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4,800 | Analysis of the solution structure parameter α in the relationship between the molar fraction and the freezing points, and hydration parameter h determined from viscosity and density measurements, for sugar alcohols and related sugars in water | The parameter α was obtained from the molar fraction of solute and the freezing points of sugar alcohols and their related sugars in water. For comparison with this parameter, simple measurement of the hydration parameter h was performed using a capillary viscometer and a density meter. This parameter was calculated from the viscosity B coefficient and the partial molar volume of solute. The viscosity B coefficient was more suitable than the partial molar volume for h calculation, as indicated by the determination coefficients of the linear regression lines. h correlated well with α for various compounds, including sugar alcohols in water, supporting the parameters' theoretical correspondence (α =-h). In addition, the activation energy required for hydration implies that the thermal stability increases with the saccharide molecular weight. |
4,801 | Accurate image super-resolution using dense connections and dimension reduction network | Recently, the convolution neural network (CNN) has achieved significant performance improvements toward the image Super-Resolution (SR) problems. Yet, the existing benchmark arts exist multiple limitations, including make use of the feature information deficiently, accompany with the gradient disappearance phenomenon and have serious time consumption. The paper utilizes a newly designed fully convolutional neural network named Accurate Image Super-resolution Using Dense Connections and Dimension Reduction Network (DCDRN) to fully exploit the image features. Contextual information of image regions utilizes efficiently and accurately through uniting dense connections and cascading small filters multiple times. And such implementation can be regarded as feature extractors to fuse local and global image features. We newly introduce 1 x 1 CNNs parallelization structure in the image reconstruction section to reduce data dimensions of the previous layers, which alleviates the computational burden effectively while avoiding the context info losing. The calculation becomes more complex and the convergence becomes slower during training because of the pre-processed images. The proposed DCDRN invents a simple and effective method which processes the original image directly and the optimization of layers and filters of CNNs shorten the cost of training significantly. Experiments on benchmark datasets with different methods show that DCDRN achieves gratifying performance against state-of-the-art methods. Code is available at https://github.com/doctorwgd/DCDRN. |
4,802 | Motion robust magnetic resonance imaging via efficient Fourier aggregation | We present a method for suppressing motion artifacts in anatomical magnetic resonance acquisitions. Our proposed technique, termed MOTOR-MRI, can recover and salvage images which are otherwise heavily corrupted by motion induced artifacts and blur which renders them unusable. Contrary to other techniques, MOTOR-MRI operates on the reconstructed images and not on k-space data. It relies on breaking the standard acquisition protocol into several shorter ones (while maintaining the same total acquisition time) and subsequent efficient aggregation in Fourier space of locally sharp and consistent information among them, producing a sharp and motion mitigated image. We demonstrate the efficacy of the technique on T2-weighted turbo spin echo magnetic resonance brain scans with severe motion corruption from both 3 T and 7 T scanners and show significant qualitative and quantitative improvement in image quality. MOTOR-MRI can operate independently, or in conjunction with additional motion correction methods. |
4,803 | Multifunctional Platform Based on a Copper(I) Complex and NaYF4:Tm3+,Yb3+ Upconverting Nanoparticles Immobilized into a Polystyrene Matrix: Downshifting and Upconversion Oxygen Sensing | This work presents an innovative approach to obtain a multifunctional hybrid material operating via combined anti-Stokes (upconversion) and Stokes (downshifting) emissions for oxygen gas sensing and related functionalities. The material is based on a Cu(I) complex exhibiting thermally activated delayed fluorescence emission (TADF) and infrared-to-visible upconverting Tm3+/Yb3+-doped NaYF4 nanoparticles supported in a polystyrene (PS) matrix. Excitation of the hybrid material at 980 nm leads to efficient transfer of Tm3+ emission in the ultraviolet/blue region to the Cu(I) complex and consequently intense green emission (560 nm) of the latter. Additionally, the green emission of the complex can also be directly generated with excitation at 360 nm. Independently of the excitation wavelength, the emission intensity is efficiently suppressed by the presence of molecular oxygen and the quenching rate is properly characterized by the Stern-Volmer plots. The results indicate that the biocompatible hybrid material can be applied as an efficient O2 sensor operating via near-infrared or ultraviolet excitation, unlike most optical oxygen sensors currently available which only work in downshifting mode. |
4,804 | Superhydrophobic Polyurethane Membrane with a Biomimetically Hierarchical Structure for Self-Cleaning | In this study, a stable and durable hexadecyltrimethoxysilane (HDTMS)/thermoplastic polyurethane (TPU) superhydrophobic film is successfully prepared by a simple and low-cost two-step method, namely, carrying out biomimetically hierarchical structures and low surface energy material modification concurrently. Meanwhile, effective parameters affecting the water contact angle (WCA) are studied and optimized. More importantly, under optimum parameters, the maximum WCA is 165°, the minimum slide angle (SA) is 3°, and the adhesion force is 13 μN, showing good self-cleaning performance. Besides, considerable mechanical stability to withstand 4000 tension or 5000 compression cycles, breathability, and moisture penetrability, as well as chemical resistance with sustained superhydrophobic properties in various harsh environments, are presented. |
4,805 | Effect of critical care pharmacist's intervention on medication errors: A systematic review and meta-analysis of observational studies | Pharmacists are integral members of the multidisciplinary team for critically ill patients. Multiple nonrandomized controlled studies have evaluated the outcomes of pharmacist interventions in the intensive care unit (ICU). This systematic review focuses on controlled clinical trials evaluating the effect of pharmacist intervention on medication errors (MEs) in ICU settings. Two independent reviewers searched Medline, Embase, and Cochrane databases. The inclusion criteria were nonrandomized controlled studies that evaluated the effect of pharmacist services vs no intervention on ME rates in ICU settings. Four studies were included in the meta-analysis. Results suggest that pharmacist intervention has no significant contribution to reducing general MEs, although pharmacist intervention may significantly reduce preventable adverse drug events and prescribing errors. This meta-analysis highlights the need for high-quality studies to examine the effect of the critical care pharmacist. |
4,806 | Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network | In contrast to images taken on land scenes, images taken over water are more prone to degradation due to the influence of the haze. However, existing image dehazing methods are mainly developed for land-scene images and perform poorly when applied to overwater images. To address this problem, we collect the first overwater image dehazing dataset and propose a Generative Adversial Network (GAN)-based method called OverWater Image Dehazing GAN (OWI-DehazeGAN). Due to the difficulties of collecting paired hazy and clean images, the dataset contains unpaired hazy and clean images taken over water. The proposed OWI-DehazeGAN is composed of an encoder-decoder framework, supervised by a forward-backward translation consistency loss for self-supervision and a perceptual loss for content preservation. In addition to qualitative evaluation, we design an image quality assessment neural network to rank the dehazed images. Experimental results on both real and synthetic test data demonstrate that the proposed method performs superiorly against several state-of-the-art land dehazing methods. Compared with the state-of-the-art, our method gains a significant improvement by 1.94% for SSIM, 7.13% for PSNR and 4.00% for CIEDE2000 on the synthetic test dataset. |
4,807 | Automatic centerline detection of small three-dimensional vessel structures | Vessel centerline detection is very important in many medical applications. In the noise and low-contrast regions, most existing methods may only produce an incomplete and disconnected extraction of the vessel centerline if no user guidance is provided. A robust and automatic method is described for extraction of the vessel centerline. First, we perform small vessel enhancement by processing with a set of line detection filters, corresponding to the 13 orientations; for each voxel, the highest filter response is kept and added to the image. Second, we extract vessel centerline segment candidates by a thinning algorithm. Finally, a global optimization algorithm is employed for grouping and selecting vessel centerline segments. We validate the proposed method quantitatively on a number of synthetic data sets, the liver artery and lung vessel. Comparisons are made with two state-of-the-art vessel centerline extraction methods and manual extraction. The experiments show that our method is more accurate and robust that these state-of-the-art methods and is, therefore, more suited for automatic vessel centerline extraction. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. |
4,808 | Synthesis, Biological, and Computational Evaluation of Novel 1,3,5-Substituted Indolin-2-one Derivatives as Inhibitors of Src Tyrosine Kinase | Several substituted indolin-2-one derivatives were synthesized and evaluated for their activities against Src kinase. Several compounds showed activity against Src, with IC50 values in the low micromolar range. Among them, compound 2f showed the most significant activity with an IC50 value of 1.02 μM. Molecular docking studies have been performed for evaluation of the binding modes of compound 2f into the Src active site. The docking structure of compound 2f disclosed that the indole NH forms a hydrogen bond with the carbonyl of Met341. These results suggest that our novel compound 2f is a promising compound for the further development of indole-based drugs targeting Src kinase. |
4,809 | Economic burden of complicated ureteral stent removal in patients with kidney stone disease in the USA | Aim: To examine the medical costs of simple versus complicated ureteral stent removal. Materials & methods: We included adults with kidney stones undergoing simple or complicated cystoscopy-based stent removal (CBSR) post ureteroscopy from the 2014 to 2018 Merative™ MarketScan® Commercial Database. The medical costs of patients with complicated and simple CBSR were compared. Results: Among 16,682 patients, 2.8% had complicated CBSR. Medical costs for patients with complicated CBSR were higher than for simple CBSR ($2182 [USD] vs $1162; p < 0.0001). Increased stenting time, increased age, southern US geography and encrusted stent diagnoses were significantly associated with complicated CBSR. Conclusion: Complicated ureteral stent removal doubled the medical costs associated with CBSR. Ureteral stents with anti-encrustation qualities may reduce the need for complicated CBSR and associated costs. |
4,810 | Plants before farming: The deep history of plant-use and representation in the rock art of Australia's Kimberley region | The orthodox notion of agriculture cumulatively and inevitably developing from foragers' gathering practices is increasingly untenable. Recent archaeological, botanical and genetic research from Asia and Australia show precocious manipulation of plant resources that continue for millennia within a forager ideology and practice without culminating in 'agriculture'. Australia's Kimberley is an especially productive research region with a wide range of environmental niches on a topographically varied landscape that has had human settlement spanning over the last 50,000 years. Previously characterised as 'foragers' until contact with travellers from Indonesia and then Europeans over the last few hundred years; new research questions this simplistic characterisation of Aboriginal people, and suggests instead a particularly complex and enduring set of people-plant relationships. This complexity is given material witness in the form of Kimberley rock art, which stands out globally in having an enormous body of direct and indirect depictions of plants, including: grasses, trees, tubers; pigment-soaked plants imprinted on rock shelter walls; anthropomorphism of plants; and plant-based material culture such as digging sticks, dilly bags, and wood-hafted stone axe. These are more than simple illustrations of a forager economic base. Instead, rock art is a primary record of long-term sophisticated physical and symbolic manipulation of plants that fits neither into the simplistic categories of 'foraging' or of 'agriculture'. Rather, we have a society in which people actively chose not to pursue orthodox agriculture while according plants a central place in their lives. (C) 2016 Elsevier Ltd and INQUA. All rights reserved. |
4,811 | Adolescent nicotine potentiates the inhibitory effect of raclopride, a D2R antagonist, on phencyclidine-sensitized psychotic-like behavior in mice | The association between schizophrenia and nicotine addiction becomes evident during adolescence. Here, to investigate interactive events that might underlie the early establishment of this comorbidity, we used phencyclidine-evoked locomotor sensitization, a proxy model of psychotic behavior, and nicotine minipump infusions in adolescent mice. Considering the involvement of dopamine D2 receptors in both schizophrenia and addiction, we further tested their role by exposing mice to raclopride. Adolescent mice that were either exposed to nicotine (24 mg/Kg/day) or not, received single daily raclopride (0.5 mg/kg, s.c.) or saline followed by phencyclidine injections (10 mg/Kg, s.c.) during open field testing for 6 consecutive days (Acquisition phase, ACQ). Phencyclidine and nicotine challenges (Sensitization Test, ST) were carried out after a 5-day withdrawal. Ambulation escalated in response to repeated phencyclidine exposure during ACQ and was increased after phencyclidine challenge, evidencing development and expression of locomotor sensitization. Raclopride prevented phencyclidine-evoked development of sensitization. However, raclopride pre-exposure during ACQ only shortened its expression in phencyclidine-challenged mice. Nicotine failed to interfere with phencyclidine stimulatory effects during ACQ but potentiated raclopride inhibition during the first ACQ days. During ST, nicotine history shortened the expression of phencyclidine-evoked sensitization. Nicotine challenge had no impact on locomotion, which is consistent with a lack of nicotine/phencyclidine cross-sensitization. In conclusion, our results show that nicotine does not worsen, and may even ameliorate phencyclidine-sensitized psychotic-like behavior in adolescent mice. The potentiation of raclopride-mediated inhibition further suggests that nicotine transiently improves the therapeutic efficacy of medication on psychotic symptoms through mechanisms that converge on D2 receptors. |
4,812 | Sustainability Research on Promoting the Inheritance of Lacquer Art Based on the E-learning Mode-Case Study of the Popularization of Lacquer Art Education in Primary Schools in Guangzhou Area | This paper investigates the sustainable inheritance of contemporary lacquer art in the Guangzhou area. Based on the traditional teaching mode dominated by folk inheritance and university education, this paper develops the advantage of contemporary information technology and proposes the application of the e-learning mode to assist the popularization of lacquer art education in society, which plays a positive role in promoting the sustainable development of the lacquer art culture in Guangzhou. This study adopts literature analysis and the questionnaire survey method, and then integrates this learning mode into the lacquer art learning curriculum in primary schools. Finally, this paper summarizes and reflects on the teaching results, and presents the effective role of this model in promoting the teaching of lacquer art culture, which is of important practical significance for consolidating the sustainable development of contemporary lacquer art culture. |
4,813 | Cultural and Artistic Design of Coastal Cities Based on Marine Landscape | The ocean has endowed open and shared spaces to coastal cities. The marine culture brought by marine landscapes affects the development and dissemination of culture and art in coastal cities and is an important source for the unique regional cultural landscapes of coastal cities. Based on the development of regional marine culture, this study aims to explore the role of marine landscapes in the cultural and artistic design of coastal cities and proposes corresponding strategies for cultural construction and development. The research results show that a beautiful marine landscape can be created in each coastal city; the basic elements of marine landscape design include conceptual elements, humanistic elements, natural elements, and artificial environment elements. The cultural and artistic design process in coastal cities must not only meet the requirements of urban development strategy, resource allocation, industrial layout, spatial pattern, and city image, but also adapt to the marine cultural atmosphere. This study provides a theoretical basis for accelerating the integration of marine culture into the cultural and artistic design of coastal cities. |
4,814 | Precise Near-Range 3-D Image Reconstruction Based on MIMO Circular Synthetic Aperture Radar | This article studies the near-range 3-D image reconstruction based on millimeter-wave multiple-input-multiple-output circular synthetic aperture radar (MIMO-CSAR). We first derive the exact forward wave model of MIMO-CSAR and then propose a range migration algorithm for near-range high-precision 3-D image reconstruction. Our formulation is strictly based on wave theory. The propagation attenuation, which is an inherent factor in wave equation and is significant for near-range wide-angle sensing case, is considered and compensated efficiently with a Fourier-domain matched filtering. Besides, the phase decoupling is achieved with a 2-D Stolt mapping. The computational complexity of the algorithm is at the same level as the state-of-the-art method. Compared with the state of the art, by considering the amplitude variation, the imaging quality can be improved for near-range measurement with large beamwidth. The results from both synthetic and practical measurement data demonstrate that a wider dynamic range (less sidelobes appeared at the same dynamic level) and a better recovery for widespread targets compared with the state-of-the-art imaging method. |
4,815 | ADMM-ADAM: A New Inverse Imaging Framework Blending the Advantages of Convex Optimization and Deep Learning | Alternating direction method of multipliers (ADMM) and adaptive moment estimation (ADAM) are two optimizers of paramount importance in convex optimization (CO) and deep learning (DL), respectively. Numerous state-of-the-art algorithms for solving inverse problems are achieved by carefully designing a convex criterion, typically composed of a data-fitting term and a regularizer. Even when the regularizer is convex, its mathematical form is often sophisticated, hence inducing a math-heavy optimization procedure and making the algorithm design a daunting task for software engineers. Probably for this reason, people turn to solve the inverse problems via DL, but this requires big data collection, quite time-consuming if not impossible. Motivated by these facts, we propose a new framework, termed as ADMM-ADAM, for solving inverse problems. As the key contribution, even just with small/single data, the proposed ADMM-ADAM is able to exploit DL to obtain a convex regularizer of very simple math form, followed by solving the regularized criterion using simple CO algorithm. As a side contribution, a state-of-the-art hyperspectral inpainting algorithm is designed under ADMM-ADAM, demonstrating its superiority even without the aid of big data or sophisticated mathematical regularization. |
4,816 | Lamellar Nanocomposite Based on a 1D Crayfish-like CeIII-Substituted Phospho(III)tungstate Semiconductor and Polyaniline Used as a High-Performance Humidity Sensing Device | In order to meet people's demand for intelligent management of daily life and health, manufacturing and developing humidity monitoring equipment with convenience, high sensitivity, easy miniaturization, and low cost is particularly important in the era of rapid development of artificial intelligence and the Internet of Things. Polyaniline (PANI) is an attractive humidity sensing material due to its designable functional properties. However, PANI modified polyoxometalates (POMs) for humidity sensing are still rare. As a proof of concept, a novel moisture sensing composite material was obtained based on PANI and a novel 1D rare-earth-substituted phospho(III)tungstate [H2N(CH3)2]9Na3H6[Ce2(H2O)3W5O13(C2O4)][HPIIIW9O33]2[(HPIII)2W15O54]·42H2O (1). Notably, the anion structure of 1 contains trivacant Keggin-type [B-α-HPIIIW9O33]8- and Dawson-like [(HPIII)2W15O54]10- subunits linked by a heterometallic [Ce2(H2O)3W5O32(C2O4)]30- cluster. Furthermore, the 1/PANI composite shows a typical semiconductive characteristic with a "band-like" conductive mechanism. The fabricated 1/PANI-based humidity sensing device exhibits a broad sensing range (11∼97% relative humidity), fast response/recovery time (3.45 s/3.24 s), good repeatability, and long-term stability (over 3 months). Additionally, the possible sensing mechanism is proposed. This work offers an enormous possibility for the design of high-performance humidity sensing materials through POM material chemistry. |
4,817 | Low-Complexity Reconfigurable and Intelligent Ultrawideband Angular Sensing | Paradigms such as multiantenna transceivers, spectrum sharing, and beamforming are critical to meet the desired throughput, reliability, latency, spectrum, and energy efficiency constraints in next-generation wireless networks. To enable these paradigms, base stations are undergoing major overhaul with complex digital signal processing, reconfigurable multistage architectures, as well as wideband sensing capability. In this article, we focus on the low-complexity multiantenna BS receiver to accomplish ultrawideband angular sensing. The proposed receiver is based on novel reconfigurable and intelligent sub-Nyquist sampling architecture. Here, reconfigurability allows the receiver to select and digitize noncontiguous frequency bands, while online learning-based intelligence allows the receiver to learn spectrum statistics and choose the frequency bands for sensing in order to maximize the throughput. Unlike the existing approaches, we show that the proposed approach does not need any prior knowledge of spectrum statistics. The efficacy of the proposed approach over state-of-the-art approaches is validated via hardware complexity as well as simulation results. We show that the proposed approach offers lower hardware complexity and achieves up to 23% lower estimation error than existing state-of-the-art methods. |
4,818 | Zero-Threshold Optical Gain in Electrochemically Doped Nanoplatelets and the Physics Behind It | Colloidal nanoplatelets (NPLs) are promising materials for lasing applications. The properties are usually discussed in the framework of 2D materials, where strong excitonic effects dominate the optical properties near the band edge. At the same time, NPLs have finite lateral dimensions such that NPLs are not true extended 2D structures. Here we study the photophysics and gain properties of CdSe/CdS/ZnS core-shell-shell NPLs upon electrochemical n doping and optical excitation. Steady-state absorption and PL spectroscopy show that excitonic effects are weaker in core-shell-shell nanoplatelets due to the decreased exciton binding energy. Transient absorption studies reveal a gain threshold of only one excitation per nanoplatelet. Using electrochemical n doping, we observe the complete bleaching of the band edge exciton transitions. Combining electrochemical doping with transient absorption spectroscopy, we demonstrate that the gain threshold is fully removed over a broad spectral range and gain coefficients of several thousand cm-1 are obtained. These doped NPLs are the best performing colloidal nanomaterial gain medium reported to date, with the lowest gain threshold and broadest gain spectrum and gain coefficients that are 4 times higher than in n-doped colloidal quantum dots. The low exciton binding energy due to the CdS and ZnS shells, in combination with the relatively small lateral size of the NPLs, results in excited states that are effectively delocalized over the entire platelet. Core-shell NPLs are thus on the border between strong confinement in QDs and dominant Coulombic effects in 2D materials. We demonstrate that this limit is in effect ideal for optical gain and that it results in an optimal lateral size of the platelets where the gain threshold per nm2 is minimal. |
4,819 | An Efficient Topological Distance-Based Tree Kernel | Tree kernels proposed in the literature rarely use information about the relative location of the substructures within a tree. As this type of information is orthogonal to the one commonly exploited by tree kernels, the two can be combined to enhance state-of-the-art accuracy of tree kernels. In this brief, our attention is focused on subtree kernels. We describe an efficient algorithm for injecting positional information into a tree kernel and present ways to enlarge its feature space without affecting its worst case complexity. The experimental results on several benchmark datasets are presented showing that our method is able to reach state-of-the-art performances, obtaining in some cases better performance than computationally more demanding tree kernels. |
4,820 | Dynamic Orthogonal Projection Constrained Discriminative Tracking | Due to the end-to-end feature learning with convolutional neural networks (CNNs), modern discriminative trackers improve the state of the art significantly. To achieve a strong discrimination, the learned features are usually high-dimensional, resulting in a massive number of parameters contained in the discriminative model and the increase of risk of over-fitting in the online tracking. In this letter, we try to alleviate the risk of over-fitting by means of the adaptive dimensionality reduction (DR) through CNNs. Specifically, an orthogonality constrained ridge regression model is proposed to reduce the dimensionality of features, and a dynamic sub-network (DOPNet) is designed to learn to perform DR. After trained with an orthogonality loss and a regression one, DOPNet generates a set of orthogonal bases (i. e., weights in FC layers) dynamically to reduce the feature dimensionality for a discriminative model in the online tracking. Based on the novel discriminative model and DOPNet, an effective and efficient tracker, DOPTracker, is developed. DOPTracker achieves the state-of-the-art results on four benchmarks, OTB-2015, VOT-2018, NfS, and GOT-10 k while running at 30 FPS. |
4,821 | Active contour model with adaptive weighted function for robust image segmentation under biased conditions | The segmentation of images under biased conditions such as low contrast, high-intensity inhomogeneity, and noise is a challenge for any image segmentation model. The ideal image segmentation model must be capable of segmenting images with maximum accuracy and a minimum false-positive rate under biased conditions. In this paper, we propose a region-based active contour model (ACM), called global signed pressure and K-means clustering based on local correntropy with the exponential family (GSLCE), to address segmentation challenges under biased conditions. An adaptive weighted function is formulated based on the global and local image differences such that a single weighted function can drive both the global and local intensities. Further, the Riemannian steepest descent method is used for convergence of the proposed GSLCE energy function, and a Gaussian kernel is applied for spatial smoothing to obviate the computationally expensive level-set re-initialization. The experimental results show that, compared with state-of-the-art ACMs, the proposed GSLCE model obtained the best visual image segmentation results for synthetic and real images under biased conditions. Further, the qualitative and quantitative experimental results validate that the proposed model outperforms the state-of-the-art ACMs by yielding higher values of performance metrics. Moreover, the proposed GSLCE model requires substantially less processing time compared to the state-of-the-art ACMs. |
4,822 | Predictors of mortality among adult people living with HIV/AIDS on antiretroviral therapy at Suhul Hospital, Tigrai, Northern Ethiopia: a retrospective follow-up study | Background: Ethiopia is striving to achieve a goal of "zero human immune deficiency virus/acquired immune deficiency syndrome (HIV/AIDS)-related deaths." However, little has been documented on the factors that hamper the progress towards achieving this goal. Therefore, the ultimate aim of this study was to determine predictors of mortality among adult people living with HIV/AIDS on antiretroviral therapy (ART). Methods: A retrospective follow-up study was employed on all adult HIV/AIDS patients who started ART between January 1 and December 30, 2010, at Suhul Hospital, Tigrai Region, Northern Ethiopia. Data were collected by trained fourth-year Public Health students using a checklist. Finally, the collected data were entered into SPSS version 16. Then after, Kaplan-Meier curves were used to estimate survival probability, the log-rank test was used for comparing the survival status, and Cox proportional hazards model were applied to determine predictors of mortality. Results: The median follow-up period was 51 months (ranging between 1 and 60 months, inter-quartile range (IQR) = 14 months). At the end of follow-up, 37 (12.5%) patients were dead. The majority of these cumulative deaths, 19 (51.4%) and 29 (78.4%), occurred within 3 and 4 years of ART initiation respectively. Consuming alcohol (adjusted hazard ratio (AHR) = 2.23, 95% CI = 1.15, 4.32), low body weight (AHR = 2.38, 95% CI = 1.03, 5.54), presence of opportunistic infections (AHR = 2.18, 95% CI = 1.09, 4.37), advanced WHO clinical stage (AHR = 2.75, 95% CI = 1.36, 5.58), and not receiving isoniazid prophylactic therapy (AHR = 3.00, 95% CI = 1.33, 6.74) were found to be independent predictors of mortality. Conclusion: The overall mortality was very high. Baseline alcohol consumption, low body weight, advanced WHO clinical stage, the presence of opportunistic infections, and not receiving isoniazid prophylactic therapy were predictors of mortality. Strengthening behavioral and nutritional counseling with close clinical follow-up shall be given much more emphasis in the ART care and support program. |
4,823 | Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation | A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images. The achieved results compare well to state-of-the-art methods, that are usually tailored to more specific questions, with Dice overlap values of 94%, 93%, 70%, and 92% for liver, kidneys, pancreas, and spleen, respectively. |
4,824 | Deformable 2D-3D Registration of Vascular Structures in a One View Scenario | Alignment of angiographic 3D scans to 2D projections is an important issue for 3D depth perception and navigation during interventions. Currently, in a setting where only one 2D projection is available, methods employing a rigid transformation model present the state of the art for this problem. In this work, we introduce a method capable of deformably registering 3D vessel structures to a respective single projection of the scene. Our approach addresses the inherent ill-posedness of the problem by incorporating a priori knowledge about the vessel structures into the formulation. We minimize the distance between the 2D points and corresponding projected 3D points together with regularization terms encoding the properties of length preservation of vessel structures and smoothness of deformation. We demonstrate the performance and accuracy of the proposed method by quantitative tests on synthetic examples as well as real angiographic scenes. |
4,825 | Review, analysis and parameterisation of techniques for copy-move forgery detection in digital images | Copy-move forgery is one of the most preliminary and prevalent forms of modification attack on digital images. In this form of forgery, region(s) of an image is(are) copied and pasted onto itself, and subsequently the forged image is processed appropriately to hide the effects of forgery. State-of-the-art copy-move forgery detection techniques for digital images are primarily motivated toward finding duplicate regions in an image. The last decade has seen lot of research advancement in the area of digital image forensics, whereby the investigation for possible forgeries is solely based on post-processing of images. In this study, the authors present a three-way classification of state-of-the-art digital forensic techniques, along with a complete survey of their operating principles. In addition, they analyse the schemes and evaluate and compare their performances in terms of a proposed set of parameters, which may be used as a standard benchmark for evaluating the efficiency of any general copy-move forgery detection technique for digital images. The comparison results provided by them would help a user to select the most optimal forgery detection technique, depending on the author requirements. |
4,826 | Optimized support vector machines for nonstationary signal classification | This letter describes an efficient method to perform nonstationary signal classification. A support vector machine (SVM) algorithm is introduced and its parameters optimized in a principled way. Simulations demonstrate that our low-complexity method outperforms state-of-the-art nonstationary signal classification techniques. |
4,827 | Single- and Mutiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes | In diffusion MRI (dMRI), a good sampling scheme is important for efficient acquisition and robust reconstruction. Diffusion weighted signal is normally acquired on single or multiple shells in q-space. Signal samples are typically distributed uniformly on different shells to make them invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The electrostatic energy minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI, was recently generalized to multi-shell schemes, called generalized EEM (GEEM). GEEM has been successfully used in the human connectome project. However, EEM does not directly address the goal of optimal sampling, i.e., achieving large angular separation between sampling points. In this paper, we propose a more natural formulation, called spherical code (SC), to directly maximize the minimal angle between different samples in single or multiple shells. We consider not only continuous problems to design single or multiple shell sampling schemes, but also discrete problems to uniformly extract sub-sampled schemes from an existing single or multiple shell scheme, and to order samples in an existing scheme. We propose five algorithms to solve the above problems, including an incremental SC (ISC), a sophisticated greedy algorithm called iterative maximum overlap construction (IMOC), an 1-Opt greedy method, a mixed integer linear programming method, and a constrained non-linear optimization method. To our knowledge, this is the first work to use the SC formulation for single or multiple shell sampling schemes in dMRI. Experimental results indicate that SC methods obtain larger angular separation and better rotational invariance than the state-of-the-art EEM and GEEM. The related codes and a tutorial have been released in DMRITool. |
4,828 | Application of fuzzy ART neural network to intelligent gas sensor arrays | In this paper a new approach to gas identification using Fuzzy Adaptive Resonance Theory (ART) neural network and polymer coated surface acoustic wave (SAW) sensors array is reported. This method provides fast learning capability in the training mode and accurate gas classification in the sniffing mode. The method can be easily extended to gas mixtures as well as other sensor technologies. Since the Fuzzy ART neural network is self organizing classifier trained in unsupervised mode, it avoids the drawbacks associated with static feed forward neural networks. |
4,829 | Water-Triggered Chemical Transformation of Perovskite Nanocrystals | Recently emerged lead-halide perovskite nanocrystals (PNCs) are promising optoelectronic material due to their easy solution processability, wide range of color tunability, as well as very high photoluminescence quantum yield. Despite their significant success in lab-scale optoelectronic applications, the long-term stability becomes the main issue, hindering them towards commercialization. The highly ionic nature of such lead-halide structure makes them extremely unstable in water and air. But a very few groups have taken the advantage of such nature of the crystal structure for water-triggered chemical transformation towards shape, composition, and morphology controlled stable and bright PNCs, which are otherwise difficult to obtain by typical direct approach. Furthermore, using polymer as an encapsulating layer for the PNCs, water-soluble stable PNCs have been prepared. In this review, the recent progress on the water-hexane interface chemistry towards chemical transformation to produce several PNCs is described. Such method not only ensure to yield several shape-controlled perovskites nanocrystals, but also formation of perovskites in aqueous phase that show promising application towards bio-imaging. |
4,830 | AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI | Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%. |
4,831 | Arithmetic in the signing brain: Differences and similarities in arithmetic processing between deaf signers and hearing non-signers | Deaf signers and hearing non-signers have previously been shown to recruit partially different brain regions during simple arithmetic. In light of the triple code model, the differences were interpreted as relating to stronger recruitment of the verbal system of numerical processing, that is, left angular and inferior frontal gyrus, in hearing non-signers, and of the quantity system of numerical processing, that is, right horizontal intraparietal sulcus, for deaf signers. The main aim of the present study was to better understand similarities and differences in the neural correlates supporting arithmetic in deaf compared to hearing individuals. Twenty-nine adult deaf signers and 29 hearing non-signers were enrolled in an functional magnetic resonance imaging study of simple and difficult subtraction and multiplication. Brain imaging data were analyzed using whole-brain analysis, region of interest analysis, and functional connectivity analysis. Although the groups were matched on age, gender, and nonverbal intelligence, the deaf group performed generally poorer than the hearing group in arithmetic. Nevertheless, we found generally similar networks to be involved for both groups, the only exception being the involvement of the left inferior frontal gyrus. This region was activated significantly stronger for the hearing compared to the deaf group but showed stronger functional connectivity with the left superior temporal gyrus in the deaf, compared to the hearing, group. These results lend no support to increased recruitment of the quantity system in deaf signers. Perhaps the reason for performance differences is to be found in other brain regions not included in the original triple code model. |
4,832 | ZoneDefense: A Fault-Tolerant Routing for 2-D Meshes Without Virtual Channels | Fault-tolerant routing is usually used to provide reliable on-chip communication for many-core processors. This paper focuses on a special class of algorithms that do not use virtual channels. One of the major challenges is to keep the network deadlock free in the presence of faults, especially those locating on network edges. State-of-the-art solutions address this problem by either disabling all nodes of the faulty network edges or including all faults into one faulty block. Therefore, a large number of fault-free nodes will be sacrificed. To address this problem, the proposed ZoneDefense routing not only includes faults into convex faulty blocks but also spreads the faulty blocks' position information in corresponding columns. The nodes, which know the position of faulty blocks, form the defense zones. Therefore, packets can find the faulty blocks and route around them in advance. Exploiting the defense zones, the proposed ZoneDefense routing could tolerate many more faults with significantly reduced sacrificed fault-free nodes compared with the state-of-the-art algorithms. Furthermore, the ZoneDefense routing does not degrade the network performance in the absence of faults, and could get similar performance as its counterparts in the presence of faults. |
4,833 | Ask the River: A Public Art and Placemaking Project | Intertwining people and place, Ask the River is a public art and placemaking project reconnecting the Brattleboro, Vermont community to the nearby Connecticut River. Ask the River is guided by the Abenaki understanding that people and place are one: we are the River, the River is us. Three artists and multiple community partners create public art for community-a riverscape of shimmering kinetic sculpture spanning the town's parking garage-and in community, in workshops using the hands-on, accessible and engaging process of cyanotype. The River is inspiration and guide. The purpose of Ask the River is for the Brattleboro community to explore seeing ourselves as a part of, not separate from, the River: to create pathways of reconnection, to enable us to hear its voice. |
4,834 | A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework | Data visualization is a powerful skill for the demonstration of meaningful data insights in an interactive and effective way. In this survey article, we collected 70 articles from last five years (2017-2022) to identify, classify, and investigate the various scopes, aspects and theories of data visualization. We also investigated the powerful applications of data visualization in various domains and fields such as visualization apps for health sector, Internet of things (IoTs), business dashboards, urban traffic management, smart buildings and environmental data visualization. However, after thorough investigation and classification, we conclude that, a comprehensive study is still missing about interactive, effective and efficient data visualization survey explaining basic current state-of-the-art best interactive visualization techniques, web-based tools and platforms, best performance theories, data structures and algorithms. In this survey article, we perform a thorough investigation to fill the gap on theoretical, analytical, statistical models and techniques for improving the performance of visualization. Current primary and domain specific future challenges are also reviewed, and related future research directions and opportunities are recommended. |
4,835 | A low-cost technique for biodiesel production in Ankistrodesmus sp. EHY by using harvested microalgal effluent | The present study aims to use Ankistrodesmus sp. EHY to develop a viable and economic lipid production strategy using recycling of harvested microalgal effluent. In comparison to the control, the highest lipid content (52.4 %) and productivity (250.72 mg L-1 d-1) were achieved when 40 % recycled medium was used. Consistent with the trend of lipid accumulation, the six key lipogenetic genes were upregulated, as well as reactive oxygen species (ROS), glutathione (GSH) and genes encoding antioxidant enzymes during cultivation in recycled medium. Moreover, the consumption of dissolved organic carbon (DOC) and the increased humic acid (HA) in the recycled medium might also be associated with lipid biosynthesis. The biodiesel parameters of alga biomass-derived lipids were fitted to the standard of commercial biodiesel. In conclusion, this study offers an economically viable strategy for microalgal biofuel production and wastewater treatment using recycling of harvested microalgal effluent. |
4,836 | Demonstration of bacterium-free very rapid reverse genetics system using mumps virus | The reverse genetics system is a very powerful tool for analyzing the molecular mechanisms of viral propagation and pathogenesis. However, full-length genome plasmid construction is highly time-consuming and laborious, and undesired mutations may be introduced by Escherichia coli. This study shows a very rapid E. coli-free method of full-genome construction using the mumps virus as an example. This method was able to reduce dramatically the time for full-genome construction, which was used very efficiently for virus rescue, from several days or more to ~2 days, with a similar accuracy and yield to the conventional method using E. coli/plasmid. |
4,837 | Polarization clustering of biological structures with Mueller matrix parameters | Mueller matrix imaging polarimetry (MMIP) is a promising technique for the characterization of biological tissues, including the classification of microstructures in pathological diagnosis. To expand the parameter space of Mueller matrix parameters, we propose new vector parameters (VPs) according to the Mueller matrix polar decomposition method. We measure invasive bladder cancer (IBC) with extensive necrosis and high-grade ductal carcinoma in situ (DCIS) with MMIP, and the regions of cancer cells and fibrotic stroma are classified with the VPs. Then the proposed and existing VPs are mapped on the Poincaré sphere with 3D visualization, and an indicator of spatial feature is defined based on the minimum enclosing sphere to evaluate the classification capability of the VPs. For both IBC and DCIS, the results show that the proposed VPs exhibit evident contrast between the regions of cancer cells and fibrotic stroma. This study broadens the fundamental Mueller matrix parameters and helps to improve the characterization ability of the MMIP technique. |
4,838 | A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva | Multiplexed detection of biomolecules is of great value in various fields, from disease diagnosis to food safety and environmental monitoring. However, accurate and multiplexed analyte detection is challenging to achieve in mixtures using a single device/material. In this paper, we demonstrate a machine learning (ML)-powered multimodal analytical device based on a single sensing material made of electrodeposited molybdenum polysulfide (eMoSx) on laser induced graphene (LIG) for multiplexed detection of tyrosine (TYR) and uric acid (UA) in sweat and saliva. Electrodeposition of MoSx shows an increased electrochemically active surface area (ECSA) and heterogeneous electron transfer rate constant, k0. Features are extracted from the electrochemical data in order to train ML models to predict the analyte concentration in the sample (both singly spiked and mixed samples). Different ML architectures are explored to optimize the sensing performance. The optimized ML-based multimodal analytical system offers a limit of detection (LOD) that is two orders of magnitude better than conventional approaches which rely on single peak analysis. A flexible and wearable sensor patch is also fabricated and validated on-body, achieving detection of UA and TYR in sweat over a wide concentration range. While the performance of the developed approach is demonstrated for detecting TYR and UA using eMoSx-LIG sensors, it is a general analytical methodology and can be extended to a variety of electrochemical sensors to enable accurate, reliable, and multiplexed sensing. |
4,839 | A Supervoxel-Based Random Forest Method for Robust and Effective Airborne LiDAR Point Cloud Classification | As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. Two International Society of Photogrammetry and Remote Sensing benchmark tests show that the proposed method achieves state-of-the-art performance, with average F1-scores of 89.16 and 83.58, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes. |
4,840 | Context awareness in biometric systems and methods: State of the art and future scenarios | In the last decade, research in biometrics has been focused on augmenting the algorithmic performance to address a growing range of applications, not limited to person authentication/recognition. The concept of context awareness emerged as a possible key-factor for both performance optimization and operational adaptation of the capture, extraction, matching and decision stages. This may be particularly effective for multi-biometrics systems. The knowledge of the context in which a task is being performed, may provide useful information to the system in several manners. For example, it may allow to adapt to a specific environmental condition, such as shadow or light exposure. On the other hand, it may be possible to select the best available algorithm, among a given set to address the task at hand, which best performs within the given context. This paper aims to provide an overall vision of the main contributions available so far in the field of context-aware biometric systems and methods. The survey is not confined to a particular biometric modality or processing stage, but rather spans the state of the art of several biometric modalities and approaches. A taxonomy of context-aware biometric systems and methods is also proposed, along with a comparison of their features, aims and performances. The analysis will be complemented with a critical discussion about the state of the art also suggesting some future application scenarios. (C) 2018 Elsevier B.V. All rights reserved. |
4,841 | A low-power current mode fuzzy-ART cell | This paper presents a very large scale integration (VLSI) implementation of a low-power current-mode fuzzy-adaptive resonance theory (ART) cell. The cell is based on a compact new current source multibit memory cell with online learning capability. A small prototype of the designed cell and its peripheral block has been fabricated in the AustriaMicroSystems (AMS)-0.35-mu m technology. The cell occupies a total area of 44 x 34 mu m(2) and consumes a maximum current of 22 nA. |
4,842 | Motion Tracking of the Carotid Artery Wall From Ultrasound Image Sequences: a Nonlinear State-Space Approach | The motion of the common carotid artery (CCA) wall has been established to be useful in early diagnosis of atherosclerotic disease. However, tracking the CCA wall motion from ultrasound images remains a challenging task. In this paper, a nonlinear state-space approach has been developed to track CCA wall motion from ultrasound sequences. In this approach, a nonlinear state-space equation with a time-variant control signal was constructed from a mathematical model of the dynamics of the CCA wall. Then, the unscented Kalman filter (UKF) was adopted to solve the nonlinear state transfer function in order to evolve the state of the target tissue, which involves estimation of the motion trajectory of the CCA wall from noisy ultrasound images. The performance of this approach has been validated on 30 simulated ultrasound sequences and a real ultrasound dataset of 103 subjects by comparing the motion tracking results obtained in this study to those of three state-of-the-art methods and of the manual tracing method performed by two experienced ultrasound physicians. The experimental results demonstrated that the proposed approach is highly correlated with (intra-class correlation coefficient >= 0.9948 for the longitudinal motion and >= 0.9966 for the radial motion) and well agrees (the 95% confidence interval width is 0.8871 mm for the longitudinal motion and 0.4159 mm for the radial motion) with the manual tracing method on real data and also exhibits high accuracy on simulated data (0.1161 similar to 0.1260 mm). These results appear to demonstrate the effectiveness of the proposed approach for motion tracking of the CCA wall. |
4,843 | Photo-Realistic Image Dehazing and Verifying Networks via Complementary Adversarial Learning | Physical model-based dehazing methods cannot, in general, avoid environmental variables and undesired artifacts such as non-collected illuminance, halo and saturation since it is difficult to accurately estimate the amount of the illuminance, light transmission and airlight. Furthermore, the haze model estimation process requires very high computational complexity. To solve this problem by directly estimating the radiance of the haze images, we present a novel dehazing and verifying network (DVNet). In the dehazing procedure, we enhanced the clean images by using a correction network (CNet), which uses the ground truth to learn the haze network. Haze images are then restored through a haze network (HNet). Furthermore, a verifying method verifies the error of both CNet and HNet using a self-supervised learning method. Finally, the proposed complementary adversarial learning method can produce results more naturally. Note that the proposed discriminator and generators (HNet & CNet) can be learned via an unpaired dataset. Overall, the proposed DVNet can generate a better dehazed result than state-of-the-art approaches under various hazy conditions. Experimental results show that the DVNet outperforms state-of-the-art dehazing methods in most cases. |
4,844 | Pansharpening Based on Deconvolution for Multiband Filter Estimation | The combination of a multispectral (MS) image and a panchromatic (PAN) image, the so-called pansharpening, allows to produce very appealing images that are useful both for visual interpretation and for feature extraction. The state-of-the-art multiresolution analysis pansharpening algorithms are based on the extraction of spatial details from the PAN image through image filters matched with the MS sensors' modulation transfer function. However, this knowledge is often poor due to measurement inaccuracies and/or its aging. Thus, deconvolution algorithms have been proposed to overcome this limitation. In this paper, we propose a multiband filter estimation (FE) approach to improve the solutions in the literature. The main idea in this paper is to exploit a preliminary pansharpened image to estimate the spatial filter used for detail extraction associated with each spectral band. We demonstrate that the proposed method outperforms the state-of-the-art FE approaches by employing data sets acquired by the IKONOS, the Quickbird, and the WorldVievv-3 sensors. |
4,845 | Visual knowledge guided intelligent generation of Chinese seal carving | We digitally reproduce the process of resource collaboration, design creation, and visual presentation of Chinese seal-carving art. We develop an intelligent seal-carving art-generation system (Zhejiang University Intelligent Seal-Carving System, http://www.next.zju.edu.cn/seal/; the website of the seal-carving search and layout system is http://www.next.zju.edu.cn/seal/search_app/) to deal with the difficulty in using a visual knowledge guided computational art approach. The knowledge base in this study is the Qiushi Seal-Carving Database, which consists of open datasets of images of seal characters and seal stamps. We propose a seal character generation method based on visual knowledge, guided by the database and expertise. Furthermore, to create the layout of the seal, we propose a deformation algorithm to adjust the seal characters and calculate layout parameters from the database and knowledge to achieve an intelligent structure. Experimental results show that this method and system can effectively deal with the difficulties in the generation of seal carving. Our work provides theoretical and applied references for the rebirth and innovation of seal-carving art. |
4,846 | Asymmetric role of non-renewable energy consumption, ICT, and financial development on ecological footprints: evidence from QARDL approach | This study examines long-term connection and short-term dynamics concerning ecological footprint and six independent variables, named fossil fuel consumption, energy consumption, financial depth, trade, GDP, and ICT for Pakistan's duration from 1960 to 2019. The "QARDL-quantile autoregressive distributed lag" technique is used for time series and panel estimation. The QARDL model exhibits the connection between variables over the quantiles range, reflecting varying stages of Pakistan's ecological footprint. The results exhibit noticeable quantile-varying co-integration connection among ecological footprint and six independent variables. The results accentuate the significant influence of energy consumption, strong financial position, economic growth, and ICT technologies on ecological well-being, which assists in understanding short and long-term impact on the environment in Pakistan. |
4,847 | Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set | Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS. |
4,848 | Speech Enhancement With Inventory Style Speech Resynthesis | We present a new method for the enhancement of speech. The method is designed for scenarios in which targeted speaker enrollment as well as system training within the typical noise environment are feasible. The proposed procedure is fundamentally different from most conventional and state-of-the-art denoising approaches. Instead of filtering a distorted signal we are resynthesizing a new "clean" signal based on its likely characteristics. These characteristics are estimated from the distorted signal. A successful implementation of the proposed method is presented. Experiments were performed in a scenario with roughly one hour of clean speech training data. Our results show that the proposed method compares very favorably to other state-of-the-art systems in both objective and subjective speech quality assessments. Potential applications for the proposed method include jet cockpit communication systems and offline methods for the restoration of audio recordings. |
4,849 | Managing Performance Overhead of Virtual Machines in Cloud Computing: A Survey, State of the Art, and Future Directions | Infrastructure-as-a-Service (IaaS) cloud computing offers customers (tenants) a scalable and economical way to provision virtual machines (VMs) on demand while charging them only for the leased computing resources by time. However, due to the VM contention on shared computing resources in datacenters, this new computing paradigm inevitably brings noticeable performance overhead (i.e., unpredictable performance) of VMs to tenants, which has become one of the primary issues of the IaaS cloud. Consequently, increasing efforts have recently been devoted to guaranteeing VM performance for tenants. In this survey, we review the state-of-the-art research on managing the performance overhead of VMs, and summarize them under diverse scenarios of the IaaS cloud, ranging from the single-server virtualization, a single mega datacenter, to multiple geodistributed datacenters. Specifically, we unveil the causes of VM performance overhead by illustrating representative scenarios, discuss the performance modeling methods with a particular focus on their accuracy and cost, and compare the overhead mitigation techniques by identifying their effectiveness and implementation complexity. With the obtained insights into the pros and cons of each existing solution, we further bring forth future research challenges pertinent to the modeling methods and mitigation techniques of VM performance overhead in the IaaS cloud. |
4,850 | MGARD plus : Optimizing Multilevel Methods for Error-Bounded Scientific Data Reduction | Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale, but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which treats the multilevel decomposition as a preconditioner and terminates the decomposition process at an appropriate level. 3) We leverage a set of algorithmic optimization strategies to significantly improve the performance of multilevel decomposition/recomposition. 4) We evaluate our proposed method using four real-world scientific datasets and compare with several state-of-the-art lossy compressors. Experiments demonstrate that our optimizations improve the decomposition/recomposition performance of the existing multilevel method by up to 70x, and the proposed compression method can improve compression ratio by up to 2x compared with other state-of-the-art error-bounded lossy compressors under the same level of data distortion. |
4,851 | A comparative evaluation of outlier detection algorithms: Experiments and analyses | We survey unsupervised machine learning algorithms in the context of outlier detection. This task challenges state-of-the-art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning. The selected methods are benchmarked on publicly available datasets and novel industrial datasets. Each method is then submitted to extensive scalability, memory consumption and robustness tests in order to build a full overview of the algorithms' characteristics. (C) 2017 Elsevier Ltd. All rights reserved. |
4,852 | High-resolution Melting Analysis for NOTCH1 c.7541-7542delCT Mutation in Chronic Lymphocytic Leukemia: Prognostic Significance in Egyptian Patients | The present study aimed to detect the prevalence of NOTCH1 c.7541-7542delCT mutation in Egyptian CLL patients using HRM assay and to assess its relation to patients' survival. The study included 50 newly diagnosed treatment-naïve CLL patients and 50 age and sex matched healthy controls. NOTCH1 c.7541-7542delCT mutation was detected using High-resolution melting (HRM) assay and direct Sanger sequencing. Outcome parameters included progression free survival (PFS) and overall survival (OS). NOTCH1 c.7541-7542delCT mutation was detected in 5 (10.0%) of CLL patients. No controls had NOTCH1 c.7541-7542delCT mutation. Similar results were obtained by direct Sanger sequencing yielding a sensitivity and specificity of 100.0% for HRM in detection of NOTCH1 c.7541-7542delCT mutation in the studied patients. In univariate analysis, predictors of OS included Trisomy 12, high LDH, presence of NOTCH1 c.7541-7542delCT mutation and lack of CR. In multivariate analysis, only lack of CR was found as a significant predictor of OS. HRM analysis is a sensitive method for detection of NOTCH1 c.7541-7542delCT mutation in CLL patients. This mutation may be linked to poor disease prognosis. |
4,853 | State of Art IoT and Edge Embedded Systems for Real-Time Machine Vision Applications | IoT and edge devices dedicated to run machine vision algorithms are usually few years lagging currently available state-of-the-art technologies for hardware accelerators. This is mainly due to the non-negligible time delay required to implement and assess related algorithms. Among possible hardware platforms which are potentially being explored to handle real-time machine vision tasks, multi-core CPU and Graphical Processing Unit (GPU) platforms remain the most widely used ones over Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC)-based platforms. This is mainly due to the availability of powerful and user friendly software development tools, in addition to their lower cost, and obviously their high computation power with reasonable form factor and power consumption. Nevertheless, the trend now is towards a System-On-Chip (SOC) processors which combine ASIC/FPGA accelerators with GPU/multicore CPUs. This paper presents different state of the art IoT and edge machine vision technologies along with their performance and limitations. It can be a good reference for researchers involved in designing state of the art IoT embedded systems for machine vision applications. |
4,854 | N-Hydroxy pipecolic acid methyl ester is involved in Arabidopsis immunity | The biosynthesis of N-hydroxy pipecolic acid (NHP) has been intensively studied, though knowledge on its metabolic turnover is still scarce. To close this gap, we discovered three novel metabolites via metabolite fingerprinting in Arabidopsis thaliana leaves after Pseudomonas infection and UV-C treatment. Exact mass information and fragmentation by tandem mass spectrometry (MS/MS) suggest a methylated derivative of NHP (MeNHP), an NHP-OGlc-hexosyl conjugate (NHP-OGlc-Hex), and an additional NHP-OGlc-derivative. All three compounds were formed in wild-type leaves but were not present in the NHP-deficient mutant fmo1-1. The identification of these novel NHP-based molecules was possible by a dual-infiltration experiment using a mixture of authentic NHP and D9-NHP standards for leaf infiltration followed by UV-C treatment. Interestingly, the signal intensity of MeNHP and other NHP-derived metabolites increased in ugt76b1-1 mutant plants. For MeNHP, we unequivocally determined the site of methylation at the carboxylic acid moiety. MeNHP application by leaf infiltration leads to the detection of a MeNHP-OGlc as well as NHP, suggesting MeNHP hydrolysis to NHP. This is in line with the observation that MeNHP infiltration is able to rescue the fmo1-1 susceptible phenotype against Hyaloperonospora arabidopsidis Noco 2. Together, these data suggest MeNHP as an additional storage or transport form of NHP. |
4,855 | A new group of eubacterial light-driven retinal-binding proton pumps with an unusual cytoplasmic proton donor | One of the main functions of microbial rhodopsins is outward-directed light-driven proton transport across the plasma membrane, which can provide sources of energy alternative to respiration and chlorophyll photosynthesis. Proton-pumping rhodopsins are found in Archaea (Halobacteria), multiple groups of Bacteria, numerous fungi, and some microscopic algae. An overwhelming majority of these proton pumps share the common transport mechanism, in which a proton from the retinal Schiff base is first transferred to the primary proton acceptor (normally an Asp) on the extracellular side of retinal. Next, reprotonation of the Schiff base from the cytoplasmic side is mediated by a carboxylic proton donor (Asp or Glu), which is located on helix C and is usually hydrogen-bonded to Thr or Ser on helix B. The only notable exception from this trend was recently found in Exiguobacterium, where the carboxylic proton donor is replaced by Lys. Here we describe a new group of efficient proteobacterial retinal-binding light-driven proton pumps which lack the carboxylic proton donor on helix C (most often replaced by Gly) but possess a unique His residue on helix B. We characterize the group spectroscopically and propose that this histidine forms a proton-donating complex compensating for the loss of the carboxylic proton donor. |
4,856 | Foreground Segmentation in Videos Combining General Gaussian Mixture Modeling and Spatial Information | We present a new statistical approach combining temporal and spatial information for robust online background subtraction (BS) in videos. Temporal information is modeled by coupling finite mixtures of generalized Gaussian distributions with foreground/background co-occurrence analysis. Spatial information is modeled by combining multiscale inter-frame correlation analysis and histogram matching. We propose an online algorithm that efficiently fuses both information to cope with several BS challenges, such as cast shadows, illumination changes, and various complex background dynamics. In addition, global video information is used through a displacement measuring technique to deal with pan-tilt-zoom camera effects. Experiments with comparison with recent state-of-the-art methods have been conducted on standard data sets. Obtained results have shown that our approach surpasses several state-of-the-art methods on the aforementioned challenges while maintaining comparable computational time. |
4,857 | A COP1-GATA2 axis suppresses AR signaling and prostate cancer | Androgen receptor (AR) signaling is crucial for driving prostate cancer (PCa), the most diagnosed and the second leading cause of death in male patients with cancer in the United States. Androgen deprivation therapy is initially effective in most instances of AR-positive advanced or metastatic PCa. However, patients inevitably develop lethal castration-resistant PCa (CRPC), which is also resistant to the next-generation AR signaling inhibitors. Most CRPCs maintain AR expression, and blocking AR signaling remains a main therapeutic approach. GATA2 is a pioneer transcription factor emerging as a key therapeutic target for PCa because it promotes AR expression and activation. While directly inhibiting GATA2 transcriptional activity remains challenging, enhancing GATA2 degradation is a plausible therapeutic strategy. How GATA2 protein stability is regulated in PCa remains unknown. Here, we show that constitutive photomorphogenesis protein 1 (COP1), an E3 ubiquitin ligase, drives GATA2 ubiquitination at K419/K424 for degradation. GATA2 lacks a conserved [D/E](x)xxVP[D/E] degron but uses alternate BR1/BR2 motifs to bind COP1. By promoting GATA2 degradation, COP1 inhibits AR expression and activation and represses PCa cell and xenograft growth and castration resistance. Accordingly, GATA2 overexpression or COP1 mutations that disrupt COP1-GATA2 binding block COP1 tumor-suppressing activities. We conclude that GATA2 is a major COP1 substrate in PCa and that COP1 promotion of GATA2 degradation is a direct mechanism for regulating AR expression and activation, PCa growth, and castration resistance. |
4,858 | Efficient Depth Fusion Transformer for Aerial Image Semantic Segmentation | Taking depth into consideration has been proven to improve the performance of semantic segmentation through providing additional geometry information. Most existing works adopt a two-stream network, extracting features from color images and depth images separately using two branches of the same structure, which suffer from high memory and computation costs. We find that depth features acquired by simple downsampling can also play a complementary part in the semantic segmentation task, sometimes even better than the two-stream scheme with the same two branches. In this paper, a novel and efficient depth fusion transformer network for aerial image segmentation is proposed. The presented network utilizes patch merging to downsample depth input and a depth-aware self-attention (DSA) module is designed to mitigate the gap caused by difference between two branches and two modalities. Concretely, the DSA fuses depth features and color features by computing depth similarity and impact on self-attention map calculated by color feature. Extensive experiments on the ISPRS 2D semantic segmentation dataset validate the efficiency and effectiveness of our method. With nearly half the parameters of traditional two-stream scheme, our method acquires 83.82% mIoU on Vaihingen dataset outperforming other state-of-the-art methods and 87.43% mIoU on Potsdam dataset comparable to the state-of-the-art. |
4,859 | Levels of neuroticism can predict attentional performance during cross-modal nonspatial repetition inhibition | Inhibition of return (IOR) refers to the slower response to targets presented at previously attended locations, and such repetition-induced inhibition has been found to be differentially associated with personality traits. Although it has been well documented how personality traits affect spatial IOR, a mechanism associated with the attentional orienting network, there is not yet a consensus as to the relationship between personality traits and nonspatial repetition inhibition, a mechanism associated with the attentional executive network. The present study herein examined how the Big Five personality traits relate to cross-modal nonspatial repetition inhibition. Participants completed the NEO-PI-R and performed a cross-modal nonspatial repetition inhibition task built on the prime-neutral cue-target paradigm, in which the relationships of the identities and modalities between the prime and the target were manipulated. The results showed a significant nonspatial inhibitory effect and the effect was larger under the visual-auditory condition than under the auditory-visual condition. More importantly, neuroticism was associated with decreased cross-modal nonspatial inhibitory effect, presumably due to impaired attentional control. However, such a result was only found in the visual-auditory condition. We propose that retrieving previous prime representations under the visual-auditory condition requires a large consumption of cognitive resources, making inhibitory control more difficult for individuals with high neuroticism. These findings provide new insight into the influence of personality traits on attentional performance requiring nonspatial inhibitory control and enrich the relationship between neuroticism and repetition-induced inhibition. |
4,860 | Auto-responsive technologies for thermal renovation of opaque facades | Auto-responsive technologies (ARTs) operate in an intrinsic mode undergoing reversible changes in one or more of their properties in direct response to external stimuli variations. The aim of this paper is to identify their potential use for the thermal renovation of opaque facades of buildings in order to reach climate adaptivity. Adaptive facade concept offers a huge potential for thermal renovation, by improving occupants' comfort, promoting sector decarbonization, and being an opportunity for adapting facades to climate change. A literature review permitted the systematization of thermal renovation adaptive strategies (TRAS) in which ARTs can be useful. The facade reversible changes (outputs) required for each strategy were identified, as well as the possible adaptation mechanisms to obtain them. The technologies responding within the recognized adaptation mechanisms were described and compared, their readiness level identified and their role within the TRAS was assigned. An approach to assess the suitability of each ART regarding TRAS for existing facades was conceived. It uses the criteria of aesthetic change, additional space and demolition needs, localization and area of intervention. The role of each ART within each TRAS, here systematized, will support the definition of the ART's operational range for each application and climate, and will promote the research and development of these technologies for thermal renovation of buildings' facades. (C) 2020 Elsevier B.V. All rights reserved. |
4,861 | Interactive Sand Art Drawing Using RGB-D Sensor | We present an interactive system using one RGB-D sensor, which allows a user to use bare hands to perform sand drawing. Our system supports the common sand drawing functions, such as sand erosion, sand spilling, and sand leaking. To use hands to manipulate the virtual sand, we design four key hand gestures. The idea is that the gesture of one hand controls the drawing actions. The motion and gesture of the other hand control the drawing positions. There are three major steps. First, our system adopts a vision-based bare-hand detection method which computes the hand position and recognizes the hand gestures. Second, the drawing positions and the drawing actions are sent to a sand drawing subsystem. Finally, the subsystem performs the sand drawing actions. Experimental results show that our system enables users to draw a rich variety of sand pictures. |
4,862 | Synthesis and Late-Stage Diversification of BN-Embedded Dibenzocorannulenes as Efficient Fluorescence Organic Light-Emitting Diode Emitters | We report the synthesis and late-stage diversification of a new class of hetero-buckybowl, BN-embedded dibenzocorannulenes (B2 N2 -DBCs). The synthesis is achieved via one-shot halogenative borylation, comprising the nitrogen-directed haloboration of alkyne and an intramolecular bora-Friedel-Crafts reaction, which provides BN-embedded dibenzocorannulene possessing two bromo substituents (B2 N2 -DBC-Br). B2 N2 -DBC-Br undergoes diversification via coupling reactions to provide a variety of arylated derivatives (B2 N2 -DBC-R), exhibiting strong blue fluorescence. An organic light-emitting diode (OLED) employing one of the derivatives as an emitter exhibited a high external quantum efficiency of 6.6 % and long operational lifetime of 907 h at an initial luminance of 1000 cd m-2 , indicating the significant potential for the development of efficient and stable hetero-buckybowl-based OLED materials. |
4,863 | Pixel Modeling Using Histograms Based on Fuzzy Partitions for Dynamic Background Subtraction | We propose a novel pixel-modeling approach for background subtraction using histograms based on strong uniform fuzzy partitions. In the proposed method, the temporal distribution of pixel values is represented by a histogram based on a triangular partition. The threshold for background segmentation is set adaptively according to the shape of the histogram. Histogram accumulation is controlled adaptively by a fuzzy controller under a supervised learning framework. Benefiting from the adaptive scheme, with no parameter tuning, the proposed algorithm functions well across a wide spectrum of challenging environments. The performance of the proposed method is evaluated against more than 20 state-of-the-art methods in complex outdoor environments, particularly in those consisting of highly dynamic backgrounds and camouflaged foregrounds. Experimental results confirm that the proposed method performs effectively in terms of both the true positive rate and the noise suppression ability. Further, it outperforms other state-of-the-art methods by a significant margin. |
4,864 | Multi-Stencil Streamline Fast Marching: A General 3-D Framework to Determine Myocardial Thickness and Transmurality in Late Enhancement Images | We propose a fully 3-D methodology for the computation of myocardial nonviable tissue transmurality in contrast enhanced magnetic resonance images. The outcome is a continuous map defined within the myocardium where not only current state-of-the-art measures of transmurality can be calculated, but also information on the location of nonviable tissue is preserved. The computation is done by means of a partial differential equation framework we have called multi-stencil streamline fast marching. Using it, the myocardial and scarred tissue thickness is simultaneously computed. Experimental results show that the proposed 3-D method allows for the computation of transmurality in myocardial regions where current 2-D methods are not able to as conceived, and it also provides more robust and accurate results in situations where the assumptions on which current 2-D methods are based-i.e., there is a visible endocardial contour and its corresponding epicardial points lie on the same slice-, are not met. |
4,865 | Art-geoscience encounters and entanglements in the watery realm | This paper critically explores a 40-year collaboration between a geomorphologist and a relief printmaker from the perspective of the emerging art-science paradigm in the geosciences. Drawing on the authors' work and practice worldwide, 'standard art-science' (the artist as communicator and observer) and emerging 'transdisciplinary/paradisciplinary' practices are explored in the watery realm. While standard art-science 'encounters' were viewed favourably from the viewpoint of community engagement, especially by commissioning bodies, they did not measurably improve the explanation of science to the public nor offer new avenues for creative investigation. In light of this, the authors undertook a series of explicitly interdisciplinary/transdisciplinary 'entanglements' by co-conceiving projects, carrying out joint fieldwork and 'data' collection and, most importantly, working together in the studio and laboratory. These projects suggest that multi-scalar approaches are required when using art-geoscience to explore environmental issues which impact significantly on individuals and communities. |
4,866 | 18β-glycyrrhetinic acid protects neuronal cells from ferroptosis through inhibiting labile iron accumulation and preventing coenzyme Q10 reduction | Ferroptosis is a new form of iron-dependent cell death. A growing body of evidence suggests that abnormal ferroptosis is involved in developing neurodegenerative diseases. 18β-glycyrrhetinic acid (GA) is a major bioactive component of licorice with multiple biological activities including neuroprotection. Give the role of ferroptosis in the neurodegenerative diseases, we hypothesized that the neuroprotective effect of GA might be associated with its ability to protect neuro-cells from ferroptosis. Results demonstrated that GA was able to prevent a well-known ferroptosis inducer ferroptosis inducer 56 (FIN56)-triggered ferroptosis in HT22 mouse neuronal cell. Further mechanistic investigation revealed that the protection of GA on ferroptosis is attributed its inhibiting effect on cellular labile iron accumulation and up-regulating coenzyme Q10 (CoQ10) levels. The findings of the present study uncovered a novel mechanism involved in the neuroprotective effect of GA, and imply that GA could be developed as a novel agent to manage ferroptosis-related diseases. |
4,867 | A Multidisciplinary Aesthetic Treatment Approach for Peg Lateral of the Maxillary Incisors | Developmental anomaly of the maxillary lateral incisors most commonly leads to the occurrence of peg lateral. It is a variant of microdontia where the lateral incisors are smaller than the normal size. This appears as unilaterally or bilaterally. This condition is characterised by the converging of the mesial and distal surfaces forming a cone shape. A variety of treatment options exist for this anomaly including orthodontic treatment, restorative technique and veneer. This case report deals with an individual presenting with peg lateral of the maxillary arch along with midline diastema. The multidisciplinary treatment protocol of orthodontic treatment involving minor tooth movement and space closure in conjunction with a restorative technique for correction was preferred. |
4,868 | Discussion: < Chronology of western Pyrenean Paleolithic cave art: A critical examination > by Blanca Ochoa and Marcos Garcia-Diez | In a recent paper by Ochoa and Garcia-Diez (2013) the available evidences for a chronology of western Pyrenean Paleolithic cave art are critically analyzed and discussed, and an alternative chronological organization is proposed on the basis of stylistic comparison. In this paper we discuss the critics made to the immediate context dating proposals in Altxerri B, Askondo and Etxeberri by giving the detailed information that has been recently published (Garate and Rios-Garaizar, 2012; Garate et al., 2012; Gonzalez-Sainz et al., 2013). We also discuss the validity of the stylistic comparisons proposed by Ochoa and Garcia-Diez (2013) for the Gravettian and the Magdalenian art. Finally we discuss the problems for establishing a reliable chronological framework for Paleolithic rock art in this area. (C) 2014 Elsevier Ltd and INQUA. All rights reserved. |
4,869 | Scalable Packet Classification on FPGA | Multi-field packet classification has evolved from traditional fixed 5-tuple matching to flexible matching with arbitrary combination of numerous packet header fields. For example, the recently proposed OpenFlow switching requires classifying each packet using up to 12-tuple packet header fields. It has become a great challenge to develop scalable solutions for next-generation packet classification that support higher throughput, larger rule sets and more packet header fields. This paper exploits the abundant parallelism and other desirable features provided by current field-programmable gate arrays (FPGAs), and proposes a decision-tree-based, 2-D multi-pipeline architecture for next-generation packet classification. We revisit the techniques for traditional 5-tuple packet classification and propose several optimization techniques for the state-of-the-art decision-tree-based algorithm. Given a set of 12-tuple rules, we develop a framework to partition the rule set into multiple subsets each of which is built into an optimized decision tree. A tree-to-pipeline mapping scheme is carefully designed to maximize the memory utilization while sustaining high throughput. The implementation results show that our architecture can store either 10K real-life 5-tuple rules or 1K synthetic 12-tuple rules in on-chip memory of a single state-of-the-art FPGA, and sustain 80 and 40 Gbps throughput for minimum size (40 bytes) packets, respectively. |
4,870 | A two-pole 900/1800-MHz microstrip filter for dual-band applications | A two-pole 900/1800-MH,7 dual-band bandpass filter using microstrip open-loop resonators is proposed. Dual-band characteristics art obtained by using the fundamental and first spurious resonance of the resonators. Both the internal and external couplings of the filter are appropriately designed to simultaneously satisfy the dual-band requirements. The measured filtering characteristics of the designed filter agree well with the simulated responses. (c) 2006 Wiley Periodicals, Inc. |
4,871 | OCT-1 Expression in Patients with Chronic Myeloid Leukemia: A Comparative Analysis with Respect to Response to Imatinib Treatment | The introduction of tyrosine kinase inhibitors (TKI) has resulted in a significant improvement in the treatment of CML patients. However, some CML patients are resistant to imatinib therapy, the initial TKI therapy in the CML. Therefore, it is important to find prognostic markers for resistance. The OCT-1 gene involved in imatinib uptake is also suspected to cause imatinib resistance. The aim of this study was to investigate the role of OCT-1 in imatinib resistance by comparing OCT-1 expression levels in imatinib resistant and imatinib sensitive patients with chronic myeloid leukemia (CML). This study was conducted on 101 patients with CML [imatinib sensitive (n = 51) and imatinib resistant (n = 50)] who were treated with imatinib. Gene expression analysis was done using QRT-PCR. The relative expression levels of OCT-1 were calculated using 2(-ΔΔCT) method. OCT1 mRNA expression levels were 0.149 (0.011-2.532) and 0.119 (0.008-2.868) in imatinib-sensitive group and imatinib-resistant group, respectively. OCT-1 expression levels were not significantly different in the imatinib-sensitive group when compared to imatinib resistant group (p > 0.05). OCT-1 expression was also similar in BCR-ABL1 kinase domain mutation positive and negative cases (p > 0.05). The imatinib-resistant group had a higher rate of hydroxyurea or interferon-alpha treatment prior to imatinib therapy and a lower rate for first-line imatinib as the only treatment than the imatinib-sensitive group (p = 0.002 and p = 0.002, respectively). According to the results of our study, OCT-1 does not have a biomarker feature in the evaluation of imatinib response. In addition, the study should be performed in larger patient groups. |
4,872 | Relative sensitivity value (RSV): A metric for measuring input parameter influence in life cycle assessment modeling | Life cycle assessment (LCA) is a commonly used tool to quantify life cycle environmental footprints of products. Uncertainty in LCA modeling, particularly from uncertainty in production practices (represented through input parameter arguments), can lead to incorrect conclusions and hamper decision-making. Characterization of uncertainty through stochastic means and sensitivity analysis is utilized in a small fraction of LCA case studies, and the majority of studies default to scenario analysis due to its lower barrier to implementation and its results are easier to interpret. In this article, we introduce a sensitivity metric, relative sensitivity value (RSV), which allows LCA practitioners to gauge the relative influence of production practices on life cycle impacts in multiple phases and impact categories. Relative sensitivity value bridges the gap between scenario analysis and global sensitivity analysis, and it allows an LCA practitioner to provide an easy-to-interpret metric for quantifying the degree to which incremental changes in production practices influences the life cycle environmental footprint. We present the methodology used to calculate RSV and provide programming code, which can be readily used by an LCA practitioner to calculate RSV for their LCA model. We demonstrate the usage of RSV through a livestock husbandry LCA case study, in which we show how RSV results may be presented and interpreted, and how conclusions regarding production practices may be drawn. Integr Environ Assess Manag 2023;19:547-555. © 2022 SETAC. |
4,873 | Generative Consistency for Semi-Supervised Cerebrovascular Segmentation From TOF-MRA | Cerebrovascular segmentation from Time-of-flight magnetic resonance angiography (TOF-MRA) is a critical step in computer-aided diagnosis. In recent years, deep learning models have proved its powerful feature extraction for cerebrovascular segmentation. However, they require many labeled datasets to implement effective driving, which are expensive and professional. In this paper, we propose a generative consistency for semi-supervised (GCS) model. Considering the rich information contained in the feature map, the GCS model utilizes the generation results to constrain the segmentation model. The generated data comes from labeled data, unlabeled data, and unlabeled data after perturbation, respectively. The GCS model also calculates the consistency of the perturbed data to improve the feature mining ability. Subsequently, we propose a new model as the backbone of the GSC model. It transfers TOF-MRA into graph space and establishes correlation using Transformer. We demonstrated the effectiveness of the proposed model on TOF-MRA representations, and tested the GCS model with state-of-the-art semi-supervised methods using the proposed model as backbone. The experiments prove the important role of the GCS model in cerebrovascular segmentation. Code is available at https://github.com/MontaEllis/SSL-For-Medical-Segmentation. |
4,874 | The role of the cave in the expression of prehistoric societies | One of the major characteristics of prehistoric arts is that they belong to a very specific spatial context, be it open air, rocks, shelters or caves. The presence of these images in these particular places is a mark of their identity and of the heritage left by these ancient societies and their beliefs, ever since the first cultural manifestations of the Upper Palaeolithic in Europe. The specific choice of a wall or of a particular background, of a location in the cave can thus be just as significant as the image that one chooses to represent or the way in which they choose to represent it. This is why the present research intends to study the links between the images and their supports, through a family which has already shown a particular affinity with the space: the family of signs. The example of the signs highlights the fundamental role of the supports in the construction of images and the important and sometimes radical influence of the cave on their graphic identity. They illustrate thus "ways of expression" of the prehistoric men, expression of their developed and complex reasoning. (C) 2015 Elsevier Ltd and INQUA. All rights reserved. |
4,875 | Filter Pruning and Re-Initialization via Latent Space Clustering | Filter pruning is prevalent for pruning-based model compression. Most filter pruning methods have two main issues: 1) the pruned network capability depends on that of source pretrained models, and 2) they do not consider that filter weights follow a normal distribution. To address these issues, we propose a new pruning method employing both weight re-initialization and latent space clustering. For latent space clustering, we define filters and their feature maps as vertices and edges to be a graph, transformed into a latent space by graph convolution, alleviating to prune zero-near weight filters only. In addition, a part of filters is re-initialized with a constraint for enhancing filter diversity, and thus the pruned model is less dependent on the source network. This approach provides more robust accuracy even when pruned from the pretrained model with low accuracy. Extensive experimental results show our method decreases 56.6% and 84.6% of FLOPs and parameters of VGG16 with negligible loss of accuracy on CIFAR100, which is the state-of-the art performance. Furthermore, our method presents outperforming or comparable pruning results against state-of-the-art models on multiple datasets. |
4,876 | LTC: A Fast Algorithm to Accurately Find Significant Items in Data Streams | Finding top-k frequent items has been a hot issue in databases. Finding top-k persistent items is a new issue, and has attracted increasing attention in recent years. In practice, users often want to know which items are significant, i.e., not only frequent but also persistent. No prior art can address both of the above two issues at the same time. Also, for high-speed data streams, prior art cannot achieve high accuracy when the memory is tight. In this paper, we define a new issue, named finding significant items, and propose a novel algorithm namely LTC to address this issue. It includes two key techniques, Long-tail Restoring and CLOCK, as well as three optimizations. In addition, LTC is extended to support finding significant items with thresholds. We theoretically derive the correct rate and error bound, and conduct extensive experiments on three real datasets to test the performance of LTC. Our experimental results show that LTC can achieve 10(5 )times higher accuracy in terms of average relative error than other related algorithms. Lastly, LTC is applied to a DDoS detection task and it shows that finding significant items is more powerful than finding frequent items. |
4,877 | Low-dose, large-angled cone-beam helical CT data reconstruction using algebraic reconstruction techniques | We report on results on the use of two variants of the algebraic reconstruction techniques (ART) for reconstructing from helical cone-beam computerized tomography (CT) data: a standard one that considers a single ray in an iterative step and a block version which treats simultaneously several cone-beam projections when calculating an iterative step. Both algorithms were implemented using the modified Kaiser-Bessel window functions, also known as blobs, placed on the body-centered cubic (bcc) grid. The algorithms were used to reconstruct phantoms from data collected for the PI-geometry for four different maximum cone-beam angles (2.39, 7.13, 9.46 and 18.43 degrees). Both scattering and quantum noise (for three different noise levels) were introduced to create noisy projections that simulate low-dose examinations. The results presented here (for both noiseless and noisy data sets) point to the facts that, as opposed to a filtered back-projection algorithm, the quality of the reconstructions produced by the ART methods does not suffer from the increase in the cone-beam angle and it is more robust in the presence of noise. (c) 2006 Elsevier B.V. All rights reserved. |
4,878 | Robust tracking with interest points: A sparse representation approach | Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,(1) we propose a robust interest point based tracker in l(1) minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l(1) minimization problem. In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos. (C) 2014 Elsevier B.V. All rights reserved. |
4,879 | Dynamic processing of hunger and thirst by common mesolimbic neural ensembles | The nucleus accumbens (NAc) is a canonical reward center that regulates feeding and drinking but it is not known whether these behaviors are mediated by same or different neurons. We employed two-photon calcium imaging in awake, behaving mice and found that during the appetitive phase, both hunger and thirst are sensed by a nearly identical population of individual D1 and D2 neurons in the NAc that respond monophasically to food cues in fasted animals and water cues in dehydrated animals. During the consummatory phase, we identified three distinct neuronal clusters that are temporally correlated with action initiation, consumption, and cessation shared by feeding and drinking. These dynamic clusters also show a nearly complete overlap of individual D1 neurons and extensive overlap among D2 neurons. Modulating D1 and D2 neural activities revealed analogous effects on feeding versus drinking behaviors. In aggregate, these data show that a highly overlapping set of D1 and D2 neurons in NAc detect food and water reward and elicit concordant responses to hunger and thirst. These studies establish a general role of this mesolimbic pathway in mediating instinctive behaviors by controlling motivation-associated variables rather than conferring behavioral specificity. |
4,880 | Nanopore Sequencing Using the Full-Length 16S rRNA Gene for Detection of Blood-Borne Bacteria in Dogs Reveals a Novel Species of Hemotropic Mycoplasma | Dogs across the globe are afflicted by diverse blood- and vector-borne bacteria (VBB), many of which cause severe disease and can be fatal. Diagnosis of VBB infections can be challenging due to the low concentration of bacteria in the blood, the frequent occurrence of coinfections, and the wide range of known, emerging, and potentially novel VBB species encounterable. Therefore, there is a need for diagnostics that address these challenges by being both sensitive and capable of detecting all VBB simultaneously. We detail the first employment of a nanopore-based sequencing methodology conducted on the Oxford Nanopore Technologies (ONT) MinION device to accurately elucidate the "hemobacteriome" from canine blood through sequencing of the full-length 16S rRNA gene. We detected a diverse range of important canine VBB, including Ehrlichia canis, Anaplasma platys, Mycoplasma haemocanis, Bartonella clarridgeiae, "Candidatus Mycoplasma haematoparvum", a novel species of hemotropic mycoplasma, and Wolbachia endosymbionts of filarial worms, indicative of filariasis. Our nanopore-based protocol was equivalent in sensitivity to both quantitative PCR (qPCR) and Illumina sequencing when benchmarked against these methods, achieving high agreement as defined by the kappa statistics (k > 0.81) for three key VBB. Utilizing the ability of the ONT' MinION device to sequence long read lengths provides an excellent alternative diagnostic method by which the hemobacteriome can be accurately characterized to the species level in a way previously unachievable using short reads. We envision our method to be translatable to multiple contexts, such as the detection of VBB in other vertebrate hosts, including humans, while the small size of the MinION device is highly amenable to field use. IMPORTANCE Blood- and vector-borne bacteria (VBB) can cause severe pathology and even be lethal for dogs in many regions across the globe. Accurate characterization of all the bacterial pathogens infecting a canine host is critical, as coinfections are common and emerging and novel pathogens that may go undetected by traditional diagnostics frequently arise. Deep sequencing using devices from Oxford Nanopore Technologies (ONT) provides a solution, as the long read lengths achievable provide species-level taxonomic identification of pathogens that previous short-read technologies could not accomplish. We developed a protocol using ONT' MinION sequencer to accurately detect and classify a wide spectrum of VBB from canine blood at a sensitivity comparable to that of regularly used diagnostics, such as qPCR. This protocol demonstrates great potential for use in biosurveillance and biosecurity operations for the detection of VBB in a range of vertebrate hosts, while the MinION sequencer's portability allows this method to be used easily in the field. |
4,881 | Z-scheme promoted heterojunction photocatalyst (Ag@AgVO3 /rGO/CeVO4) with improved interfacial charge transfer for efficient removal of aqueous organics irradiated under LED light | A facile hydrothermal route was followed to obtain a ternary composite Ag@AgVO3/rGO/CeVO4 with in-situ deposition of Ag nanoparticles over the AgVO3 nano-belts. The in-situ deposition was promoted and enhanced with the introduction of GO. The as-synthesized composite demonstrated remarkable visible light harvesting efficiency greater than 75% in the visible region. The charge separation and light harvesting properties were achieved through the Z-scheme mechanism mediated through rGO and the electron trapping/Schottky barrier effect from Ag nanoparticles. The reduction in the width of space charge region (∼2.5 times) and simultaneous increase in the density of charge carriers (2.3∗1018) promoted the LED irradiated photocatalytic performance. The decay time of the charge carriers were prolonged in the order of 4.46 s implying the enhancement in the charge separation. The studies were extended to charge trapping and the band structure modelling. The later emphasized on the prominence of Z-scheme mechanism with hole mediated degradation pathway. The LED photocatalysis demonstrated a removal efficiency of 87.20% for MB and 55.51% for phenol with a average AQE of 29.28% (MB) and 13.90% (phenol) for the ternary. The mineralization efficiency determined through TOC analysis was found to be 71.72%, and 66.43% for MB and phenol system respectively. |
4,882 | MANet: Multi-Scale Attention Network for Correspondence Learning | Establishing reliable correspondences from a putative correspondence set is a challenging task. Most of state-of-the-art methods utilize the local context and global context to address the task. However, the local and global context often contains large number of outliers, which have a negative impact on capturing scene geometry. In this paper, we propose a Multi-scale Attention Network (called MANet), which introduces the attention mechanism for feature matching, to improve the ability of capturing scene geometry. Specifically, we first fuse the features of low and high levels by an multi-scale strategy network to enhance the representative ability of features. Then, we propose an attentive PointCN block and an attentive pooling layer, to discriminatively capture global context and local context information, respectively. We demonstrate through extensive experiments on both indoor and outdoor datasets that MANet provides a significant improvement in the performance of the two-view geometry and correspondences accuracy compared to the state-of-the-art methods. |
4,883 | Unconventional and Dynamically Anisotropic Thermal Conductivity in Compressed Flexible Graphene Foams | Although a variety of methods to predict the effective thermal conductivity of porous foams have been proposed, the response of such materials under dynamic compressive loading has generally not been considered. Understanding the dynamic thermal behavior will widen the potential applications of porous foams and provide insights into methods of modifying material properties to achieve desired performance. Previous experimental work on the thermal conductivity of a flexible graphene composite under compression showed intriguing behavior: the cross-plane thermal conductivity remained approximately constant with increasing compression, despite the increasing mass density. In this work, we use molecular dynamics (MD) simulations and finite element analysis to study the variation in both the cross-plane and in-plane thermal conductivities by compressing isotropic graphene foams. We have found that, interestingly, the cross-plane thermal conductivity decreases with compression while the in-plane thermal conductivity increases; hence, the dynamic thermal transport of the graphene foam becomes anisotropic with a significant anisotropy ratio. Such observations cannot be explained by the conventional effective medium theory, which describes the increase of thermal conductivity to be proportional to mass density. Thus, we propose a model that can describe such anisotropic effective thermal conductivity of highly porous open-cell media during compression. The model is validated against the MD simulations as well as a larger-scale finite element simulation of an open-cell foam geometry. |
4,884 | Tight binding enantiomers of pre-clinical drug candidates | MTDIA is a picomolar transition state analogue inhibitor of human methylthioadenosine phosphorylase and a femtomolar inhibitor of Escherichia coli methylthioadenosine nucleosidase. MTDIA has proven to be a non-toxic, orally available pre-clinical drug candidate with remarkable anti-tumour activity against a variety of human cancers in mouse xenografts. The structurally similar compound MTDIH is a potent inhibitor of human and malarial purine nucleoside phosphorylase (PNP) as well as the newly discovered enzyme, methylthioinosine phosphorylase, isolated from Pseudomonas aeruginosa. Since the enantiomers of some pharmaceuticals have revealed surprising biological activities, the enantiomers of MTDIH and MTDIA, compounds 1 and 2, respectively, were prepared and their enzyme binding properties studied. Despite binding less tightly to their target enzymes than their enantiomers compounds 1 and 2 are nanomolar inhibitors. |
4,885 | Improving LiDAR compression efficiency on small packets | Several high-quality methods for compressing LiDAR data stored in the LAS format have evolved in recent years. They offer good compression for large datasets, but are less efficient on small data packets, which are needed in web applications. This problem is focused on by analysing two state-of-the-art implementations for large LAS datasets and then proposing improvements suitable for small data packets. |
4,886 | Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications | Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87 & x0025; accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications. |
4,887 | Sampling as a Baseline Optimizer for Search-Based Software Engineering | Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. An alternative approach, proposed in this paper, is to start with a very large population and sample down to just the better solutions. We call this method "SWAY", short for "the sampling way". This paper compares SWAY versus state-of-the-art search-based SE tools using seven models: five software product line models; and two other software process control models (concerned with project management, effort estimation, and selection of requirements) during incremental agile development. For these models, the experiments of this paper show that SWAY is competitive with corresponding state-of-the-art evolutionary algorithms while requiring orders of magnitude fewer evaluations. Considering the simplicity and effectiveness of SWAY, we, therefore, propose this approach as a baseline method for search-based software engineering models, especially for models that are very slow to execute. |
4,888 | Exosomes as Potential Functional Nanomaterials for Tissue Engineering | Exosomes are cell-derived extracellular vesicles of 40-160 nm diameter, which carry numerous biomolecules and transmit information between cells. They are used as functional nanomaterials with great potential in biomedical areas, such as active agents and delivery systems for advanced drug delivery and disease therapy. In recent years, potential applications of exosomes in tissue engineering have attracted significant attention, and some critical progress has been made. This review gives a complete picture of exosomes and their applications in the regeneration of various tissues, such as the central nervous systems, kidney, bone, cartilage, heart, and endodontium. Approaches employed for modifying exosomes to equip them with excellent targeting capacity are summarized. Furthermore, current concerns and future outlook of exosomes in tissue engineering are discussed. |
4,889 | The Use of Online CB-ART Interventions in the Context of COVID-19: Enhancing Salutogenic Coping | Community crises require the provision of short-term reflective intervention methods to help service users identify stressors, and access and intensify their adaptive coping. Here, we demonstrate the use of a single-session online cognitive behavioral- and art-based (CB-ART) intervention within the context of the COVID-19 pandemic. In this method, the individual draws three images: his/her COVID-19-related stress, his/her perceived resources, and an integration of stress and resources. This method provided a reflective space in which individuals could identify their experienced stressors, acknowledge their coping resources, and integrate these two elements within the context of the current pandemic. In this article, we use illustrative examples from a study implemented during the first national lockdown in Israel and present a tool that can be easily implemented by mental-health professionals in ongoing community crises. The aims of this intervention were to co-create knowledge with service users, access their self-defined needs and strengths, and enhance their coping by enabling them to view stress and coping as part of the salutogenic continuum. |
4,890 | An evaluation of recent neural sequence tagging models in Turkish named entity recognition | Named entity recognition (NER) is an extensively studied task that extracts and classifies named entities in a text. NER is crucial not only in downstream language processing applications such as relation extraction and question answering but also in large scale big data operations such as real-time analysis of online digital media content. Recent research efforts on Turkish, a less studied language with morphologically rich nature, have demonstrated the effectiveness of neural architectures on well-formed texts and yielded state-of-the art results by formulating the task as a sequence tagging problem. In this work, we empirically investigate the use of recent neural architectures (Bidirectional long short-term memory (BiLSTM) and Transformer-based networks) proposed for Turkish NER tagging in the same setting. Our results demonstrate that transformer-based networks which can model long-range context overcome the limitations of BiLSTM networks where different input features at the character, subword, and word levels are utilized. We also propose a transformer-based network with a conditional random field (CRF) layer that leads to the state-of-the-art result (95.95% f-measure) on a common dataset. Our study contributes to the literature that quantifies the impact of transfer learning on processing morphologically rich languages. |
4,891 | Evaluating the Integrated Methadone and Anti-Retroviral Therapy Strategy in Tanzania Using the RE-AIM Framework | There are an estimated 50,000 people who inject drugs in Tanzania, with an HIV prevalence in this population of 42%. The Integrated Methadone and Anti-Retroviral Therapy (IMAT) strategy was developed to integrate HIV services into an opioid treatment program (OTP) in sub-Saharan Africa and increase anti-retroviral therapy (ART) initiation rates. In this paper, we evaluate the IMAT strategy using an implementation science framework to inform future care integration efforts in the region. IMAT centralized HIV services into an OTP clinic in Dar Es Salaam, Tanzania: HIV diagnosis, ART initiation, monitoring and follow up. A mixed-methods, concurrent design, was used for evaluation: quantitative programmatic data and semi-structured interviews with providers and clients addressed 4 out of 5 components of the RE-AIM framework: reach, effectiveness, adoption, implementation. Results showed high reach: 98% of HIV-positive clients received HIV services; effectiveness: 90-day ART initiation rate doubled, from 41% pre-IMAT to 87% post-IMAT (p < 0.001); proportion of HIV-positive eligible clients on ART increased from 71% pre-IMAT to 98% post-IMAT (p < 0.001). There was high adoption and implementation protocol fidelity. Qualitative results informed barriers and facilitators of RE-AIM components. In conclusion, we successfully integrated HIV care into an OTP clinic in sub-Saharan Africa with increased rates of ART initiation. The IMAT strategy represents an effective care integration model to improve HIV care delivery for OTP clients. |
4,892 | A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices | Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies the given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success of our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN, EigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time. Moreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that FPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics. |
4,893 | DeepQGHO: Quantized Greedy Hyperparameter Optimization in Deep Neural Networks for on-the-Fly Learning | On-the-fly learning is unavoidable for applications that demand instantaneous deep neural network (DNN) training or where transferring data to the central system for training is costly. Hyperparameter optimization plays a significant role in the performance and reliability in deep learning. Many hyperparameter optimization algorithms have been developed for obtaining better validation accuracy in DNNs. Most state-of-the-art hyperparameter optimization techniques are computationally expensive due to the focus on validation accuracy. Therefore, they are unsuitable for on-the-fly learning applications that require faster computation on resource constraint devices (e.g., edge devices). In this paper, we develop a novel greedy approach-based hyperparameter optimization (GHO) algorithm enabling faster computing on edge devices for on-the-fly learning applications. In GHO, we obtain the validation accuracy locally for each of the hyperparameter configurations. The GHO algorithm optimizes each hyperparameter while keeping the others constant in order to converge to the locally optimal solution in the expectation that this choice will lead to a globally optimal solution. We perform an empirical study to compute the performance such as computation time and energy consumption of the GHO and compare it with two state-of-the-art hyperparameter optimization algorithms. We also deploy the GHO algorithm in an edge device to validate the performance of our algorithm. We perform post-training quantization on the GHO algorithm to reduce the inference time. Our GHO algorithm is more than 3x energy efficient and 2x faster than two state-of-the-art hyperparameter optimization techniques on both DNNs and datasets studied in this paper. |
4,894 | A Case of Sex Cord-Stromal Tumor Originating in the Retroperitoneal Space | A 54-year-old man was seen in the clinic with the chief complaint of epigastric pain radiating to the left groin region and a predominant postprandial abdominal discomfort. Upon examination, a painless round mass with reduced mobility was felt in the left flank during deep palpation of the abdomen. His past medical history was irrelevant. Ultrasound and IV contrast-enhanced CT scan confirmed the presence of a large tumor and an exploratory laparotomy for removal of the tumor was performed. The microscopic examination of the specimen confirmed the primary diagnosis of retroperitoneal tumor (RPT) and identified it as an extragonadal germ cell tumor with a vestigial origin, which is a rare type affecting the kidney and adrenal gland. Primitive RPTs are histologically classified as mesenchymal and neuroectodermal or vestigial. These histological types are rarely found in surgical practice and are challenging to diagnose and treat due to the peculiarities of the site of origin where they develop. RPTs are extremely rare and approximately 80% are malignant and detected lately during the disease's course, commonly discovered in advanced stages of local or systemic evolution. Currently, surgical intervention remains the only effective method of treating these tumors. |
4,895 | Thyroid Nodules on Ultrasound in Children and Young Adults: Comparison of Diagnostic Performance of Radiologists' Impressions, ACR TI-RADS, and a Deep Learning Algorithm | BACKGROUND. In current clinical practice, thyroid nodules in children are generally evaluated on the basis of radiologists' overall impressions of ultrasound images. OBJECTIVE. The purpose of this article is to compare the diagnostic performance of radiologists' overall impression, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS), and a deep learning algorithm in differentiating benign and malignant thyroid nodules on ultrasound in children and young adults. METHODS. This retrospective study included 139 patients (median age 17.5 years; 119 female patients, 20 male patients) evaluated from January 1, 2004, to September 18, 2020, who were 21 years old and younger with a thyroid nodule on ultrasound with definitive pathologic results from fine-needle aspiration and/or surgical excision to serve as the reference standard. A single nodule per patient was selected, and one transverse and one longitudinal image each of the nodules were extracted for further evaluation. Three radiologists independently characterized nodules on the basis of their overall impression (benign vs malignant) and ACR TI-RADS. A previously developed deep learning algorithm determined for each nodule a likelihood of malignancy, which was used to derive a risk level. Sensitivities and specificities for malignancy were calculated. Agreement was assessed using Cohen kappa coefficients. RESULTS. For radiologists' overall impression, sensitivity ranged from 32.1% to 75.0% (mean, 58.3%; 95% CI, 49.2-67.3%), and specificity ranged from 63.8% to 93.9% (mean, 79.9%; 95% CI, 73.8-85.7%). For ACR TI-RADS, sensitivity ranged from 82.1% to 87.5% (mean, 85.1%; 95% CI, 77.3-92.1%), and specificity ranged from 47.0% to 54.2% (mean, 50.6%; 95% CI, 41.4-59.8%). The deep learning algorithm had a sensitivity of 87.5% (95% CI, 78.3-95.5%) and specificity of 36.1% (95% CI, 25.6-46.8%). Interobserver agreement among pairwise combinations of readers, expressed as kappa, for overall impression was 0.227-0.472 and for ACR TI-RADS was 0.597-0.643. CONCLUSION. Both ACR TI-RADS and the deep learning algorithm had higher sensitivity albeit lower specificity compared with overall impressions. The deep learning algorithm had similar sensitivity but lower specificity than ACR TI-RADS. Interobserver agreement was higher for ACR TI-RADS than for overall impressions. CLINICAL IMPACT. ACR TI-RADS and the deep learning algorithm may serve as potential alternative strategies for guiding decisions to perform fine-needle aspiration of thyroid nodules in children. |
4,896 | The Shigir idol in the context of early art in Eurasia | In 1890 the so called "Shigir Idol" was found in a peat bog and for a long time discussion on the dating of the wooden sculpture was going on. In the 1990s first conventional radiocarbon dates suggested a Mesolithic context, but a series of recent AMS-results date the object close to the beginning of the Holocene (c. 10,000 calBC). The surprisingly early date makes the find the earliest monumental wooden sculpture of the world. A direct parallel is not available and this hampers the interpretation and contextualization of the find. Here we discuss the find according to aspects such as wood working and the type of sculpture and decoration in the Late Palaeolithic to Early Mesolithic context of Eurasia. We can show that there is a long tradition of wood working since the Lower Palaeolithic and the very limited evidence of wooden objects from the Palaeolithic and Mesolithic is due to preservation conditions. Anthropomorphic figures are sometimes present in Late Glacial art and less anthropomorphic representations are available from the Early Mesolithic. The geometric patterns of the Shigir sculpture such as simple lines and zigzag-ornaments are common elements of Late Palaeolithic and Early Mesolithic decoration. In conclusion the different elements of the Shigir sculpture fit better to the record of Late Glacial to Early Mesolithic art in Eurasia than expected. We see this as a confirmation of the early date of the monumental sculpture. The figure demonstrates a complex expression of symbolic behavior and art of hunter-gatherers at that time. The only general parallel of monumental anthropomorphic figures from that time can be mentioned from the far distant Gobekli Tepe site in eastern Anatolia. |
4,897 | Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images | Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance. |
4,898 | Energy and building aesthetics. Slovenian examples of good practice | The well-being of people, the industry and the economy depends on a safe, reliable, sustainable, affordable, efficient and economical use of energy. At the same time, energy-related emissions represent almost 80% of the total greenhouse gas emissions in the European Union (EU). Therefore the question of efficient energy use is one of the biggest challenges facing Europe and Slovenia in the coming decades. The building industry is responding to the energy and environmental crisis with a development of new low energy systems for the heating and cooling of the buildings. New technologies pose designers and architects new tasks and challenges, as well as opportunities for creating new, low energy and sustainable environment and buildings. With a multidisciplinary approach, featuring technical disciplines, as well as architecture as an art, it is possible to transform new technological arrangements and buildings into poetic living spaces for people. The possibilities of such an approach are illustrated by seven examples of low energy buildings in Slovenia. (C) 2015 Published by Elsevier B.V. |
4,899 | Robust key point descriptor for multi-spectral image matching | Histogram of collinear gradient-enhanced coding (HCGEC), a robust key point descriptor for multi-spectral image matching, is proposed. The HCGEC mainly encodes rough structures within an image and suppresses detailed textural information, which is desirable in multi-spectral image matching. Experiments on two multi-spectral data sets demonstrate that the proposed descriptor can yield significantly better results than some state-of-the-art descriptors. |
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