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3,400 | Synergistic induction of apoptosis by a polo-like kinase 1 inhibitor and microtubule-interfering drugs in Ewing sarcoma cells | Since polo-like kinase 1 (PLK1) is highly expressed in Ewing sarcoma (ES), we evaluated the therapeutic potential of the PLK1 inhibitor BI 6727. Here, we identify a synergistic induction of apoptosis by BI 6727 and several microtubule-interfering drugs in ES cells, including vincristine (VCR), vinblastine (VBL), vinorelbine (VNR) and eribulin. Synergistic drug interaction is confirmed by calculation of combination index (CI). Also, BI 6727 and VCR act in concert to reduce long-term clonogenic survival. Mechanistically, BI 6727/VCR co-treatment cooperates to trigger mitotic arrest, phosphorylation of BCL-2 and BCL-XL and downregulation of MCL-1. This inactivation of anti-apoptotic BCL-2 family proteins in turn promotes activation of BAX and BAK, activation of caspase-9 and -3 and caspase-dependent apoptosis. Overexpression of BCL-2 or simultaneous knockdown of BAX and BAK significantly rescue BI 6727/VCR-induced apoptosis, indicating that engagement of the mitochondrial pathway is critical for BI 6727/VCR-mediated apoptosis. The clinical relevance of PLK1 inhibitor-based combination therapies is underscored by the fact that BI 6727 is currently evaluated in phase I clinical trials in childhood cancer. In conclusion, PLK1 inhibitors such as BI 6727 may provide a new strategy to chemosensitize ES. |
3,401 | Gesture, Music and Computer: The Centro di Sonologia Computazionale at Padova University, a 50-Year History | With the advent of digital technologies, the computer has become a generalized tool for music production. Music can be seen as a creative form of human-human communication via a computer, and therefore, research on human-computer and computer-human interfaces is very important. This paper, for the Sensors Special Issue on 800 Years of Research at Padova University, presents a review of the research in the field of music technologies at Padova University by the Centro di Sonologia Computazionale (CSC), focusing on scientific, technological and musical aspects of interaction between musician and computer and between computer and audience. We discuss input devices for detecting information from gestures or audio signals and rendering systems for audience and user engagement. Moreover, we discuss a multilevel conceptual framework, which allows multimodal expressive content processing and coordination, which is important in art and music. Several paradigmatic musical works that stated new lines of both musical and scientific research are then presented in detail. The preservation of this heritage presents problems very different from those posed by traditional artworks. CSC is actively engaged in proposing new paradigms for the preservation of digital art. |
3,402 | Social Sciences, Art and Physical Activity in Leisure Environments. An Inter-Disciplinary Project for Teacher Training | Factors such as social change and increasing urbanization processes in the early years of the 21st century have caused a reduction in the amount of time that children devote to leisure activities in the open-air, resulting in more sedentary lifestyles than children in previous decades. An education in healthy habits from early ages to increase children's physical and mental well-being together with their level of cultural knowledge contributes to the acquisition of a Leisure Culture that allows children to perceive the close environment as a scene for learning and enjoyment. It is thus be necessary for schools to foster pedagogical experiences, taking the physical and cultural environment as teaching resources. An innovation project is proposed which will be implemented with 25 university students from the School of Teacher Training and Education at the University of Oviedo (Oviedo, Spain). The project will consist of the proposal of educational itineraries through the city of Oviedo and Mount Naranco. As teachers-to-be, students must combine knowledge of the related areas and generate inter-disciplinary activities throughout the routes that will foster respect for the environment and leisure based on culture and physical activity, attitudes that they will transmit to their own students in the future. |
3,403 | In Defense of Locality-Sensitive Hashing | Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training data by solving binary optimization problems with millions of variables, thus usually requiring intensive computations. Despite simplicity and efficiency, locality-sensitive hashing (LSH) has never been recognized as a good way to generate such codes due to its poor performance in traditional approximate neighbor search. We claim in this paper that the true merit of LSH lies in transforming the semantic labels to obtain the binary codes, resulting in an effective and efficient two-step hashing framework. Specifically, we developed the locality-sensitive two-step hashing (LS-TSH) that generates the binary codes through LSH rather than any complex optimization technique. Theoretically, with proper assumption, LS-TSH is actually a useful LSH scheme, so that it preserves the label-based semantic similarity and possesses sublinear query complexity for hash lookup. Experimentally, LS-TSH could obtain comparable retrieval accuracy with state of the arts with two to three orders of magnitudes faster training speed. |
3,404 | Fast and energy-efficient low-voltage level shifters | This paper presents two novel low-voltage level shifter designs: one based on cross-coupled PMOS transistors and the other using current mirror structure. These two level shifters are designed to address the problems of the existing state-of-the-art level shifters. Simulation at 65 nm shows that both of the proposed level shifters achieve significantly better performance (up to 12 x) and energy consumption (up to 8 x) than the state-of-the-art level shifters with similar or less area consumption while operating from near-threshold to super-threshold region, making them optimal for level shifting in low-power systems with multiple scalable voltage domains. (C) 2014 Elsevier Ltd. All rights reserved. |
3,405 | An Efficient Heterogeneous Memristive XNOR for In-Memory Computing | Resistive RAM (RRAM) technologies are gaining importance due to their appealing characteristics, which include non-volatility, small form factor, low power consumption, and ability to perform logic operations in memory. These characteristics make RRAM highly suited for Internet of Things devices and similarly resource-constrained systems. This paper proposes a novel memristor-based XNOR gate that enables the execution of XNOR/XOR function in the memristive crossbar memory. The proposed two-input XNOR gate requires two steps to perform the XNOR function. The design of the circuit utilizes bipolar and unipolar memristors and permits cascading by only adding an extra step and one computing memristor. To the best of our knowledge, this is the first native stateful XNOR logic implementation. Spice simulations have been used to verify the functionality of the proposed circuit. This includes bench-marking the proposed design against the state-of-the-art stateful memristor-based logic circuits. The results for implementing three-input XOR using the proposed circuit demonstrate efficient performance in terms of energy, latency, and area. The gate shows 56% saving in energy, 54% less number of steps (latency), and 50% less number of computing MR (area) compared with the state-of-the-art stateful XOR/XNOR implementations. |
3,406 | State-of-the-art review of backfill practices for sublevel stoping system | Backfilling is one of the most critical tasks for sublevel stoping mining methods with delayed backfill. Selection of the type of backfill is a significant component of backfill design at preliminary stage of mine development. This paper presents a state-of-the-art review of backfill practices with focus on backfill operations in North America. Major difficulties and solutions in backfill practice have been laid down in this review. Preliminary recommendations are made for the case study mine based on the knowledge gained from the literature review and in situ observations. |
3,407 | Emerging trends in supply chain architecture | This paper traces significant developments in technology, quality, measurement, and relationships that have led to the study of supply chain networks. The paper introduces five principles of supply chain networks-velocity, variability, vocalize, visualize, and value-that have moved supply chain architecture from an art to a science. Finally, the paper uses each principle to point to an important, emerging trend in supply chain architecture. |
3,408 | Origin, form and function of extraembryonic structures in teleost fishes | Teleost eggs have evolved a highly derived early developmental pattern within vertebrates as a result of the meroblastic cleavage pattern, giving rise to a polar stratified architecture containing a large acellular yolk and a small cellular blastoderm on top. Besides the acellular yolk, the teleost-specific yolk syncytial layer (YSL) and the superficial epithelial enveloping layer are recognized as extraembryonic structures that play critical roles throughout embryonic development. They provide enriched microenvironments in which molecular feedback loops, cellular interactions and mechanical signals emerge to sculpt, among other things, embryonic patterning along the dorsoventral and left-right axes, mesendodermal specification and the execution of morphogenetic movements in the early embryo and during organogenesis. An emerging concept points to a critical role of extraembryonic structures in reinforcing early genetic and morphogenetic programmes in reciprocal coordination with the embryonic blastoderm, providing the necessary boundary conditions for development to proceed. In addition, the role of the enveloping cell layer in providing mechanical, osmotic and immunological protection during early stages of development, and the autonomous nutritional support provided by the yolk and YSL, have probably been key aspects that have enabled the massive radiation of teleosts to colonize every ecological niche on the Earth. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'. |
3,409 | Data-parallel intra decoding for block-based image and video coding on massively parallel architectures | With the increasing number of processor cores available in modern computing architectures, task or data parallelism is required to maximally exploit the available hardware and achieve optimal processing speed. Current state-of-the-art data-parallel processing methods for decoding image and video bitstreams are limited in parallelism by dependencies introduced by the coding tools and the number of synchronization points introduced by these dependencies, only allowing task or coarse-grain data parallelism. In particular, entropy decoding and data prediction are bottleneck coding tools for parallel image and video decoding. We propose a new data-parallel processing scheme for block-based intra sample and coefficient prediction that allows fine-grain parallelism and is suitable for integration in current and future state-of-the-art image and video codecs. Our prediction scheme enables maximum concurrency, independent of slice or tile configuration, while minimizing synchronization points. This paper describes our data-parallel processing scheme for one- and two-dimensional prediction and investigates its application to block-based image and video codecs using JPEG XR and H.264/AVC Intra as a starting point. We show how our scheme enables faster decoding than the state-of-the-art wavefront method with speedup factors of up to 21.5 and 7.9 for JPEG XR and H.2641AVC Intra coding tools respectively. Using the H.264/AVC Intra coding tool, we discuss the requirements of the algorithm and the impact on decoded image quality when these requirements are not met. Finally, we discuss the impact on coding rate in order to allow for optimal parallel intra decoding. (C) 2012 Elsevier B.V. All rights reserved. |
3,410 | An IEEE 1451 Standard-based Plug-and-Play Architecture to Empower the Internet of Things | In this paper, we introduce a new Internet of Things (IoT) solution based on a Plug-and-Play (PnP) architecture, aiming to stimulate new paths set by the concept of the Smart Environment. In this sense, the IoT ecosystem is considered, and the layers are presented from the higher level of software to the lowest level of sensors/actuators. We extend the state of art solutions since our proposal features are transparent in both hardware and software domains, supporting the identification of sensors through an easy and intuitive tool responsible for creating and managing all resources and logic programming in an IoT environment. To show the main contributions of this research, we will introduce the architecture, adding the IEEE 1451 standard, based on well-defined message protocols. The contribution of this work encompasses all PnP levels in the IoT architecture, from the platform to the user interface, which are different from all approaches currently existent in state of the art. Moreover, a proof of concept is described at the end of this article. |
3,411 | Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration | Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration. These methods generally follow the nonlocal filtering processing chain, aiming at circumventing the staircase effect and preserving the details of phase variations. In this article, we propose an alternative approach for InSAR phase restoration, that is, Complex Convolutional Sparse Coding (ComCSC) and its gradient regularized version. To the best of the authors' knowledge, this is the first time that we solve the InSAR phase restoration problem in a deconvolutional fashion. The proposed methods can not only suppress interferometric phase noise, but also avoid the staircase effect and preserve the details. Furthermore, they provide an insight into the elementary phase components for the interferometric phases. The experimental results on synthetic and realistic high- and medium-resolution data sets from TerraSAR-X StripMap and Sentinel-1 interferometric wide swath mode, respectively, show that our method outperforms those previous state-of-the-art methods based on nonlocal InSAR filters, particularly the state-of-the-art method: InSAR-BM3D. The source code of this article will be made publicly available for reproducible research inside the community. |
3,412 | A 0.66mW 400 MHz/900 MHz Transmitter C for In-Body Bio-Sensing Applications | A sub-1GHz transmitter (TX) integrated chip (IC) with ultra-low power consumption and moderately high adjacent channel power rejection (ACPR) is presented for in-body bio-sensing applications. The 400 MHz 12-phase digital power amplifier (DPA) is implemented with the proposed 16QAM modulation scheme to improve the energy efficiency. The TX IC also contains a 900 MHz FSK TX realized with a symmetrical edge-combiner, which can be used in the low accuracy mode. A fully digital modulator with band shaping is integrated on the chip for the improvement of ACPR performance. Fabricated in 65-om CMOS process, the chip occupies an active area of 0.75 mm(2). Under 0.5 V supply voltage, the TX consumes less than 0.66 mW power consumption while delivering -15 dBm of output power when operating at both bands. The presented TX has an energy efficiency performance comparable to the state-of-the-arts low power designs, with the measured average energy consumption of 64.5/220 p.J/bit, and the measured figure-of-merit (FoM) of 2.04/6.98 nJ/(bit . mW) for the two bands. Compared with the state-of-the-arts sub-lmW designs in literatures, the ACPR is improved by at least 13 dB. |
3,413 | Efficient Generation of Low Autocorrelation Binary Sequences | Simple and efficient algorithm based on heuristic search by shotgun hill climbing to construct binary sequences with small peak sidelobe levels (PSL) is suggested. The algorithm is applied for generation of binary sequences of lengths between 106 and 300. Improvements are obtained in almost half of the considered lengths while for the rest of the lengths, binary sequences with the same PSL values as reported in the state-of-the-art publications are found. |
3,414 | Japanese nationwide observational multicenter study of tumor BRCA1/2 variant testing in advanced ovarian cancer | The association between germline BRCA1 and BRCA2 pathogenic variants (mutations: gBRCAm) and ovarian cancer risk is well established. Germline testing alone cannot detect somatic BRCA1/2 pathogenic variants (sBRCAm), which is calculated based on the proportion of tumor BRCAm (tBRCAm) from tumor samples and gBRCAm. Homologous recombination deficiency (HRD) results mainly from genetic/epigenetic alterations in homologous recombination repair-related genes and can be evaluated by genomic instability status. In Japan, the prevalence of tBRCAm, sBRCAm, and HRD remains unclear. This multicenter, cross-sectional, observational study, CHaRacterIzing the croSs-secTional approach to invEstigate the prevaLence of tissue BRCA1/2 mutations in newLy diagnosEd advanced ovarian cancer patients (CHRISTELLE), evaluated the prevalence of tBRCAm, sBRCAm, and HRD in tumor specimens from newly diagnosed patients with ovarian cancer who underwent gBRCA testing. Of the 205 patients analyzed, 26.8% had a tBRCAm, including tBRCA1m (17.6%) and tBRCA2m (9.3%). The overall prevalence of tBRCAm, gBRCAm, sBRCAm, and HRD-positive status was 26.8%, 21.5%, 6.3%, and 60.0%, respectively. The calculated sBRCAm/tBRCAm ratio was 23.6% (13/55), and the prevalence of gBRCA variant of uncertain significance was 3.9%. These results suggest gBRCA testing alone cannot clearly identify the best course of treatment, highlighting the importance of sBRCA testing in Japan. The present results also suggest that testing for tBRCA and HRD should be encouraged in advanced ovarian cancer patients to drive precision medicine. |
3,415 | A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction | The core objective of this paper is to develop and validate a comprehensive visual sensing concept for robustly classifying house types. Previous studies regarding this type of classification show that this type of classification is not simple (i.e., tough) and most classifier models from the related literature have shown a relatively low performance. For finding a suitable model, several similar classification models based on convolutional neural network have been explored. We have found out that adding/involving/extracting better and more complex features result in a significant accuracy related performance improvement. Therefore, a new model taking this finding into consideration has been developed, tested and validated. The model developed is benchmarked with selected state-of-art classification models of relevance for the "house classification" endeavor. The test results obtained in this comprehensive benchmarking clearly demonstrate and validate the effectiveness and the superiority of our here developed deep-learning model. Overall, one notices that our model reaches classification performance figures (accuracy, precision, etc.) which are at least 8% higher (which is extremely significant in the ranges above 90%) than those reached by the previous state-of-the-art methods involved in the conducted comprehensive benchmarking. |
3,416 | Assessment of water, sanitation, and hygiene services in district health care facilities in rural area of Mekong Delta, Vietnam | Access to sufficient water, sanitation, and hygiene (WASH) services is a crucial requirement for patients during therapy and general well-being in the hospital. However, in low- and middle-income countries, these services are often inadequate, resulting in increased morbidity and mortality of patients. This study aimed at assessing the current situation of WASH services in six District Health Care Facilities (DHCFs) in rural areas of the Mekong Delta provinces, Vietnam. The results showed that these services were available with inappropriate quality, which did not compromise the stakeholders' needs. The revealed WASH infrastructures have raised concerns about the prolonged hospital stays for patients and push nosocomial infections to a high level. The safety of the water supply was doubted as the high E. coli (> 60%) and total coliform incidence (86%) was observed with very low residual chlorine concentration (< 0.1 mg/L) in water quality assessment. Moreover, water supply contained a high concentration of iron (up to 15.55 mg/L) in groundwater in one DHCF. Technical assessment tool analysis proved that the improper management and lack of knowledge by human resources were the primary roots of the observed status WASH services. Improvement of the perceptions of WASH should be done for the hospital staff with collaboration and support from the government to prevent incidents in the future. |
3,417 | A Primer on Hardware Security: Models, Methods, and Metrics | The multinational, distributed, and multistep nature of integrated circuit (IC) production supply chain has introduced hardware-based vulnerabilities. Existing literature in hardware security assumes ad hoc threat models, defenses, and metrics for evaluation, making it difficult to analyze and compare alternate solutions. This paper systematizes the current knowledge in this emerging field, including a classification of threat models, state-of-the-art defenses, and evaluation metrics for important hardware-based attacks. |
3,418 | Robust Localization Using IMM Filter Based on Skew Gaussian-Gamma Mixture Distribution in Mixed LOS/NLOS Condition | This article proposes a new skewed outlier-robust localization algorithm that is based on time-difference of arrival (TDOA) measurements at an airport. A new outlier-robust filtering framework is derived based on the skew Gaussian-gamma mixture (SGGM) distribution, where the state, a mixing parameter, a shape parameter, a scale matrix, and the degrees of freedom (DOFs) are inferred simultaneously using variational Bayesian (VB) approach. An interacting multiple-model (IMM) filter with different kinematic system models is implemented to handle the multimodal dynamics of the vehicle, yielding the IMM-SGGM algorithm. In particular, a new measurement likelihood based on the SGGM distribution is derived utilizing VB inference for the combination procedure in the proposed IMM-SGGM algorithm. Car-mounted experiments using TDOA measurements at an airport were conducted to verify the effectiveness of the proposed algorithm. The performance of the proposed IMM-SGGM algorithm is evaluated through comparisons with the state-of-the-art approaches. The experimental results demonstrate that the proposed IMM-SGGM algorithm has better localization accuracy and robustness to skewed outlier measurements than the state-of-the-art approaches. |
3,419 | PSIGAN: Joint Probabilistic Segmentation and Image Distribution Matching for Unpaired Cross-Modality Adaptation-Based MRI Segmentation | We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images. Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution using a new structure discriminator. The structure discriminator computes structure of interest focused adversarial loss by combining the generated pseudo MRI with probabilistic segmentations produced by a simultaneously trained segmentation sub-network. The segmentation sub-network is trained using the pseudo MRI produced by the generator sub-network. This leads to a cyclical optimization of both the generator and segmentation sub-networks that are jointly trained as part of an end-to-end network. Extensive experiments and comparisons against multiple state-of-the-art methods were done on four different MRI sequences totalling 257 scans for generating multi-organ and tumor segmentation. The experiments included, (a) 20 T1-weighted (T1w) in-phase mdixon and (b) 20 T2-weighted (T2w) abdominal MRI for segmenting liver, spleen, left and right kidneys, (c) 162 T2-weighted fat suppressed head and neck MRI (T2wFS) for parotid gland segmentation, and (d) 75 T2w MRI for lung tumor segmentation. Our method achieved an overall average DSC of 0.87 on T1w and 0.90 on T2w for the abdominal organs, 0.82 on T2wFS for the parotid glands, and 0.77 on T2w MRI for lung tumors. |
3,420 | Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing | Eectromechanical systems built by Simulink or Ptolemy have been widely used in industry fields, such as autonomous systems and robotics. It is an urgent need to ensure the safety and security of those systems. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art testing tools employ model-checking techniques or search-based methods to generate test cases. Traditional search-based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model-checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works. |
3,421 | Cross-Domain Correspondence for Sketch-Based 3D Model Retrieval Using Convolutional Neural Network and Manifold Ranking | Due to the huge difference in the representation of sketches and 3D models, sketch-based 3D model retrieval is a challenging problem in the areas of graphics and computer vision. Some state-of-the-art approaches usually extract features from 2D sketches and produce multiple projection views of 3D models, and then select one view of 3D models to match sketch. It's hard to find "the best view" and views from different perspectives of a 3D model may be completely different. Other methods apply learning features to retrieve 3D models based on 2D sketch. However, sketches are abstract images and are usually drawn subjectively. It is difficult to be learned accurately. To address these problems, we propose cross-domain correspondence method for sketch-based 3D model retrieval based on manifold ranking. Specifically, we first extract learning features of sketches and 3D models by two-parts CNN structures. Subsequently, we generate cross-domain undirected graphs using learning features and semantic labels to create correspondence between sketches and 3D models. Finally, the retrieval results are computed by manifold ranking. Experimental results on SHREC 13 and SHREC 14 datasets show the superior performance in all 7 standard metrics, compared to the state-of-the-art approaches. |
3,422 | HMTNet: 3D Hand Pose Estimation From Single Depth Image Based on Hand Morphological Topology | Thanks to the rapid development of CNNs and depth sensors, great progress has been made in 3D hand pose estimation. Nevertheless, it is still far from being solved for its cluttered circumstance and severe self-occlusion of hand. In this paper, we propose a method that takes advantage of human hand morphological topology (HMT) structure to improve the pose estimation performance. The main contributions of our work can be listed as below. Firstly, in order to extract more powerful features, we concatenate original and last layer of initial feature extraction module to preserve hand information better. Next, regression module inspired from hand morphological topology is proposed. In this submodule, we design a tree-like network structure according to hand joints distribution to make use of high order dependency of hand joints. Lastly, we conducted sufficient ablation experiments to verify our proposed method on each dataset. Experimental results on three popular hand pose dataset show superior performance of our method compared with the state-of-the-art methods. On ICVL and NYU dataset, our method outperforms great improvement over 2D state-of-the-art methods. On MSRA dataset, our method achieves comparable accuracy with the state-of-the-art methods. To summarize, our method is the most efficient method which can run at 220.7 fps on a single GPU compared with approximate accurate methods at present. |
3,423 | Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning | Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer's Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns. |
3,424 | ACER: An Agglomerative Clustering Based Electrode Addressing and Routing Algorithm for Pin-Constrained EWOD Chips | The problem of pin-constrained electrowetting-ondielectric (EWOD) biochips becomes a serious issue to realize complex bio-chemical operations. Due to limited number of control pins and routing resources, additional Printed Circuit Board (PCB) routing layers may be required which potentially raises the fabrication cost. Previous state-of-the-art work has tried to develop a framework that uses a network-flow-based method for broadcast electrodeaddressing EWOD biochips. Nevertheless, greedily merging of electrical pins in previous works is at high risk of producing unroutable design. Routability should have higher priority than pin reduction. While previous works dedicated their effort on pin reduction, we have addressed our attention on routability of broadcast addressing. Experimental results demonstrate that taking routability into consideration can even have higher pin reduction. Viewed in this light, we present ACER, a routability driven clustering algorithm followed by escape routing using integer linear programming that effectively solves both pin merging and routing in broadcast addressing framework. Our proposed algorithm does not greedily focus on pin-reduction. Instead, routability is taken into consideration through agglomerative clustering. Compared to previous state-of-the-art, our proposed algorithm can further reduce required control pins by an average of 13% and route the design using 68% less wirelength. |
3,425 | Pollution Pods: The merging of art and psychology to engage the public in climate change | Environmental artists have risen to the challenge of communicating the urgency of public action to address environmental problems such as air pollution and climate change. Joining this challenge, the immersive artwork Pollution Pods (PPs) was created through a synthesis of knowledge from the fields of environmental psychology, empirical aesthetics, and activist art. This study summarizes the scientific process in this transdisciplinary project and reports the findings from a questionnaire study (N = 2662) evaluating the effect of the PPs on visitors. Data were collected at the first two exhibitions of the installation, one in a public park in Trondheim, Norway, and one at Somerset House, London, UK. Intentions to act were strong and slightly increased after visiting the art installation. Individual changes in intentions were positively associated with self-reported emotions of sadness, helplessness, and anger and self-reported cognitive assessment their awareness of the environmental consequences of their action, their willingness to take responsibility for their consequences, and belief in the relevance of environmental problems for daily life. Education and age were negatively associated with intentions. Despite favorable intentions, however, taking advantage of an actual behavioral opportunity to track one's climate change emissions behavior after visiting the PPs could not be detected. We conclude that environmental art can be useful for environmental communication and give recommendations for communicators on how to best make use of it. We emphasize the potential benefits of art that encourages personal responsibility and the need for valid behavior measures in environmental psychological research. |
3,426 | Face Verification Using the LARK Representation | We present a novel face representation based on locally adaptive regression kernel (LARK) descriptors. Our LARK descriptor measures a self-similarity based on "signal-induced distance" between a center pixel and surrounding pixels in a local neighborhood. By applying principal component analysis (PCA) and a logistic function to LARK consecutively, we develop a new binary-like face representation which achieves state-of-the-art face verification performance on the challenging benchmark "Labeled Faces in the Wild" (LFW) dataset. In the case where training data are available, we employ one-shot similarity (OSS) based on linear discriminant analysis (LDA). The proposed approach achieves state-of-the-art performance on both the unsupervised setting and the image restrictive training setting (72.23% and 78.90% verification rates), respectively, as a single descriptor representation, with no preprocessing step. As opposed to combined 30 distances which achieve 85.13%, we achieve comparable performance (85.1%) with only 14 distances while significantly reducing computational complexity. |
3,427 | Pooling the Convolutional Layers in Deep ConvNets for Video Action Recognition | Deep ConvNets have shown their good performance in image classification tasks. However, there still remains problems in deep video representations for action recognition. On one hand, current video ConvNets are relatively shallow compared with image ConvNets, which limits their capability of capturing the complex video action information; on the other hand, temporal information of videos is not properly utilized to pool and encode the video sequences. Toward these issues, in this paper we utilize two state-of-the-art ConvNets, i.e., the very deep spatial net (VGGNet [1]) and the temporal net from Two-Stream ConvNets [2], for action representation. The convolutional layers and the proposed new layer, called frame-diff layer, are extracted and pooled with two temporal pooling strategies: Trajectory pooling and Line pooling. The pooled local descriptors are then encoded with vector of locally aggregated descriptors (VLAD) [3] to form the video representations. In order to verify the effectiveness of the proposed framework, we conduct experiments on UCF101 and HMDB51 data sets. It achieves accuracy of 92.08% on UCF101, which is the state-of-the-art, and the accuracy of 65.62% on HMDB51, which is comparable to the state-of-the-art. In addition, we propose the new Line pooling strategy, which can speed up the extraction of feature and achieve the comparable performance of the Trajectory pooling. |
3,428 | Small Program, Big Needs | Resources are typically scarce in any safety program, but they may be especially limited and difficult to access when the program is relatively small. California State University, Fullerton, has a broad-scope radioactive material license with around ten faculty-users, so while the amount of radioactive material used is not large, its obligation to provide support to those users is. Research is conducted primarily using undergraduate students, so emphasis on safety is paramount. Additionally, when one's duties also include oversight to the biosafety program and support of the hazardous waste program, the ability of a single person to provide what is needed becomes part art, part miracle. I have created a program that conforms to the license's regulatory requirements, while drawing on resources in a variety of places to produce a highly-visible, high-quality program from this one-person office (me). Using humor, respect, good science and good grammar, anyone can get a higher quality, and more compliant radiation safety program than he or she thought possible. This model could be expanded to fit larger programs as well. Health Phys.99(Supplement 2): S164-S167; 2010 |
3,429 | Feature-Based Image Patch Approximation for Lung Tissue Classification | In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts. |
3,430 | Reconfigurable Security Architecture (RESA) Based on PUF for FPGA-Based IoT Devices | Cybersecurity is a challenge in the utilization of IoT devices. One of the main security functions that we need for IoT devices is authentication. In this work, we used physical unclonable function (PUF) technology to propose a lightweight authentication protocol for IoT devices with long lifetimes. Our focus in this project is a solution for FPGA-based IoT devices. We evaluated the resiliency of our solution against state-of-the-art machine learning attacks. |
3,431 | Cross-Correlated Attention Networks for Person Re-Identification | Deep neural networks need to make robust inference in the presence of occlusion, background clutter, pose and viewpoint variations -to name a few- when the task of person re-identification is considered. Attention mechanisms have recently proven to be successful in handling the aforementioned challenges to some degree. However previous designs fail to capture inherent inter-dependencies between the attended features; leading to restricted interactions between the attention blocks. In this paper, we propose a new attention module called Cross-Correlated Attention (CCA); which aims to overcome such limitations by maximizing the information gain between different attended regions. Moreover, we also propose a novel deep network that makes use of different attention mechanisms to learn robust and discriminative representations of person images. The resulting model is called the Cross-Correlated Attention Network (COIN). Extensive experiments demonstrate that the CCAN comfortably outperforms current state-of-the-art algorithms by a tangible margin. Modeling the inherentspatial relations between different attended regions within the deep architecture. joint end-to-end cross correlated attention and representational learning. State-of-the-art results in terms of mAP and Rank-1 accuracies across several challenging datasets. (C) 2020 Elsevier B.V. All rights reserved. |
3,432 | Abnormal Event Detection and Localization via Adversarial Event Prediction | We present adversarial event prediction (AEP), a novel approach to detecting abnormal events through an event prediction setting. Given normal event samples, AEP derives the prediction model, which can discover the correlation between the present and future of events in the training step. In obtaining the prediction model, we propose adversarial learning for the past and future of events. The proposed adversarial learning enforces AEP to learn the representation for predicting future events and restricts the representation learning for the past of events. By exploiting the proposed adversarial learning, AEP can produce the discriminative model to detect an anomaly of events without complementary information, such as optical flow and explicit abnormal event samples in the training step. We demonstrate the efficiency of AEP for detecting anomalies of events using the UCSD-Ped, CUHK Avenue, Subway, and UCF-Crime data sets. Experiments include the performance analysis depending on hyperparameter settings and the comparison with existing state-of-the-art methods. The experimental results show that the proposed adversarial learning can assist in deriving a better model for normal events on AEP, and AEP trained by the proposed adversarial learning can surpass the existing state-of-the-art methods. |
3,433 | Compressed Sensing Based Real-Time Dynamic MRI Reconstruction | This work addresses the problem of real-time online reconstruction of dynamic magnetic resonance imaging sequences. The proposed method reconstructs the difference between the previous and the current image frames. This difference image is sparse. We recover the sparse difference image from its partial k-space scans by using a nonconvex compressed sensing algorithm. As there was no previous fast enough algorithm for real-time reconstruction, we derive a novel algorithm for this purpose. Our proposed method has been compared against state-of-the-art offline and online reconstruction methods. The accuracy of the proposed method is less than offline methods but noticeably higher than the online techniques. For real-time reconstruction we are also concerned about the reconstruction speed. Our method is capable of reconstructing 128 x 128 images at the rate of 6 frames/s, 180 x 180 images at the rate of 5 frames/s and 256 256 images at the rate of 2.5 frames/s. |
3,434 | HYDRHA: Hydrogels of hyaluronic acid. New biomedical approaches in cancer, neurodegenerative diseases, and tissue engineering | In the last decade, hyaluronic acid (HA) has attracted an ever-growing interest in the biomedical engineering field as a biocompatible, biodegradable, and chemically versatile molecule. In fact, HA is a major component of the extracellular matrix (ECM) and is essential for the maintenance of cellular homeostasis and crosstalk. Innovative experimental strategies in vitro and in vivo using three-dimensional (3D) HA systems have been increasingly reported in studies of diseases, replacement of tissue and organ damage, repairing wounds, and encapsulating stem cells for tissue regeneration. The present work aims to give an overview and comparison of recent work carried out on HA systems showing advantages, limitations, and their complementarity, for a comprehensive characterization of their use. A special attention is paid to the use of HA in three important areas: cancer, diseases of the central nervous system (CNS), and tissue regeneration, discussing the most innovative experimental strategies. Finally, perspectives within and beyond these research fields are discussed. |
3,435 | As-similar-as-possible saliency fusion | Salient region detection has gradually become a popular topic in multimedia and computer vision research. However, existing techniques exhibit remarkable variations in methodology with inherent pros and cons. In this paper, we propose fusing the saliency hypotheses, namely the saliency maps produced by different methods, by accentuating their advantages and attenuating the disadvantages. To this end, our algorithm consists of three basic steps. First, given the test image, our method finds the similar images and their saliency hypotheses by comparing the similarity of the learned deep features. Second, the error-aware coefficients are computed from the saliency hypotheses. Third, our method produces a pixel-accurate saliency map which covers the objects of interest and exploits the advantages of the state-of-the-art methods. We then evaluate the proposed framework on three challenging datasets, namely MSRA-1000, ECSSD and iCoSeg. Extensive experimental results show that our method outperforms all state-of-the-art approaches. In addition, we have applied our method to the SquareMe application, an autonomous image resizing system. The subjective user-study experiment demonstrates that human prefers the image retargeting results obtained by using the saliency maps from our proposed algorithm. |
3,436 | Counting and locating high-density objects using convolutional neural network | This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement combined with a multi-sigma refinement of the confidence map. The proposed method was evaluated in two counting datasets: trees and cars. For the tree dataset, our method returned a mean absolute error (MAE) of 2.05, a root-mean-squared error (RMSE) of 2.87 and a coefficient of determination (R-2) of 0.986. For the car dataset (CARPK and PUCPR+), our method was superior to state-of-the-art methods. In the these datasets, our approach achieved an MAE of 4.45 and 3.16, an RMSE of 6.18 and 4.39, and an R-2 of 0.975 and 0.999, respectively. We conclude that the proposed method is suitable for dealing with high object-density, returning a state-of-the-art performance for counting and locating objects. |
3,437 | Accurate statistical description of random dopant-induced threshold voltage variability | We have studied the detailed threshold voltage distribution in a state-of-the-art n-channel MOSFET in the presence of random discrete dopants. A ground-breaking sample of 100 000 transistors with statistically unique random dopant distributions were simulated using the Glasgow 3-D device simulator and advanced grid computing technologies. The results indicate that the threshold voltage distribution deviates substantially from a Gaussian distribution, which may have significant implications for the margins used in circuit design, particularly in SRAM cells. |
3,438 | Orthogonal Crosslinking: A Strategy to Generate Novel Protein Topology and Function | Compared to the disulfide bond, other naturally occurring intramolecular crosslinks have received little attention, presumably due to their rarity in the vast protein space. Here we presented examples of natural non-disulfide crosslinks, which we refer to as orthogonal crosslinks, emphasizing their effect on protein topology and function. We summarize recent efforts on expanding orthogonal crosslinks by using either the enzymes that catalyze protein circularization or the genetic code expansion strategy to add electrophilic amino acids site-specifically in proteins. The advantages and disadvantages of each method are discussed, along with their applications to generate novel protein topology and function. In particular, we highlight our recent work on spontaneous orthogonal crosslinking, in which a carbamate-based crosslink was generated in situ, and its applications in designing orthogonally crosslinked domain antibodies with their topology-mimicking bacterial adhesins. |
3,439 | No-reference stereo image quality assessment based on joint wavelet decomposition and statistical models | The widespread use of 3D acquisition and display technologies has increased the interest of stereo image dataset in various application fields. As a result, it becomes necessary to have an efficient 3D quality assessment method to measure the human perception of stereoscopic images. While most of the state-of-the-art methods belong to the class of full-reference methods which require the original stereo images to be able to assess the quality, we propose in this paper a no-reference quality metric which does not require any information of the original stereo images. The proposed method operates in the wavelet transform domain and adopts a statistical framework to predict the quality of stereo images. More precisely, a joint wavelet decomposition is first performed on the stereo images to exploit simultaneously the intra and inter-views redundancies. A wavelet transform is also applied to their associated estimated disparity maps. Then, relevant features are extracted from the resulting wavelet subbands by resorting to appropriate statistical models. Simulations, carried out on the standard Live 3D image quality database, show that our proposed design model achieves significant improvement compared to the state-of-the-art 3D quality assessment methods. |
3,440 | The Association between Assisted Reproduction Technology (ART) and Social Perception of Childbearing Deadline Ages: A Cross-Country Examination of Selected EU Countries | The advancement of assisted reproductive technologies (ART) has gained much attention in relation to childbearing postponement. Our study's purpose was to empirically examine how perceptions of childbearing deadline age vary in association with availability and prevalence of ART across different countries. The present study used data from the 2006 European Social Survey and the 2006 European Society of Human Reproduction and Embryology to examine selected EU countries. A total sample of 17,487 respondents was examined. Multilevel regression modeling was used. Results showed that first, younger generations were more generous with maternal childbearing ages but stricter with paternal deadline ages. Second, respondents residing in countries with higher percentage of reproductive clinics per population were more generous with maternal ages, however no significant association was observed with regard to paternal childbearing ages. Third, on the contrary, respondents residing in countries with higher utilization of ART treatments were stricter with maternal ages, which may be because they are more likely to be aware of the physiological and financial difficulties associated with ART treatments. The present study is meaningful in that it is the first study to empirically examine social perceptions of childbearing ages in relation with ART. |
3,441 | Prediction of banana maturity based on the sweetness and color values of different segments during ripening | To predict the maturity of bananas, the present study used non-destructive methods to analyze changes in the sweetness and color of the stalks, middles, and tips of bananas during ripening. The results indicated that the respective maturation of these three segments did not occur simultaneously, as indicated by the differential enzyme activity and gene expression levels recorded in these segments. A principal component analysis and cluster plots were used to review the classification of banana maturity, highlighting that banana maturation can be divided into six stages. Two distinct maturity prediction algorithms were established using random forest, artificial neural network, and support vector machines, and they also indicated that dividing the maturity of bananas into six stages was adequate. These findings contribute to the development of quality evaluation and of a rapid grading system for processing, which improves the quality and sale of banana fruits and the related processed products. |
3,442 | Experimental Study of Diamond-like Carbon Film on Aermet100 Steel and First-Principles Calculation of Interfacial Adhesion | Three different types of surface-modified layers of N, C, and N+C are successfully prepared on AerMet100 steel by plasma-assisted thermochemical treatment, and diamond-like carbon (DLC) films are formed on the top surfaces of the latter two. The results show that the DLC films produced by prenitriding and then carburizing (N+C) exhibit a smoother and finer morphology and higher sp3 content than that without prenitriding (C). In addition, the wear resistance of the N+C specimen with a high hardness nitrided layer as the support for the outermost DLC films is superior to that of the C specimen. In view of the catalytic effect of the Fe3C phase on the growth of DLC films, the interfacial properties of Fe3C(001)/diamond(111) are investigated using first-principles calculations. On the basis of the most preferred Fe-terminated HCP site model, the effects of alloyed cementite (Fe2MC) on interfacial adhesion of Fe2MC(001)/diamond(111) are also investigated. Furthermore, the mechanisms of interfacial adhesion for two representative dopings (Zr weakened and V enhanced) are revealed in detail. These results are expected to provide a potential promising means for future experimental works on the preparation of high-performance DLC films on alloy steel surfaces by plasma carburizing. |
3,443 | Disability Among Older Adults in South-Eastern Nigeria | Disability is a common reason for the loss of independence. There is a dearth of data on older adults with disability in south-eastern Nigeria. Using a multistage sampling technique and disability indexes, we assessed 816 persons aged 65 years and above living with a disability. While respondents' experiences of abuse and property inheritance differ by gender, they have poor health status. Elevated risks of disability were associated with gender, increased age, education, smoking, alcohol use, and engagement in physical exercise. Findings suggest urgency in formulating and implementing ageing welfare policy in this African community undergoing demographic and social changes. While this is underway, we recommend a massive health promotion among older adults in this community. We also suggest the integration of courses on ageing in schools' curriculum since ageing is a life course phenomenon. This in the long run would provide ageing-friendly education that averts old age's deleterious effects. |
3,444 | Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features | Recurrent neural network (RNN) and long short-term memory (LSTM) have achieved great success in processing sequential multimedia data and yielded the state-of-the-art results in speech recognition, digital signal processing, video processing, and text data analysis. In this paper, we propose a novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network. First, deep features are extracted from every sixth frame of the videos, which helps reduce the redundancy and complexity. Next, the sequential information among frame features is learnt using DB-LSTM network, where multiple layers are stacked together in both forward pass and backward pass of DB-LSTM to increase its depth. The proposed method is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval. Experimental results show significant improvements in action recognition using the proposed method on three benchmark data sets including UCF-101, YouTube 11 Actions, and HMDB51 compared with the state-of-the-art action recognition methods. |
3,445 | A semantic-mediation architecture for interoperable supply-chain applications | This paper presents a semantic-mediation architecture that enables standards-based interoperability between heterogeneous supply-chain applications. The architecture was implemented using a state-of-the-art semantic-mediation toolset for design-time and run-time integration tasks. The design-time tools supported a domain ontology definition, message annotations, message schema transformations and reconciliation rules specifications. The run-time tools performed exchanges, transformations, and reconciliations of the messages. The architecture supports a supply-chain integration scenario where heterogeneous automotive manufacturing supply-chain applications exchange inventory information. |
3,446 | Circular regional mean completed local binary pattern for texture classification | The local binary pattern (LBP) is a simple yet efficient texture operator, and the completed local binary pattern (CLBP) is a completed modeling for LBP that has been adopted in many texture classification methods. However, existing CLBP operators are sensitive to noise and they cannot extract the regional structure information efficiently. To overcome these disadvantages, we propose a circular regional mean completed local binary pattern (CRMCLBP) by introducing a circular regional mean operator to modify the traditional CLBP. We also present two encoding schemes for CRMCLBP. The proposed CRMCLBP not only achieves rotation invariance and completed representation capability but also has high robustness to image noise. In order to evaluate the performance, we compare the CRMCLBP with recent state-of-the-art methods by extensive experiments on two popular texture databases including Outex database and Columbia-Utrecht reflection and texture database. Excellent experimental results demonstrate that the proposed CRMCLBP is comparable with recent state-of-the-art texture descriptors and superior to other approaches for robustness. (C) 2018 SPIE and IS&T |
3,447 | Identifying the origins of obsidian artifacts in the Deh Luran Plain (Southwestern Iran) highlights community connections in the Neolithic Zagros | Exchange networks created by Neolithic pastoral transhumance have been central to explaining the distant transport of obsidian since chemical analysis was first used to attribute Near Eastern artifacts to their volcanic origins in the 1960s. Since then, critical reassessments of floral, faunal, and chronological data have upended long-held interpretations regarding the emergence of food production and have demonstrated that far-traveled, nomadic pastoralists were more myth than reality, at least during the Neolithic. Despite debates regarding their proposed conveyance mechanisms, obsidian artifacts' transport has received relatively little attention compared with zooarchaeological and archaeobotanical lines of investigation. The rise of nondestructive and portable instruments permits entire obsidian assemblages to be traced to their sources, renewing their significance in elucidating connections among early pastoral and agricultural communities. Here we share our findings about the obsidian artifacts excavated from the sites of Ali Kosh and Chagha Sefid in the southern Zagros. In the 1960s and 1970s, 28 obsidian artifacts from the sites were destructively tested, and the remainder were sorted by color. Our results emphasize a dynamic, accelerating connectivity among the Early and Late Neolithic communities. Here we propose and support an alternative model for obsidian distribution among more settled communities. In brief, diversity in the obsidian assemblage accelerated diachronically, an invisible trend in the earlier studies. Our model of increasing population densities is supported by archaeological data and computational simulations, offering insights regarding the Neolithic Demographic Transition in the Zagros, an equivalent of which is commonly thought to have occurred around the world. |
3,448 | Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability | Networks derived from many disciplines, such as social relations, web contents, and cancer progression, are temporal and incomplete. Link prediction in temporal networks is of theoretical interest and practical significance because spurious links are critical for investigating evolving mechanisms. In this study, we address the temporal link prediction problem in networks, i.e. predicting links at time T + 1 based on a given temporal network from time 1 to T. To address the relationships among matrix decomposition based algorithms, we prove the equivalence between the eigendecomposition and nonnegative matrix factorization (NMF) algorithms, which serves as the theoretical foundation for designing NMF-based algorithms for temporal link prediction. A novel NMF-based algorithm is proposed based on such equivalence. The algorithm factorizes each network to obtain features using graph communicability, and then collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches. (C) 2017 Elsevier Ltd. All rights reserved. |
3,449 | Revision of cytogenetic dosimetry in the IAEA manual 2011 based on data about radio-sensitivity and dose-rate findings contributing | In order to achieve the goal of rapid response, effective controland protection of life inlarge-scale radiation events, the IAEA Manual 2011 has been revised based on the data of radio-sensitivity, dose-rate findings. Analyze individual differences in radiation sensitivity using 60 Co radiation (0.27 Gy/min). Chromosomal aberrations with different irradiation dose rates were used to establish the biological dose curve and analyze the excess of the "dicentric + ring" caused by the dose rate at each dose point; DAPI-images and Metafer 4 were used to capture metaphase images and make further analysis. The data were collected in 2020, Dicentric + ring/100 Cells was 17.5-43.8, the average value was28.32 ± 6.98. The mean value of Dicentric + ring/100 Cells was 31.37 in males while 25.27 in females, there are significant differences (p < .01). The irradiation dose is dominant, At each dose point, the value of"(dicentric chromosome + centric rings)/cell" is proportional to "dose rate", that is, Y = kx + b, within the dose range of 1-5 Gy, "(dicentric chromosome + centric rings)/Cell" holds a quadratic linear relationship with dose rate, that is, y = ax2 + bx + c; The DAPI-images might give you more hints than those of conventional Giemsa-stain. The authors recommend that the IAEA Manual 2011 could be revised based on data of radio-sensitivity and dose-rate, which may contribute to the establishment of a unified dose-response calibration curve and stimulation of potential for automation in cytogenetic biodosimetry. (1) Individual differences of radiosensitivity are very large. (2) At each dose point, "(dicentric chromosome + centric rings)/cell" is proportional to "dose rate", that is, Y = kx + b. (3) "(dicentric chromosome + centric rings)/Cell" is a quadratic linear relationship with dose rate, that is, y = ax2 + bx + c. (4) We created a "Unity Standard Curve of Biological Dose Estimation". Creating a Unity Standard Curve of Biological Dose, under these circumstances, we can form a joint and rapid response to a nuclear and radiological accident. |
3,450 | Modern possibilities of neurosurgical treatment of brain metastases | Despite significant progress in neuroimaging and introduction of new combined treatments for solid tumors, brain metastases are still adverse factor for overall survival. Brain metastases are diagnosed in 8-10% of patients and associated with extremely poor prognosis. These lesions result focal and general cerebral symptoms. Literature review highlights the current principles of surgical treatment of metastatic brain lesions in patients with solid tumors. |
3,451 | Superpixel-based object boundary gimmicking using optimized conditional random fields with random associations | Superpixel-based clustering approach gains attention as an efficient pre-processing step for image segmentation during the last decade. In general, color similarity and position of the pixels are used as similarity metrics for segmentation. In this proposed work, a two-level object segmentation framework is proposed, where the mid-level cues are meritoriously utilized for efficient object segmentation. In the first level, a superpixel-based object boundary gimmicking algorithm with improved distance measure is used to gimmick the cluster boundaries. To make the picture visualization as human vision friendly, the regular clusters are decomposed literally only at the required places to adhere with object boundaries. Spurious Clusters are identified and merged to remove the noisy superpixels. As a post-processing step, superpixel-based optimized conditional random field (CRF) with random association (SOCRA) algorithm is proposed for efficient segmentation. Here homogeneity of the superpixel is imposed by optimized CRF to augment the object clusters with random associations. CRFs are employed for its excellent capability to characterize the relationship among the random variables. Extensive performance evaluation shows the proposed boundary gimmicking approach significantly competes with the state-of-the-art methods. Results obtained with Berkeley segmentation dataset (BSDS300) are compared with state-of-the-art methods in terms of under segmentation error, boundary recall, and achievable segmentation accuracy. |
3,452 | Changes in MCP-1, HGF, and IGF-1 expression in endometrial stromal cells, PBMCs, and PFMCs of endometriotic women following 1,25(OH)2D3 treatment | 1,25(OH)2D3 has anti-inflammatory and growth inhibitory effects. Our study explored the effect of 1,25(OH)2D3 treatment on the expression of monocyte chemotactic protein-1 (MCP-1), hepatocyte growth factor (HGF), and insulin-like growth factor-1 (IGF-1) by peripheral blood mononuclear cells (PBMCs), peritoneal fluid mononuclear cells (PFMCs), endometrial stromal cells (ESCs), and its effect on the proliferation of PBMCs and PFMCs of patients with endometriosis compared with controls. PBMCs, PFMCs, and ESCs were obtained from 10 endometriosis patients and 10 non-endometriotic individuals. After treating cells with 0.1 μM of 1,25(OH)2D3 for 6, 24, and 48 h, the gene and protein expression of mentioned factors were evaluated by real-time PCR and ELISA methods, respectively. 1,25(OH)2D3 treatment significantly reduced the protein expression of MCP-1, HGF, and IGF-1 in PBMCs and PFMCs of endometriotic patients at 48 h (p < 0.05-<0.01). Also, this treatment significantly reduced MCP-1, HGF, and IGF-1 gene and/or protein expression in EESCs and EuESCs at 24 and 48 h (p < 0.05-<0.01). 1,25(OH)2D3 treatment also reduced the proliferation of PBMCs and PFMCs of endometriotic patients compared with controls (p < 0.01). 1,25(OH)2D3 can be considered as a potentially effective agent in the prevention and treatment of endometriosis along with other therapies. |
3,453 | In silico structural inhibition of ACE-2 binding site of SARS-CoV-2 and SARS-CoV-2 omicron spike protein by lectin antiviral dyad system to treat COVID-19 | Spike glycoprotein of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) binds angiotensin-converting enzyme-2 (ACE-2) receptors via its receptor-binding domain (RBD) and mediates virus-to-host cell fusion. Recently emerged omicron variant of SARS-CoV-2 possesses around 30 mutations in spike protein where N501Y tremendously increases viral infectivity and transmission. Lectins interact with glycoproteins and mediate innate immunity displaying antiviral, antibacterial, and anticarcinogenic properties. In this study, we analyzed the potential of lectin, and lectin-antibody (spike-specific) complex to inhibit the ACE-2 binding site of wild and N501Y mutated spike protein by utilizing in silico molecular docking and simulation approach. Docking of lectin at reported ACE-2 binding spike-RBD residues displayed the ZDock scores of 1907 for wild and 1750 for N501Y mutated spike-RBD. Binding of lectin with antibody to form proposed dyad complex gave ZDock score of 1174 revealing stable binding. Docking of dyad complex with wild and N501Y mutated spike-RBD, at lectin and antibody individually, showed high efficiency binding hence, effective structural inhibition of spike-RBD. MD simulation of 100 ns of each complex proved high stability of complexes with RMSD values ranging from 0.2 to 1.5 nm. Consistent interactions of lead ACE-2 binding spike residues with lectin during simulation disclosed efficient structural inhibition by lectin against formation of spike RBD-ACE-2 complex. Hence, lectins along with their ability to induce innate immunity against spike glycoprotein can structurally inhibit the spike-RBD when given as lectin-antibody dyad system and thus can be developed into a dual effect treatment against COVID-19. Moreover, the high binding specificity of this system with spike-RBD can be exploited for development of diagnostic and drug-delivery systems. |
3,454 | A quick eye to anger: An investigation of a differential effect of facial features in detecting angry and happy expressions | Detection of angry and happy faces is generally found to be easier and faster than that of faces expressing emotions other than anger or happiness. This can be explained by the threatening account and the feature account. Few empirical studies have explored the interaction between these two accounts which are seemingly, but not necessarily, mutually exclusive. The present studies hypothesised that prominent facial features are important in facilitating the detection process of both angry and happy expressions; yet the detection of happy faces was more facilitated by the prominent features than angry faces. Results confirmed the hypotheses and indicated that participants reacted faster to the emotional expressions with prominent features (in Study 1) and the detection of happy faces was more facilitated by the prominent feature than angry faces (in Study 2). The findings are compatible with evolutionary speculation which suggests that the angry expression is an alarming signal of potential threats to survival. Compared to the angry faces, the happy faces need more salient physical features to obtain a similar level of processing efficiency. |
3,455 | Detecting abnormality with separated foreground and background: Mutual Generative Adversarial Networks for video abnormal event detection | As one of the most important tasks in intelligent video analysis, video abnormal event detection has been extensively studied. Prior arts have made a great process in designing frameworks to capture spatio-temporal features of video frames. However, video frames usually consist of various objects. It is challenging to grasp the nuances of anomalies against noisy backgrounds. To tackle the bottleneck, we propose a novel Foreground-Background Separation Mutual Generative Adversarial Network (FSM-GAN) framework. The FSMGAN permits the separation of video frames into the foreground and background. The separated foreground and background are utilized as the input of mutual generative adversarial networks, which transform raw pixel images in optical-flow representations and vice versa. In the networks, the background is regarded as known conditions and the model focuses on learning the high-level spatio-temporal foreground features to represent the event with the given conditions during the mutual adversarial training. In the test stage, these high-level features instead of low-level visual primitives are utilized to measure the abnormality in the semantic level. Compared with state-of-the-art methods and other abnormal event detection approaches, the proposed framework demonstrates its effectiveness and reliability across various scenes and events. |
3,456 | Our Tradition Our Enemy: A Qualitative Study of Barriers to Women's HIV Care in Jimma, Southwest Ethiopia | Evidence exists that suggests that women are vulnerable to negative HIV treatment outcomes worldwide. This study explored barriers to treatment outcomes of women in Jimma, Southwest Ethiopia. We interviewed 11 HIV patients, 9 health workers, 10 community advocates and 5 HIV program managers from 10 institutions using an in-depth interview guide designed to probe barriers to HIV care at individual, community, healthcare provider, and government policy levels. To systematically analyze the data, we applied a thematic framework analysis using NVivo. In total, 35 participants were involved in the study and provided the following interrelated barriers: (i) Availability-most women living in rural areas who accessed HIV cared less often than men; (ii) free antiretroviral therapy (ART) is expensive-most women who have low income and who live in urban areas sold ART drugs illegally to cover ART associated costs; (iii) fear of being seen by others-negative consequences of HIV related stigma was higher in women than men; (iv) the role of tradition-the dominance of patriarchy was found to be the primary barrier to women's HIV care and treatment outcomes. In conclusion, barriers related to culture or tradition constrain women's access to HIV care. Therefore, policies and strategies should focus on these contextual constrains. |
3,457 | Analysis of college martial arts teaching posture based on 3D image reconstruction and wavelet transform | Martial arts is a traditional sports event of the Chinese nation, which carries history and culture. With the development of "Martial Arts on Campus Activities" in recent years, more and more schools have opened general martial arts courses. However, due to the more complex technical movements of martial arts, there are often varying degrees of gaps between the movements and standard movements. Based on this, this research introduces three-dimensional imaging technology on the basis of traditional physical education teaching methods, aiming to explore new martial arts teaching models through image reconstruction and posture analysis. First of all, in order to obtain a three-dimensional point cloud and three-dimensional line, this paper extracts and matches feature points and feature lines on the input image. Secondly, on the basis of obtaining dense matching and straight-line matching, this paper selects the image with the most feature line matching for reprojection. Wavelet transform is used in the process of image compression and coding, including signal decomposition and reconstruction steps. Finally, through the experimental test of martial arts teaching posture images in colleges and universities, it shows that the method of combining three-dimensional image reconstruction and wavelet transform proposed in this paper has good applicability and efficiency, and can provide a scientific reference for college martial arts teaching. |
3,458 | Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting | Due to the chaotic nature of the underlying physical processes, even state-of-the-art models cannot perfectly forecast the solar irradiance at the surface of the earth. There is, therefore, a growing interest in the research community for forecasting methods that can quantify their own uncertainty. This paper proposes a novel probabilistic framework for forecasting day-ahead hourly solar irradiance. A principal component analysis (PCA) is used to tightly combine a high-resolution mesoscale numerical weather prediction (NWP) model with a quantile gradient boosting algorithm. A thorough evaluation of the deterministic and probabilistic properties of the model is conducted for a full year in the tropical island of Singapore. The impact of the sky conditions on its performance is also considered. Furthermore, a rigorous statistical framework is employed to systematically benchmark our model against two state of the art methods, a Lasso model output statistic procedure and an analog ensemble (AnEn). Our model significantly improves the numerical weather prediction model: it achieves a 41% reduction of the MAE and 39% reduction of the RMSE. It is also slightly more accurate than Lasso and has a CRPS 4% lower than that of AnEn. |
3,459 | Developmental Resonance Network | Adaptive resonance theory (ART) networks deal with normalized input data only, which means that they need the normalization process for the raw input data, under the assumption that the upper and lower bounds of the input data are known in advance. Without such an assumption, ART networks cannot be utilized. To solve this problem and improve the learning performance, inspired by the ART networks, we propose a developmental resonance network (DRN) by employing new techniques of a global weight and node connection and grouping processes. The proposed DRN learns the global weight converging to the unknown range of the input data and properly clusters by grouping similar nodes into one. These techniques enable DRN to learn the raw input data without the normalization process while retaining the stability, plasticity, and memory usage efficiency without node proliferation. Simulation results verify that our DRN, applied to the unsupervised clustering problem, can cluster raw data properly without a prior normalization process. |
3,460 | Neurite Tracing With Object Process | In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection, modeling to digital reconstruction. First, we present an automatic, unsupervised object detection framework using stochastic marked point process. It extracts connected neuronal networks by fitting special configuration of marked objects to the centreline of the neurite branches in the image volume giving us position, local width and orientation information. Semantic modeling of neuronal morphology in terms of critical nodes like bifurcations and terminals, generates various geometric and morphology descriptors such as branching index, branching angles, total neurite length, internodal lengths for statistical inference on characteristic neuronal features. From the detected branches we reconstruct neuronal tree morphology using robust and efficient numerical fast marching methods. We capture a mathematical model abstracting out the relevant position, shape and connectivity information about neuronal branches from the microscopy data into connected minimum spanning trees. Such digital reconstruction is represented in standard SWC format, prevalent for archiving, sharing, and further analysis in the neuroimaging community. Our proposed pipeline outperforms state of the art methods in tracing accuracy and minimizes the subjective variability in reconstruction, inherent to semi-automatic methods. |
3,461 | Effect of empirical antifungal treatment on mortality in non-neutropenic critically ill patients: a propensity-matched retrospective cohort study | To evaluate the effect of empirical antifungal treatment (EAFT) on mortality in critically ill patients without invasive fungal infections (IFIs). This was a single-center propensity score-matched retrospective cohort study involving non-transplanted, non-neutropenic critically ill patients with risk factors for invasive candidiasis (IC) in the absence of IFIs. We compared all-cause hospital mortality and infection-attributable hospital mortality in patients who was given EAFT for suspected IC as the cohort group and those without any systemic antifungal agents as the control group. Among 640 eligible patients, 177 patients given EAFT and 177 control patients were included in the analyses. As compared with controls, EAFT was not associated with the lower risks of all-cause hospital mortality [odds ratio (OR), 0.911; 95% CI, 0.541-1.531; P = 0.724] or infection-attributable hospital mortality (OR, 1.149; 95% CI, 0.632-2.092; P = 0.648). EAFT showed no benefit of improvement of infection at discharge, duration of mechanical ventilation, and antibiotic-free days. However, the later initiation of EAFT was associated with higher risks of all-cause hospital mortality (OR, 1.039; 95% CI, 1.003 to 1.076; P = 0.034) and infection-attributable hospital mortality (OR, 1.046; 95% CI, 1.009 to 1.085; P = 0.015) in patients with suspected IC. This effect was also found in infection-attributable hospital mortality (OR, 1.042; 95% CI, 1.005 to 1.081; P = 0.027) in septic patients with suspected IC. EAFT failed to decrease hospital mortality in non-neutropenic critically ill patients without IFIs. The timing may be critical for EAFT to improve mortality in these patients with suspected IC. ChiCTR2000038811, registered on Oct 3, 2020. |
3,462 | Human action recognition using deep rule-based classifier | In recent years, numerous techniques have been proposed for human activity recognition (HAR) from images and videos. These techniques can be divided into two major categories: handcrafted and deep learning. Deep Learning-based models have produced remarkable results for HAR. However, these models have several shortcomings, such as the requirement for a massive amount of training data, lack of transparency, offline nature, and poor interpretability of their internal parameters. In this paper, a new approach for HAR is proposed, which consists of an interpretable, self-evolving, and self-organizing set of 0-order If...THEN rules. This approach is entirely data-driven, and non-parametric; thus, prototypes are identified automatically during the training process. To demonstrate the effectiveness of the proposed method, a set of high-level features is obtained using a pre-trained deep convolution neural network model, and a recently introduced deep rule-based classifier is applied for classification. Experiments are performed on a challenging benchmark dataset UCF50; results confirmed that the proposed approach outperforms state-of-the-art methods. In addition to this, an ablation study is conducted to demonstrate the efficacy of the proposed approach by comparing the performance of our DRB classifier with four state-of-the-art classifiers. This analysis revealed that the DRB classifier could perform better than state-of-the-art classifiers, even with limited training samples. |
3,463 | Impact of gulf war toxic exposures after mild traumatic brain injury | Chemical and pharmaceutical exposures have been associated with the development of Gulf War Illness (GWI), but how these factors interact with the pathophysiology of traumatic brain injury (TBI) remains an area of study that has received little attention thus far. We studied the effects of pyridostigmine bromide (an anti-nerve agent) and permethrin (a pesticide) exposure in a mouse model of repetitive mild TBI (r-mTBI), with 5 impacts over a 9-day period, followed by Gulf War (GW) toxicant exposure for 10 days beginning 30 days after the last head injury. We then assessed the chronic behavioral and pathological sequelae 5 months after GW agent exposure. We observed that r-mTBI and GWI cumulatively affect the spatial memory of mice in the Barnes maze and result in a shift of search strategies employed by r-mTBI/GW exposed mice. GW exposure also produced anxiety-like behavior in sham animals, but r-mTBI produced disinhibition in both the vehicle and GW treated mice. Pathologically, GW exposure worsened r-mTBI dependent axonal degeneration and neuroinflammation, increased oligodendrocyte cell counts, and increased r-mTBI dependent phosphorylated tau, which was found to colocalize with oligodendrocytes in the corpus callosum. These results suggest that GW exposures may worsen TBI-related deficits. Veterans with a history of both GW chemical exposures as well as TBI may be at higher risk for worse symptoms and outcomes. Subsequent exposure to various toxic substances can influence the chronic nature of mTBI and should be considered as an etiological factor influencing mTBI recovery. |
3,464 | A Low-Power Photoplethysmogram-Based Heart Rate Sensor Using Heartbeat Locked Loop | In this paper, we present an ultralow power heart rate (HR) monitoring photoplethysmography (PPG) sensor using a heartbeat locked loop (HBLL). The HBLL generates a narrow window that turns on the LED and analog-front-end only when a peak is expected in the PPG signal. The prototype PPG sensor implemented in 0.18 mu m CMOS has an effective duty-cycle of 0.01% and consumes only 43.4 mu W at a HR of 60 b/m, which is the lowest power consumption compared with previous state-of-the-art PPG sensors. The HR error of the proposed sensor is less than 2.1 b/m for HR below 180 b/m. |
3,465 | Texture classification using block intensity and gradient difference (BIGD) descriptor | In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD). In an image patch, we randomly sample multi-scale block pairs and utilize the intensity and gradient differences of pairwise blocks to construct the local BIGD descriptor. The random sampling strategy and the multi-scale framework help BIGD descriptors capture the distinctive patterns of patches at different orientations and spatial granularity levels. We use vectors of locally aggregated descriptors (VLAD) or improved Fisher vector (IFV) to encode local BIGD descriptors into a full image descriptor, which is then fed into a linear support vector machine (SVM) classifier for texture classification. We compare the proposed descriptor with typical and state-of-the-art ones by evaluating their classification performance on five public texture data sets including Brodatz, CUReT, KTH-TIPS, and KTH-TIPS-2a and -2b. Experimental results show that the proposed BIGD descriptor with stronger discriminative power yields 0.12% similar to 6.43% higher classification accuracy than the state-of-the-art texture descriptor, dense microblock difference (DMD). |
3,466 | Knowledge Transfer with Citizen Science: Luft-Leipzig Case Study | Community-based participatory research initiatives such as "hackAir", "luftdaten.info", "senseBox", "CAPTOR", "CurieuzeNeuzen Vlaanderen", "communityAQ", and "Healthy Air, Healthier Children" campaign among many others for mitigating short-lived climate pollutants (SLCPs) and improving air quality have reported progressive knowledge transfer results. These research initiatives provide the research community with the practical four-element state-of-the-art method for citizen science. For the preparation-, measurements-, data analysis-, and scientific support-elements that collectively present the novel knowledge transfer method, the Luft-Leipzig project results are presented. This research contributes to science by formulating a novel method for SLCP mitigation projects that employ citizen scientists. The Luft-Leipzig project results are presented to validate the four-element state-of-the-art method. The method is recommended for knowledge transfer purposes beyond the scope of mitigating short-lived climate pollutants (SLCPs) and improving air quality. |
3,467 | HEADS-JOIN: Efficient Earth Mover's Distance Similarity Joins on Hadoop | The Earth Mover's Distance (EMD) similarity join has a number of important applications such as near duplicate image retrieval and distributed based pattern analysis. However, the computational cost of EMD is super cubic and consequently the EMD similarity join operation is prohibitive for datasets of even medium size. We propose to employ the Hadoop platform to speed up the operation. Simply porting the state-of-the-art metric distance similarity join algorithms to Hadoop results in inefficiency because they involve excessive distance computations and are vulnerable to skewed data distributions. We propose a novel framework, named HEADS-JOIN, which transforms data into the space of EMD lower bounds and performs pruning and partitioning at a low cost because computing these EMD lower bounds has constant or linear complexity. We investigate both range and top-k joins, and design efficient algorithms on three popular Hadoop computation paradigms, i.e., MapReduce, Bulk Synchronous Parallel, and Spark. We conduct extensive experiments on both real and synthetic datasets. The results show that HEADS-JOIN outperforms the state-of-the-art metric similarity join technique, i.e., Quickjoin, by up to an order of magnitude and scales out well. |
3,468 | Photo-catalytic and biomedical applications of one-step, plant extract-mediated green-synthesized cobalt oxide nanoparticles | In the present work, for the first time, green chemically synthesized and stabilized Co3O4 nanoparticles were employed for catalytic conversion of isopropyl alcohol to acetone by dehydrogenation of IPA. Plant extract of Rosmarinus officinalis was used as a reducing and stabilizing agent for this synthesis. The biosynthesized Co3O4 nanoparticles were annealed at 450℃ followed by their physiochemical characterizations through XRD, SEM, AFM, and FTIR. Size distribution information collected through XRD and AFM back each other, and it was found to be 6.5 nm, having the highest number of nanoparticles in this size range. While SEM confirms the self-arranging property of synthesized nanoparticles due to their magnetic nature, furthermore, the biogenic Co3O4 nanoparticles were studied for their catalytic potential to convert isopropyl alcohol to acetone with the help of a UV-Visible spectrophotometer. The highest photocatalytic conversion of 99% was obtained in time period of 48 s. For the first time ever, nanoparticles were used for 5 cycles to evaluate their recyclable nature and conversion fell from 99 to 86% and the end of the 5th cycle. Later anti-bacterial activity against 3 Gram-positive and 3 Gram-negative strains gave the highest inhibition value of 99% against Streptococcus pneumoniae at 500 µg/mL. Finally, a cytotoxicity study on synthesized nanomaterials was carried out by exposing freshly drawn human macrophages to them. It was found that even at the highest concentration of 500 µg/mL, the nanoparticles showed only 28% lysis. |
3,469 | ConvSequential-SLAM: A Sequence-Based, Training-Less Visual Place Recognition Technique for Changing Environments | Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place under changing viewpoints and appearances. A large number of handcrafted and deep-learning-based VPR techniques exist, where the former suffer from appearance changes and the latter have significant computational needs. In this paper, we present a new handcrafted VPR technique, namely ConvSequential-SLAM, that achieves state-of-the-art place matching performance under challenging conditions. We utilise sequential information and block-normalisation to handle appearance changes, while using regional-convolutional matching to achieve viewpoint-invariance. We analyse content-overlap in-between query frames to find a minimum sequence length, while also re-using the image entropy information for environment-based sequence length tuning. State-of-the-art performance is reported in contrast to 9 contemporary VPR techniques on 4 public datasets. Qualitative insights and an ablation study on sequence length are also provided. |
3,470 | Quality Analysis of Autologous Platelet-Rich Plasmapheresis | To summarized the technology of autologous platelet-rich plasmapheresis and analyzed the product quality, in order to provide safe and effective product guarantee service for clinical treatment. Technical parameters were set according to patient age, weight, height, and preoperative routine blood indices. Autologous platelet-rich plasma (PRP) was collected, and the product quality and adverse reactions of patients were statistically analyzed. Autologous PRP had platelet (PLT), white blood cell (WBC), and red blood cell (RBC) counts of (1250.26 ± 435.88) × 109/L, (1.19 ± 1.95) × 109/L, and (0.05 ± 0.04) × 1012/L, respectively. The PLT enrichment ratio in PRP was 5.66 ± 1.66. There was no significant difference in PLT, RBC, WBC, or hematocrit before and after apheresis (P > 0.05). The incidence of adverse reactions was 8%, and all were mild. When clinical patients use PRP in the treatment of diseases, autologous platelet-rich plasmapheresis technology was used to apheresis PRP, which has good product quality and few adverse reactions, and thus can be adopted more widely. |
3,471 | Self-reported voice difficulties in educational professionals during COVID-19 in Quebec: a cross-sectional mixed-methods study | Background: The health measures imposed by COVID-19 on workplaces created adverse communication settings. Our cross-sectional study aimed to document the nature and severity of the vocal difficulties experienced by educational professionals a few weeks after the implementation of health measures in schools and early childhood settings in Quebec, Canada while teaching in class.Methods: To this end, we conducted a self-report survey containing nine close-ended questions and one open-ended question regarding self-reported vocal difficulties and the implementation of health measures. The survey was answered by 194 educational professionals in October 2020.Results: Since the introduction of the health measures, respondents reported often or always: having difficulty making themselves heard (66.5%), needing to strain their voice (68.1%), having throat pain after work (38.1%), and being concerned about their vocal health (25.2%). 35.6% perceived that their voice changed moderately or a lot and 75.3% did not feel equipped to take care of their vocal health. Fisher's exact tests revealed the difficulties overall were more present in women (p < 0.05).Discussion: The qualitative analysis of open-ended question answers shows a circular process at play, where the vocal responses to the COVID-19-induced communication barriers contribute to creating more problematic communication settings, thus increasing the challenges for vocal health. Better equipping the professionals to take care of their vocal health by developing resources in their professional settings to help them face vocal challenges in both every day as well as extreme situations, should be a priority. |
3,472 | A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription | Automatic music transcription (AMT) is a critical problem in the field of music information retrieval (MIR). When AMT is faced with deep neural networks, the variety of timbres of different instruments can be an issue that has not been studied in depth yet. The goal of this work is to address AMT transcription by analyzing how timbre affect monophonic transcription in a first approach based on the CREPE neural network and then to improve the results by performing polyphonic music transcription with different timbres with a second approach based on the Deep Salience model that performs polyphonic transcription based on the Constant-Q Transform. The results of the first method show that the timbre and envelope of the onsets have a high impact on the AMT results and the second method shows that the developed model is less dependent on the strength of the onsets than other state-of-the-art models that deal with AMT on piano sounds such as Google Magenta Onset and Frames (OaF). Our polyphonic transcription model for non-piano instruments outperforms the state-of-the-art model, such as for bass instruments, which has an F-score of 0.9516 versus 0.7102. In our latest experiment we also show how adding an onset detector to our model can outperform the results given in this work. |
3,473 | Deep Convolutional Neural Networks for Unconstrained Ear Recognition | This paper employs state-of-the-art Deep Convolutional Neural Networks (CNNs), namely AlexNet, VGGNet, Inception, ResNet and ResNeXt in a first experimental study of ear recognition on the unconstrained EarVN1.0 dataset. As the dataset size is still insufficient to train deep CNNs from scratch, we utilize transfer learning and propose different domain adaptation strategies. The experiments show that our networks, which are fine-tuned using custom-sized inputs determined specifically for each CNN architecture, obtain state-of-the-art recognition performance where a single ResNeXt101 model achieves a rank-1 recognition accuracy of 93.45%. Moreover, we achieve the best rank-1 recognition accuracy of 95.85% using an ensemble of fine-tuned ResNeXt101 models. In order to explain the performance differences between models and make our results more interpretable, we employ the t-SNE algorithm to explore and visualize the learned features. Feature visualizations show well-separated clusters representing ear images of the different subjects. This indicates that discriminative and ear-specific features are learned when applying our proposed learning strategies. |
3,474 | Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images | Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of good seed points. 3D neuron critical points, including terminations, branch points and cross-over points, are good candidates for such seed points. However, a method that can simultaneously detect all types of critical points has barely been explored. In this work, we present a method to simultaneously detect all 3 types of 3D critical points in neuron microscopy images, based on a spherical-patches extraction (SPE) method and a 2D multi-stream convolutional neural network (CNN). SPE uses a set of concentric spherical surfaces centered at a given critical point candidate to extract intensity distribution features around the point. Then, a group of 2D spherical patches is generated by projecting the surfaces into 2D rectangular image patches according to the orders of the azimuth and the polar angles. Finally, a 2D multi-stream CNN, in which each stream receives one spherical patch as input, is designed to learn the intensity distribution features from those spherical patches and classify the given critical point candidate into one of four classes: termination, branch point, cross-over point or non-critical point. Experimental results confirm that the proposed method outperforms other state-of-the-art critical points detection methods. The critical points based neuron reconstruction results demonstrate the potential of the detected neuron critical points to be good seed points for neuron reconstruction. Additionally, we have established a public dataset dedicated for neuron critical points detection, which has been released along with this article. |
3,475 | Genome-wide selection signatures reveal widespread synergistic effects of two different stressors in Drosophila melanogaster | Experimental evolution combined with whole-genome sequencing (evolve and resequence (E&R)) is a powerful approach to study the adaptive architecture of selected traits. Nevertheless, so far the focus has been on the selective response triggered by a single stressor. Building on the highly parallel selection response of founder populations with reduced variation, we evaluated how the presence of a second stressor affects the genomic selection response. After 20 generations of adaptation to laboratory conditions at either 18°C or 29°C, strong genome-wide selection signatures were observed. Only 38% of the selection signatures can be attributed to laboratory adaptation (no difference between temperature regimes). The remaining selection responses are either caused by temperature-specific effects, or reflect the joint effects of temperature and laboratory adaptation (same direction, but the magnitude differs between temperatures). The allele frequency changes resulting from the combined effects of temperature and laboratory adaptation were more extreme in the hot environment for 83% of the affected genomic regions-indicating widespread synergistic effects of the two stressors. We conclude that E&R with reduced genetic variation is a powerful approach to study genome-wide fitness consequences driven by the combined effects of multiple environmental factors. |
3,476 | A simple single-sensor MPPT solution | Maximum power point trackers (MPPTs) are used to ensure optimal utilization of solar cells. The implementation essentially involves sensing input current and voltage. An MPPT algorithm uses this information to maximize power drawn from the solar cells. Understandably, such realization is costly. Current state of the art allows replacing one of the sensors by complicated computations. In the present work, an empirical observation is used to develop a strategy, which employs a single voltage sensor and carries out simple computations for a buck converter-based MPPT. |
3,477 | Structure-Driven Unsupervised Domain Adaptation for Cross-Modality Cardiac Segmentation | Performance degradation due to domain shift remains a major challenge in medical image analysis. Unsupervised domain adaptation that transfers knowledge learned from the source domain with ground truth labels to the target domain without any annotation is the mainstream solution to resolve this issue. In this paper, we present a novel unsupervised domain adaptation framework for cross-modality cardiac segmentation, by explicitly capturing a common cardiac structure embedded across different modalities to guide cardiac segmentation. In particular, we first extract a set of 3D landmarks, in a self-supervised manner, to represent the cardiac structure of different modalities. The high-level structure information is then combined with another complementary feature, the Canny edges, to produce accurate cardiac segmentation results both in the source and target domains. We extensively evaluate our method on the MICCAI 2017 MM-WHS dataset for cardiac segmentation. The evaluation, comparison and comprehensive ablation studies demonstrate that our approach achieves satisfactory segmentation results and outperforms state-of-the-art unsupervised domain adaptation methods by a significant margin. |
3,478 | CgIκB3, the third novel inhibitor of NF-kappa B (IκB) protein, is involved in the immune defense of the Pacific oyster, Crassostrea gigas | Inhibitor of NF-κB (IκB), the important regulator of NF-κB/Rel signaling pathway, plays the crucial role in immune response of both vertebrates and invertebrates. Here, a novel homologue of IκB was cloned from Crassostrea gigas, and designated as CgIκB3. The complete CgIκB3 cDNA was 1282 bp in length, including a 942 bp open reading frame (ORF), a 51 bp 5' UTR and a 289 bp 3' UTR. The ORF encodes a putative protein of 313 amino acids with a predicted molecular weight of approximately 34.7 kDa. Sequence analysis reveals that CgIκB3 contains a conserved degradation motif but with only five ankyrin repeats. Neither a PEST domain nor a C-terminal casein kinase II phosphorylation site was identified through either alignment or bioinformatic prediction. Phylogenetic analysis suggested that CgIκB3 shares common ancestor with CgIκB1 rather CgIκB2, and theoretically it may originate from one duplication event prior to divergence of CgIκB1 and CgIκB2. Tissue expression analyses demonstrated that CgIκB3 mRNA is the most abundant in gills and heart. The expression following PAMP infection showed that CgIκB3 was significantly up-regulated in a similar pattern when challenged with LPS, HKLM or HKVA, respectively. Moreover, similar to CgIκB1 and CgIκB2, CgIκB3 can also inhibit Rel dependent NF-κB activation in HEK293 cells in a dose-dependent manner. In summary, these findings suggest that CgIκB3 can be as the functional inhibitor of NF-κB/Rel and involved in the host defense of C. gigas. The discovery of the third IκB emphasizes the complexity and importance of the regulation on NF-κB activation. |
3,479 | A phase 1/2, open-label, multicenter study of isatuximab in combination with cemiplimab in patients with lymphoma | Patients with relapsed or refractory lymphoma have limited treatment options, requiring newer regimens. In this Phase 1/2 study (NCT03769181), we assessed the safety, efficacy, and pharmacokinetics of isatuximab (Isa, anti-CD38 antibody) in combination with cemiplimab (Cemi, anti-programmed death-1 [PD-1] receptor antibody; Isa + Cemi) in patients with classic Hodgkin lymphoma (cHL), diffuse large B-cell lymphoma (DLBCL), and peripheral T-cell lymphoma (PTCL). In Phase 1, we characterized the safety and tolerability of Isa + Cemi with planned dose de-escalation to determine the recommended Phase 2 dose (RP2D). Six patients in each cohort were treated with a starting dose of Isa + Cemi to determine the RP2D. In Phase 2, the primary endpoints were complete response in Cohort A1 (cHL anti-PD-1/programmed death-ligand 1 [PD-L1] naïve), and objective response rate in Cohorts A2 (cHL anti-PD-1/PD-L1 progressors), B (DLBCL), and C (PTCL). An interim analysis was performed when the first 18 (Cohort A1), 12 (Cohort A2), 17 (Cohort B), and 11 (Cohort C) patients in Phase 2 had been treated and followed up for 24 weeks. Isa + Cemi demonstrated a manageable safety profile with no new safety signals. No dose-limiting toxicities were observed at the starting dose; thus, the starting dose of each drug was confirmed as the RP2D. Based on the Lugano 2014 criteria, 55.6% (Cohort A1), 33.3% (Cohort A2), 5.9% (Cohort B), and 9.1% (Cohort C) of patients achieved a complete or partial response. Pharmacokinetic analyses suggested no effect of Cemi on Isa exposure. Modest clinical efficacy was observed in patients with cHL regardless of prior anti-PD-1/PD-L1 exposure. In DLBCL or PTCL cohorts, interim efficacy analysis results did not meet prespecified criteria to continue enrollment in Phase 2 Stage 2. Isa + Cemi did not have a synergistic effect in these patient populations. |
3,480 | Self-adaptive Algorithm for Simulating Sand Painting in Real-Time | Sand painting is a form of combination of arts and modern aesthetic, which relies on profound cultural heritage and cultural connotation. To provide the public and artists with an opportunity to better understand sand painting and make art creations surprisingly, this paper proposes a self-adaptive algorithm to simulate sand painting in a real-time way. Our simulation system exploits the height field to simulate sand flow to achieve a fast even real-time target. Seven frequently-used styles of painting techniques are elaborately defined and successfully simulated in our system, including pouring, seeping, dotting, stroking, sweeping, multi-stroking, and pinching. The procedure of sand flow is mainly consist of two key parts: sand accumulation and collapse. The direction field is introduced into the system to control a similar appearance of a normal distribution, which will be of benefit to sand accumulation algorithm. A self-adaptive approach is taken advantage of into sand collapse algorithm to present certain appearances with various details. A color factor is also considered for realistic simulation in this paper in two ways: one is the background color of sand table/canvas and the other is the natural color of sand particles themselves. User feedbacks and experimental results reveal that the algorithm of sand painting simulation in this paper can realize kinds of sand painting arts of creations easily, realistically, effectively and interactively. |
3,481 | Integration of Remote-Sensing Techniques for the Preventive Conservation of Paleolithic Cave Art in the Karst of the Altamira Cave | Rock art offers traces of our most remote past and was made with mineral and organic substances in shelters, walls, or the ceilings of caves. As it is notably fragile, it is fortunate that some instances remain intact-but a variety of natural and anthropogenic factors can lead to its disappearance. Therefore, as a valuable cultural heritage, rock art requires special conservation and protection measures. Geomatic remote-sensing technologies such as 3D terrestrial laser scanning (3DTLS), drone flight, and ground-penetrating radar (GPR) allow us to generate exhaustive documentation of caves and their environment in 2D, 2.5D, and 3D. However, only its combined use with 3D geographic information systems (GIS) lets us generate new cave maps with details such as overlying layer thickness, sinkholes, fractures, joints, and detachments that also more precisely reveal interior-exterior interconnections and gaseous exchange; i.e., the state of senescence of the karst that houses the cave. Information of this kind is of great value for the research, management, conservation, monitoring, and dissemination of cave art. |
3,482 | Prime Editing: An Emerging Tool in Cancer Treatment | Prime Editing is a CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) based genome editing technique having promising potential in terms of reducing off target activity. It introduces fragments of DNA sequences into the target site using a guide RNA (gRNA) molecule, composed of both the sequence that is to be inserted into the target site along with an inactive Cas9 nickase and a reverse transcriptase. Prime Editing can cause insertions, deletions, and various point mutations for reverting the phenetic characteristics of a disease specially tested in human adult stem cells and cancer cell lines. The main aim of our review is to explore how Prime Editing and its various forms are being utilized as an emerging tool to cure deleterious diseases like cancer, also as a delivery strategy of the tool into cells. There are almost five generations of Prime Editors (PE) with increasing levels of efficiency from one level to another that have huge clinical potential in correcting mutations; however, the necessity for a pegRNA design is extremely significant. But besides having such advantages, the limitations of this technology particularly include generation of double nicks while optimizing the efficiency of PE3. So, it is important to consider all such consequences and customize PE as per requirements. |
3,483 | Hyperspectral Face Recognition With Spatiospectral Information Fusion and PLS Regression | Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition. We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum. |
3,484 | A review on state-of-the-art applications of data-driven methods in desalination systems | The substitution of conventional mathematical models with fast and accurate modeling tools can result in the further development of desalination technologies and tackling the need for freshwater. Due to the great capability of data-driven methods in analyzing complex systems, several attempts have been made to study various desalination systems using data-driven approaches. In this state-of-the-art review, the application of various artificial intelligence and design of experiment data-driven methods for analyzing different desalination technologies have been thoroughly investigated. According to the applications of data-driven methods in the field of desalination, the reviewed investigations are classified into five categories namely performance prediction using operational parameters, performance prediction using design parameters, optimization and correlation development, maintenance, and control of desalination systems. For each category, valuable information about the data-driven methods such as inputs, outputs, hyper-parameter tuning methods, and size of datasets have been provided and the main remarks are reported. The findings showed that data-driven methods can play a vital role in each aforementioned application for both thermal and membrane-based desalination technologies. Eventually, |
3,485 | Continuous Histone Deacylase Activity Assays | Protein lysine acylation represents one of the most common post-translational modifications. Obviously, highly reactive metabolic intermediates, like thioesters and mixed anhydrides between phosphoric acid and organic acids, modify lysine residues spontaneously. Additionally, enzymes using acyl-CoAs as co-substrates transfer the acyl residue specifically to defined sequences within proteins. The counteracting enzymes are called histone deacetylases (HDACs), releasing the free lysine side chain. Such enzymatic activities are involved in different cellular processes like tumor progression, immune response, regulation of metabolism, and aging. Modulators of such enzymatic activities represent valuable tools in drug discovery. Therefore, direct and continuous assays to monitor enzymatic activity of HDACs are needed. Here we describe different assay formats allowing both monitoring of Zn2+-dependent HDACs via UV-Vis-spectroscopy and NAD+-dependent HDACs (sirtuins) by fluorescence-based assay formats. Additionally, we describe methods enabling efficient screening of HDAC-inhibitors via fluorescence displacement assays. |
3,486 | Agent alliance formation using ART-networks as agent belief models | In today's hyper-competitive business environments virtual organisations are becoming highly dynamic and unpredictable. Individuals may want to work together across organisation boundaries but do not have much prior knowledge about potential partners. The semantic web and its associated new standards appear very promising as candidates to support a new generation of virtual organisations. Whilst knowledge can be represented in a machine interpretable way, social-like behaviours can be expected in a virtual organisation. In this paper ontology definition techniques from the semantic web are applied to define a virtual state space of a virtual organisation. Actors involved in an organisation, from high level strategy making members to low level physical devices, advertise their skills and local knowledge in a community. A task initiator, with a virtual sensor to perceive the advertised skills and with an adaptive belief model about the community, seeks for the best matched partners for cooperation. The belief model is a fuzzy neural network based on Adaptive Resonance Theory which takes the advertisements of actors as its initial belief and learns actors' actual capabilities through interaction experience. Dynamic alliances can then take place in an automated/semi-automated way that exhibit adaptive ability, self-organisation, unsupervised learning and competition ability. The alliances thus exhibit the inherent characteristics of realistic enterprises or human societies. |
3,487 | Efficient Computer-Aided Design of Dental Inlay Restoration: A Deep Adversarial Framework | Restoring the normal masticatory function of broken teeth is a challenging task primarily due to the defect location and size of a patient's teeth. In recent years, although some representative image-to-image transformation methods (e.g. Pix2Pix) can be potentially applicable to restore the missing crown surface, most of them fail to generate dental inlay surface with realistic crown details (e.g. occlusal groove) that are critical to the restoration of defective teeth with varying shapes. In this article, we design a computer-aided Deep Adversarial-driven dental Inlay reStoration (DAIS) framework to automatically reconstruct a realistic surface for a defective tooth. Specifically, DAIS consists of a Wasserstein generative adversarial network (WGAN) with a specially designed loss measurement, and a new local-global discriminator mechanism. The local discriminator focuses on missing regions to ensure the local consistency of a generated occlusal surface, while the global discriminator aims at defective teeth and adjacent teeth to assess if it is coherent as a whole. Experimental results demonstrate that DAIS is highly efficient to deal with a large area of missing teeth in arbitrary shapes and generate realistic occlusal surface completion. Moreover, the designed watertight inlay prostheses have enough anatomical morphology, thus providing higher clinical applicability compared with more state-of-the-art methods. |
3,488 | Synthesis, characterization of Ag-WO3/bentonite nanocomposites and their application in photocatalytic degradation of humic acid in water | In this study, Ag-WO3/bentonite nanocomposites were synthesized through a sol-gel process, a microwave irradiation technique, and a sol-immobilization process to examine their impact on the photocatalytic activity in the degradation of humic acids. The optical and structural properties of the synthesized materials were characterized using X-ray diffraction (XRD), Fourier-transform-infrared spectra (FTIR), field emission scanning electron microscope (FE-SEM) with energy dispersive X-ray (EDX), UV-Vis diffused reflectance spectra (UV-Vis DRS), Brunauer-Emmett-Teller (BET) method, and transmission electron microscope (TEM). The presence of Ag and WO3 peaks in the XRD and EDX spectra confirmed the synthesis of Ag-WO3 nanoparticles in the composite. The monoclinic structure of the produced WO3 samples are shown by powder X-ray diffraction patterns. The WO3-based nanocomposites' photocatalytic activity was improved by the composition of Ag and bentonite, which reduced the optical bandgap energy of WO3. The binary (Ag-WO3) nanocomposite showed improved photocatalytic activity towards the degradation of humic acid (HA) from 58% (pristine WO3) to 82% (Ag-WO3) when compared with the pristine WO3 sample under the visible light irradiation. Notably, the ternary (Ag-WO3/bent) nanocomposite demonstrated an outstanding photocatalytic efficiency of HA degradation (91.0%) under normal conditions (pH = 7.0 and 25 °C). Humic acid degradation in Ag-WO3/bent was expressed by the pseudo-first-order kinetic. To summarize, integrating Ag, WO3, bentonite, and visible light radiation to activate HA efficiently can be offered as a successful and promising technique for wastewater treatment. |
3,489 | Generic integration tools for reconfigurable laser micromachining systems | Laser micro-machining (LMM) is an attractive manufacturing process due to its intrinsic machining characteristics such as such as non-contact processing and capabilities to machine complex free-form surfaces in a wide range of materials. Nevertheless, state-of-art LMM platforms still do not offer the repeatability, reproducibility and operability of conventional machining centres, e.g. the flexibility to realise complex machining configurations and also to combine LMM with other complementary processes in hybrid manufacturing systems and production lines. The paper presents the development of three generic integration tools for improving the system-level performance of reconfigurable LMM platforms. In particular, the research reports the design and implementation of modular workpiece holding device, automated work piece setting up routine and automated strategy for multi-axis LMM machining employing rotary stages. An experimental validation of their accuracy, repeatability and reproducibility (ARR) are performed on a representative state-of-art LMM platform. The results demonstrate the flexibility and operability of the proposed tools to address important system-level issues in LMM by creating the necessary pre-requisites for achieving machining ARR better than +/- 10 mu m. (C) 2015 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved. |
3,490 | Assessing Climate Change Impact on the US East Coast Hurricane Hazard: Temperature, Frequency, and Track | This paper presents a study to assess the impact of possible future climate change on the hurricane wind hazard along the eastern coastline of the United States. Initially, climate change scenarios were coupled with state-of-the-art hurricane genesis, wind field, and tracking models to examine possible changes in hurricane intensity (maximum wind speed) and hurricane size (radius to maximum winds). A number of different postulated climate change models (IPCC scenarios) were considered. Each scenario suggested changes in sea surface temperature (SST), which is the driving parameter in most modern hurricane models. The evolution of hurricane genesis frequency was then considered both independently and jointly with hurricane intensification. State-of-the-art probabilistic event modeling and simulation techniques were used to generate 10,000 years of hurricane events under the 2005 and future climate conditions. The annual maximum wind speed distribution and the joint distribution of maximum wind speed and storm size, under 2005 and future climate scenarios, are then compared. Finally, the evolution of hurricane tracks was examined in an effort to establish a trend over time. (C) 2014 American Society of Civil Engineers. |
3,491 | Assessing the Feasibility and Implementation of Palliative Care Triggers in a Surgical Intensive Care Unit to Improve Interdisciplinary Collaboration for Patient and Family Care | Although palliative care focuses on supporting patients and families through serious illness, it is underutilized in the surgical intensive care unit (SICU). In 2020, patients in the SICU represented only 2.75% of our palliative team's consults. We hypothesize that utilization of palliative care triggers in the SICU will increase collaboration between SICU and palliative care teams and improve patient/family experiences. After reviewing our team's consultation records and the published literature, a consult trigger program was implemented for patients with a SICU length of stay >10 days, unplanned SICU readmission, or new diagnosis of metastatic cancer. A pre-intervention survey assessed SICU providers' perceptions of palliative care. Retrospective analysis evaluated qualitative and quantitative measures. 97% of SICU providers felt increased palliative care would be helpful. During the 6-month project, January 1, 2021 - June 30, 2021, our palliative team performed 27 triggered consults, representing 3.3% of the total 818 consults performed during this period and thus a 20% increase in SICU palliative consults. Triggered consults represented many primary surgical services and the most common consult reason was length-of-stay. All consults included discussions about goals of care and 16 of the 27 patients/families expressed restorative goals. Numerous notes documented family appreciation. |
3,492 | Efficacy of Olanzapine for Symptom Relief in Cancer Patients | Olanzapine is an atypical antipsychotic and is widely used for prophylaxis of chemotherapy-induced nausea and vomiting in cancer patients. Previous studies have suggested its potential efficacy for the relief of various symptoms in cancer patients, especially gastrointestinal and psychiatric symptoms. We retrospectively reviewed the prescription of olanzapine to cancer patients at our hospital. Between 2008 and 2020, olanzapine was prescribed to 41 patients for relief of symptoms associated with cancer other than prophylaxis of chemotherapy-induced nausea and vomiting. Of those patients, symptom relief was seen in 53.7%. Notably, olanzapine was effective in 13 of 14 patients with chemotherapy-induced nausea and vomiting refractory to guideline-recommended prophylaxis. Of 16 patients in whom this symptom was not relieved by olanzapine, 13 (81.3%) continued taking olanzapine even after it was judged ineffective. No treatment-related adverse events were seen in this study. Our observation implies good efficacy of olanzapine for refractory chemotherapy-induced nausea and vomiting and a tendency to continue olanzapine even in those for whom it was ineffective. |
3,493 | Cognitive and encrypted communications: state of the art and a new approach for frequency-agile filters | Several communication techniques are investigated in the first part of this paper: software radio, cognitive radio and encrypted communications. State of the art of research on agile and reconfigurable filters, passive as well as active, necessary for transceivers is then made and various tables for comparison are given. In the third part, a new theory for a 2(nd)-order frequency-agile filter is introduced. The center frequency of the filter is proportional to the gain of a feedback amplifier and thus can be tuned over a wide frequency range. This new theory is thereafter generalized to the n(th)-class leading to a center frequency proportional to (A)(n/2). Simulation results of band pass agile filters in current mode and made from second-generation current controlled conveyors (CCCII+) in 0.25 mu m SiGe BiCMOS technology are given for n = 1 and n = 2. These simulation results along with results of measurements carried out on the fabricated filters entirely confirm the new approach. They also highlight the improvements to be expected for cognitive and encrypted communications. |
3,494 | Soft Frequency Reuse With Allocation of Resource Plans Based on Machine Learning in the Networks With Flying Base Stations | Flying base stations (FlyBSs) enable ubiquitous communications in the next generation mobile networks with a flexible topology. However, a deployment of the FlyBSs intensifies interference, which can result in a degradation in the throughput of cell-edge users. In this paper, we introduce a flexible soft frequency reuse (F-SFR) that enables a self-organization of a common SFR in the networks with an unpredictable and dynamic topology with the FlyBSs. We propose a graph theory-based algorithm for an allocation of resource plans, which is understood as a bandwidth allocation and a transmission power setting in the context of SFR. Furthermore, we introduce a low-complexity implementation of the proposed resource allocation using deep neural network (DNN) to significantly reduce the computation complexity. We show that the proposed F-SFR increases the throughput of cell-edge users by 16% to 26% and, at the same time, improves the satisfaction of the cell-edge users by up to 25% compared to the state-of-the-art solutions. We also demonstrate that the proposed scheme ensures a higher fairness in the throughput among the users with respect to the state-of-the-art solutions. The implementation via DNN also outperforms all state-of-the-art solutions despite its very low complexity. |
3,495 | Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images | Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great importance for improved diagnosis, growth rate prediction, and treatment planning. However, automating this process is challenging due to the presence of severe partial volume effect and considerable variability in tumor structures, as well as imaging conditions, especially for the gliomas. In this paper, we introduce a new methodology that combines random forests and active contour model for the automated segmentation of the gliomas from multimodal volumetric MR images. Specifically, we employ a feature representations learning strategy to effectively explore both local and contextual information from multimodal images for tissue segmentation by using modality specific random forests as the feature learning kernels. Different levels of the structural information is subsequently integrated into concatenated and connected random forests for gliomas structure inferring. Finally, a novel multiscale patch driven active contour model is exploited to refine the inferred structure by taking advantage of sparse representation techniques. Results reported on public benchmarks reveal that our architecture achieves competitive accuracy compared to the state-of-the-art brain tumor segmentation methods while being computationally efficient. |
3,496 | Organic Solar Cells-The Path to Commercial Success | Organic solar cells have the potential to become the cheapest form of electricity, beating even silicon photovoltaics. This article summarizes the state of the art in the field, highlighting research challenges, mainly the need for an efficiency increase as well as an improvement in long-term stability. It discusses possible current and future applications, such as building integrated photovoltaics or portable electronics. Finally, the environmental footprint of this renewable energy technology is evaluated, highlighting the potential to be the energy generation technology with the lowest carbon footprint of all. |
3,497 | Exploring the Influence of Perceived Epidemic Severity and Risk on Well-Being in Nature-Based Tourism-Taking China's Post-1990 Generation as an Example | The impacts of perceived risk (PR) and perceived severity (PS) on personal well-being (WB) during the COVID-19 epidemic have often been overlooked, especially in the context of China's post-1990 generation. Therefore, this research intends to explore how members of the post-1990 generation obtain personal benefits through PR through the Attention Restoration Theory (ART). A total of 276 online questionnaires were collected by snowball sampling and analyzed in SPSS 21.0. This research found that PR, NC, and the ART are mediating variables which affect WB. The higher the PR, the more likely it is that the post-1990 generation will engage in nature tourism. These discoveries undoubtedly demonstrate a breakthrough in the theoretical gap, and provide a proposal for the sustainable development of China's tourism industry. |
3,498 | Improving visual odometry pipeline with feedback from forward and backward motion estimates | Estimating motion from visual cameras has become a promising art in the area of autonomous navigation and constant efforts are being made toward improving the accuracy of these estimates. In this paper, an improvement in the visual odometry algorithm is proposed that takes cues from both the forward and backward motion estimates. An error is formulated based on the consistency which measures the difference between the forward and backward motion. This error is used in a feedback mechanism to improve the triangulated 3D point estimates, thereby improving the pose estimate. Additionally, a novel means to incorporate information from multiple stereo camera setups has been devised to improve the pose estimate. The proposed scheme of joint forward-backward VO with multiple cameras and feedback mechanism (JFBVO-FM) is validated on two publicly available datasets having different environmental conditions and camera motion, that is, KITTI and EuRoC Micro Aerial Vehicle (MAV) datasets. The results are analyzed both qualitatively and quantitatively, and the proposed scheme is found to perform better as compared to the state-of-the-art methods in most of the sequences. |
3,499 | Representation and Preservation of Heritage Crafts | This work regards the digital representation of tangible and intangible dimensions of heritage crafts, towards craft preservation. Based on state-of-the-art digital documentation, knowledge representation and narrative creation approach are presented. Craft presentation methods that use the represented content to provide accurate, intuitive, engaging, and educational ways for HC presentation and appreciation are proposed. The proposed methods aim to contribute to HC preservation, by adding value to the cultural visit, before, and after it. |
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