reduction. |
2,417 | Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units | Fatigue driving has become one of the major causes of traffic accidents. The authors propose an effective method capable of detecting fatigue state via the spatial-temporal feature of driver's eyes. In this work, the authors consider fatigue detection as image-based sequence recognition and an end-to-end trainable convolutional neural network with long short-term memory (LSTM) units is designed. First, the authors apply a deep cascaded multi-task framework to extract eye region from infrared videos. Then the spatial features are learned by deep convolutional layers and the relationships between adjacent frames are analysed via LSTM units. Finally, through authors' model, a sequence-level prediction for driving state is produced. The proposed method achieves superior accuracy over the state-of-the-art techniques on authors' own dataset. Experimental results demonstrate the feasibility of authors' method. |
2,418 | A new focus evaluation operator based on max-min filter and its application in high quality multi-focus image fusion | Multi-focus image fusion plays an important role in the field of image recognition and analysis. However, current focus evaluation operators are complex and inefficiency. In this paper, a new focus evaluation operator based on max-min filter is proposed. In new focus measure, we use max-min filter with the help of average filter and median filter (MMAM) to evaluate the focus degree of source images. This evaluation algorithm can well measure the sharpness of different regions of the image, and the selected clear region will be more useful for human visual or machine perception. The experiment proved that MMAM can perform better than sum-of-modified-Laplacian in most of cases. Later, MMAM is used to fused multi-focus image by combined structure-driven fused regions and depth information of blurred images. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art fusion algorithms on image quality and objective fusion criteria. This paper firstly proposes the concept and computational processing of MMAM, which provides a new research direction and innovative idea for multi-focus image fusion base on filter, and MMAM can embedded into state-of-the-art fusion algorithm to achieve high quality multi-focus image fusion. |
2,419 | Generalized Quadrature Data Weighted Averaging | This brief focuses on quadrature multibit digital-to-analog conversion with data weighted averaging (DWA) as a linearization method. Previous study on generalized DWA is expanded here, into the complex domain, and is compared with prior art on complex DWA. It will be shown that the generalized DWA in the complex domain has several advantages such as reduced complexity, reduced spurious behavior, and inherent quadrature error suppression. |
2,420 | A thermoresponsive cationic block copolymer brush-grafted silica bead interface for temperature-modulated separation of adipose-derived stem cells | Adipose-derived mesenchymal stem cells (ADSCs) have beneficial effects in cell transplantation therapy; these cells are collected from adipose tissue using low-invasive methods. However, to prepare ADSCs for cell therapy, a cell separation method that neither involves modification of the cell surface nor causes loss of cell activity is needed. Here, we aimed to develop ADSC separation columns using thermoresponsive cationic block copolymer brush-grafted beads as packing materials. The block copolymer brush was formed by a bottom cationic segment, poly(N,N-dimethylaminopropylacrylamide) (PDMAPAAm), and an upper thermoresponsive segment, poly(N-isopropylacrylamide) (PNIPAAm), and was grafted in two atom transfer radical polymerization reactions. The copolymer brush-grafted silica beads were packed into a column. An ADSC suspension was introduced into the columns at 37 °C and adsorbed on the copolymer brush-modified beads through electrostatic and hydrophobic interactions with the PDMAPAAm and PNIPAAm segments, respectively. The adsorbed ADSCs eluted from the column by lowering the temperature to 4 °C. In contrast, most Jurkat and vascular endothelial cells eluted at 37 °C, because of the relatively weaker electrostatic interactions with the block copolymer brush compared to ADSCs. Using the prepared column, a mixture of ADSCs and Jurkat cells was separated by changing the column temperature. The recovered ADSCs exhibited cell activity. The developed cell separation column may be useful for isolating ADSCs without cell surface modification, while maintaining cell activity. |
2,421 | Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI | Susceptibility induced distortion is a major artifact that affects the diffusion MRI (dMRI) data analysis. In the Human Connectome Project (HCP), the state-of-the-art method adopted to correct this kind of distortion is to exploit the displacement field from the B0 image in the reversed phase encoding images. However, both the traditional and learning-based approaches have limitations in achieving high correction accuracy in certain brain regions, such as brainstem. By utilizing the fiber orientation distribution (FOD) computed from the dMRI, we propose a novel deep learning framework named DistoRtion Correction Net (DrC-Net), which consists of the U-Net to capture the latent information from the 4D FOD images and the spatial transformer network to propagate the displacement field and back propagate the losses between the deformed FOD images. The experiments are performed on two datasets acquired with different phase encoding (PE) directions including the HCP and the Human Connectome Low Vision (HCLV) dataset. Compared to two traditional methods topup and FODReg and two deep learning methods S-Net and flow-net, the proposed method achieves significant improvements in terms of the mean squared difference (MSD) of fractional anisotropy (FA) images and minimum angular difference between two PEs in white matter and also brainstem regions. In the meantime, the proposed DrC-Net takes only several seconds to predict a displacement field, which is much faster than the FODReg method. |
2,422 | Verification of road databases using multiple road models | In this paper a new approach for automatic road database verification based on remote sensing images is presented. In contrast to existing methods, the applicability of the new approach is not restricted to specific road types, context areas or geographic regions. This is achieved by combining several stateof-the-art road detection and road verification approaches that work well under different circumstances. Each one serves as an independent module representing a unique road model and a specific processing strategy. All modules provide independent solutions for the verification problem of each road object stored in the database in form of two probability distributions, the first one for the state of a database object (correct or incorrect), and a second one for the state of the underlying road model (applicable or not applicable). In accordance with the Dempster-Shafer Theory, both distributions are mapped to a new state space comprising the classes correct, incorrect and unknown. Statistical reasoning is applied to obtain the optimal state of a road object. A comparison with state-of-the-art road detection approaches using benchmark datasets shows that in general the proposed approach provides results with larger completeness. Additional experiments reveal that based on the proposed method a highly reliable semiautomatic approach for road data base verification can be designed. (C) 2017 Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). |
2,423 | Art Painting Diagnostic Before Restoration with Terahertz and Millimeter Waves | Art painting diagnostic is commonly performed using electromagnetic waves at wavelengths from terahertz to X-ray. These former techniques are essential in conservation and art history research, but they could be also very useful for restoring artwork. While most studies use time domain imaging technique, in this study, a painting has been investigated using both time domain imaging (TDI) and frequency-modulated continuous wave (FMCW) system in the millimeter frequency range. By applying these systems to a painting of the eighteenth century, we detect and analyze the structure of some defects. This study underlines the differences between FMCW and TDI. We present the advantages and disadvantages of each technique on a real artwork. |
2,424 | Saliency Based Ulcer Detection for Wireless Capsule Endoscopy Diagnosis | Ulcer is one of the most common symptoms of many serious diseases in the human digestive tract. Especially for the ulcers in the small bowel where other procedures cannot adequately visualize, wireless capsule endoscopy (WCE) is increasingly being used in the diagnosis and clinical management. Because WCE generates large amount of images from the whole process of inspection, computer-aided detection of ulcer is considered an indispensable relief to clinicians. In this paper, a two-staged fully automated computer-aided detection system is proposed to detect ulcer from WCE images. In the first stage, we propose an effective saliency detection method based on multi-level superpixel representation to outline the ulcer candidates. To find the perceptually and semantically meaningful salient regions, we first segment the image into multi-level superpixel segmentations. Each level corresponds to different initial region sizes of the superpixels. Then we evaluate the corresponding saliency according to the color and texture features in superpixel region of each level. In the end, we fuse the saliency maps from all levels together to obtain the final saliency map. In the second stage, we apply the obtained saliency map to better encode the image features for the ulcer image recognition tasks. Because the ulcer mainly corresponds to the saliency region, we propose a saliency max-pooling method integrated with the Locality-constrained Linear Coding (LLC) method to characterize the images. Experiment results achieve promising 92.65% accuracy and 94.12% sensitivity, validating the effectiveness of the proposed method. Moreover, the comparison results show that our detection system outperforms the state-of-the-art methods on the ulcer classification task. |
2,425 | Hydrological impacts of future climate and land use/cover changes in the Lower Mekong Basin: a case study of the Srepok River Basin, Vietnam | This study presents hydrological impacts of future climate change (CC) and land use/cover change (LUCC) for the Srepok River Basin (SRB) in the Vietnam's Central Highlands. The hydrology cycle of this basin was reproduced using Soil and Water Assessment Tool (SWAT) allowing an evaluation of hydrological responses to CC and LUCC. Future climate scenarios of the 2015-2100 period under Representative Concentration Pathways (RCP) 4.5 simulated by five General Circulation Models (GCMs) and LUCC scenario in 2050 were developed. Compared to the reference scenario (1980-2005), future LUCC increases the streamflow (0.25%) and surface runoff (1.2%) and reduces the groundwater discharge (2.1%). Climate change may cause upward trends in streamflow (0.1 to 2.7%), surface runoff (0.4 to 4.3%), and evapotranspiration (0.8 to 3%), and a change in the groundwater discharge (- 1.7 to 0.1%). The combination of CC and LUCC increases the streamflow (0.2 to 2.8%), surface runoff (1.6 to 5.6%), and evapotranspiration (1.0 to 3.1%), and reduces the groundwater discharge (1.5 to 2.7%) with respect to the reference scenario. Moreover, the results noted that the water scarcity may happen in the dry-seasonal months. |
2,426 | Exploring the intersection of structural racism and ageism in healthcare | The American Geriatrics Society (AGS) has consistently advocated for a healthcare system that meets the needs of older adults, including addressing impacts of ageism in healthcare. The intersection of structural racism and ageism compounds the disadvantage experienced by historically marginalized communities. Structural racism and ageism have long been ingrained in all aspects of US society, including healthcare. This intersection exacerbates disparities in social determinants of health, including poor access to healthcare and poor outcomes. These deeply rooted societal injustices have been brought to the forefront of the collective public consciousness at different points throughout history. The COVID-19 pandemic laid bare and exacerbated existing inequities inflicted on historically marginalized communities. Ageist rhetoric and policies during the COVID-19 pandemic further marginalized older adults. Although the detrimental impact of structural racism on health has been well-documented in the literature, generative research on the intersection of structural racism and ageism is limited. The AGS is working to identify and dismantle the healthcare structures that create and perpetuate these combined injustices and, in so doing, create a more just US healthcare system. This paper is intended to provide an overview of important frameworks and guide future efforts to both identify and eliminate bias within healthcare delivery systems and health professions training with a particular focus on the intersection of structural racism and ageism. |
2,427 | Necrotizing Hepatitis Associated with Clostridium perfringens in Broiler Chicks | In this retrospective study we describe unusual cases of clostridial hepatitis associated with high mortality in young broiler chicks. Eleven cases of necrotizing hepatitis in broiler chicks from four companies were submitted to the Poultry Diagnostic and Research Center or the Georgia Poultry Laboratory Network between 2017 and 2020. In most flocks, increased 3-day mortality was followed by an elevated 7-day mortality. Gross lesions included green to dark brown discoloration of the liver, congested lungs, serosanguineous fluid in the caudoventral aspect of the abdomen, and emphysema in the yolk sacs. In birds older than a week of age, disease with neurologic signs became evident and consisted of tremors, stargazing, and incoordination. Histopathologic evaluation revealed multifocal to coalescing fibrinoheterophilic and necrotizing hepatitis associated with gram-positive, long, rod-shaped bacteria. Formalin-fixed liver samples from six cases out of eight cases tested were positive for Clostridium perfringens by immunohistochemistry. Liver samples from two cases were culture positive for Clostridium spp., and C. perfringens was isolated from one sample. Toxinotyping by PCR performed in seven samples revealed the presence of the genes that code for alpha toxin phospholipase C (cpa or plc) and necrotic enteritis toxin B-like (netB) in six samples and as well as C. perfringens large cytotoxin (tpeL) in one sample. Broiler breeders are the suspected source of the infection, and testing revealed C. perfringens in hatchery samples and among broiler breeder flocks. Antimicrobial therapy was coupled with enhanced sanitation at the farm and hatchery in that company, markedly decreasing the mortality and clinical signs. This is the first comprehensive evaluation of clostridial necrotizing hepatitis in newly hatched chicks, and the second ever reported in the literature. |
2,428 | Brucellosis unusually presented as septic knee arthritis: A case report | Brucellosis is one of the world's most prevalent zoonotic illnesses. The most often afflicted joints are the sacroiliac joints, although spondylitis and peripheral arthritis are becoming increasingly prevalent. We described a case of a 40-year-old male patient with Brucellosis presented as septic knee arthritis. |
2,429 | Influencing factors of wide pulse pressure in an elderly Chinese population: A cross-sectional study | Blood pressure and pulse pressure (PP) had their own characteristics in the elderly population. This cross-sectional study including 5030 elderly participants was conducted to describe the distribution of blood pressure and wide PP in the elderly population and find influencing factors of wide PP. Wide PP was defined as PP equal to or more than 65 mmHg, and was classified three types as low systolic blood pressure (SBP) and low diastolic blood pressure (DBP) (LSLD), high SBP and low DBP (HSLD), and high SBP and high DBP (HSHD). Using multivariate logistic regression models to analyze the associations of demographic factors, health-related factors and lifestyle factors with different wide PP types. The associations of lifestyles with wide PP by gender were estimated by subgroup analyses. Among 5030 elderly participants, 2727 (54.2%) participants had wide PP. Logistic regression models showed older age (OR = 2.48, 95%CI: 2.14-2.88), female (OR = 1.31, 95%CI: 1.07-1.60), not married (OR = 1.26, 95%CI: 1.07-1.49), having chronic diseases (OR = 1.28, 95%CI: 1.09-1.50), current alcohol drinker (OR = 1.29, 95%CI: 1.11-1.50) were positively associated, and higher body height (OR = .78, 95%CI: .62-.99), higher education level (OR = .60, 95%CI: .43-.82), current smoker (OR = .79, 95%CI: .64-.97) were negatively associated with wide PP. Among three different types of wide PP including LSLD, HSLD, HSHD, these factors had different effects. Subgroup analyses found that only among male, current smoker was negatively associated and current alcohol drinker was positively associated with wide PP. |
2,430 | Intravenous fluid therapy in patients with severe acute pancreatitis admitted to the intensive care unit: a narrative review | Patients with acute pancreatitis (AP) often require ICU admission, especially when signs of multiorgan failure are present, a condition that defines AP as severe. This disease is characterized by a massive pancreatic release of pro-inflammatory cytokines that causes a systemic inflammatory response syndrome and a profound intravascular fluid loss. This leads to a mixed hypovolemic and distributive shock and ultimately to multiorgan failure. Aggressive fluid resuscitation is traditionally considered the mainstay treatment of AP. In fact, all available guidelines underline the importance of fluid therapy, particularly in the first 24-48 h after disease onset. However, there is currently no consensus neither about the type, nor about the optimal fluid rate, total volume, or goal of fluid administration. In general, a starting fluid rate of 5-10 ml/kg/h of Ringer's lactate solution for the first 24 h has been recommended. Fluid administration should be aggressive in the first hours, and continued only for the appropriate time frame, being usually discontinued, or significantly reduced after the first 24-48 h after admission. Close clinical and hemodynamic monitoring along with the definition of clear resuscitation goals are fundamental. Generally accepted targets are urinary output, reversal of tachycardia and hypotension, and improvement of laboratory markers. However, the usefulness of different endpoints to guide fluid therapy is highly debated. The importance of close monitoring of fluid infusion and balance is acknowledged by most available guidelines to avoid the deleterious effect of fluid overload. Fluid therapy should be carefully tailored in patients with severe AP, as for other conditions frequently managed in the ICU requiring large fluid amounts, such as septic shock and burn injury. A combination of both noninvasive clinical and invasive hemodynamic parameters, and laboratory markers should guide clinicians in the early phase of severe AP to meet organ perfusion requirements with the proper administration of fluids while avoiding fluid overload. In this narrative review the most recent evidence about fluid therapy in severe AP is discussed and an operative algorithm for fluid administration based on an individualized approach is proposed. |
2,431 | Projection based weight normalization: Efficient method for optimization on oblique manifold in DNNs | Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling based weight space symmetry (SBWSS) in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over the Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art neural networks. The experimental results show that our method is able to improve the performance of different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method achievers the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively. (C) 2020 Elsevier Ltd. All rights reserved. |
2,432 | Using computer vision to recognize construction material: A Trustworthy Dataset Perspective | Data quality is vital in machine learning models, and data scientists spend a substantial amount of time on data quality certificates before model training. With the objective of building trustworthy construction material datasets in this study, we first built construction material datasets, namely those for concrete, brick, metal, wood, and stone data, by collecting images of different construction materials. In particular, these datasets include the main construction material (i.e., concrete, brick, metal, wood, and stone), background, and uncertainty cate-gories. Subsequently, we propose a novel cross-review framework that applies the entire corresponding datasets as the input (e.g., brick datasets) and returns clean datasets. Subsequently, several state-of-the-art deep con-volutional neural networks such as VGG16, GoogleNet, and ResNet were selected to verify the quality and effectiveness of the construction material datasets under two pipelines: friendly mobile devices and unfriendly architectures. The experimental results show that the proposed construction waste material datasets have satisfactory quality and can be effectively recognized by these state-of-the-art deep neural network models. Additionally, the results indicate the necessity to clean the data before training a deep-learning model. Further research should be combining a novel deep construction material recognition model with the cross-review framework to further improve the recognition performance. |
2,433 | Comprehensive data of 4502 patients newly diagnosed with colorectal liver metastasis between 2015 and 2017, and prognostic data of 2427 patients newly diagnosed with colorectal liver metastasis in 2013 and 2014: Third report of a nationwide survey in Japan | To improve treatment outcomes in patients with colorectal liver metastasis (CRLM), the Joint Committee for Nationwide Survey on CRLM was established by the Japanese Society for Cancer of the Colon and Rectum and the Japanese Society of Hepato-Biliary-Pancreatic Surgery. The aim of the study was to evaluate transition in the characteristics and treatment strategy in CRLM patients and analyze prognostic factors using large-scale data. The present study summarizes the data of patients newly diagnosed between 2015 and 2017 and presents prognostic data of patients newly diagnosed in 2013 and 2014. Survival curves were generated by the Kaplan-Meier method and compared by log-rank test. Multivariate analyses were carried out using Cox proportional hazard modeling. The data of 4502 patients newly diagnosed with CRLM between 2015 and 2017 and the prognostic data of 2427 patients diagnosed in 2013 and 2014 are included. Regarding the 2013 and 2014 prognostic data, the 5-year overall survival (OS) rates of patients who underwent hepatectomy alone was 59.8%. Multivariate analyses identified age at diagnosis of CRLM ≥70 years, concomitant extrahepatic metastasis at diagnosis of CRLM, tumor depth of primary lesion ≥subserosa/pericolic or perirectal tissue, mutant KRAS status, number of CRLM ≥5, maximum diameter of CRLM >5 cm, and surgical curability R1/R2 as independent predictors of OS. Analysis of the latest nationwide database of patients diagnosed with CRLM revealed changes in patients and oncological characteristics, a transition in treatment strategy, and different independent prognosticators to those reported previously. |
2,434 | OANet: Learning Two-View Correspondences and Geometry Using Order-Aware Network | Establishing correct correspondences between two images should consider both local and global spatial context. Given putative correspondences of feature points in two views, in this paper, we propose Order-Aware Network, which infers the probabilities of correspondences being inliers and regresses the relative pose encoded by the essential or fundamental matrix. Specifically, this proposed network is built hierarchically and comprises three operations. First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix. These clusters are in canonical order and invariant to input permutations. Next, the clusters are spatially correlated to encode the global context of correspondences. After that, the context-encoded clusters are interpolated back to the original size and position to build a hierarchical architecture. We intensively experiment on both outdoor and indoor datasets. The accuracy of the two-view geometry and correspondences are significantly improved over the state-of-the-arts. Besides, based on the proposed method and advanced local feature, we won the first place in CVPR 2019 image matching workshop challenge and also achieve state-of-the-art results in the Visual Localization benchmark. Code is available at https://github.com/zjhthu/OANet. |
2,435 | Systems analysis, design, and optimization of geothermal energy systems for power production and polygeneration: State-of-the-art and future challenges | Geothermal energy has significant potential to reduce fossil fuel consumptions and environmental impacts. To improve energy conversion efficiency of geothermal energy systems, numerous systems designs have been proposed and their optimization sought. At this point, it is worth reviewing current developed geothermal energy systems because understanding configurations and principles of basic and state-of-the-art technologies is important for developing advanced energy systems. A comprehensive review of the geothermal energy systems is carried out from the perspective of systems analysis, design, and optimization. Results illustrate that limited sets of parameters have been considered in most studies on design and optimization, though these studies provide great insight into specific designs. However, all influential factors have to be fully considered for practical applications. This study identifies and organizes influential factors for geothermal energy systems. In addition, critical analyses of studies on systems design and optimization are performed to determine limitations of current studies. As polygeneration systems produce various energy products (electricity, heat, and/or cooling), it might play key roles to maximize utilization of geothermal energy. Especially, polygeneration systems with binary technology, which can effectively produce electricity from moderate temperature geothermal resources, have significant potentials to enhance the overall performance. In this regard, the energy production strategy and technology selection are of significant importance to meet electric, heating, and cooling loads efficiently. To fill the knowledge gaps and to maximize geothermal energy utilization, this review proposes state-of-the-art multi-scale modeling and optimization framework. |
2,436 | State of the Art Sub-Terahertz Switching Solutions | In this paper the state of the art in RF switches for mm-wave frequency range is summarized and evaluated. Several leading technologies is presented from typical semiconductor devices based on transistors and diodes on Si, SiGe or III-V semiconductor substrates to more unconventional solutions such as microelectromechanical or phase-change material switches. The most important parameters and characteristics for those technologies are gathered and compiled for comparison. Besides different technologies, also various switch topologies of mm-wave switches are presented, assessed and compared. Furthermore, new emerging technological solutions approaching mm-wave range involving two-dimensional materials are also presented. Their evaluation is focused on proposed designs and current results for experimentally evaluated prototypes. Although the performance of these devices are currently not competitive with more traditional approaches, some reported results near the mm-wave range makes them a promising solution for future mm-wave switches and an interesting topic for further research and development. |
2,437 | Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks | Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input-output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN. |
2,438 | Quantifying cell adhesion through forces generated by acoustic streaming | The strength of cell adhesion is important in understanding the cell's health and in culturing them. Quantitative measurement of cell adhesion strength is a significant challenge in bioengineering research. For this, the present study describes a system that can measure cell adhesion strength using acoustic streaming induced by Lamb waves. Cells are cultured on an ultrasound transducer using a range of preculture and incubation times with phosphate-buffered saline (PBS) just before the measurement. Acoustic streaming is then induced using several Lamb wave intensities, exposing the cells to shear flows and eventually detaching them. By relying upon a median detachment rate of 50 %, the corresponding detachment force, or force of cell adhesion, was determined to be on the order of several nN, consistent with previous reports. The stronger the induced shear flow, the more cells were detached. Further, we employed a preculture time of 8 to 24 h and a PBS incubation time of 0 to 60 min, producing cell adhesion forces that varied from 1.2 to 13 nN. Hence, the developed system can quantify cell adhesion strength over a wide range, possibly offering a fundamental tool for cell-based bioengineering. |
2,439 | Effects of natural and anthropogenic storm-stranded debris in upper-beach arthropods: Is wrack a prey hotspot for birds? | Storm-stranded debris (i.e., wrack) are important components for the functioning of beach ecosystems. With the current increase in extreme storm events, beached wrack is expected to change globally. However, little is known about how different types of wrack can affect beach biodiversity. Here, we hypothesized that natural debris (algae and land-plant debris) would optimize the short-term aggregation of benthic arthropods on the beach ecosystem, while anthropogenic debris (plastics) would not perform this function. We also expected that short-term aggregations of arthropods in the natural debris would create a transient prey hotspot (i.e., points of high prey concentration) for birds on the beach. Thus, we performed manipulative field experiments with debris addition and predator exclusion by cage on a short temporal scale (maximum 20 days). We found that natural debris aggregated higher community abundances than anthropic debris and treatments without debris, while community richness was not affected by wrack. No differences were noted when comparing the community aggregation on plastic debris and treatments without debris. The coleopterans were the group responsible for this aggregation, mainly represented by Phaleria testacea, which aggregated on natural debris with abundances five times greater than those on plastic debris. Nevertheless, we did not find any evidence of increased predation by birds on the coleopterans aggregated in the natural debris. We conclude that arthropod aggregation in the wrack is a phenomenon primarily associated with natural debris, not occurring in plastic debris, although the role of this faunal aggregation as a prey hotspot for birds was not evident in the short term. These results showed that the wrack type matters in terms of consequences for beach arthropods, creating concerns against beach cleaning methods that are adopted indiscriminately, also signaling the need for long-term studies to proceed with investigating the wrack functions for top predators on sandy beaches. |
2,440 | Uncovering Heterogeneous Associations Between Disaster-Related Trauma and Subsequent Functional Limitations: A Machine-Learning Approach | This study examined heterogeneity in the association between disaster-related home loss and functional limitations of older adults, and identified characteristics of vulnerable subpopulations. Data were from a prospective cohort study of Japanese older survivors of the 2011 Japan Earthquake. Complete home loss was objectively assessed. Outcomes in 2013 (n = 3,350) and 2016 (n = 2,664) included certified physical disability levels, self-reported activities of daily living, and instrumental activities of daily living. We estimated population average associations between home loss and functional limitations via targeted maximum likelihood estimation with SuperLearning and its heterogeneity via the generalized random forest algorithm. We adjusted for 55 characteristics of survivors from the baseline survey conducted 7 months before the disaster. While home loss was consistently associated with increased functional limitations on average, there was evidence of effect heterogeneity for all outcomes. Comparing the most and least vulnerable groups, the most vulnerable group tended to be older, not married, living alone, and not working, with preexisting health problems before the disaster. Individuals who were less educated but had higher income also appeared vulnerable for some outcomes. Our inductive approach for effect heterogeneity using machine learning algorithm uncovered large and complex heterogeneity in postdisaster functional limitations among Japanese older survivors. |
2,441 | Deep convolutional features for image retrieval | Nowadays, the use of Convolutional Neural Networks (CNNs) has led to tremendous achievements in several computer vision challenges. CNN-based image retrieval methods vary in complexity, growing capacity, and execution time. This work presents a state-of-the-art review in Deep Convolutional Features for image retrieval, pointing out their scope, advantages, and limitations. Moreover, the paper presents a procedure that adopts the latest architectures of pre-trained CNNs that have been initially proposed for image classification to shape image retrieval features. It investigates their suitability on several image retrieval tasks, without any optimization procedure, exhaustive preparatory work, and tuning. Each network's performance is evaluated in two different setups: one employing global and one using local representations. Extensive experiments on several well-known benchmark datasets demonstrate that a simple normalization on the pre-trained networks yields results comparable to state-of-the-art approaches. The global descriptor shapes a plug-and-play approach, which can be adopted for description and retrieval without any prior initialization or training. Moreover, the descriptor's localized version outperforms significantly much more sophisticated and complex methods of the recent literature. |
2,442 | Integration of Wearable Sensors Measurements for Indoor Pedestrian Tracking | Wearable sensors have great potential to ensure the safety and security of humans in hazardous and unknown environments. Real-time tracking and mapping are key aspects of safety and security. Integrating body-mounted wearable sensors measurements for human tracking is challenging due to the high degree of freedom of human motion. Particularly, time synchronization and spatial alignment between different sensors make the integration of measurements challenging. Also, weight, power, and computational resources impose additional constraints. This article addresses these challenges by demonstrating the design and implementation of a multisensor pedestrian tracking system that integrates, spatially aligns, and time synchronizes state-of-art wearable tracking and positioning sensors/technologies. The integrated measurements are packaged into labelled datasets that will be publicly available and easily accessible for researchers. The dataset includes raw and processed data of multiple wearable inertial sensors, a stereo camera, a handheld LiDAR, and an ultrawide-band (UWB) receiver. The dataset is supported by a high-accuracy ground truth reference generated by a post-processed 3D LiDAR SLAM engine. Assessment of state-of-art tracking technologies/sensors is covered, and details of tracking algorithms and their performance and challenges are demonstrated. |
2,443 | Deep Interactive Denoiser (DID) for X-Ray Computed Tomography | Low-dose computed tomography (LDCT) is desirable for both diagnostic imaging and image-guided interventions. Denoisers are widely used to improve the quality of LDCT. Deep learning (DL)-based denoisers have shown state-of-the-art performance and are becoming mainstream methods. However, there are two challenges to using DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs, which are sometimes needed for different clinical tasks; and 2) the model's generalizability might be an issue when the noise level in the testing images differs from that in the training dataset. To address these two challenges, in this work, we introduce a lightweight optimization process that can run on top of any existing DL-based denoiser during the testing phase to generate multiple image candidates with different noise-resolution tradeoffs suitable for different clinical tasks in real time. Consequently, our method allows users to interact with the denoiser to efficiently review various image candidates and quickly pick the desired one; thus, we termed this method deep interactive denoiser (DID). Experimental results demonstrated that DID can deliver multiple image candidates with different noise-resolution tradeoffs and shows great generalizability across various network architectures, as well as training and testing datasets with various noise levels. |
2,444 | Investigations on the Role of Iron (III) and Silica-Iron (III) for DNA Protection Against Highly Intense UV Radiation: Tracking the Connection of Prebiotic Chemistry to Biology | The mineral reaction pathways that yield organic compounds of increasing complexity would have required a means of protective screening against strong ultraviolet radiation for macromolecular assembly on early Earth. In this study, a bacterial chromosomal plasmid DNA was used as a model biomolecule that represents a complex polymeric nucleic acid containing genetic information. The plasmid DNA was exposed to UV radiation through a medium containing air, water, iron (Fe3+), or silica-iron rich aqueous solutions. Our results demonstrate that the plasmid DNA underwent covalent breakage in an aqueous solution when exposed to UV radiation but was shielded against damage due to the presence of iron and silica. It is demonstrated that a suspension of ca. 40 nm colloidal particles of silica gel embedded with Fe3+ ions adsorbed on silanol groups that formed nanoclusters of noncrystalline iron hydroxide is an extremely efficient shelter against intense UV radiation. The implications for our understanding of primitive Earth and Earth-like planets, moons, and asteroids are discussed. The stability of a chromosomal DNA molecule against UV radiation in the presence of iron and silica may provide support on how macromolecules endured early Earth environments and brought forth important implications on early molecular survival against UV radiation. |
2,445 | 14-3-3τ drives estrogen receptor loss via ERα36 induction and GATA3 inhibition in breast cancer | About one-fourth of recurrent estrogen receptor-positive (ER+) breast cancers lose ER expression, leading to endocrine therapy failure. However, the mechanisms underlying ER loss remain to be fully explored. We now show that 14-3-3τ, up-regulated in ∼60% of breast cancer, drives the conversion of ER+ to ER- and epithelial-to-mesenchymal transition (EMT). We identify ERα36, an isoform of ERα66, as a downstream effector of 14-3-3τ. Overexpression of 14-3-3τ induces ERα36 in xenografts and tumor spheroids. The regulation is further supported by a positive correlation between ERα36 and 14-3-3τ expression in human breast cancers. ERα36 can antagonize ERα66 and inhibit ERα66 expression. Isoform-specific depletion of ERα36 blocks the ER conversion and EMT induced by 14-3-3τ overexpression in tumor spheroids, thus establishing ERα36 as a key mediator in 14-3-3τ-driven ER loss and EMT. ERα36 promoter is repressed by GATA3, which can be phosphorylated by AKT at consensus binding sites for 14-3-3. Upon AKT activation, 14-3-3τ binds phosphorylated GATA3 and facilitates the degradation of GATA3 causing GATA3 to lose transcriptional control over its target genes ERα66 and ERα36. We also demonstrate a role for the collaboration between 14-3-3τ and AKT in ERα36 induction and endocrine therapy resistance by three-dimensional spheroid and tamoxifen treatment models in MCF7 and T47D ER+ breast cancer cells. Thus, the 14-3-3τ-ERα36 regulation provides a previously unrecognized mechanism for ER loss and endocrine therapy failure. |
2,446 | Jointly Extract Entities and Their Relations From Biomedical Text | Entity recognition and relation extraction have become an important part of knowledge acquisition, and which have been widely applied in various fields, such as Bioinformatics. However, prior state-of-the-art extraction models heavily rely on the external features obtained from hand-craft or natural language processing (NLP) tools. As a result, the performance of models depends directly on the accuracy of the obtained features. Moreover, current joint extraction approaches cannot effectively tackle the multi-head problem (i.e. an entity is related to multiple entities). In this paper, we firstly present a novel tagging scheme and then propose a joint approach based deep neural network for producing unique tagging sequences. Our approach can not only simultaneously perform entity resolution and relation extraction without any external features, but also effectively solve the multi-head problem. Besides, since arbitrary tokens may provide important cues for two components, we exploit self-attention to explicitly capture long-range dependencies among them and character embeddings to learn the features of lexical morphology, which make our method less susceptible to cascading errors. The results demonstrate that the joint method proposed outperforms the other state-of-the-art joint models. Our work is beneficial for biomedical text mining, and the construction of the biomedical knowledge base. |
2,447 | Robust metric learning based on subspace learning with l(p) - norm | Distance metric learning has been an important technique in machine learning field recently due to its high effectiveness in improving the performance of distance related methods. In order to take advantages of both subspace learning and metric learning to overcome the limitations of metric learning, in this work we intend to learn a robust discriminative subspace and a distance metric simultaneously by maximizing the ratio of inter-class covariance to inner-class covariance using l(p) - norm (0 < p <= 2), where the l(p) - norm is used to enhance the robustness. The proposed model is a more general framework compared to the state-of-art algorithms. Moreover, a modified gradient ascending algorithm is designed to optimize the problem, and the convergence of the algorithm and complexity are analyzed. To verify the proposed method, we carry out numerical experiments on artificial data sets and benchmark data sets. Under different evaluation criterions, experiment results show that the proposed method achieves better performance than the state-of-art algorithms in most cases. (C) 2021 Published by Elsevier B.V. |
2,448 | Long noncoding RNA SNHG5 promotes podocyte injury via the microRNA-26a-5p/TRPC6 pathway in diabetic nephropathy | Podocyte injury is a characteristic pathological hallmark of diabetic nephropathy (DN). However, the exact mechanism of podocyte injury in DN is incompletely understood. This study was conducted using db/db mice and immortalized mouse podocytes. High-throughput sequencing was used to identify the differentially expressed long noncoding RNAs in kidney of db/db mice. The lentiviral shRNA directed against long noncoding RNA small nucleolar RNA host gene 5 (SNHG5) or microRNA-26a-5p (miR-26a-5p) agomir was used to treat db/db mice to regulate the SNHG5/miR-26a-5p pathway. Here, we found that the expression of transient receptor potential canonical type 6 (TRPC6) was significantly increased in injured podocytes under the condition of DN, which was associated with markedly decreased miR-26a-5p. We determined that miR-26a-5p overexpression ameliorated podocyte injury in DN via binding to 3'-UTR of Trpc6, as evidenced by the markedly reduced activity of luciferase reporters by miR-26a-5p mimic. Then, the upregulated SNHG5 in podocytes and kidney in DN was identified, and it was proved to sponge to miR-26a-5p directly using luciferase activity, RNA immunoprecipitation, and RNA pull-down assay. Knockdown of SNHG5 attenuated podocyte injury in vitro, accompanied by an increased expression of miR-26a-5p and decreased expression of TRPC6, demonstrating that SNHG5 promoted podocyte injury by controlling the miR-26a-5p/TRPC6 pathway. Moreover, knockdown of SNHG5 protects against podocyte injury and progression of DN in vivo. In conclusion, SNHG5 promotes podocyte injury via the miR-26a-5p/TRPC6 pathway in DN. Our findings provide novel insights into the pathophysiology of podocyte injury and a potential new therapeutic strategy for DN. |
2,449 | Carrageenan functional film integrated with Pickering Emulsion of Oregano Oil Stabilized by Cationic Nanocellulose for Active Packaging | Consumers are worried about potential contaminants, especially during any pandemic event, and are demanding more biodegradable food packaging with little to no chemical preservatives. This study aims to prepare carrageenan film containing essential oil with antibacterial properties. Oregano essential oil is successfully added into the carrageenan-based film using the Pickering emulsion method with cationic nanocellulose as stabilizer. The positive charge of nanocellulose enhances the stability of emulsion through strong electrostatic interaction with the Oregano Oil. FTIR spectra and SEM micrographs show the Oregano Pickering emulsion (OrePE) well dispersed in the polymer matrix and good compatibility with carrageenan film. The mechanical and thermal properties of carrageenan film were only slightly affected by the addition of OrePE. The tensile strength of films significantly decreased, whereas the elongation break increased following the addition of OrePE. Moreover, the addition of OrePE to the carageen film provides inhibitory effects on gram-positive (S. aureus) and gram negative (E. coli) bacteria. This innovative incorporation of essential oil into biopolymer films by Pickering emulsion technology may have implications for extending the shelf life of food products which is indicates that the material has the potential to be used in active packaging. |
2,450 | Higher sirt1 is associated with a better body composition in master sprinters and untrained peers | HighlightsLower levels of Sirt1 are associated with higher body fat.Master athlete lifestyle seems to promote higher Sirt1 Levels. |
2,451 | Modelling and solving a cost-oriented resource-constrained multi-model assembly line balancing problem | A line balancing problem considers the assignment of operations to workstations in an assembly line. While assembly lines are usually associated to mass production of standardised goods, their advantages have led to their widespread use whenever a product-oriented production system is applicable and the benefits of the labour division and specialisation are significant, even when some of its characteristics may deviate from classical assembly lines. In this work, we study a line balancing problem found in the textile industry in which the line must be balanced for multiple types of goods taking into account resource requirements. In order to solve the problem, a hybrid method that combines classical methods for line balancing with an Estimation of Distribution Algorithm is proposed. Computational experiments show that the new procedure improves upon the state of the art when compared using a benchmark set derived from the literature, as well as when compared using data from the manufacturer that originated this research work. |
2,452 | Efficacy increase of lipid nanoparticles in vivo by inclusion of bis(monoacylglycerol)phosphate | Aim: To investigate the effect of incorporating bis(monoacylglycerol)phosphate (BMP) lipid into a lipid nanoparticle and the functional transport of mRNA by the formulated nanoparticles in vivo. Materials & methods: The nanoparticles were prepared from ionizable lipid, 1,2-distearoyl-sn-glycerol-3-phosphocholine, cholesterol, 1,2-dimyristoyl-sn-glycerol PEG 2000, BMP and formulated mRNA encoding human erythropoietin. We measured the effect of BMP on physicochemical properties and impact on functional efficacy to transport mRNA to its target cells/tissue as measured by protein expression both in vitro and in vivo. Results: Lipid nanoparticles composed of BMP displayed increased endosomal membrane fusion and improved mRNA delivery to the cytosol. Conclusion: The results establish the foundation for future development of these nanoparticulated entities by designing new BMP derivatives and correlating structures to enhanced pharmacokinetic profiles. |
2,453 | Evaluation System of CG Art Communication Platform Based on User Experience | With the rapid development of computer technology in recent years, CG art has gradually entered the ranks of mainstream visual art and received more attention. CG art refers to digital visual technology works created by computer, graphic design software, digital photography technology and computer-aided drawing software. The communication platform of CG art is mainly online, while the research on relevant online platforms mainly focuses on the content and form of art works. There is still a gap between the research on the platform user experience. From the perspective of user experience, this research uses a combination of qualitative and quantitative methods to study the evaluation of user emotional experience of CG art communication platform. Through grounded theory qualitative research methods, this research summarizes the evaluation system indicators of CG art communication platform user experience. The Delphi method and analytic hierarchy process are used to obtain the weight of relevant indicators of the evaluation system. Then take three CG art communication platforms as samples. Using the fuzzy comprehensive evaluation method to compare and verify the user evaluation system constructed. The results show that the evaluation system can effectively evaluate the user experience level of CG art platform. And provide a basis of the construction of CG art communication platform and the optimization of user experience. |
2,454 | Corneal Endothelial Cell Segmentation by Classifier-Driven Merging of Oversegmented Images | Corneal endothelium images obtained by in vivo specular microscopy provide important information to assess the health status of the cornea. Estimation of clinical parameters, such as cell density, polymegethism, and pleomorphism, requires accurate cell segmentation. State-of-the-art techniques to automatically segment the endothelium are error-prone when applied to images with low contrast and/or large variation in cell size. Here, we propose an automatic method to segment the endothelium. Starting with an oversegmented image comprised of superpixels obtained from a stochastic watershed segmentation, the proposed method uses intensity and shape information of the superpixels to identify and merge those that constitute a cell, using support vector machines. We evaluated the automatic segmentation on a data set of in vivo specular microscopy images (Topcon SP-1P), obtaining 95.8% correctly merged cells and 2.0% undersegmented cells. We also evaluated the parameter estimation against the results of the vendor's built-in software, obtaining a statistically significant better precision in all parameters and a similar or better accuracy. The parameter estimation was also evaluated on three other data sets from different imaging modalities (confocal microscopy, phase-contrast microscopy, and fluorescence confocal microscopy) and tissue types (ex vivo corneal endothelium and retinal pigment epithelium). In comparison with the estimates of the data sets' authors, we achieved statistically significant better accuracy and precision in all parameters except pleomorphism, where a similar accuracy and precisionwere obtained. |
2,455 | Toward Fog-Based Mobile Crowdsensing Systems: State of the Art and Opportunities | MCS is an emerging paradigm that leverages the pervasiveness of mobile, wearable, and vehicle-mounted devices to collect data from urban environments for ubiquitous service provisioning. In order to manage MCS application data streams efficiently, a scalable computing infrastructure hosting heterogeneous and distributed resources is critical. FC, as a geo-distributed computing paradigm, is a key enabler for this requirement as it bridges cloud servers and smart mobile devices. Research on the integration of MCS with FC has recently started to be explored, recognizing the requirements of MCS and their coexistence with cyber-physical systems. In this article, we analyze the state of the art of FC solutions in MCS systems. After a brief overview of MCS, we emphasize the link between MCS and FC. We then investigate the existing fog-based MCS architectures in detail by focusing on their building blocks, as well as the challenges that remain unaddressed. Our detailed review on the subject results in a taxonomy of FC solutions in MCS systems. In particular, we highlight the node structures, the information exchanged, the resource and service management, and the type of solutions adopted concerning privacy and security. Moreover, we provide a thorough discussion on the open issues and challenges by reporting useful insights for researchers in MCS and FC. |
2,456 | Local approach for face verification in polar frequency domain | We present a face verification system inspired by known properties of the human visual system. In the proposed algorithm the face is normalized for geometry and luminance, and Fourier-Bessel (FB) descriptors are extracted from three locations in the eyes region (local analysis). The resulting representations are embedded in a dissimilarity space, where each image is represented by its distance to all the other images, and a Pseudo-Fisher discriminator is built. Using the FERET database, we submitted the system to a battery of tests under a wide variation of imaging conditions, including expression, age, and illumination variations. Results showed that the system outperformed previous state-of-the-art methods in most testing conditions. To deal with partial occlusions, we implemented an occluded region detector that resulted in low performance loss under up to 50% occlusion level. Finally, we automated the registration step by implementing face and eye detection algorithms. We also showed that the local-FB analysis outperforms the global-FB version of the system and an alternative polar frequency representation. In conclusion, the intermediate-scale local analysis approach used in the proposed system resulted in state-of-the-art face verification performance and high robustness to common problems such as expression, age, and illumination variations and to strong occlusions. (c) 2006 Elsevier B.V. All rights reserved. |
2,457 | Engineering nanostructured Ag doped α-MnO2 electrocatalyst for highly efficient rechargeable zinc-air batteries | Engineering of highly active, and non-precious electrocatalysts are vital to enhance the air-electrodes of rechargeable zinc-air batteries (ZABs). We report a facile co-precipitation technique to develop Ag doped α-MnO2 nanoparticles (NPs) and investigate their application as cathode materials for ZABs. The electrochemical and physical characteristics of α-MnO2 and Ag doped α-MnO2 NPs were compared and examined via CP, CV, TGA/DTA, FT-IR, EIS, and XRD analysis. CV result displayed higher potential and current for ORR in Ag doped α-MnO2 NPs than α-MnO2; but, ORR performance decreased when the Ag doping was raised from 7.5 to10 mmol. Moreover, α-MnO2 and Ag doped α-MnO2 NPs showed 2.1 and 3.8 electron transfer pathway, respectively, showing Ag doped α-MnO2 performance to act as an active ORR electrocatalyst for ZABs. The EIS investigation exhibited that charge-transfer resistance for Ag doped α-MnO2 was extremely lower associated to the MnO2 demonstrating that the successful loading of Ag in α-MnO2. A homemade ZAB based on Ag-MnO2-7.5 showed a high open circuit potential, low ohmic resistances, and excellent discharge profile at a constant current density of 1 mA/g. Moreover, Ag-MnO2-7.5 show a specific capacity of 795 mA h g-1 with corresponding high energy density ∼875 Wh kg-1 at 1 mA cm-2 discharging conditions. |
2,458 | MALS-Net: A Multi-Head Attention-Based LSTM Sequence-to-Sequence Network for Socio-Temporal Interaction Modelling and Trajectory Prediction | Predicting the trajectories of surrounding vehicles is an essential task in autonomous driving, especially in a highway setting, where minor deviations in motion can cause serious road accidents. The future trajectory prediction is often not only based on historical trajectories but also on a representation of the interaction between neighbouring vehicles. Current state-of-the-art methods have extensively utilized RNNs, CNNs and GNNs to model this interaction and predict future trajectories, relying on a very popular dataset known as NGSIM, which, however, has been criticized for being noisy and prone to overfitting issues. Moreover, transformers, which gained popularity from their benchmark performance in various NLP tasks, have hardly been explored in this problem, presumably due to the accumulative errors in their autoregressive decoding nature of time-series forecasting. Therefore, we propose MALS-Net, a Multi-Head Attention-based LSTM Sequence-to-Sequence model that makes use of the transformer's mechanism without suffering from accumulative errors by utilizing an attention-based LSTM encoder-decoder architecture. The proposed model was then evaluated in BLVD, a more practical dataset without the overfitting issue of NGSIM. Compared to other relevant approaches, our model exhibits state-of-the-art performance for both short and long-term prediction. |
2,459 | Design of narrow transition band variable bandwidth digital filter | This study presents a novel implementation technique for linear phase variable bandwidth finite impulse response (FIR) filters with a noticeable reduction in transition bandwidth as well as hardware complexity. In this proposition, concept of Farrow structure based design technique is effectively utilised; whereas the fixed sub-filters are constructed from a generalised interpolated bandpass method based low-pass FIR filter by means of different techniques such as interpolation, up-sampling and masking. Simulation results have shown the frequency response characteristics of several variable bandwidth FIR filters, designed with the help of the proposed architecture. Simulation results have shown a drastic reduction of transition bandwidth resulting from the proposed variable bandwidth FIR filters of up to similar to 70-80%, when compared with other state-of-the-art filtering techniques, with a significant reduction in hardware complexity of up to similar to 50%. |
2,460 | Implications of Non-Uniform Deadline Scaling to Quality of Service Under Single Errors | Fault-tolerant real-time systems for emerging critical applications like wearable electronic healthcare monitors, consumer-grade unmanned aerial vehicles, or environmental monitoring have to tolerate errors during operation. If they fail, the consequences are dire. But often their budgets for error mitigation capabilities are low, which requires to limit mitigation capabilities to the most critical functions of a system. Still, less critical functions of a system should operate as long as possible, but current state-of-the-art scheduling approaches either ignore them or suffer from low acceptance rates. Our fully static and verification friendly mixed-criticality approach guarantees mentioned systems that the most critical system functions are always available, and maximizes the time where less critical system functions are operational: We prove that our approach is feasible, and extends the system operation time while providing full service by a factor of 1.93 with a probability of 0.92. With 1.56 higher acceptance rates compared to similar state-of-the-art approaches, the integration of functionalities of different criticality in one fault-tolerant system succeeds more and more often, which especially benefits emerging critical applications with limited budgets for error mitigation. |
2,461 | Rare Diagnostic and Clinical Manifestations in an Acute Hepatitis A Infection: A Case Report | The hepatitis A virus (HAV) is a common cause of infectious hepatitis worldwide. In adults, clinical manifestations typically involve fever, nausea/vomiting, fatigue, abdominal pain, and jaundice, although rarer manifestations may be observed. Acute hepatitis A infection is detected via anti-HAV IgM antibodies, which are present in almost all patients at symptom onset. In this case, we present a patient who not only tested negative for acute HAV infection at symptom onset, but also presented with uncommon, extrahepatic manifestations including maculopapular skin rash and polyarthralgia. Wariness of such a presentation can facilitate the timely diagnosis of atypical cases of HAV infection. We report the case of a 51-year-old man who presented with fever, abdominal pain, headaches, and diarrhea for one week with elevated liver enzymes and leukocytosis. Workup consisting of viral hepatitis panels, various infectious studies, and rheumatologic antibody titers did not initially reveal an etiology for the patient's presentation. Computed tomography (CT) abdomen and pelvis, abdominal ultrasound, magnetic resonance cholangiopancreatography (MRCP), and hepatobiliary iminodiacetic acid (HIDA) scan did not reveal acute pathology. The patient's symptoms worsened over the following days, and he additionally developed bilateral wrist pain, digital arthralgias, paraspinal back pain, diffuse muscular weakness, and a pruritic maculopapular rash affecting the flanks and extremities. Eventually, viral hepatitis studies were repeated which revealed elevated levels of anti-HAV IgM antibodies, indicating acute hepatitis A infection. The patient was treated supportively while hospitalized with subsequent improvement of symptoms and lab abnormalities. Since discharge, the patient had not experienced persistent sequelae of the disease. This case of acute viral hepatitis A infection is notable for two reasons: (1) the patient experienced uncommon, delayed, extrahepatic manifestations of disease, and (2) the initial viral hepatitis studies revealed undetectable anti-HAV IgM levels despite having experienced symptoms of illness for several days. This case suggests that repeat viral hepatitis testing may be warranted in patients who continue to experience manifestations of the infection after initially testing negative. It also emphasizes the importance of recognizing potential atypical manifestations of acute hepatitis A infection. |
2,462 | The challenge of o-phenylphenol detection in coffee: How "OPP-conjugates" hide their presence in green and roasted samples | o-Phenylphenol (OPP) is not a commonly used pesticide in the coffee production chain. Although it has only been detected in roasted coffee, it is unlikely that OPP can be formed during roasting. Its acidic nature may lead to the formation of conjugates with natural matrix components. The objective of this study is to optimize an analytical method to discover how these conjugates may mask the presence of OPP in coffee. Sample extraction with hexane followed by basic hydrolysis and then a QuEChERS method allows the presence of OPP to be quantitatively detected via UPLC-MS/MS. The optimized method was applied to the same Arabica coffee (Brazil), and the quantification of comparable amounts of OPP was observed in both green and roasted samples (34.8 vs 32.2 μg/kg). The optimized procedure detected twice the amount of OPP in roasted samples, compared to the QuEChERS method, suggesting that roasting causes the partial hydrolysis of OPP conjugates. |
2,463 | Peptide vaccine from cancer-testis antigen ODF2 can potentiate the cytotoxic T lymphocyte infiltration through IL-15 in non-MSI-H colorectal cancer | About 85% of patients with colorectal cancer (CRC) have the non-microsatellite instability-high (non-MSI-H) subtype, and many cannot benefit from immune checkpoint blockade. A potential reason for this is that most non-MSI-H colorectal cancers are immunologically "cold" due to poor CD8+ T cell infiltration. In the present study, we screened for potential cancer-testis antigens (CTAs) by comparing the bioinformatics of CD8+ T effector memory (Tem) cell infiltration between MSI-H and non-MSI-H CRC. Two ODF2-derived epitope peptides, P433 and P609, displayed immunogenicity and increased the proportion of CD8+ T effector memory (Tem) cells in vitro and in vivo. The adoptive transfer of peptide pool-induced CTLs inhibited tumor growth and enhanced CD8+ T cell infiltration in tumor-bearing NOD/SCID mice. The mechanistic study showed that knockdown of ODF2 in CRC cells promoted interleukin-15 expression, which facilitated CD8+ T cell proliferation. In conclusion, ODF2, a CTA, was negatively correlated with CD8+ T cell infiltration in "cold" non-MSI-H CRC and was selected based on the results of bioinformatics analyses. The corresponding HLA-A2 restricted epitope peptide induced antigen-specific CTLs. Immunotherapy targeting ODF2 could improve CTA infiltration via upregulating IL-15 in non-MSI-H CRC. This tumor antigen screening strategy could be exploited to develop therapeutic vaccines targeting non-MSI-H CRC. |
2,464 | THE CONTRIBUTION OF THE SOVIET "KINETIC" ART OF THE1960s-1970s TO THE DEVELOPMENT OF THE THEORY AND PRACTICE OF ARCHITECTURAL LIGHTING TECHNOLOGY | The paper analyses the original works in the field of architectural light and engineering belonging to the most prominent representatives of the domestic kinetic art of the 1960s (F. Infante, V.F. Koleichuk, L.N. Nusberg, as well as A. Lanin and B.M. Galeev). The conceptual basis of these works is revealed including a special attitude to light as a unique physical phenomenon and, at the same time, the most important component of a new architectural and artistic language. The practical value of the projects of special dynamic lighting of art objects, interiors of exhibition spaces, individual architectural objects and the urban environment as a whole, proposed by these authors and partly implemented by them, is revealed. It is shown in what way and for what purpose this type of synthetic art was formed at the intersection of architecture, design and engineering, which made a significant contribution to the development of both the theory and practice of architectural light engineering. In conclusion, the idea is expressed about the importance of using the considered historical experience at the present time, when every year new technical possibilities appear for creating an expressive light-colour environment, but deep content, philosophical and theoretical foundations for its design, which inspired the masters who stood at the origins of this the most important architectural and artistic direction, are lost. |
2,465 | CoW Bimetallic Carbide Nanocatalysts: Computational Exploration, Confined Disassembly-Assembly Synthesis and Alkaline/Seawater Hydrogen Evolution | Earth-abundant tungsten carbide exhibits potential hydrogen evolution reaction (HER) catalytic activity owing to its Pt-like d-band electronic structure, which, unfortunately, suffers from the relatively strong tungsten-hydrogen binding, deteriorating its HER performance. Herein, a catalyst design concept of incorporating late transition metal into early transition metal carbide is proposed for regulating the metal-H bonding strength and largely enhancing the HER performance, which is employed to synthesize CoW bi-metallic carbide Co6 W6 C by a "disassembly-assembly" approach in a confined environment. Such synthesized Co6 W6 C nanocatalyst features the optimal Gibbs free energy of *H intermediate and dissociation barrier energy of H2 O molecules as well by taking advantage of the electron complementary effect between Co and W species, which endows the electrocatalyst with excellent HER performance in both alkaline and seawater/alkaline electrolytes featuring especially low overpotentials, elevated current densities, and much-enhanced operation durability in comparison to commercial Pt/C catalyst. Moreover, a proof-of-concept Mg/seawater battery equipped with Co6 W6 C-2-600 as cathode offers a peak power density of 9.1 mW cm-2 and an open-circuit voltage of ≈1.71 V, concurrently realizing hydrogen production and electricity output. |
2,466 | Real-Time Self-Supervised Monocular Depth Estimation Without GPU | Single-image depth estimation represents a longstanding challenge in computer vision and although it is an ill-posed problem, deep learning enabled astonishing results leveraging both supervised and self-supervised training paradigms. State-of-the-art solutions achieve remarkably accurate depth estimation from a single image deploying huge deep architectures, requiring powerful dedicated hardware to run in a reasonable amount of time. This overly demanding complexity makes them unsuited for a broad category of applications requiring devices with constrained resources or memory consumption. To tackle this issue, in this paper a family of compact, yet effective CNNs for monocular depth estimation is proposed, by leveraging self-supervision from a binocular stereo rig. Our lightweight architectures, namely PyD-Net and PyD-Net2, compared to complex state-of-the-art trade a small drop in accuracy to drastically reduce the runtime and memory requirements by a factor ranging from 2x to 100x. Moreover, our networks can run real-time monocular depth estimation on a broad set of embedded or consumer devices, even not equipped with a GPU, by early stopping the inference with negligible (or no) loss in accuracy, making it ideally suited for real applications with strict constraints on hardware resources or power consumption. |
2,467 | Solving the robot-world hand-eye(s) calibration problem with iterative methods | Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end-effector and a camera, as well as the transformation between the robot base and the world coordinate system. This relationship has been modeled as , where and are unknown homogeneous transformation matrices. The successful execution of many robot manipulation tasks depends on determining these matrices accurately, and we are particularly interested in the use of calibration for use in vision tasks. In this work, we describe a collection of methods consisting of two cost function classes, three different parameterizations of rotation components, and separable versus simultaneous formulations. We explore the behavior of this collection of methods on real datasets and simulated datasets and compare to seven other state-of-the-art methods. Our collection of methods returns greater accuracy on many metrics as compared to the state-of-the-art. The collection of methods is extended to the problem of robot-world hand-multiple-eye calibration, and results are shown with two and three cameras mounted on the same robot. |
2,468 | Soft-Output Successive Cancellation Stack Polar Decoder | Polar coding has been ratified for employment in the 3GPP New Radio standard and several soft-decision decoders achieved comparable performance to that of the state-of-the-art successive cancellation list decoder. Aiming for further improving the performance of the soft-decision polar decoders, we propose a soft-output successive cancellation stack (SSCS) polar decoder, which jointly exploits the benefits of the depth-first search of the stack decoder and the soft information output of the belief propagation decoder. This has the substantial benefit of facilitating soft-input soft-output (SISO) decoding and seamless iterative information exchange in turbo-style receivers. As a further contribution, we intrinsically amalgamate our SSCS decoder into polar-coded large-scale multiple-input multiple-output (MIMO) systems and conceive an iterative turbo receiver, operating on the basis of logarithmic likelihood ratios (LLRs). Our simulation results show that the proposed SSCS decoder is capable of outperforming the state-of-the-art SISO polar decoders, despite requiring a lower complexity at moderate to high signal-to-noise ratios (SNRs). Additionally, compared with the non-iterative hard-output SCS decoder, our SSCS scheme attained 1.5 dB SNR gain at a bit error ratio level of 10(-5), when decoding the [256,512] polar code of a (64 x 64) MIMO system. |
2,469 | Probability-based approach to rectilinear Steiner tree problems | The rectilinear Steiner tree (RST) problem is of essential importance to the automatic interconnect optimization for VLSI design. In this paper, we present a class of probability-based approaches toward the best solutions under statistical sense and show their performance in comparison with the state-of-the-art algorithm. Experiments conducted on both small- and large-size problems indicate that the proposed approaches lead to promising results in terms of wire length and/or CPU time. The potential advantages with our technique are also discussed for further applications. |
2,470 | MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data | Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package. |
2,471 | Rotation-reversal invariant HOG cascade for facial expression recognition | This paper presents a novel classification framework derived from AdaBoost to classify facial expressions. The proposed framework adopts rotation-reversal invariant HOG as features. The framework is implemented by configuring the area under receiver operating characteristic curve of the weak classifier with HOG, which is a discriminative classification framework. The proposed classification framework is evaluated with three very popular and representative public databases: CK+, MMI, and AFEW. The results showed that the proposed classification framework outperforms the state-of-the-art methods. |
2,472 | Conjugation of cysteamine functionalized nanodiamond to gold nanoparticles for pH enhanced colorimetric detection of Cr3+ ions demonstrated by real water sample analysis | A cysteamine functionalized nanodiamond (NDC) was conjugated to gold nanoparticles (AuNPs) to deliver NDC@AuNPs and utilized in enhanced colorimetric detection of Cr3+ at pH 6 environment. The conjugation was validated using FTIR, TEM, PXRD, DLS, and zeta potential investigations. At pH 6, superior sensory response of NDC@AuNPs to Cr3+ than that of other ions was validated by UV-vis spectroscopy and colorimetric photographs. Results from UV-vis titrations displayed a linear regression from 0.01 to 0.4 µM with a LOD of 0.236 ± 0.005 nM. The particle aggregation, size variations, potential changes, and binding modes are investigated using TEM, DLS, and FTIR techniques to explore the underlying mechanisms. By adding the EDTA, sensory response is reversible up to 4 cycles. Finally, spiked real water experiments show improved sensing of Cr3+ at pH 6 via the observed recovery between 96 and 110 %, which is in good agreement with the ICP-mass data. |
2,473 | EndoL2H: Deep Super-Resolution for Capsule Endoscopy | Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics. Enhanced-resolution endoscopy has shown to improve adenoma detection rate for conventional endoscopy and is likely to do the same for capsule endoscopy. In this work, we propose and quantitatively validate a novel framework to learn a mapping from low-to-high-resolution endoscopic images. We combine conditional adversarial networks with a spatial attention block to improve the resolution by up to factors of 8x, 10x, 12x, respectively. Quantitative and qualitative studies demonstrate the superiority of EndoL2H over state-of-the-art deep super- resolution methods Deep Back-Projection Networks (DBPN), Deep Residual Channel Attention Networks (RCAN) and Super Resolution Generative Adversarial Network (SRGAN). Mean Opinion Score (MOS) testswere performedby 30 gastroenterologists qualitatively assess and confirm the clinical relevance of the approach. EndoL2H is generally applicable to any endoscopic capsule system and has the potential to improve diagnosis and better harness computational approaches for polyp detection and characterization. Our code and trained models are available at https:// github. com/ CapsuleEndoscope/ EndoL2H. |
2,474 | Dense 3D surface reconstruction of large-scale streetscape from vehicle-borne imagery and LiDAR | Accurate and efficient three-dimensional (3D) streetscape reconstruction is the fundamental ability for an exploration vehicle to navigate safely and perform high-level tasks. Recently, remarkable progress has been made in streetscape reconstruction with visual images and light detection and ranging (LiDAR), but they have difficulties either in scaling and reconstructing large-scale outdoors or in efficient processing. To address these issues, this paper proposed an automatic method for incremental dense reconstruction of large-scale 3D streetscapes from coarse to fine at near real time. Firstly, the pose of vehicle is estimated by visual and laser odometry (VLO) and the state-of-the-art pyramid stereo matching network (PSMNet) is introduced to estimate depth information. Then, incremental dense 3D streetscape reconstruction is conducted by key-frame selection and coarse registration with local optimization. Finally, redundant and noise points are removed through multiple filtering, resulting good quality of dense reconstruction. Comprehensive experiments were undertaken to check the visual effect, trajectory pose error and multi-scale model to model cloud comparison (M3C2) based on reference trajectories and reconstructions provided by the state-of-the-art method, showing the precision, recall and F-score of sampling core points (SCPs) are over 80.42%, 71.68% and 77.19%, respectively, which verified the proposed method. |
2,475 | Frequency-Resolved Optical Gating Recovery via Smoothing Gradient | Frequency-resolved optical gating (FROG) is a popular technique for complete characterization of ultrashort laser pulses. The acquired data in FROG, called FROG trace, is the Fourier magnitude of the product of the unknown pulse with a time-shifted version of itself, for several different shifts. To estimate the pulse from the FROG trace, we propose an algorithm that minimizes a smoothed non-convex least-squares objective function. The method consists of two steps. First, we approximate the pulse by an iterative spectral algorithm. Then, the attained initialization is refined based upon a sequence of block stochastic gradient iterations. The algorithm is theoretically simple, numerically scalable, and easy-to-implement. Empirically, our approach outperforms the state-of-the-art when the FROG trace is incomplete, that is, when only few shifts are recorded. Simulations also suggest that the proposed algorithm exhibits similar computational cost compared to a state-of-the-art technique for both complete and incomplete data. In addition, we prove that in the vicinity of the true solution, the algorithm converges to a critical point. A Matlab implementation is publicly available at https://github.com/samuelpinilla/FROG. |
2,476 | VCSEL Technology for Green Optical Interconnects | State-of-the-art vertical-cavity surface-emitting laser (VCSEL) technology will be reviewed in terms of power consumption and reliability to realize the energy-efficient high-performance computing (HPC) and data centers. Intermediate wavelength (between short and long wavelength) VCSELs emitting at 1060 nm are promising candidates for the upcoming higher performance computing with energy saving. |
2,477 | Writer identification in handwritten musical scores with bags of notes | Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. (C) 2012 Elsevier Ltd. All rights reserved. |
2,478 | Multimodal recognition of frustration during game-play with deep neural networks | Frustration, which is one aspect of the field of emotional recognition, is of particular interest to the video game industry as it provides information concerning each individual player's level of engagement. The use of non-invasive strategies to estimate this emotion is, therefore, a relevant line of research with a direct application to real-world scenarios. While several proposals regarding the performance of non-invasive frustration recognition can be found in literature, they usually rely on hand-crafted features and rarely exploit the potential inherent to the combination of different sources of information. This work, therefore, presents a new approach that automatically extracts meaningful descriptors from individual audio and video sources of information using Deep Neural Networks (DNN) in order to then combine them, with the objective of detecting frustration in Game-Play scenarios. More precisely, two fusion modalities, namely decision-level and feature-level, are presented and compared with state-of-the-art methods, along with different DNN architectures optimized for each type of data. Experiments performed with a real-world audiovisual benchmarking corpus revealed that the multimodal proposals introduced herein are more suitable than those of a unimodal nature, and that their performance also surpasses that of other state-of-the-art approaches, with error rate improvements of between 40% and 90%. |
2,479 | A CMA-ES-Based Adversarial Attack on Black-Box Deep Neural Networks | Deep neural networks(DNNs) are widely used in AI-controlled Cyber-Physical Systems (CPS) to controll cars, robotics, water treatment plants and railways. However, DNNs have vulnerabilities to well-designed input samples that are called adversarial examples. Adversary attack is one of the important techniques for detecting and improving the security of neural networks. Existing attacks, including state-of-the-art black-box attack have a lower success rate and make invalid queries that are not beneficial to obtain the direction of generating adversarial examples. For these reasons, this paper proposed a CMA-ES-based adversarial attack on black-box DNNs. Firstly, an efficient method to reduce the number of invalid queries is introduced. Secondly, a black-box attack of generating adversarial examples to fit a high-dimensional independent Gaussian distribution of the local solution space is proposed. Finally, a new CMA-based perturbation compression method is applied to make the process of reducing perturbation smoother. Experimental results on ImageNet classifiers show that the proposed attack has a higher success-rate than the state-of-the-art black-box attack but reduce the number of queries by 30% equally. |
2,480 | Sequential hybridization may have facilitated ecological transitions in the Southwestern pinyon pine syngameon | Multispecies interbreeding networks, or syngameons, have been increasingly reported in natural systems. However, the formation, structure, and maintenance of syngameons have received little attention. Through gene flow, syngameons can increase genetic diversity, facilitate the colonization of new environments, and contribute to hybrid speciation. In this study, we evaluated the history, patterns, and consequences of hybridization in a pinyon pine syngameon using morphological and genomic data to assess genetic structure, demographic history, and geographic and climatic data to determine niche differentiation. We demonstrated that Pinus edulis, a dominant species in the Southwestern US and a barometer of climate change, is a core participant in the syngameon, involved in the formation of two drought-adapted hybrid lineages including the parapatric and taxonomically controversial fallax-type. We found that species remain morphologically and genetically distinct at range cores, maintaining species boundaries while undergoing extensive gene flow in areas of sympatry at range peripheries. Our study shows that sequential hybridization may have caused relatively rapid speciation and facilitated the colonization of different niches, resulting in the rapid formation of two new lineages. Participation in the syngameon may allow adaptive traits to be introgressed across species barriers and provide the changes needed to survive future climate scenarios. |
2,481 | Practicing physical geography: An actor-network view of physical geography exemplified by the rock art stability index | This paper explores the use of a new pedagogy, the rock art stability index (RASI), to engender deeper understanding of weathering science concepts by students. Owing to its dynamic nature, RASI represents a quintessential actor network for weathering science, because it links task in the landscape with an active material practice and an alternative materialistic world-view recently called for in positivistic science, to create place. Using concept maps as an assessment tool, 571 college undergraduate students and 13 junior high school integrated science students (ages 12-13) were evaluated for increased learning potential between pre- and post-field experiences. Further, this article demonstrates that when students use RASI to learn the fundamental complex science of weathering they make in-depth connections between weathering form and process not achieved through traditional, positivistic weathering pedagogy. We argue that RASI draws upon inherent actor networks which allow students to link weathering form and process to an animate conceptualization of landscape. Conceptualizing landscape as sentient actor networks removes weathering science disciplinary connections and their inherent pedagogic practices. Our focus in this paper is not to challenge weathering ontology and epistemology, but rather to argue that there is a need for a pedagogical paradigm shift in weathering science. |
2,482 | PolarPose: Single-Stage Multi-Person Pose Estimation in Polar Coordinates | Regression based multi-person pose estimation receives increasing attention because of its promising potential in achieving realtime inference. However, the challenges in long-range 2D offset regression have restricted the regression accuracy, leading to a considerable performance gap compared with heatmap based methods. This paper tackles the challenge of long-range regression through simplifying the 2D offset regression to a classification task. We present a simple yet effective method, named PolarPose, to perform 2D regression in Polar coordinate. Through transforming the 2D offset regression in Cartesian coordinate to quantized orientation classification and 1D length estimation in the Polar coordinate, PolarPose effectively simplifies the regression task, making the framework easier to optimize. Moreover, to further boost the keypoint localization accuracy in PolarPose, we propose a multi-center regression to relieve the quantization error during orientation quantization. The resulting PolarPose framework is able to regress the keypoint offsets in a more reliable way, and achieves more accurate keypoint localization. Tested with the single-model and single scale setting, PolarPose achieves the AP of 70.2% on COCO test-dev dataset, outperforming the state-of-the-art regression based methods. PolarPose also achieves promising efficiency, e.g., 71.5% AP at 21.5FPS and 68.5%AP at 24.2FPS and 65.5%AP at 27.2FPS on COCO val2017 dataset, faster than current state-of-the-art. |
2,483 | Low power & mobile hardware accelerators for deep convolutional neural networks | This article provides a comprehensive review of recent developments in the field of computational hardware for mobile low power machine learning hardware accelerators. The article provides an introduction to neural networks, convolutional neural networks and details recent developments in state of the art deep convolutional neural networks. The key considerations in the design of low power hardware accelerators are discussed with reference to a conceptual system. Strategies for reducing the energy cost of memory access and computation in state of the art hardware accelerators are detailed. This includes techniques such as dataflow, reduced precision, model compression and sparsity. Recent reported digital mobile accelerators for deep convolutional neural networks with power consumptions of less than 3.3 W are observed to have 4x-20x better efficiency than the reference GPU accelerator at 16-bit precision, and can achieve 20x-1171x better efficiency at less than 4-bit precision. Efficiency improvements of 20x-1171x over a GPU is observed for reported mobile accelerators with reduced precision. |
2,484 | A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT | Available super-resolution techniques for 3-D images are either computationally inefficient prior-knowledge-based iterative techniques or deep learning methods which require a large database of known low-resolution and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this paper, this factorization framework is investigated for single image resolution enhancement with an offline estimate of the system point spread function. The technique is applied to 3-D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-of-the-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time-2 min compared to 2 h for a dental volume of 282 x 266 x 392 voxels. Furthermore, it also offers slightly improved quantitative results ( peak signal-to-noise ratio and segmentation quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes in its parameters, proposing an ease of use. |
2,485 | A novel visual tracking method using stochastic fractal search algorithm | Recently metaheuristic algorithms have attracted the attention of many researchers in various disciplines for their simplicity of application and their efficiency. Visual tracking is one of the most promising fields of application of these methods, and although many approaches have been proposed, their main disadvantage is the convergence at local minima which make them unable to find the exact position. To overcome this drawback, we propose to use an algorithm that provides an efficient exploration of the search space, which is stochastic fractal search (SFS) algorithm. SFS is used as a localization method, to find the most similar candidate to a previous defined template. Standard kernel-based spatial color histogram of the object bounding box, is evaluated in order to model the object appearance. Subsequently, Bhattacharyya distance is measured between the two histograms of the model and the candidate to define the fitness function, in which optimization is sought. To assess fairly the robustness of our approach, we have evaluated its performance on 20 video sequences from the OTB-100 sequences dataset and compared it to 11 other state-of-the-art trackers. Quantitative and qualitative evaluations on challenging situations provided satisfying results of SFS-based tracker compared to other state-of-the-art algorithms. |
2,486 | Phenotype-first hypotheses, spandrels and early metazoan evolution | Against the neo-Darwinian assumption that genetic factors are the principal source of variation upon which natural selection operates, a phenotype-first hypothesis strikes us as revolutionary because development would seem to constitute an independent source of variability. Richard Watson and his co-authors have argued that developmental memory constitutes one such variety of phenotypic variability. While this version of the phenotype-first hypothesis is especially well-suited for the late metazoan context, where animals have a sufficient history of selection from which to draw, appeals to developmental memory seem less plausible in the evolutionary context of the early metazoans. I provide an interpretation of Stuart Newman's account of deep metazoan phylogenesis that suggests that spandrels are, in addition to developmental memory, an important reservoir of phenotypic variability. I conclude by arguing that Gerd Müller's "side-effect hypothesis" is an illuminating generalization of the proposed non-Watsonian version of the phenotype-first hypothesis. |
2,487 | Differences in the cross-sectional area along the ankle tendons with both age and sex | Increasing age appears to influence several morphologic changes in major tendons. However, the effects of aging on the cross-sectional area (CSA) of different ankle tendons are much less understood. Furthermore, potential differences in specific tendon regions along the length of the tendons have not been investigated in detail. Sixty healthy adult participants categorized by age as young (n = 20; mean ± SD age = 22.5 ± 4.5 years), middle-age (n = 20; age = 40.6 ± 8. 0 years), or old (n = 20; age = 69.9 ± 9.1 years), from both sexes, were included. The tendon CSA of tibialis anterior (TA), tibialis posterior (TP), fibularis (FT), and Achilles (AT) was measured from T1-weighted 1.5 T MR images in incremental intervals of 10% along its length (from proximal insertion) and compared between different age groups and sexes. The mean CSA of the AT was greater in the middle-age group than both young and old participants (p < 0.01) and large effect sizes were observed for these differences (Cohen's d > 1). Furthermore, there was a significant difference in CSA in all three groups along the length of the different tendons. Region-specific differences between groups were observed in the distal portion (90% and 100% of the length), in which the FT presented greater CSA comparing middle-age to young and old (p < 0.05). In conclusion, (1) great magnitude of morpho-structural differences was discovered in the AT; (2) there are region-specific differences in the CSA of ankle tendons within the three groups and between them; and (3) there were no differences in tendon CSA between sexes. |
2,488 | Copper-catalyzed aerobic autoxidation of N-hydroxycarbamates probed by mass spectrometry | We present herein a mechanistic investigation by nanoelectrospray ionization mass spectrometry of copper-catalyzed aerobic oxidative processes involved in the N-nitrosocarbonyl aldol reaction of N-hydroxycarbamates. Protonated amine and copper as charge-tags aided the detection of reaction intermediates, which verified the enamine mechanism together with a competing enol process. Our experimental results reveal that the copper-catalyzed aerobic oxidation of N-hydroxycarbamates may proceed through an autoxidation catalytic mechanism in which a CbzNHO(.) radical abstracts a hydrogen from the bound N-hydroxycarbamate to release the nitroso intermediate through a bimolecular hydrogen-atom transfer. In this process, the chiral diamine also works as a ligand for copper to facilitate the aerobic oxidative step. The dual role of the chiral vicinal diamine as both an aminocatalyst and a bidentate ligand was finally uncovered. |
2,489 | Human ageing is associated with more rigid concept spaces | Prevalence-induced concept change describes a cognitive mechanism by which someone's definition of a concept shifts as the prevalence of instances of that concept changes. While this phenomenon has been established in young adults, it is unclear how it affects older adults. In this study, we explore how prevalence-induced concept change affects older adults' lower-level, perceptual, and higher-order, ethical judgements. We find that older adults are less sensitive to prevalence-induced concept change than younger adults across both domains. Using computational modeling, we demonstrate that these age-related changes in judgements reflect more cautious and deliberate responding in older adults. Based on these findings, we argue that while overly cautious responding by older adults may be maladaptive in some cognitive domains, in the case of prevalence-induced concept change, it might be protective against biased judgements. |
2,490 | Cationic Modified PVA Hydrogels Provide Low Friction and Excellent Mechanical Properties for Potential Cartilage and Orthopedic Applications | Poly(vinyl alcohol) (PVA) hydrogel is a promising candidate for articular cartilage repair yet restrained by its mechanical strength and tribological property. Current work reports a newly designed PVA-based hydrogel modified by glycerol (g), bacterial cellulose (BC), and a cationic polymer poly (diallyl dimethylammonium chloride) (PDMDAAC), which is a novel cationic strengthening choice. The resultant PVA-g-BC-PDMDAAC hydrogel proves the effectiveness of this modification scheme, with a confined compressive modulus of 19.56 MPa and a friction coefficient of 0.057 at a joint-equivalent load and low sliding speed. The water content, swelling property, and creep behavior of this hydrogel are also within a cartilage-mimetic range. The properties of PVA-based hydrogels before PDMDAAC addition are likewise studied as a cross-reference. Besides, PDMDAAC-modified PVA hydrogel realizes ideal mechanical and lubrication properties with a relatively low PVA concentration (10 wt.%) and facile fabrication process, which lays a foundation for mass production and marketization in the future. |
2,491 | Sequential Subspace Clustering via Temporal Smoothness for Sequential Data Segmentation | This paper develops a novel sequential subspace clustering method for sequential data. Inspired by the state-of-the- art methods, ordered subspace clustering, and temporal subspace clustering, we design a novel local temporal regularization term based on the concept of temporal predictability. Through minimizing the short-term variance on historical data, it can recover the temporal smoothness relationships in sequential data. Moreover, we claim that the local temporal regularization is more important than the global structural regularization for a specific task, such as sequential subspace clustering, which leads to a concise minimization objective function. To solve the bi-convex objective function, a simple and efficient optimization algorithm based on the alternate convex search method is devised to jointly learn the coding matrix and the dictionary. Furthermore, five baseline methods are also devised for comparison with our proposed method from different aspects. Extensive experimental results and comparisons with the state-of-the-art methods on three data sets demonstrate the effectiveness of the proposed temporal smoothness sequential subspace clustering method for sequential data. |
2,492 | Developments in xEVs charging infrastructure and energy management system for smart microgrids including xEVs | The swiftly growing structure of urbanization and smart cities facilitating the transportation era at peak. Thus, the rising pattern of conventional automobiles leading to the high contribution of greenhouse gas (GHG) emission. To mitigate the hazardous profile of GHG emissions, the electric vehicles (xEVs) as the part of smart cities, are gaining immense consideration. However, the unscheduled EVs connectivity with conventional grid system leading unreliable and interrupted power supply, which may lead to the grid failure. In such state of affairs (i.e. GHG emission and rising power demand), the smart microgrids including Renewable Energy Sources (RESs) based charging infrastructure are becoming the most viable paradigm. In this paper, two most emerging technologies belonging to smart cities i.e. xEVs and RESs based smart Microgrid has been covered. The xEVs part of the presented manuscript discusses the detailed study of rising advancement in xEVs charging infrastructure, enhancement in international standards for proper xEVs deployment, and state of art in the xEVs application such as the vehicle to grid (V2G) and vehicle to home (V2H). The second part of the presented work elaborates the state of art in research of smart microgrids energy management system (EMS) including xEVs to enhance the reliability of charging infrastructure. |
2,493 | Coordination of Pnictogenylboranes Towards Tl(I) Salts and a Tl- Mediated P-P Coupling | The coordination chemistry of only Lewis-base (LB)-stabilized pnictogenylboranes EH2 BH2 ⋅NMe3 (E=P, As) towards Tl(I) salts has been studied. The reaction of Tl[BArCl ] (BArCl =[B(3,5-C6 H3 Cl2 )4 ]- ) with the corresponding pnictogenylborane results in the formation of [Tl(EH2 BH2 ⋅NMe3 )][BArCl ] (1 a: E=P; 1 b: E=As). Whereas the Tl ion in 1 a/b is monocoordinated, the exchange of the weakly coordinating anion (WCA) in the Tl(I) salt leads to the formation of a trigonal pyramidal coordination mode at the Tl atom by coordination of three equivalents of EH2 BH2 ⋅ NMe3 in [Tl(EH2 BH2 ⋅ NMe3 )3 ][WCA] (2 a: E=P, WCA=TEFCl ; 2 b: E=As, WCA=TEF) (TEF=[Al{OC(CF3 )3 }4 ]- , TEFCl =[Al{(OC(CF3 )2 (CCl3 )}4 ]- ). Furthermore, by using two equivalents of PH2 BH2 ⋅NMe3 , a Tl(I)-mediated P-P coupling takes place in CH2 Cl2 as solvent resulting in [Me3 N⋅BH2 PH2 PHBH2 ⋅NMe3 ][WCA] (WCA=TEF, 3 a; BArCl , 3 b; TEFCl , 3 c). In contrast, for the arsenic derivatives 1 b and 2 b, no coupling reaction is observed. The underlying chemical processes are elucidated by quantum chemical computations. |
2,494 | Biodegradable Redox-Responsive AIEgen-Based-Covalent Organic Framework Nanocarriers for Long-Term Treatment of Myocardial Ischemia/Reperfusion Injury | Timely restoration of blood supply after myocardial ischemia is imperative for the treatment of acute myocardial infarction but causes additional myocardial ischemia/reperfusion (MI/R) injury, which has not been hitherto effectively targeted by interventions for MI/R injury. Hence, the development of advanced nanomedicine that can reduce apoptosis of cardiomyocytes while protecting against MI/R in vivo is of utmost importance. Herein, a redox-responsive and emissive TPE-ss covalent organic framework (COF) nanocarrier by integrating aggregation-induced emission luminogens and redox-responsive disulfide motifs into the COF skeleton is developed. TPE-ss COF allows for efficient loading and delivery of matrine, a renowned anti-cryptosporidial drug, which significantly reduces MI/R-induced functional deterioration and cardiomyocyte injury when injected through the tail vein into MI/R models at 5 min after 30 min of ischemia. Moreover, TPE-ss COF@Matrine shows a drastic reduction in cardiomyocyte apoptosis and improvements in cardiac function and survival rate. The effect of the TPE-ss COF carrier is further elucidated by enhanced cardiomyocyte viability and triphenyltetrazolium chloride staining in vitro. This work demonstrates the cardioprotective effect of TPE-ss COFs for MI/R injury, which unleashes the immense potential of using COFs as smart drug carriers for the peri-reperfusion treatment of ischemic heart disease with low cost, high stability, and single postoperative intervention. |
2,495 | Ranavirus infection-induced avoidance behaviour in wood frog juveniles: do amphibians socially distance? | Hosts may limit exposure to pathogens through changes in behaviour, such as avoiding infected individuals or contaminated areas. Here, we tested for a behavioural response to ranavirus infection in juvenile wood frogs (Rana sylvatica) because the majority of dispersal between populations occurs during this life stage. We hypothesized that if infections are transmissible and detectable at this life stage, then susceptibles would display avoidance behaviours when introduced to an infected conspecific. Despite no apparent signs of infection, we observed a greater distance between susceptible-infected pairs, compared to pairs of either two infected or two susceptible animals. Further, distances between susceptible-infected pairs were positively related to the infection intensity of the focal exposed frog, suggesting the cue to avoid infected conspecifics may become more detectable with more intense infections. Although we did not quantify whether the transmission was affected by their distancing, our findings suggest that juvenile frogs have the potential to reduce terrestrial transmission of ranaviruses through avoidance behaviours. |
2,496 | Formulating Spatially Varying Performance in the Statistical Fusion Framework | To date, label fusion methods have primarily relied either on global [e. g., simultaneous truth and performance level estimation (STAPLE), globally weighted vote] or voxelwise (e. g., locally weighted vote) performance models. Optimality of the statistical fusion framework hinges upon the validity of the stochastic model of how a rater errs (i.e., the labeling process model). Hitherto, approaches have tended to focus on the extremes of potential models. Herein, we propose an extension to the STAPLE approach to seamlessly account for spatially varying performance by extending the performance level parameters to account for a smooth, voxelwise performance level field that is unique to each rater. This approach, Spatial STAPLE, provides significant improvements over state-of-the-art label fusion algorithms in both simulated and empirical data sets. |
2,497 | Skin lightness affects ultraviolet A-induced oxidative stress: Evaluation using ultraweak photon emission measurement | The human skin is usually exposed to ultraviolet A (UVA) in the sunlight and experiences oxidative stress associated with skin disorders and aging. Although oxidative stress caused by UVA exposure is assumed to be dependent on skin colour, few studies have demonstrated this dependency. We investigated the effects of skin colour on UVA-induced oxidative stress using ultraweak photon emission (UPE) generated from the skin during oxidation processes. The UPE intensities of skin samples were detected using a photomultiplier tube every second without any labelling. We irradiated skin tissue of different colours with UVA and measured UPE over time. UVA-induced UPE could be detected from immediately after irradiation to 2 h after irradiation, indicating persistent oxidative stress. Skin lightness (L*) positively correlates with UPE intensity. Lighter-coloured skin exhibited more UVA-induced UPE, indicating higher oxidative stress. Additionally, oxidative stress persisted significantly more in lighter skin compared with darker skin. Skin tissues exhibited pigment darkening after UVA irradiation. Our results suggest that skin lightness affects oxidative stress induced by UV irradiation. Our study demonstrated the relationship between skin lightness and UVA-induced oxidative stress for the first time and offers new photodermatological insights into the human skin. |
2,498 | ART 2 - an unsupervised neural network for PD pattern recognition and classification | This paper introduces a method of classifying partial discharges of unknown origin. The innovative trend of using Artificial Neural Network (ANN) towards classification of Partial Discharge (PD) patterns is cogent and discernible. The Adaptive Resonance Theory (ART), a type of neural network which is suitable for PD pattern recognition is explained here. To ensure the suitability and reliability of chosen network for PD pattern recognition, the network is tested with the well known Iris plant database and alphabet character for recognition & classification. Further more the network is trained with various combinations of (phi-q-n distributions of PD patterns and tested. It is shown that the ART 2 network is able to classify the PD patterns. The paper ends with analyzing the efficacy of multifarious features selected in the measurement space. Also the validation of input features is done using 'Hold-One-Out' method and partial set training technique (C) 2005 Elsevier Ltd. All rights reserved. |
2,499 | A 30.1 mW/mu m(2) SiGe:C HBT Featuring an Implanted Collector in a 55-nm CMOS Node | This letter deals with the load-pull measurements at 94 GHz of 450 GHz Si/SiGe f(T) HBTs. On the one hand the technological modifications performed to improve large signal performances are presented and on the other hand, load-pullmeasurements are presented after having describing the measurement setup. A state-of-the-art 30.1 mW / mu m(2) Si/SiGe HBT is demonstrated thanks to a layout optimization. |
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