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5,600 | Making Use of Auditory Models for Better Mimicking of Normal Hearing Processes With Cochlear Implants: The SAM Coding Strategy | Mimicking the human ear on the basis of auditory models has become a viable approach in many applications by now. However, only a few attempts have been made to extend the scope of physiological ear models to be employed in cochlear implants (CI). Contemporary CI systems rely on much simpler filter banks and simulate the natural signal processing of a healthy cochlea to only a very limited extent. When looking at rehabilitation outcomes, current systems seem to have reached their peak potential, which signals the need for better algorithms and/or technologies. In this paper, we present a novel sound processing strategy, SAM (Stimulation based on Auditory Modeling), that is based on neurophysiological models of the human ear and can be employed in auditory prostheses. It incorporates active cochlear filtering (basilar membrane and outer hair cells) along with the mechanoelectrical transduction of the inner hair cells, so that several psychoacoustic phenomena are accounted for inherently. Although possible, current implementation does not make use of parallel stimulation of the electrodes, which matches state-of-the-art CI hardware. This paper elaborates on SAM's signal processing and provides a computational evaluation of the strategy. Results show that aspects of normal cochlear processing that are missing in common strategies can be replicated by SAM. This is supposed to improve overall CI user performance, which we have at least partly proven in a pilot study with implantees. |
5,601 | Patterns of reported infection and reinfection of SARS-CoV-2 in England | One of the key features of any infectious disease is whether infection generates long-lasting immunity or whether repeated reinfection is common. In the former, the long-term dynamics are driven by the birth of susceptible individuals while in the latter the dynamics are governed by the speed of waning immunity. Between these two extremes a range of scenarios is possible. During the early waves of SARS-CoV-2, the underlying paradigm was for long-lasting immunity, but more recent data and in particular the 2022 Omicron waves have shown that reinfection can be relatively common. Here we investigate reported SARS-CoV-2 cases in England, partitioning the data into four main waves, and consider the temporal distribution of first and second reports of infection. We show that a simple low-dimensional statistical model of random (but scaled) reinfection captures much of the observed dynamics, with the value of this scaling, k, providing information of underlying epidemiological patterns. We conclude that there is considerable heterogeneity in risk of reporting reinfection by wave, age-group and location. The high levels of reinfection in the Omicron wave (we estimate that 18% of all Omicron cases had been previously infected, although not necessarily previously reported infection) point to reinfection events dominating future COVID-19 dynamics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics". |
5,602 | Blunt dopamine transmission due to decreased GDNF in the PFC evokes cognitive impairment in Parkinson's disease | Studies have found that the absence of glial cell line-derived neurotrophic factor may be the primary risk factor for Parkinson's disease. However, there have not been any studies conducted on the potential relationship between glial cell line-derived neurotrophic factor and cognitive performance in Parkinson's disease. We first performed a retrospective case-control study at the Affiliated Hospital of Xuzhou Medical University between September 2018 and January 2020 and found that a decreased serum level of glial cell line-derived neurotrophic factor was a risk factor for cognitive disorders in patients with Parkinson's disease. We then established a mouse model of Parkinson's disease induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and analyzed the potential relationships among glial cell line-derived neurotrophic factor in the prefrontal cortex, dopamine transmission, and cognitive function. Our results showed that decreased glial cell line-derived neurotrophic factor in the prefrontal cortex weakened dopamine release and transmission by upregulating the presynaptic membrane expression of the dopamine transporter, which led to the loss and primitivization of dendritic spines of pyramidal neurons and cognitive impairment. In addition, magnetic resonance imaging data showed that the long-term lack of glial cell line-derived neurotrophic factor reduced the connectivity between the prefrontal cortex and other brain regions, and exogenous glial cell line-derived neurotrophic factor significantly improved this connectivity. These findings suggested that decreased glial cell line-derived neurotrophic factor in the prefrontal cortex leads to neuroplastic degeneration at the level of synaptic connections and circuits, which results in cognitive impairment in patients with Parkinson's disease. |
5,603 | Interpretable temporal-spatial graph attention network for multi-site PV power forecasting | Accurate forecasting of photovoltaic (PV) and wind production is crucial for the integration of more renewable energy sources into the power grid. To address the limited resolution and costs of methods based on numerical weather predictions (NWP), we take PV production data as main input for forecasting. Since PV power is affected by weather and cloud dynamics, we model spatio-temporal correlations between production data by representing PV systems as nodes of a dynamic graph and embedding production data, geographical information and clear-sky irradiance as signals on that graph. We introduce a new temporal-spatial multi -windows graph attention network (TSM-GAT) for predicting future PV power production. TSM-GAT can adapt to the dynamics of the problem, by learning different graphs over time. It consists of temporal attention with an overlapping-window mechanism that finds the temporal correlations and spatial attention with a multi-window mechanism, which captures different dynamical spatio-temporal correlations for different parts of the forecasting horizon. Thus, it is possible to interpret which PV stations have the most influence when making a prediction for short-, medium-and long-term intra-day forecasts. TSM-GAT outperforms multi-site state-of-the-art models for four to six hours ahead predictions, with average NRMSE 12.4% and 10.5% on a real and synthetic dataset, respectively. Furthermore, it outperforms state-of-the-art models that use NWP as inputs for up to five hours ahead predictions. TSM-GAT yields predicted signals with a closer shape to ground truth than state-of-the-art models, which indicates that it is better at capturing cloud motion and may lead to better generalization capabilities. |
5,604 | Performance Improvement in Geographic Routing for Vehicular Ad Hoc Networks | Geographic routing is one of the most investigated themes by researchers for reliable and efficient dissemination of information in Vehicular Ad Hoc Networks (VANETs). Recently, different Geographic Distance Routing (GEDIR) protocols have been suggested in the literature. These protocols focus on reducing the forwarding region towards destination to select the Next Hop Vehicles (NHV). Most of these protocols suffer from the problem of elevated one-hop link disconnection, high end-to-end delay and low throughput even at normal vehicle speed in high vehicle density environment. This paper proposes a Geographic Distance Routing protocol based on Segment vehicle, Link quality and Degree of connectivity (SLD-GEDIR). The protocol selects a reliable NHV using the criteria segment vehicles, one-hop link quality and degree of connectivity. The proposed protocol has been simulated in NS-2 and its performance has been compared with the state-of-the-art protocols: P-GEDIR, J-GEDIR and V-GEDIR. The empirical results clearly reveal that SLD-GEDIR has lower link disconnection and end-to-end delay, and higher throughput as compared to the state-of-the-art protocols. It should be noted that the performance of the proposed protocol is preserved irrespective of vehicle density and speed. |
5,605 | Isolation and Characterization of Fetal Liver Hematopoietic Stem Cells | Hematopoietic stem cells (HSCs) are responsible for the generation and maintenance of pools of multipotent precursors that ultimately give rise to all fully differentiated blood and immune cells. Proper identification and isolation of HSCs for functional analysis has greatly facilitated our understanding of both normal and abnormal adult hematopoiesis. Whereas adult hematopoiesis in mice and humans is driven by quiescent HSCs that reside almost exclusively within the bone marrow (BM), developmental hematopoiesis is characterized by a series of transient progenitors driving waves of increasingly mature hematopoietic cell production that occur across multiple anatomical sites. These waves of hematopoietic cell production are also responsible for the generation of distinct immune cell populations during development that persist into adulthood and contribute uniquely to adult immunity. Therefore, methods to properly isolate and characterize fetal progenitors with high purity across development become increasingly important not only for defining developmental hematopoietic pathways, but also for understanding the contribution of developmental hematopoiesis to the immune system. Here, we describe and discuss methods and considerations for the isolation and characterization of HSCs from the fetal liver, the primary hematopoietic organ during fetal development. |
5,606 | PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition | Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. Recently, neural networks have been applied to tackle audio pattern recognition problems. However, previous systems are built on specific datasets with limited durations. Recently, in computer vision and natural language processing, systems pretrained on large-scale datasets have generalized well to several tasks. However, there is limited research on pretraining systems on large-scale datasets for audio pattern recognition. In this paper, we propose pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature. Our best PANN system achieves a state-of-the-art mean average precision (mAP) of 0.439 on AudioSet tagging, outperforming the best previous system of 0.392. We transfer PANNs to six audio pattern recognition tasks, and demonstrate state-of-the-art performance in several of those tasks. We have released the source code and pretrained models of PANNs: https://github.com/qiuqiangkong/audioset_tagging_cnn. |
5,607 | Signs associated with figurative representations Aurignacian. Examples from Grotte Chauvet and the Swabian Jura | Signs in Upper Palaeolithic art provide an important criterion of assessing cultural affinities or differences. The Swabian Jura in southern Germany and Grotte Chauvet in the Ardeche region in southern France - both inscribed in the list of the UNESCO world heritage - supply the most important examples of Aurignacian art known so far. Of a particular interest in this respect are characteristic types of signs which can be associated with depictions of animals. This paper examines the distinctive ways in which this association is realized in these two regions, including the question which type of signs was employed. Despite of the small data base and the lack of sophisticated statistical tests, this study may help to provide first hints to the question if the Aurignacian groups of the Ardeche and the Swabian Jura, whose areas are linked by important river systems, used similar or different sets of signs and in which animal contexts these signs were used. These results may give indications to what extent these groups were part of the same or different cultural entities and subsequently shared feelings of a common ethnicity and belief-world or not. (c) 2018 Elsevier Ltd and INQUA. All rights reserved. |
5,608 | Approximate Sparse Multinomial Logistic Regression for Classification | We propose a new learning rule for sparse multinomial logistic regression (SMLR). The new rule is the generalization of the one proposed in the pioneering work by Krishnapuram et al. In our proposed method, the parameters of SMLR are iteratively estimated from log-posterior by using some approximations. The proposed update rule provides a faster convergence compared to the state-of the-art methods used for SMLR parameter estimation. The estimated parameters are tested on the pixel-based classification of hyperspectral images. The experimental results on real hyperspectral images show that the classification accuracy of proposed method is also better than those of the state-of-the-art methods. |
5,609 | Teamwork in the performing arts | This paper addresses the nature of teams and teamwork in the performing arts, including symphony, chamber orchestra, chorus, and jazz, as well as musical theater straight theatre, improv, ballet, and puppetry. The results of an interview study of performing arts leaders in these domains are reported. These results suggest an "ecology" of performance. The characteristics of this ecology strongly influence the nature and roles of teams, as well as how teams are created and supported. Potential relationships among the attributes of this characterization are discussed. |
5,610 | Correlation-Weighted Sparse Representation for Robust Liver DCE-MRI Decomposition Registration | Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast agent from original liver DCE-MR images. This framework allows the robust registration of motion components over time without intensity variances. Existing sparse coding techniques recover a 3D image containing only contrast agents (named contrast enhancement component) from a manually labeled dictionary, whose column has the same size with the original 3D volume (3D-t mode). The high dimension of the recovery target (3D volume) and the indistinguishability between the unenhanced and enhanced images make accurate coding difficult. In this paper, we predefine an ideal time-intensity curve containing only contrast agents (named contrast agent curve) and recover it from the transpose dictionary (t-3D mode), whose column has been updated into the original time-intensity curves. The low dimension of the target (1D curve) and the significant intergroup difference between contrast agent curves and non-contrast agent curves can estimate a series of pure contrast agent curves. A "correlation-weighted"constraint is introduced for the selection of a coding subset with more contrast agent curves, leading to an efficient and accurate sparse recovery process. Then, the contrast enhancement component can be estimated by the solved sparse coefficients' map and the ideal curve and subtracted from the original DCE-MRI. Finally, we register the de-enhanced images and apply the obtained deformation fields for the original DCE-MRI to achieve the goal of motion correction. We conduct the experiments on both simulated and real liver DCE-MRI data. Compared with other state-of-the-art DCE-MRI registration methods, the experimental results show that our method achieves a better registration performance with less computational efficiency. |
5,611 | Accelerating Decision Tree Based Traffic Classification on FPGA and Multicore Platforms | Machine learning (ML) algorithms have been shown to be effective in classifying a broad range of applications in the Internet traffic. In this paper, we propose algorithms and architectures to realize online traffic classification using flow level features. First, we develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL (Minimum Description Length) discretization algorithm. It achieves an overall accuracy of 97.92 percent for classifying eight major applications. Next we propose approaches to accelerate the classifier on FPGA (Field Programmable Gate Array) and multicore platforms. We optimize the original classifier by merging it with discretization. Our implementation of this optimized decision tree achieves 7500+Million Classifications Per Second (MCPS) on a state-of-the-art FPGA platform and 75-150 MCPS on two state-of-the-art multicore platforms. We also propose a divide and conquer approach to handle imbalanced decision trees. Our implementation of the divide-and-conquer approach achieves 10,000+MCPS on a state-of-the-art FPGA platform and 130-340 MCPS on two state-of-the-art multicore platforms. We conduct extensive experiments on both platforms for various application scenarios to compare the two approaches. |
5,612 | Association of Plasma Metabolites and Lipoproteins with Rh and ABO Blood Systems in Healthy Subjects | This study investigated the associations between the levels of 27 plasma metabolites, 114 lipoprotein parameters, determined using nuclear magnetic resonance spectroscopy, and the ABO blood groups and the Rhesus (Rh) blood system in a cohort of n = 840 Italian healthy blood donors of both sexes. We observed good multivariate discrimination between the metabolomic and lipoproteomic profiles of subjects with positive and negative Rh. In contrast, we did not observe significant discrimination for the ABO blood group pairwise comparisons, suggesting only slight metabolic differences between these group-specific metabolic profiles. We report univariate associations (P-value < 0.05) between the subfraction HDL1 related to Apo A1, the subfraction HDL2 related to cholesterol and phospholipids, and the particle number of LDL2 related to free cholesterol, cholesterol, phospholipids, and Apo B and the ABO blood groups; we observed association of the lipid main fraction LDL4 related to free cholesterol, triglycerides, and Apo B; creatine; the particle number of LDL5; the subfraction LDL5 related to Apo B; the particle number of LDL4; and the subfraction LDL4 related to Apo B with Rh blood factors. These results suggest blood group-dependent (re)shaping of lipoprotein metabolism in healthy subjects, which may provide relevant information to explain the differential susceptibility to certain diseases observed in different blood groups. |
5,613 | The Innovative Application of Visual Communication Design in Modern Art Design | In the context of the rapid development of economy and culture, people's requirements for material and culture are constantly increasing, and the relationship between graphic design and human life is also increasingly close. With the continuous development of Internet of Things technology, information exchange between people and things can be realized by using various sensing devices, and innovative modern art can be established. Therefore, the focus of culture has gradually shifted to the field of visualization. However, in the visual communication design, the product design method of "mainly two-dimensional plan and supplemented by three-dimensional model", on the one hand, cannot maximize the intuitive effect of three-dimensional modeling, thus affecting the efficiency of design; on the other hand, problems such as information incoordination and resource waste are likely to occur. In short, people's requirements for design have changed from a two-dimensional plane space to a three-dimensional space, and a two-dimensional plane design can no longer meet people's growing demand for artistic design. Aiming at tackling these problems, this paper proposed to establish a visual communication system based on artificial intelligence (AI). Through this method, the image can be made clearer and has a larger field of view and magnification. At the same time, the system was applied to modern art design, which is a type of innovation. The experimental results showed that the maximum distortion of the system designed in this paper was approximately 15%, and the maximum distortion of the traditional sample was about 20%. Compared with conventional samples, this system has great advantages in graphics transformation. In addition, the chromatic aberration of the optical system can be corrected to improve the imaging effect. |
5,614 | Patterning principles of morphogen gradients | Metazoan embryos develop from a single cell into three-dimensional structured organisms while groups of genetically identical cells attain specialized identities. Cells of the developing embryo both create and accurately interpret morphogen gradients to determine their positions and make specific decisions in response. Here, we first cover intellectual roots of morphogen and positional information concepts. Focusing on animal embryos, we then provide a review of current understanding on how morphogen gradients are established and how their spans are controlled. Lastly, we cover how gradients evolve in time and space during development, and how they encode information to control patterning. In sum, we provide a list of patterning principles for morphogen gradients and review recent advances in quantitative methodologies elucidating information provided by morphogens. |
5,615 | HANAC Syndrome Col4a1 Mutation Causes Neonate Glomerular Hyperpermeability and Adult Glomerulocystic Kidney Disease | Hereditary angiopathy, nephropathy, aneurysms, and muscle cramps (HANAC) syndrome is an autosomal dominant syndrome caused by mutations in COL4A1 that encodes the α1 chain of collagen IV, a major component of basement membranes. Patients present with cerebral small vessel disease, retinal tortuosity, muscle cramps, and kidney disease consisting of multiple renal cysts, chronic kidney failure, and sometimes hematuria. Mutations producing HANAC syndrome localize within the integrin binding site containing CB3[IV] fragment of the COL4A1 protein. To investigate the pathophysiology of HANAC syndrome, we generated mice harboring the Col4a1 p.Gly498Val mutation identified in a family with the syndrome. Col4a1 G498V mutation resulted in delayed glomerulogenesis and podocyte differentiation without reduction of nephron number, causing albuminuria and hematuria in newborns. The glomerular defects resolved within the first month, but glomerular cysts developed in 3-month-old mutant mice. Abnormal structure of Bowman's capsule was associated with metalloproteinase induction and activation of the glomerular parietal epithelial cells that abnormally expressed CD44,α-SMA, ILK, and DDR1. Inflammatory infiltrates were observed around glomeruli and arterioles. Homozygous Col4a1 G498V mutant mice additionally showed dysmorphic papillae and urinary concentration defects. These results reveal a developmental role for the α1α1α2 collagen IV molecule in the embryonic glomerular basement membrane, affecting podocyte differentiation. The observed association between molecular alteration of the collagenous network in Bowman's capsule of the mature kidney and activation of parietal epithelial cells, matrix remodeling, and inflammation may account for glomerular cyst development and CKD in patients with COL4A1-related disorders. |
5,616 | Subversion-Resistant and Consistent Attribute-Based Keyword Search for Secure Cloud Storage | Secure cloud search service allows resource-constrained clients to effectively search over encrypted cloud storage. Towards enabling owner-enforced search authorization, the notion of attribute-based keyword search (ABKS) has been introduced and widely deployed in practice. To enhance traditional security of ABKS, two state-of-the-art solutions are presented to address keyword guessing attacks or setup inconsistency for secret key. Nevertheless, they have not simultaneously considered the following threats to a data user: (i) inconsistent secret key/cipher-index caused by outside dishonest authority and/or data owner; (ii) algorithm substitution attacks (ASA) launched by inside adversarial eavesdropping. These attacks may unfortunately lead to cloud data breach and user information exposure. To tackle such outside and inside threats, we introduce subversion-resistance and consistency for secure and fine-grained cloud document search services. In particular, we propose a consistent ABKS system with cryptographic reverse firewalls (CRF). Technically, we refer to verifiable functional encryption and employ non-interactive zero-knowledge proofs of discrete logarithm equality to ensure strong input consistency for ABKS. In addition, we build a trusted CRF zone for sanitizing algorithm outputs against ASA attacks. Moreover, we formalize the security model and formally prove security of our system. To clarify practical performance, we implement state-of-the-art solutions and our system in real cloud environment based on Enron dataset. The results show that our system achieves more enhanced security properties without obviously sacrificing performance. In particular, our system achieves comparable time and storage cost for document-index encryption and document search, as compared to state-of-the-art solutions. |
5,617 | Lipid bands of approx. 1740 cm-1 as spectral biomarkers and image of tissue oxidative stress | Studies with the use of FTIR and FTR methods to find spectroscopic biomarkers within the 1740 cm-1 band of pathological tissues found that oxidative stress, including damage to epidermis and structural changes in pathological amnion and placenta tissue, are associated with the occurrence of products of lipid peroxidation and have impact on fluidity and transport function of membranes. In particular, the findings show that the absence of a marker lipid band of approx. 1743 cm-1 and the occurrence of a minimum of 1764 cm-1 (FTIR) and 1734 cm-1 (FTR) testify to the integrity and absence of damage in the allogeneic dermis, while the presence the 1743 or 1747 cm-1 bands indicates denaturation of the thermally or electrically burned epidermis. The absence of a marker lipid band of approx. 1736-1740 cm-1 for a healthy placental and amniotic tissue and the presence of a band of 1740 cm-1 indicate placental gestosis, while the presence of a band of 1742 cm-1 indicates hypotrophy. The 1738 cm-1 bands indicate amniotic macrosomia. Structural changes caused by tissue modification with antioxidants, which were observed on individual samples: the L-ascorbic acid (presence of a lipid band marker at a frequency of 1755 cm-1), sodium ascorbate (disappearance of the marker band), orthosilicic acid (disappearance or decrease in the intensity of the marker band with a decrease in the concentration of the modifier), as well as graphene oxide (separation of the marker lipid band of 1755 cm-1), inform us about the effect of modifiers on the tissue repair process. The studies also tracked spectral changes identified in serum. Withing the range of the lipid band and the amide I and II bands (α → β conversion), there are clear differences between normal and pathological serum lyophilisates and a sample analyzed from the solution. |
5,618 | Video cloning for paintings via artistic style transfer | In the past, visual arts usually represented the static art like paintings, photography and sculptures. In recent years, many museums, artwork galleries, and even art exhibitions demonstrated dynamic artworks for visitors to relish. The most famous dynamic artwork is "The moving painting of Along the River During the Qingming Festival". Nevertheless, it took 2 years to complete this work. They had to plan each action for every character at first, then drew each video frame by animators. Finally, it could achieve seamless stitching by using lots of projectors to render scene on the screen. In our research, we develop a method for generating animated paintings. It only needs a number of videos on a network of existing databases and requires users to perform some simple auxiliary operations to achieve the effect of animation synthesis. First, our system lets users select an object with the same class from the first video frame. We then employ random forests as learning algorithm to retrieve from a video the object which users want to insert into an artwork. Second, we utilize style transferring, which enables the video frames to be consistent with the style of painting. At last, we use the seamless image cloning algorithm to yield seamless synthesizing result. Our approach allows different users to synthesize animating paintings up to their selected styled video frames. The resulting work not only maintains the original author's painting style, but also generates a variety of artistic conception for people to enjoy. |
5,619 | Dielectric Resonator Antennas: A Historical Review and the Current State of the Art | This article presents a historical review of the research carried out on dielectric resonator antennas (DRAs) over the last three decades. Major research activities in each decade are highlighted. The current state of the art of dielectric-resonator-antenna technology is then reviewed. The achievable performance of dielectric resonator antennas designed for compactness, wide impedance bandwidth, low profiles, circular polarization, or high gain are illustrated. The latest developments in dielectric-resonator-antenna arrays and fabrication techniques are also examined. |
5,620 | Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD) | Defects are the primary problem affecting steel product quality in the steel industry. The specific challenges in developing detect defectors involve the vagueness and tiny size of defects. To solve these problems, we propose incorporating super-resolution technique, sequential feature pyramid network, and boundary localization. Initially, the ensemble of enhanced super-resolution generative adversarial networks (ESRGAN) was proposed for the preprocessing stage to generate a more detailed contour of the original steel image. Next, in the detector section, the latest state-of-the-art feature pyramid network, known as De-tectoRS, utilized the recursive feature pyramid network technique to extract deeper multi-scale steel features by learning the feedback from the sequential feature pyramid network. Finally, Side-Aware Boundary Localization was used to precisely generate the output prediction of the defect detectors. We named our approach EnsGAN-SDD. Extensive experimental studies showed that the proposed methods improved the defect detector's performance, which also surpassed the accuracy of state-of-the-art methods. Moreover, the proposed EnsGAN achieved better performance and effectiveness in processing time compared with the original ESRGAN. We believe our innovation could significantly contribute to improved production quality in the steel industry. |
5,621 | Evolution of polyphenolic, anthocyanin, and organic acid components during coinoculation fermentation (simultaneous inoculation of LAB and yeast) and sequential fermentation of blueberry wine | This research aims to investigate the effects of both sequential fermentation and coinoculation fermentation with yeast and lactic acid bacterial (LAB) on the dynamics of changes in basic quality parameters and organic acid, anthocyanin, and phenolic components as well as antioxidant activity during the fermentation of blueberry. The coculture-fermented blueberry wine showed significant decreases in total phenolics, flavonoids, and anthocyanins,by 23.9%, 15.9%, and 13.7%, respectively, as compared with those before fermentation Fermentation changed the contents of organic acids in each group, with a more than 7-fold increase in lactic acid contents as well as a more than 4-fold reduction in quinic acid and malic acid contents. The content of all investigated anthocyanins first increased and then decreased. Moreover, different fermentation strategies exerted a profound influence on the dynamic change in phenolic components during fermentation; specifically, most of the phenolic acids showed a trend of increasing first, then decreasing, and finally increasing. Gallic acid, p-coumaric acid, quercetin, and myricetin were increased by 116.9%, 130.1%, 127.2% and 177.6%, respectively, while syringic acid, ferulic acid, cinnamic acid, and vanillic acid were decreased by 49.5%, 68.5%, and 37.1% in sequentially fermented blueberry wine. Coinoculation fermentation with yeast and LAB produces faster dynamic variations and higher organic acid, anthocyanin, and phenolic profiles than sequential inoculation fermentation. PRACTICAL APPLICATION: In this work, brewing technology of sequential fermentation and coinoculation fermentation with yeast and LAB (Lactobacillus plantarum SGJ-24 and Oenococcus oeni SD-2a) was adopted to ferment blueberry wine. This is an innovative technology of fruit wine brewing technology to produce wine products. Compared with traditional sequential brewing, simultaneous inoculation brewing can significantly accelerate the brewing process of fruit wine and slightly improve the quality of fruit wine in terms of active ingredients. |
5,622 | Tertiary lymphoid tissues in kidney diseases: a perspective for the pediatric nephrologist | Chronic kidney disease (CKD) is a major public health problem worldwide. In the pediatric population, CKD is also an important health issue because it causes several comorbid conditions that can have long-term consequences beyond the pediatric age. Chronic inflammation is a common pathological feature of CKD, irrespective of etiology, and leads to maladaptive repair and kidney dysfunction. Tertiary lymphoid tissues (TLTs) are ectopic lymphoid structures that develop in non-lymphoid organs under chronic inflammation caused by pathological conditions, including infections, autoimmune diseases, and cancers. TLTs in the kidneys have been poorly researched due to the lack of an animal model. We have recently found that, in aged but not young mice, TLTs develop in multiple kidney injury models, and the analysis of age-dependent TLTs has brought about several novel insights into the development and pathogenic impacts of TLTs in the kidney. Age-dependent TLT formation is also observed in human kidneys. In addition to aged kidneys, TLT development is also reported in several human kidney diseases including kidney allografts, lupus nephritis, and IgA nephropathy in both adults and children. In this review, we describe the novel findings on TLTs in the kidney obtained mainly from the analysis of age-dependent TLTs and discuss the clinical relevance of TLTs in kidney diseases. |
5,623 | Printed circuit board recycling: A state-of-the-art survey | This survey is done with an intention of providing a clear and comprehensive review of current practices and recent developments in the area of printed circuit boards (PCB) recycling, The aim of this paper is to be a reference for research and implementation for PCB recycling process. Original information is collected from the companies engaged in PCB recycling industry and articles published after 1990. The paper gives an overview of PCB structure, material composition and different recycling processes. |
5,624 | Enhanced molecular mobility of ordinarily structured regions drives polyglutamine disease | Polyglutamine expansion is a hallmark of nine neurodegenerative diseases, with protein aggregation intrinsically linked to disease progression. Although polyglutamine expansion accelerates protein aggregation, the misfolding process is frequently instigated by flanking domains. For example, polyglutamine expansion in ataxin-3 allosterically triggers the aggregation of the catalytic Josephin domain. The molecular mechanism that underpins this allosteric aggregation trigger remains to be determined. Here, we establish that polyglutamine expansion increases the molecular mobility of two juxtaposed helices critical to ataxin-3 deubiquitinase activity. Within one of these helices, we identified a highly amyloidogenic sequence motif that instigates aggregation and forms the core of the growing fibril. Critically, by mutating residues within this key region, we decrease local structural fluctuations to slow ataxin-3 aggregation. This provides significant insight, down to the molecular level, into how polyglutamine expansion drives aggregation and explains the positive correlation between polyglutamine tract length, protein aggregation, and disease severity. |
5,625 | Shadow detection and removal for moving objects using Daubechies complex wavelet transform | Shadow detection and removal is a challenging problem for several computer vision applications because shadow always makes object misclassified. A number of shadow detection and removal algorithms have been reported, and some of these algorithms require manual calibration in terms of some hypothesis and predefined specific parameters whereas others do not require manual intervention, but fail to give accurate result in various lighting and environmental conditions. This paper introduces a novel method for shadow detection and removal with Daubechies complex wavelet domain. Daubechies complex wavelet transform has been used in the proposed algorithm due to its strong edge detection, approximate shift-invariance as well as approximate rotation invariance properties. For shadow detection, we have proposed a new threshold in the form of coefficient of variation of wavelet coefficients. This threshold is automatically determined and does not require any manual calibration and training. Results of shadow detection and removal from moving objects after applying the proposed method are compared with the those of other state-of-the-art methods in terms of visual performance and number of quantitative performance evaluation parameters. The proposed method is found to perform better than other state-of-the-art methods. |
5,626 | Navigation With Time Limits in Transportation Networks: A Fourth Moment Approach | This paper investigates the stochastic on-time arrival (SOTA) problem in transportation networks. We propose a fourth moment approach (FMA), which calculates the tight lower bound of a given routing policy's on-time-arrival probability, through estimating the first four moments of the policy's travel time. Then, we employ the generalized policy iteration (GPI) scheme to gradually improve the policy towards the optimal one. Different from state-of-the-art algorithms for the SOTA problem, which require the full travel time distribution and usually incur high computational cost due to the convolution integration operation, FMA only requires the moments of travel-time statistics, which are easily estimated from the statistics perspective. Moreover, the algorithm's computational complexity analysis indicates the relatively light computational load requirement of FMA. Experimental results in a range of transportation networks show FMA's superior performance over state of the arts. |
5,627 | Compressive sensing reconstruction via decomposition | When recovering images from a small number of Compressive Sensing (CS) measurements, a problem arises whereby image features (e.g., smoothness, edges, textures) cannot be preserved well in reconstruction, especially textures at small-scale. Since the missing information still remains in the residual measurement, we propose a novel Decomposition-based CS-recovery framework (DCR) which utilizes residual reconstruction and state-of-the-art filters. The proposed method iteratively refines residual measurement which is closely related to the denoise-boosting techniques. DCR is further incorporated with a weighted total variation and nonlocal structures in the gradient domain as priors to form the proposed Decomposition based Texture preserving Reconstruction (DETER). We subsequently demonstrate robustness of the proposed framework to noise and its superiority over the other state-of-the-art methods, especially at low subrates. Its fast implementation based on the split Bregman technique is also presented. |
5,628 | Learn to Threshold: ThresholdNet With Confidence-Guided Manifold Mixup for Polyp Segmentation | The automatic segmentation of polyp in endoscopy images is crucial for early diagnosis and cure of colorectal cancer. Existing deep learning-based methods for polyp segmentation, however, are inadequate due to the limited annotated dataset and the class imbalance problems. Moreover, these methods obtained the final polyp segmentation results by simply thresholding the likelihood maps at an eclectic and equivalent value (often set to 0.5). In this paper, we propose a novel ThresholdNet with a confidence-guided manifold mixup (CGMMix) data augmentation method, mainly for addressing the aforementioned issues in polyp segmentation. The CGMMix conducts manifold mixup at the image and feature levels, and adaptively lures the decision boundary away from the under-represented polyp class with the confidence guidance to alleviate the limited training dataset and the class imbalance problems. Two consistency regularizations, mixup feature map consistency (MFMC) loss and mixup confidence map consistency (MCMC) loss, are devised to exploit the consistent constraints in the training of the augmented mixup data. We then propose a two-branch approach, termed ThresholdNet, to collaborate the segmentation and threshold learning in an alternative training strategy. The threshold map supervision generator (TMSG) is embedded to provide supervision for the threshold map, thereby inducing better optimization of the threshold branch. As a consequence, ThresholdNet is able to calibrate the segmentation result with the learned threshold map. We illustrate the effectiveness of the proposed method on two polyp segmentation datasets, and our methods achieved the state-of-the-art result with 87.307% and 87.879% dice score on the EndoScene dataset and the WCE polyp dataset. The source code is available at https://github.com/Guo-Xiaoqing/ThresholdNet. |
5,629 | A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition | Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery. |
5,630 | A toolkit for wide-screen dynamic area of interest measurements using the Pupil Labs Core Eye Tracker | Eye tracking measurements taken while watching a wide field screen are challenging to perform. Commercially available remote eye trackers typically do not measure more than 35 degrees in eccentricity. Analysis software was developed using the Pupil Core Eye Tracking data to analyze viewing behavior under circumstances as natural as possible, on a 1.55-m-wide screen allowing free head movements. Additionally, dynamic area of interest (AOI) analyses were performed on data of participants viewing traffic scenes. A toolkit was created including software for simple allocation of dynamic AOIs (semi-automatically and manually), measurement of parameters such as dwell times and time to first entry, and overlaying gaze and AOIs on video. Participants (n =11) were asked to look at 13 dynamic AOIs in traffic scenes from appearance to disappearance in order to validate the setup and software. Different AOI margins were explored for the included objects. The median ratio between total appearance time and dwell time was about 90% for most objects when appropriate margins were chosen. This validated open-source toolkit is readily available for researchers who want to perform dynamic AOI analyses with the Pupil Core eye tracker, especially when measurements are desired on a wide screen, in various fields such as psychology, transportation, and low vision research. |
5,631 | Begomovirus populations in single plants are complex and may include both well-adapted and poorly-adapted viruses | Begomoviruses (single-stranded DNA plant viruses transmitted by whiteflies) are economically important pathogens causing epidemics worldwide. Tomato-infecting begomoviruses emerged in Brazil in the 1990's following the introduction of Bemisia tabaci Middle East-Asia Minor 1. It is believed that these viruses evolved from indigenous viruses infecting non-cultivated hosts. However, tomato-infecting viruses are rarely found in non-cultivated hosts, and vice-versa. It is possible that viral populations in a given host are composed primarily of viruses which are well adapted to this host, but also include a small proportion of poorly adapted viruses. Following transfer to a new host, the composition of the viral population would shift rapidly, with the viruses which are better adapted to the new host becoming predominant. To test this hypothesis, we collected tomato and Sida plants growing next to each other at two locations in 2014 and 2018. Total DNA was extracted from tomato and Sida samples from each location and year and used as a template for high-throughput sequencing. Reads were mapped following a highly stringent set of criteria. For the 2014 samples, >98% of the Sida reads mapped to Sida micrantha mosaic virus (SiMMV), but 0.1% of the reads mapped to tomato severe rugose virus (ToSRV). Conversely, >99% of the tomato reads mapped to ToSRV, with 0.18% mapping to SiMMV. For the 2018 samples, 41% of the Sida reads mapped to three Sida-adapted viruses and 0.1% of the reads mapped to ToSRV, while 99.9% of the tomato reads mapped to ToSRV. These results are consistent with the hypothesis that viral populations in a single plant are composed primarily of the virus that is better adapted to the host but also include a small proportion of viruses that are poorly adapted. |
5,632 | Refining deep convolutional features for improving fine-grained image recognition | Fine-grained image recognition, a computer vision task filled with challenges due to its imperceptible inter-class variance and large intra-class variance, has been drawing increasing attention. While manual annotation can be utilized to effectively enhance performance in this task, it is extremely time-consuming and expensive. Recently, Convolutional Neural Networks (CNN) achieved state-of-the-art performance in image classification. We propose a fine-grained image recognition framework by exploiting CNN as the raw feature extractor along with several effective methods including a feature encoding method, a feature weighting method, and a strategy to better incorporate information from multi-scale images to further improve recognition ability. Besides, we investigate two dimension reduction methods and successfully merge them to our framework to compact the final image representation. Based on the discriminative and compact framework, we achieved the state-of-the-art performance in terms of classification accuracy on several fine-grained image recognition benchmarks based on weekly supervision. |
5,633 | Dilated U-Block for Lightweight Indoor Depth Completion With Sobel Edge | Depth completion has proven to be the key to obtaining high-precision depth maps from a sparse set of measurements and a single RGB image. However, state-of-the-art depth completion algorithms are based on rather complex deep networks that are unfriendly for multiple applications with limited computational resources and energy. In this paper, we consider the task of lightweight indoor depth completion. We propose an efficient convolution block, called Dilated U-Block (DUB), for multi-level feature extraction and integration. The DUBs and an auxiliary Sobel edge prediction network are used to decrease the number of parameters and model complexity. Comprehensive experiments are performed on the indoor NYU-Depth-v2 dataset. The results show that our proposed approach achieves similar accuracy while requiring only about 5% of parameter size and 20% model complexity compared to the state-of-the-art methods. |
5,634 | Enhancing crop yields through improvements in the efficiency of photosynthesis and respiration | The rate with which crop yields per hectare increase each year is plateauing at the same time that human population growth and other factors increase food demand. Increasing yield potential ( Y p ) of crops is vital to address these challenges. In this review, we explore a component of Y p that has yet to be optimised - that being improvements in the efficiency with which light energy is converted into biomass ( ε c ) via modifications to CO2 fixed per unit quantum of light (α), efficiency of respiratory ATP production ( ε prod ) and efficiency of ATP use ( ε use ). For α, targets include changes in photoprotective machinery, ribulose bisphosphate carboxylase/oxygenase kinetics and photorespiratory pathways. There is also potential for ε prod to be increased via targeted changes to the expression of the alternative oxidase and mitochondrial uncoupling pathways. Similarly, there are possibilities to improve ε use via changes to the ATP costs of phloem loading, nutrient uptake, futile cycles and/or protein/membrane turnover. Recently developed high-throughput measurements of respiration can serve as a proxy for the cumulative energy cost of these processes. There are thus exciting opportunities to use our growing knowledge of factors influencing the efficiency of photosynthesis and respiration to create a step-change in yield potential of globally important crops. |
5,635 | LSV-ANet: Deep Learning on Local Structure Visualization for Feature Matching | Feature matching is a fundamental and important task in many applications of remote sensing and photogrammetry. Remote sensing images often involve complex spatial relationships due to the ground relief variations and imaging viewpoint changes. Therefore, using a pre-defined geometrical model will probably lead to inferior matching accuracy. In order to find good correspondences, we propose a simple yet efficient deep learning network, which we term the x201C;local structure visualization-attentionx201D; network (LSV-ANet). Our main aim is to transform outlier detection into a dynamic visual similarity evaluation. Specifically, we first map the local spatial distribution into a regular grid as descriptor LSV, and then customized a spatial SCale Attention (SCA) module and a spatial STructure Attention (STA) module, which explicitly allows structure manipulation and scale selection of LSV within the network. Finally, the embedded SCA and STA deduce optimal LSV for solving feature matching task by training the LSV-ANet end-to-end. In order to demonstrate the robustness and universality of our LSV-ANet, extensive experiments on various real image pairs for general feature matching are conducted and compared against eight state-of-the-art methods. The experiment results demonstrate the superiority of our method over state of the art. |
5,636 | State-of-the-art in product-service systems | A Product-Service System (PSS) is an integrated combination of products and services. This Western concept embraces a service-led competitive strategy, environmental sustainability, and the basis to differentiate from competitors who simply offer lower priced products. This paper aims to report the state-of-the-art of PSS research by presenting a clinical review of literature currently available on this topic. The literature is classified and the major outcomes of each study are addressed and analysed. On this basis, this paper defines the PSS concept, reports on its origin and features, gives examples of applications along with potential benefits and barriers to adoption, summarizes available tools and methodologies, and identifies future research challenges. |
5,637 | A Deep Neural Architecture for Real-Time Access Point Scheduling in Uplink Cell-Free Massive MIMO | In this paper, a novel hybrid architecture is proposed combining expert knowledge for optimal power allocation and deep artificial neural networks (ANN) to address the access-point scheduling problem in cell-free massive multiple-input multiple-output (MIMO) communication systems with a serial bandwidth-limited fronthaul architecture. The scheduling task is formulated as an image segmentation problem for which a supervised encoder-decoder like ANN is proposed. It consists of serially concatenated contraction and expansion layers to maximize the (regularized) cross-entropy, followed by a binary projection to undo the relaxation problem. Besides the robustness to scenarios with a time-varying system load and fronthaul bandwidth, the proposed architecture provides a complexity-efficient solution that fulfills the fronthaul bandwidth constraints and satisfies the real-time considerations. Our experimental results verify the competitive performance of the proposed solution with respect to both nonlinear solvers and state-of-art convex algorithms while the time efficiency of the ANN model outperforms the state of the art, especially, in scenarios with a large number of users. |
5,638 | A new neighbourhood structure for job shop scheduling problems | Job shop scheduling problem (JSP) is a widely studied NP-complete combinatorial optimisation problem. Neighbourhood structures play a critical role in solving JSP. At present, there are three state-of-the-art neighbourhood structures, i.e. N5, N6, and N7. Improving the upper bounds of some famous benchmarks is inseparable from the role of these neighbourhood structures. However, these existing neighbourhood structures only consider the movement of critical operations within a critical block. According to our experiments, it is also possible to improve the makespan of a scheduling scheme by moving a critical operation outside its critical block. According to the above finding, this paper proposes a new N8 neighbourhood structure considering the movement of critical operations within a critical block and the movement of critical operations outside the critical block. Besides, a neighbourhood clipping method is designed to avoid invalid movement, discarding non-improving moves. Tabu search (TS) is a commonly used algorithm framework combined with neighbourhood structures. This paper uses this framework to compare the N8 neighbourhood structure with N5, N6, and N7 neighbourhood structures on four famous benchmarks. The experimental results verify that the N8 neighbourhood structure is more effective and efficient in solving JSP than the other state-of-the-art neighbourhood structures. |
5,639 | Towards optimal VLAD for human action recognition from still images | Human action recognition from still image has recently drawn increasing attention in human behavior analysis and also poses great challenges due to the huge inter ambiguity and intra variability. Vector of locally aggregated descriptors (VLAD) has achieved state-of-the-art performance in many image classification tasks based on local features. The great success of VLAD is largely due to its high descriptive ability and computational efficiency. In this paper, towards optimal VLAD representations for human action recognition from still images, we improve VLAD by tackling three important issues including empty cavity, ambiguity and pooling strategies. The empty cavity limits the performance of VLAD and has long been overlooked. We investigate the empty cavity and provide an effective solution to deal with it, which improves the performance of VLAD; we enhance the codewords with middle level of assignments which are more reliable and can provide more useful information for realistic activity; we propose incorporating the generalized max pooling to replace sum pooling in VLAD, which is more reliable for the final representation. We have conducted extensive experiments on four widely-used benchmarks to validate the proposed method for human action recognition from still images. Our method produces competitive performance with state-of-the-art algorithms. (C) 2016 Elsevier B.V. All rights reserved. |
5,640 | Ensemble Fuzzy Clustering Using Cumulative Aggregation on Random Projections | Random projection is a popular method for dimensionality reduction due to its simplicity and efficiency. In the past few years, random projection and fuzzy c-means based cluster ensemble approaches have been developed for high-dimensional data clustering. However, they require large amounts of space for storing a big affinity matrix, and incur large computation time while clustering in this affinity matrix. In this paper, we propose a new random projection, fuzzy c-means based cluster ensemble framework for high-dimensional data. Our framework uses cumulative agreement to aggregate fuzzy partitions. Fuzzy partitions of random projections are ranked using external and internal cluster validity indices. The best partition in the ranked queue is the core (or base) partition. Remaining partitions then provide cumulative inputs to the core, thus, arriving at a consensus best overall partition built from the ensemble. Experimental results with Gaussian mixture datasets and a variety of real datasets demonstrate that our approach outperforms three state-of-the-art methods in terms of accuracy and space-time complexity. Our algorithm runs one to two orders of magnitude faster than other state-of-the-arts algorithms. |
5,641 | Power Line Communications: State of the art in research, development and application | Power Line Communications (PLC) is currently well-known technology. In the last 10-15 years its development and improvements became considerable so that the mass deployment takes place. In this paper we describe the main trends of PLC development. (C) 2014 Elsevier GmbH. All rights reserved. |
5,642 | A concise review of the bioactivity and pharmacological properties of the genus Codium (Bryopsidales, Chlorophyta) | The genus Codium is one of the most important genera of marine green macroalgae. Its distribution is widespread worldwide and it has a high degree of diversity in species and characteristics. This genus plays an important ecological role in marine ecosystems as it is a primary producer. However, some species in the genus Codium are invasive species and may disturb the functioning of the ecosystem. Economically, Codium has promising potential as a source of diverse nutritional and pharmacological compounds. Codium is edible, has a high nutrient value, and is rich in bioactive compounds. Hence, some species of Codium have been consumed as food and used as herbal medicines in some Asian countries. In recent decades, studies of the bioactivity and pharmacological properties of the genus Codium have attracted the attention of scientists. This review aims to identify gaps in studies analyzing Codium that have been conducted in the past three decades by assessing published research articles on its bioactivity and pharmacological properties. Compounds obtained from Codium have demonstrated significant biological activities, such as immunostimulatory, anticoagulant, anticancer, anti-inflammatory, antioxidant, antiviral, antibacterial, antifungal, antitumor, anti-angiogenic, osteoprotective, and anti-obesity activities. This review provides information that can be used as a future guideline for sustainably utilizing the genus Codium. |
5,643 | A Smart CMOS Assay SoC for Rapid Blood Screening Test of Risk Prediction | A micro-controller unit (MCU) assisted immunoassay lab-on-a-chip is realized in 0.35 mu m CMOS technology. The MCU automatically controls the detection procedure including blood filtration through a nonporous aluminum oxide membrane, bimolecular conjugation with antibodies attached to magnetic beads, electrolytic pumping, magnetic flushing and threshold detection based on Hall sensor array readout analysis. To verify the function of this chip, in-vitro Tumor necrosis factor-alpha (TNF-alpha) and N-terminal pro-brain natriuretic peptide (NT-proBNP) tests are performed by this 9 mm(2)-sized single chip. The cost, efficiency and portability are considerably improved compared to the prior art. |
5,644 | Online Disinhibition and Online Trolling Among Chinese College Students: The Mediation of the Dark Triad and the Moderation of Gender | The Dark Triad (Machiavellianism, psychopathy, narcissism) is associated with online disinhibition and antisocial online behaviors. However, the mediating role between online disinhibition and online trolling has never previously been investigated. We examined direct and indirect associations between online disinhibition and online trolling via the Dark Triad among 1,303 participants. The results showed that online disinhibition is positively correlated with online trolling, and their link is partly mediated by Machiavellianism and psychopathy. Furthermore, men exhibited higher levels of Dark Triad traits and were more likely to engage in online trolling than women. Moderated mediation analyses indicated that gender moderated the relationship between psychopathy and online trolling. The study provided a promising perspective for the intervention of online trolling, namely netizens should be taught to reduce impulsivity and improve empathy and self-control, especially for men. |
5,645 | Scalar Product Lattice Computation for Efficient Privacy-Preserving Systems | Privacy-preserving (PP) applications allow users to perform online daily actions without leaking sensitive information. The PP scalar product (PPSP) is one of the critical algorithms in many private applications. The state-of-the-art PPSP schemes use either computationally intensive homomorphic (public-key) encryption techniques, such as the Paillier encryption to achieve strong security (i.e., 128 b) or random masking technique to achieve high efficiency for low security. In this article, lattice structures have been exploited to develop an efficient PP system. The proposed scheme is not only efficient in computation as compared to the state-of-the-art but also provides a high degree of security against quantum attacks. Rigorous security and privacy analyses of the proposed scheme have been provided along with a concrete set of parameters to achieve 128-b and 256-b security. Performance analysis shows that the scheme is at least five orders faster than the Paillier schemes and at least twice as faster than the existing randomization technique at 128-b security. Also the proposed scheme requires six-time fewer data compared to the Paillier and randomization-based schemes for communications. |
5,646 | Highly efficient neoteric histogram-entropy-based rapid and automatic thresholding method for moving vehicles and pedestrians detection | Thresholding for segmentation is an important key step and necessary process in various applications. Estimating an accurate threshold value for a complex and coarse image is computationally expensive and lacks accuracy and stability. This study is aimed at developing a general histogram-entropy-based thresholding method, referred as our HEBT method, for fast and efficient automatic threshold value evaluation. In the proposed method, the probability density function and Shannon entropy derived from 1D bimodal histogram have been used to find the optimal threshold values automatically. The proposed method implemented with a three-frame differencing segmentation technique has been tested on real-time datasets - change detection 2012, change detection 2014, and Wallflower - to identify pedestrians and vehicles. The performance of our HEBT method has been compared with six state-of-the-art automatic thresholding methods. The experimental segmented image results confirmed that our HEBT method is more adaptable and better suited for real-time systems with severe challenging conditions of great variations. Further, the new HEBT method achieved the best segmentation results with highest values of several performance parameters, i.e. recall, precision, similarity, and f-measure. Interestingly, the computation time is the lowest for the proposed method than the state-of-the-art methods, promising its application for a fast and effective image segmentation. |
5,647 | Additive models and separable pooling, a new look at structural similarity | Objective quality metrics predict perceived quality of image signals computationally and can: (i) benchmark and monitor compression and processing algorithms and (ii) optimise their performance for a given application (content, bandwidth, packet loss...). Structural similarity, represented by the well known SSIM index is a framework for objective assessment of image quality well known for its relative simplicity and robustness. Despite its practical appeal, SSIM's performance level, measured as agreement with subjective quality scores, lags more complex state-of-the-art metrics. We present a new look into structural similarity that uses an additive model and a spatial pooling approach that decouples individual structural comparisons and utilises the quality driven aggregation paradigm. We apply this new approach to both baseline intensity SSIM and gradient SSIM (GSSIM) frameworks and show, through extensive evaluation on four publicly available subjective datasets that it provides considerably more ordered (linear) relationship between objective and subjective quality for a variety of input conditions. We demonstrate that newly formulated structural similarity metrics using this approach are capable of equal or even better performance than more complex state-of-the-art objective metrics in the process lending support to the theory that humans base their opinion on the worst sections of the observed signal. (C) 2013 Elsevier B.V. All rights reserved. |
5,648 | A novel neural network based approach to latent overlapped fingerprints separation | Overlapped fingerprints are often found in latent fingerprints lifted from crime scenes and in live-scan fingerprint images when the surface of fingerprint sensors contains residues of fingerprints of previous users. Such overlapped fingerprints usually cannot be processed accurately by contemporary commercial fingerprint matchers, which has led many researchers to propose methods designed to separate the overlapped fingerprints. In this paper, we propose a novel latent overlapped fingerprints separation algorithm based on neural networks. Our algorithm works in a block-based fashion. After producing an initial estimation of the orientation fields present in the overlapped fingerprint image, it uses a neural network to separate the mixed orientation fields, which are then post-processed to correct remaining errors and enhanced using the global orientation field enhancement model. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithm in terms of accuracy on the Tsinghua Overlapped Latent Fingerprint Database (containing real-world overlapped fingerprints obtained by forensic methods), while also showing encouraging results (second only to state-of-the-art) on the Tsinghua Simulated Overlapped Fingerprint Database (containing artificially overlapped fingerprints of a good quality). |
5,649 | Cross-SRN: Structure-Preserving Super-Resolution Network With Cross Convolution | It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details. Existing deep learning works almost neglect the inherent structural information of images, which acts as an important role for visual perception of SR results. In this paper, we design a hierarchical feature exploitation network to probe and preserve structural information in a multi-scale feature fusion manner. First, we propose a cross convolution upon traditional edge detectors to localize and represent edge features. Then, cross convolution blocks (CCBs) are designed with feature normalization and channel attention to consider the inherent correlations of features. Finally, we leverage multi-scale feature fusion group (MFFG) to embed the cross convolution blocks and develop the relations of structural features in different scales hierarchically, invoking a lightweight structure-preserving network named as Cross-SRN. Experimental results demonstrate the Cross-SRN achieves competitive or superior restoration performances against the state-of-the-art methods with accurate and clear structural details. Moreover, we set a criterion to select images with rich structural textures. The proposed Cross-SRN outperforms the state-of-the-art methods on the selected benchmark, which demonstrates that our network has a significant advantage in preserving edges. |
5,650 | Retrospective study of the clinicopathological characteristics and prognostic factors of gastrointestinal stromal tumors in Chinese patients | The purpose of this study was to investigate the clinicopathological characteristics and prognostic factors of patients with gastrointestinal stromal tumors (GISTs) in mainland China. We retrospectively analyzed the clinicopathological characteristics and survival data of 149 patients with GISTs admitted to Shengjing Hospital of China Medical University from July 2011 to October 2017. The following details were collected from all patients: sex, age, symptoms, preoperative examination, pathology, surgical procedures, and follow-up data. Recurrence-free survival (RFS) and overall survival (OS) were used to assess survival outcomes. The Kaplan-Meier method was performed to draw survival curves and calculate the survival rate. The log-rank test was performed for univariate analysis, and the significant factors were included in multivariate analysis using a Cox proportional hazards model to determine prognostic factors. The 5-year RFS rate was 78.5 % and 5-year OS rate was 83.2 %. The univariate analysis showed that the following prognostic factors could significantly predict 5-year RFS and OS: tumor size, initial status, modified NIH classification, mitotic index, CD117 expression, Ki67 index, and surgical procedure (P < 0.05). The multivariate analysis showed that mitotic index, CD117, and Ki67 index were independent prognostic factors associated with 5-year RFS and 5-year OS. This study provides a reference for the clinicopathological characteristics and prognostic factors of patients with GISTs in mainland China, and the results suggest that focusing on immunohistochemical markers in clinical practice may be more reliable for the prediction of clinical outcomes. |
5,651 | MIPSGPU: Minimizing Pipeline Stalls for GPUs With Non-Blocking Execution | Improving the latency hiding ability is important for GPU performance. Although existing works, which mainly target on either improving thread level parallelism or optimizing memory hierarchy, are effective at improving GPUs' latency hiding ability, warps are still blocked after executing long latency operations, reducing the number of schedulable warps. This article revisits the recently proposed non-blocking execution for GPUs to improve the latency hiding ability of GPUs. With non-blocking execution, instructions from warps blocked by long latency operations can be pre-executed to make full use of GPU resources. However, we find that the state-of-the-art non-blocking GPU architecture gains limited performance improvement. Through in-depth analysis, we observe that the poor performance is largely due to inefficient pre-execution state management, duplicate instruction extraction, frequent early eviction and severe resource congestion. To make non-blocking execution actually useful for GPUs and minimize hardware overheads, we carefully redesign the non-blocking architecture for GPUs based on our analysis and propose MIPSGPU. Our evaluations show that MIPSGPU, relative to the state-of-the-art non-blocking GPU architecture, improves performance of memory intensive applications by 19.05 percent, and reduces memory to SM traffics by 14 percent. |
5,652 | A progressive lossless/near-lossless image compression algorithm | A novel image compression technique is presented that incorporates progressive transmission and near-lossless compression in a single framework. Experimental performance of the proposed coder proves to be competitive with the state-of-the-art compression schemes. |
5,653 | Aberrant expression of microRNA-4443 (miR-4443) in human diseases | miRNA is a small endogenous RNA and an important regulator of gene expression. miR-4443 is abnormally expressed in 12 diseases including cancer. The expression of miR-4443 is regulated by 3 upstream factors. miR-4443 has 12 downstream target genes. miR-4443 inhibits the expression of its target genes, thereby affecting the migration, proliferation, and invasion of pathological cells. miR-4443 participates in 4 signaling pathways and plays a role in the occurrence and development of several diseases. In addition, miR-4443 can also promote resistance to multiple drugs. Here, this article summarizes the aberrant expression of miR-4443 and its pathogenic molecular mechanisms in human diseases, which provides clues and directions for the follow-up research of miR-4443. |
5,654 | The Role of Community Centre-based Arts, Leisure and Social Activities in Promoting Adult Well-being and Healthy Lifestyles | Developed countries are experiencing high levels of mental and physical illness associated with long term health conditions, unhealthy lifestyles and an ageing population. Given the limited capacity of the formal health care sector to address these public health issues, attention is turning to the role of agencies active in civil society. This paper sought to evaluate the associations between participation in community centre activities, the psycho-social wellbeing and health related behaviours. This was based on an evaluation of the South West Well-being programme involving ten organisations delivering leisure, exercise, cooking, befriending, arts and crafts activities. The evaluation consisted of a before-and-after study with 687 adults. The results showed positive changes in self-reported general health, mental health, personal and social well-being. Positive changes were associated with diet and physical activity. Some activities were different in their outcomes-especially in cases where group activities were combined with one-to-one support. The results suggest that community centre activities of this nature offer benefits that are generically supportive of health behaviour changes. Such initiatives can perform an important role in supporting the health improvement objectives of formal health care services. For commissioners and partner agencies, accessibility and participation are attractive features that are particularly pertinent to the current public health context. |
5,655 | Automatic Annotation of Hyperspectral Images and Spectral Signal Classification of People and Vehicles in Areas of Dense Vegetation with Deep Learning | Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera-installed on the ground or possibly a drone-and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train a classifier to detect people and cars. Our results show that, based only on spectral signature classification, we can achieve an Matthews Correlation Coefficient of 0.83. We evaluate our classification method in areas with varying vegetation and discuss the limitations and constraints that the current hyperspectral imaging technology has. We conclude that spectral signature classification is possible with high accuracy in uncontrolled outdoor environments. Nevertheless, even with state-of-the-art compact passive hyperspectral imaging technology, high dynamic range of illumination and relatively low image resolution continue to pose major challenges when developing object detection algorithms for areas of dense vegetation. |
5,656 | Chroma 422 subsampling for Bayer pattern in H.264 video coding | The optimal strategy of chroma 422 subsampling for Bayer pattern in H.264 video coding is proposed. The state-of-the-art work uses a predetermined conversion method from colour filter array to YCbCr 422. Derived from this structure, first, 64 subsampling methods are designed to exploit more possibilities. Secondly, among which, the four optimal methods are found by the pre-screening process, which show their better performances than the state-of-the-art method by up to 2.57 dB in CPSNR averaged for different videos, demonstrating the practicability of the proposed methods. Finally, for the experiment consisting of the whole conversion and H.264 video encoding/decoding procedures, the proposed optimal methods perform better than the state-of-the-art method under all different operating bitrates and different tested videos, by up to 2.25 dB in BDPSNR. |
5,657 | Strategies to overcome the main challenges of the use of exosomes as drug carrier for cancer therapy | Exosomes are naturally occurring nanosized particles that aid intercellular communication by transmitting biological information between cells. Exosomes have therapeutic efficacy that can transfer their contents between cells as natural carriers. In addition, the exosomal contents delivered to the recipient pathological cells significantly inhibit cancer progression. However, exosome-based tumor treatments are inadequately precise or successful, and various challenges should be adequately overcome. Here, we discuss the significant challenges that exosomes face as drug carriers used for therapeutic targets and strategies for overcoming these challenges in order to promote this new incoming drug carrier further and improve future clinical outcomes. We also present techniques for overcoming these challenges. |
5,658 | An overview of process systems engineering approaches for process intensification: State of the art | Process intensification offers the potential to drastically reduce the energy consumption and cost of producing chemicals from both bulk and distributed feedstocks. This review article aims to offer an extensive survey on state-of-the-art process systems engineering (PSE) approaches for process intensification. From both academic and industrial perspectives, this paper provides an overview of the development of various process intensification technologies, specifically those under the categories of separation, reaction, hybrid reaction/separation, and alternative energy sources. A current status analysis in the areas of modeling and simulation is then provided. An indicative list of PSE publications specialized on process intensification is presented to illustrate the progresses made so far towards the deployment of novel process intensification technologies. We also highlight some recent advances for the modeling, design, and synthesis of intensified systems, as well as for the assessment of their controllability/operability/safety performance. Key open questions in these areas include: (i) how to systematically derive intensified designs, and (ii) how to ensure the operability and optimality of the derived intensified structures while delivering their expected functionality. |
5,659 | Discriminalve cluster adaptive training | Multiple-cluster schemes, such as cluster adaptive training (CAT) or eigenvoice systems, are a popular approach for rapid speaker, and environment adaptation. Interpolation I weights are used to transform a multiple-cluster, canonical, mode (HMM) set representative to a standard hidden Markov model e, of an individual speaker or acoustic environment. Maximum likelihood training for CAT has previously been investigated. However, in state-of-the-art large vocabulary continuous. speech ' recognition systems, discriminative training is commonly employed. This paper investigates applying discriminative training to multiple-cluster systems. In particular,. minimum phone error (MPE) update formulae for CAT systems are derived. In order to use MPE in this case, modifications to the standard MPE smoothing function and the prior. distribution associated with MPE training are required. A more complex adaptive training near trans scheme combining both interpolation weights and in forms, a structured transform (ST), is also discussed within the MPE training framework. Discriminatively trained CAT and ST systems were evaluated on a state-of-the-art conversational telephone speech task. These Multiple-cluster systems ,were found to outperform both standard and adaptively trained systems. |
5,660 | Laparoscopic Cholecystectomy with Intraoperative Cholangiogram and Antegrade Biliary Stenting in Acute Gallstone Pancreatitis: A Pilot Study | Aims: To demonstrate feasibility and efficacy of simultaneous intraoperative cholangiogram (IOC) and antegrade biliary stenting (ABS) with laparoscopic cholecystectomy (LC) compared with preoperative biliary investigation and delayed LC in acute gallstone pancreatitis (AGP). Methods: A retrospective case-control study was performed comparing patients who had a simultaneous IOC ± ABS with LC at index admission with those who had delayed LC in the treatment of AGP. 74 patients were included in this study from January 2016 to October 2018. All patients who underwent LC for AGP were included in a prospective database with 1 year follow-up. Results: 30 (40.5%) patients underwent simultaneous IOC ± ABS with LC, 11 of these required ABS insertion. 2 (6.7%) patients also underwent magnetic resonance cholangiopancreatography (MRCP). No patients underwent endoscopic retrograde cholangiopancreatography (ERCP). No patients were readmitted with AGP or symptomatic gallbladder. Mean length of total hospital admission was 10.1 days. 44 (59.5%) patients underwent delayed LC. Of this cohort, 7 (15.9%) patients underwent ERCP and 19 (43.2%) underwent MRCP. In total, there were 19 (43.2%) readmissions in this group with pancreatitis or symptomatic gallbladder. Mean length of total hospital admission was 13 days. Conclusions: In our pilot study we demonstrated that performing simultaneous IOC ± ABS with LC is a feasible option in the secondary care setting. Using this surgical technique, we have demonstrated a reduction in readmissions with AGP and symptomatic gallbladder while also reducing the number of CBD investigations required. Using simultaneous IOC ± ABS with LC reduced the mean total length of stay in hospital. |
5,661 | Are predictions of cancer response to targeted drugs, based on effects in unrelated tissues, the 'Black Swan' events? | Adverse effects of targeted drugs on normal tissues can predict the cancer response. Rash correlates with efficacy of erlotinib, cetuximab and gefitinib and onset of arterial hypertension with response to bevacizumab, sunitinib, axitinib and sorafenib, possible examples of 'Black Swan' events, unexpected scientific observations, as described by Karl Popper in 1935. The proposition is that our patients have individual intrinsic variants of cell growth control, important for tumor response and adverse effects on tumor-unrelated tissue. This means that the lack of predictive side effects in healthy tissue is linked with poor results of tumor therapy when tumor resistance is caused by mechanisms that protect all cells of that patient from the targeted drug effects. |
5,662 | Efficient single image super-resolution via graph-constrained least squares regression | We explore in this paper an efficient algorithmic solution to single image super-resolution (SR). We propose the gCLSR, namely graph-Constrained Least Squares Regression, to super-resolve a high-resolution (HR) image from a single low-resolution (LR) observation. The basic idea of gCLSR is to learn a projection matrix mapping the LR image patch to the HR image patch space while preserving the intrinsic geometric structure of the original HR image patch manifold. Even if gCLSR resembles other manifold learning-based SR methods in preserving the local geometric structure of HR and LR image patch manifolds, the innovation of gCLSR lies in that it preserves the intrinsic geometric structure of the original HR image patch manifold rather than the LR image patch manifold, which may be contaminated by image degeneration (e.g., blurring, down-sampling and noise). Upon acquiring the projection matrix, the target HR image can be simply super-resolved from a single LR image without the need of HR-LR training pairs, which favors resource-limited applications. Experiments on images from the public database show that gCLSR method can achieve competitive quality as state-of-the-art methods, while gCLSR is much more efficient in computation than some state-of-the-art methods. |
5,663 | A Simplified Adaptive Neural Network Prescribed Performance Controller for Uncertain MIMO Feedback Linearizable Systems | In this paper, the problem of deriving a continuous, state-feedback controller for a class of multiinput multioutput feedback linearizable systems is considered with special emphasis on controller simplification and reduction of the overall design complexity with respect to the current state of the art. The proposed scheme achieves prescribed bounds on the transient and steady-state performance of the output tracking errors despite the uncertainty in system nonlinearities. Contrary to the current state of the art, however, only a single neural network is utilized to approximate a scalar function that partly incorporates the system nonlinearities. Furthermore, the loss of model controllability problem, typically introduced owing to approximation model singularities, is avoided without attaching additional complexity to the control or adaptive law. Simulations are performed to verify and clarify the theoretical findings. |
5,664 | Super-Resolution of Sentinel-2 Images Using a Spectral Attention Mechanism | Many visual applications require high-resolution images for an adequate interpretation of the data stored within them. In remote sensing, the appearance of satellites such as Sentinel or Landsat has facilitated the access to data thanks to their free offer of multispectral images. However, the spatial resolution of these satellites is insufficient for many tasks. Therefore, the objective of this work is to apply deep learning techniques to increase the resolution of the Sentinel-2 Read-Green-Blue-NIR (RGBN) bands from the original 10 m to 2.5 m. This means multiplying the number of pixels in the resulting image by 4, improving the perception and visual quality. In this work, we implement a state-of-the-art residual learning-based model called Super-Resolution Residual Network (SRResNet), which we train using PlanetScope-Sentinel pairs of images. Our model, named SARNet (Spectral Attention Residual Network), incorporates Residual Channel Attention Blocks (RCAB) to improve the performance of the network and the visual quality of the results. The experiments we have carried out show that SARNet offers better results than other state-of-the-art methods. |
5,665 | What is known about the care and support provided for an ageing population with lived experience of chronic viral hepatitis as they near end-of-life: A scoping review | Ageing with a chronic hepatitis B (HBV) or hepatitis C (HCV) infection is an emerging public health priority. For people living with chronic viral hepatitis, their disease progression into old age is both underpinned by their existing blood borne virus and the potential emergence of other infectious and non-infectious conditions. These twinned pathways bring additional challenges to the care and support for people as they near end of life. This scoping review sought to examine what is known about the experiences of the end-of-life phase of an increasing population ageing with HBV and HCV in studies conducted in high-income settings and published in peer reviewed literature (2010-2021). In interpreting this literature, we found that challenges in determining the end-of life phase for people with lived experience of HBV or HCV are exacerbated by the conflation of aetiologies into a singular diagnosis of end-stage liver disease. Studies overwhelmingly reported the clinical aspects of end-of-life care (i.e. prognosis assessment and symptom management) with less attention paid to educative aspects (i.e. advance care directives and surrogate decision makers, discussion of treatment options and determining goals of care). Psychosocial interventions (i.e. quality of life beyond symptom management, including emotional/spiritual support and family and bereavement support) received limited attention in the literature, though there was some recognition that psychosocial interventions should be part of end-of-life care provision. Given the focus on the prominent disease presentation of liver cirrhosis and/or end-stage liver disease, the social and cultural dimensions of these infections have received less attention in the literature on end-of-life in the context of chronic viral hepatitis. |
5,666 | Chimpanzee susceptibility to hepatitis C virus infection correlates with presence of Pt-KIR3DS2 and Pt-KIR2DL9: paired activating and inhibitory natural killer cell receptors | Infection of humans and chimpanzees with Hepatitis C virus (HCV) results in either the resolution of the acute infection or its progression to a persistent infection associated with chronic liver disease. In cohorts of human patients, resolution of HCV infection has been associated with homozygosity for both C1(+)HLA-C and its cognate inhibitory receptor, KIR2DL3. Compared here are the killer cell immunoglobulin-like receptors (KIR) and major histocompatibility complex (MHC) class I factors of chimpanzees who resolve, or resist, HCV infection with those chimpanzees who progress to chronic infection. Analysis of Pt-KIR gene content diversity associated two of the 12 Pt-KIR with clinical outcome. Activating Pt-KIR3DS2 and inhibitory Pt-KIR2DL9 are strong receptors specific for the C2 epitope. They are encoded by neighboring genes within the Pt-KIR locus that are in strong linkage disequilibrium. HCV-infected chimpanzees with KIR genotypes containing Pt-KIR3DS2 and KIR2DL9 are significantly more likely to progress to chronic infection than infected chimpanzees lacking the genes (p = 0.0123 and p = 0.0045, respectively), whereas human HLA-B allotypes having the C1 epitope are unusual, such allotypes comprise about one quarter of the chimpanzee Patr-B allotypes. Homozygous C1 (+) Patr-B are enriched in chimpanzees with chronic HCV infection, and the compound genotype of homozygous C1 (+) Patr-B combined with either Pt-KIR3DS2 or Pt-KIR2DL9 is more strongly associated with disease progression than either factor alone (p = 0.0031 and p = 0.0013, respectively). Thus, despite similarities suggesting a common basis in disease resistance, there are substantial differences in the KIR and MHC class I correlations observed for HCV-infected humans and chimpanzees, consistent with the divergence of their KIR and MHC class I systems. |
5,667 | Factors Associated With Visual Acuity in Non-arteritic Ischemic Optic Neuropathy Patients: A Five-Year Cross-Sectional Study | Background and objective Non-arteritic ischemic optic neuropathy (NAION) is a common cause of optic neuropathy in elderly patients. Currently, there is no definitive treatment for this condition, and the factors influencing visual outcomes have not yet been conclusively identified. In this study, we aimed to evaluate factors that affect visual outcomes and those that are predictors of the development of NAION in a Thai population. Methods All patients diagnosed with NAION at the Rajavithi Hospital between January 1, 2016, and December 31, 2020, were retrospectively reviewed to evaluate the improvement in their best-corrected visual acuity (BCVA) and determine the factors that are predictive of visual outcomes. Results The 80 patients reviewed were predominantly male (55%) with a mean age of 55.8 ±9.89 years. Their most common comorbidities were dyslipidemia (DLP) (67.5%), diabetes mellitus (DM) (61.3%), and hypertension (HT) (48.8%). At the 12-week follow-up visit, there was a significant improvement of at least 0.2 logarithm of the minimum angle of resolution (logMAR) in BCVA (p=0.001). A significantly greater percentage of patients with higher age, DM, and HT was observed in the unfavorable visual recovery (UVR) group (p=0.002, p=0.001, and p=0.005 respectively). In contrast, neither baseline visual acuity nor cup-to-disc ratio (CDR) affected the result of visual recovery (p=0.275 and p=0.076, respectively). In multivariate logistic analysis, older age increased the odds of worse visual recovery [odds ratio (OR): 4.014; 95% CI: 1.038-15.515; p=0.044], as did having DM (OR: 3.809; 95% CI: 1.168-12.421; p=0.027), and HT (OR: 4.577; 95% CI: 1.491-14.049; p=0.008). Conclusions None of the baseline visual status parameters (visual acuity, CDR, or visual field defect) was able to determine the outcome of visual recovery at 12 weeks in our NAION patients. Regarding systemic vascular diseases, diabetes and HT are significant risk factors and also predictors of poor visual improvement in Thai populations. NAION patients who are elderly or have vascular diseases such as DM or HT should be closely followed up and advised about the likelihood of having inferior visual recovery. |
5,668 | Skin rash associated with combined cytotoxic chemotherapy and immunotherapy for cancer: A retrospective single-center case series | In recent years, the development of combination therapies with immune checkpoint inhibitors (ICIs) and cytotoxic anticancer drugs has radically changed the management of diverse malignancies and significantly improved patient outcomes. Several clinical trials have shown that skin rash caused by combination therapy with ICIs and cytotoxic drugs may be more frequent and severe than that developing after administration of ICIs alone or cytotoxic drug monotherapy. However, most reports provide little information on severity, treatment, post-diagnosis course, and recurrence of rashes on drug rechallenges. We aimed to describe the experience of skin rashes developing within 2 weeks from the first administration of combination therapy with ICIs and cytotoxic drugs in 11 patients visiting our dermatology department. This study included seven men and four women, and the patients' median age was 52 years. The primary disease was non-small-cell lung cancer in eight patients, cervical cancer in two patients, and esophageal cancer in one patient. Nine patients had a maculopapular rash and two patients developed erythema multiforme-like eruptions. The skin rash was often accompanied by extracutaneous symptoms, such as fever (n = 9), mucositis (n = 4), and liver dysfunction (n = 2). In all cases, the symptoms improved with topical steroid therapy alone, with no patients exhibiting severe symptoms requiring systemic steroids or immunosuppressive agents. In addition, when the causative drugs were re-administered after recovery from the rash, only two patients relapsed with accompanying systemic symptoms, and all patients except one were able to continue treatment using the same drug regimen. Although it was suggested that the rash caused by the combination therapy of ICIs and cytotoxic drugs may be more prominent than that caused by each agent alone, comprehensive judgment, including histopathological examination, may indicate the feasibility of continuing the treatment regimen for cancer. |
5,669 | A Adaptive Algorithm for Online Interference Cancellation in EMG Sensors | One of the biggest issues encountered in the analysis of sensitive electromyography (EMG) sensor data is the power line interference (PLI). Conventional methods in literature either lose valuable sensor data or inadequately decrease the power line noise. Instead of filtering out predetermined frequencies, adaptively estimating the spectrum of PM can provide better performance. This paper introduces an online adaptive algorithm that removes the power line interference in real time without disturbing the true EMG data. Our method sequentially processes the biomedical signal to properly estimate and remove the PLI component. Through experiments with real EMG data, we compared our method to the five state-of-the-art techniques. Our algorithm outperformed all of them with the highest SNR gain (3.6 dB on average) and with the least disturbance of the true EMG signal (0.0152 dB loss on average). Our method reduces the PLI the most while keeping the valuable sensor data loss at its minimum in comparison to the state-of-the-art. Reducing the noise without disturbing the valuable sensor data provides higher quality signals with decreased interference, which can be better processed and used in biomedical research. |
5,670 | Real-Time Optical Flow for Vehicular Perception With Low- and High-Resolution Event Cameras | Event cameras capture changes of illumination in the observed scene rather than accumulating light to create images. Thus, they allow for applications under high-speed motion and complex lighting conditions, where traditional frame-based sensors show their limits with blur and over- or under-exposed pixels. Thanks to these unique properties, they represent nowadays an highly attractive sensor for ITS-related applications. Event-based optical flow (EBOF) has been studied following the rise in popularity of these neuromorphic cameras. The recent arrival of high-definition neuromorphic sensors, however, challenges the existing approaches, because of the increased resolution of the events pixel array and a much higher throughput. As an answer to these points, we propose an optimized framework for computing optical flow in real-time with both low- and high-resolution event cameras. We formulate a novel dense representation for the sparse events flow, in the form of the "inverse exponential distance surface". It serves as an interim frame, designed for the use of proven, state-of-the-art frame-based optical flow computation methods. We evaluate our approach on both low- and high-resolution driving sequences, and show that it often achieves better results than the current state of the art, while also reaching higher frame rates, 250Hz at 346 x 260 pixels and 77Hz at 1280 x 720 pixels. |
5,671 | Fuzzy multilevel graph embedding | Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. (c) 2012 Elsevier Ltd. All rights reserved. |
5,672 | Alternative caregivers` role in food choices for young children in semi-urban and urban Mexico | Alternative caregivers (i.e., someone besides the primary caregiver who also takes care of children) make food choices for children. This study investigated what alternative caregivers consider when making food choices for children and their perspectives on their role in making food choices to feed children. In-depth interviews were conducted with 16 alternative caregivers of children aged 1-5 years old in semi-urban and urban areas of the State of Mexico in Mexico. Interviews were recorded, transcribed, coded, and analyzed using constant comparative method. Alternative caregivers described spaces and situations that exposed children to food while under their care. Alternative caregivers who spent longer periods of time with the child described more involvement in what the child ate. Healthy or nutritious food, cost of food and affection for children were important considerations for alternative caregivers when deciding what to feed the child. Alternative caregivers had a substantial role in child feeding, decisions about cooking, and advising mothers on how to feed their children. Efforts to promote healthy food choices for children should include targeting of alternative caregivers. |
5,673 | A molecular characterization of marsupial filarioid nematodes of the genus Breinlia | Here we present the genetic relationships of 26 specimens of the genus Breinlia (Nematoda: Filarioidea) from a range of Australian marsupials using markers in the small subunit of nuclear ribosomal RNA and mitochondrial cytochrome c oxidase subunit 1 (cox1) genes and compare them with morphological determinations. The molecular data support the validity of most of the morpho-species included in the study and provide provisional insights into the phylogeny of the genus in Australian mammals, with dasyuroid marsupials appearing to be the original hosts. The recent discovery of Breinlia annulipapillata in the eye of a human brings this genus of parasites into the group of emerging infectious parasitic diseases. |
5,674 | Meal patterns and the quality of breakfast and snacks in relation to adolescents' dental health in southeast of Iran | Aim: The objective was to determine the frequency pattern and nutritional quality of breakfast and snacks in Iranian adolescents and to investigate these dietary habits in relation to tooth decay and tooth erosion. Methods: A multistage cluster random sampling method was adopted to recruit 600 adolescents with equal sex distribution in the city of Kerman/southeast of Iran. Decayed, Missing and Filled Teeth (DMFT) and Tooth Wear Index (TWI) were recorded for each subject. Snacking and breakfast quality, frequency of snacking and regular/irregular use of main meals were also recorded. Poisson regression and Firth's bias-reduced penalized-likelihood logistic regression were used for data analysis. Results: DMFT score of adolescents who consumed low-quality snacks were 1.13 times more than those who consumed high-quality snacks. Regular use of all three main meals was associated with a lower DMFT score. DMFT score of adolescents who did not have regular use of breakfast was 1.19 times more than those who consumed breakfast on a regular basis. Also, regarding adolescents who had an irregular use of lunch, the DMFT score was 1.3 times more than those who had a regular lunch schedule. In addition, participants with irregular dinner consumption had 1.24 times more DMFT scores compared to those with a regular dinner schedule. Conclusions: Regular breakfast consumption, decreased snacking occasions, use of higher nutritional quality snacks, and increased nutritional education are important in order to prevent a higher chance of dental caries and promote dental health status. |
5,675 | SBAUPT: Azimuth SBUPT for Frequent Full Attitude Correction of Smartphone-Based PDR | The growing market of smart devices make them appealing for various applications. Motion tracking can be achieved using such devices, and is important for various applications such as navigation, search and rescue, health monitoring, and quality of life-style assessment. Step detection is a crucial task that affects the accuracy and quality of such applications. In this paper, a new step detection technique is proposed, which can be used for step counting and activity monitoring for health applications as well as part of a Pedestrian Dead Reckoning (PDR) system. Inertial and Magnetic sensors measurements are analyzed and fused for detecting steps under varying step modes and device pose combinations using a free-moving handheld device (smartphone). Unlike most of the state of the art research in the field, the proposed technique does not require a classifier, and adaptively tunes the filters and thresholds used without the need for presets while accomplishing the task in a real-time operation manner. Testing shows that the proposed technique successfully detects steps under varying motion speeds and device use cases with an average performance of 99.6% and outperforms some of the state of the art techniques that rely on classifiers and commercial wristband products. |
5,676 | Improvement of ascorbic acid delivery into human skin via hyaluronic acid-coated niosomes | Hyaluronic acid (HA) as a covering agent was incorporated into the ascorbic acid (AA)-niosomes to improve the performance of AA delivery systems into the skin. The preparation method: Thin film hydration. Characterisation tests: Field emission scanning electron microscopy, fourier transform infra-red spectroscopy, dynamic light scattering, UV-Visible, zeta potential, Franz diffusion cell, and flowcytometry. The niosomes with 10% w/w HA possessed the largest mean particle diameter of 341.0 ± 48.09 nm with PDI value of 0.29 ± 0.05, and the lowest zeta potential of -38.70 ± 0.27 mv. The drug encapsulation efficiency of this sample was 56.55 ± 0.99%, and in-vitro drug release test showed AA released in two slow and fast phases. Moreover, the highest amount of drug penetration and accumulation was related to this sample, recorded 116.55 ± 7.54 and 134.8 ± 10.04 µg/cm2, respectively. Niosomes coated with 10% w/w HA showed the greatest potential for improving the antioxidant activity of AA penetration into the skin. |
5,677 | METAMORPHOSE European Doctoral Programs on Metamaterials - State-of-the-art | A new European Doctoral Program on Metamaterials has been initiated by the European Union (EU) Network of Excellence METAMORPHOSE. So far, twenty European academic institutions have established a consortium that operates a geographically distributed doctoral school in the emerging and multidisciplinary field of metamaterials. |
5,678 | The Secret to That Old Black Magic: Mastering the Art of Sparkless Commutation | DC machines have been in operation for more than 100 years. Engineers and technicians understand the dc-motor structure and components since they have experience rebuilding, testing, and making performance adjustments to them. Those that understand commutation, [2], [4] the commutation zone (CZ), and the characteristics of the carbon brush are few in number. Traditionally, the lack of a visible arc has been the defining quality of good commutation; hence, the term "black." The knowledge gap and relatively high-level physical science involved with commutation have led to an art known as "black magic" for achieving the invisible-arc condition [5], [6]. Black magic refers to both the brush coloration and commutation. The purpose of this article is to define the CZ and present the critical components that create it. An explanation of specific brush properties and their contribution to successful commutation is included. |
5,679 | A deep neural network approach to QRS detection using autoencoders*,** | Objective: In this paper, a stacked autoencoder deep neural network is proposed to extract the QRS complex from raw ECG signals without any conventional feature extraction phase. Methods: A simple architecture has been deeply trained on many datasets to ensure the generalization of the network at inference. Results: The proposed method achieved a QRS detection accuracy of 99.6% using more than 1042000 beats which is competitive with all state-of-the-art QRS detectors. Moreover, the proposed method produced only 0.82% of Detection Error Rate using six unseen datasets containing more than 1470000 beats. Thus confirms the high performance of our method to detect QRSs. Conclusion: Stacked autoencoder neural networks are very effective in QRS detection. At inference, our algorithm processes 1042309 beats in less than 25.32 s. Thus, it is favorably comparable with state-of-the-art deep learning methods. Significance: The stacked autoencoder is an efficient tool for QRS detection, which could replace conventional systems to help practitioners make fast and accurate decisions. |
5,680 | Lipid binding protein response to a bile acid library: a combined NMR and statistical approach | Primary bile acids, differing in hydroxylation pattern, are synthesized from cholesterol in the liver and, once formed, can undergo extensive enzyme-catalysed glycine/taurine conjugation, giving rise to a complex mixture, the bile acid pool. Composition and concentration of the bile acid pool may be altered in diseases, posing a general question on the response of the carrier (bile acid binding protein) to the binding of ligands with different hydrophobic and steric profiles. A collection of NMR experiments (H/D exchange, HET-SOFAST, ePHOGSY NOESY/ROESY and (15) N relaxation measurements) was thus performed on apo and five different holo proteins, to monitor the binding pocket accessibility and dynamics. The ensemble of obtained data could be rationalized by a statistical approach, based on chemical shift covariance analysis, in terms of residue-specific correlations and collective protein response to ligand binding. The results indicate that the same residues are influenced by diverse chemical stresses: ligand binding always induces silencing of motions at the protein portal with a concomitant conformational rearrangement of a network of residues, located at the protein anti-portal region. This network of amino acids, which do not belong to the binding site, forms a contiguous surface, sensing the presence of the bound lipids, with a signalling role in switching protein-membrane interactions on and off. |
5,681 | Aqua-(2-formylbenzoato)triphenyltin(IV) induces cell cycle arrest and apoptosis in hypoxic triple negative breast cancer cells | Hypoxia plays a vital role in tumor microenvironment by allowing development and maintenance of cancer cells thereby led to major hindrance for effective anticancer therapy and main reason for failure of most anticancer drugs. We herein investigated the therapeutic efficacy and molecular mechanism of action of aqua-(2-formylbenzoato) triphenyltin (IV) compound (OTC) in MDA-MB-231 cell line. Cobalt chloride induced hypoxic MDA-MB-231 cells treated with OTC were used to access cytotoxicity, ROS, cellular apoptosis, and cell cycle progression. Further, expression of HIF-1α and VEGF, as well as apoptotic proteins like p53, Bax, Bcl-2 and caspase 3 were assessed. The findings indicated that OTC is more effective towards CoCl2 induced hypoxic cells when compared to normoxic cells and the results are far superior to doxorubicin. Additionally, our study revealed that OTC facilitates more ROS production induced cell cycle arrest and promote apoptosis. Furthermore, OTC significantly down regulates the expression of Hif-1α, VEGF and Bcl-2 in hypoxic condition and elevates the level of p53, Bax, cytochrome-C and Caspase 3. Our in vitro studies demonstrated that OTC showed better efficacy than doxorubicin, corroborating that OTC could be a promising compound for hypoxic cancer that also display multi drug resistant. |
5,682 | Understanding ART-based neural algorithms as statistical tools for manufacturing process quality control | Neural networks have recently received a great deal of attention in the field of manufacturing process quality control, where statistical techniques have traditionally been used. In this paper, a neural-based procedure for quality monitoring is discussed from a statistical perspective. The neural network is based on Fuzzy ART, which is exploited for recognising any unnatural change in the state of a manufacturing process. Initially, the neural algorithm is analysed by means of geometrical arguments. Then, in order to evaluate control performances in terms of errors of Types I and II, the effects of three tuneable parameters are examined through a statistical model. Upper, bound limits for the error rates are analytically computed, and then numerically illustrated for different combinations of the tuneable parameters. Finally, a criterion for the neural network designing is proposed and validated in a specific test case through simulation. The results demonstrate the effectiveness of the proposed neural-based procedure for manufacturing quality monitoring. (c) 2005 Elsevier Ltd. All rights reserved. |
5,683 | Compressive Sensing of Electrocardiogram Signals by Promoting Sparsity on the Second-Order Difference and by Using Dictionary Learning | A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the l(p) pseudo-norm of the second-order difference, called as the l(p)(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art l(p)(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm. |
5,684 | A 36.7 mW, 28 GHz receiver frontend using 40 nm RFCMOS technology with improved Figure of Merit | High carrier frequency requirement (Sub 6, 28 GHz) to accomplish the high bandwidth specification for millimeter wave band wireless communication, has reduced the ratio of operating carrier frequency (f(c)) and unity current gain frequency (f(t)) of MOSFETs in state of the art RFCMOS technology. This poses a challenge for designing a high gain and low noise receiver with better linearity. In an attempt to realize such receiver, this paper presents a 28 GHz receiver front-end in 40 nm RFCMOS technology. It includes 3-stage low noise amplifier incorporating push pull topology, current bleeding down converting gilbert cell based mixer with common gate transconductance stage followed by a standard Gm-C filter. By incorporating these techniques, the performance of the proposed receiver improved in terms of linearity as compared to the state of the art designs. For a comprehensive analysis, the combined effect of performance parameters has been compiled into a single metric i.e. Figure of Merit (FOM). The proposed receiver exhibits conversion gain of 30.5 dB and 2.15 dB noise figure with linearity parameter IIP3 being -21.7 dBm while consuming 36.7mW power resulting in FOM value of 0.27. |
5,685 | Development of Pyrazine-Anilinobenzamides as Histone Deacetylase HDAC1-3 Selective Inhibitors and Biological Testing Against Pancreas Cancer Cell Lines | Class I histone deacetylase (HDAC) enzymes are key regulators of cell proliferation and are frequently dysregulated in cancer cells. Here we describe the synthesis of a novel series of class-I selective HDAC inhibitors containing anilinobenzamide moieties as ZBG connected with a central (piperazin-1-yl)pyrazine moiety. Compounds were tested in vitro against class-I HDAC1, 2, and 3 isoforms. Some highly potent HDAC inhibitors were obtained and were tested in pancreatic cancer cells and showed promising activity. Moreover, we summarize how the growth-inhibitory effects of these compounds can be determined in murine pancreatic cancer cell lines. |
5,686 | Pipelines for HDR Video Coding Based on Luminance Independent Chromaticity Preprocessing | We consider the chromaticity in high dynamic range (HDR) video coding and show the advantages of a constant luminance color space for encoding. For this, we introduce two constant luminance HDR video coding pipelines, which convert the source video to linear Y u' v'. A content dependent scaling of the chromaticity components serves as color quality parameter. This reduces perceivable color artifacts while remaining fully compatible with core High Efficiency Video Coding or other video coding standards. One of the pipelines further combines the scaling with a dedicated chromaticity transform to optimize the representation of the chromaticity components for encoding. We validate both pipelines with subjective user studies in addition to an objective comparison to the other state-of-the-art methods. The user studies show a significant improvement in perceived color quality at medium to high compression rates without sacrificing luminance quality compared with current standard coding pipelines. The objective evaluation suggests that both pipelines perform at least comparable to the current state-of-the-art methods. |
5,687 | High-rate activated sludge system for carbon management--Evaluation of crucial process mechanisms and design parameters | The high-rate activated sludge (HRAS) process is a technology suitable for the removal and redirection of organics from wastewater to energy generating processes in an efficient manner. A HRAS pilot plant was operated under controlled conditions resulting in concentrating the influent particulate, colloidal, and soluble COD to a waste solids stream with minimal energy input by maximizing sludge production, bacterial storage, and bioflocculation. The impact of important process parameters such as solids retention time (SRT), hydraulic residence time (HRT) and dissolved oxygen (DO) levels on the performance of a HRAS system was demonstrated in a pilot study. The results showed that maximum removal efficiencies of soluble COD were reached at a DO > 0.3 mg O2/L, SRT > 0.5 days and HRT > 15 min which indicates that minimizing the oxidation of the soluble COD in the high-rate activated sludge process is difficult. The study of DO, SRT and HRT exhibited high degree of impact on the colloidal and particulate COD removal. Thus, more attention should be focused on controlling the removal of these COD fractions. Colloidal COD removal plateaued at a DO > 0.7 mg O2/L, SRT > 1.5 days and HRT > 30 min, similar to particulate COD removal. Concurrent increase in extracellular polymers (EPS) production in the reactor and the association of particulate and colloidal material into sludge flocs (bioflocculation) indicated carbon capture by biomass. The SRT impacted the overall mass and energy balance of the high-rate process indicating that at low SRT conditions, lower COD mineralization or loss of COD content occurred. In addition, the lower SRT conditions resulted in higher sludge yields and higher COD content in the WAS. |
5,688 | The impact of the art-ICA control technology on the performance, energy consumption and greenhouse gas emissions of full-scale wastewater treatment plants | Advanced real time - Instrumentation, Control, and Automation (Art-ICA) controllers are an advanced control solution for biological nutrient removal wastewater treatment plants. Art-ICA has been previously shown to be capable of enhancing nutrient removal performance in BNR plants, at lower energy expenditures. However, the impact that this control solution has on the greenhouse gas emissions from full-scale wastewater treatment plants has not previously been addressed. This work addresses the effect of art-ICA on the performance, energy consumption and greenhouse gas emissions of two full-scale WWTPs, Chelas and Castelo Branco (Portugal). The raw wastewater, nitrous oxide emissions, energy consumption and water discharges were quantified in two independent trains operated under different operational modes, conventional operation and art-ICA control. The implementation of the art-ICA strategy improved the effluent quality and reduced the operational costs, resulting in a better performance of these WWTPs. The art-ICA controllers activation led to a reduction of 54% and 7-10% of the total nitrogen effluent and in the specific energy consumption, respectively. Moreover, process control with art-ICA did not have a negative impact on the N2O emissions of the plants, and contributed to lower global warming potential by the facilities. The lower indirect carbon dioxide production due to lower energy consumption contributes to the observation that art-ICA control is environmentally preferable to conventional control. (C) 2018 Elsevier Ltd. All rights reserved. |
5,689 | Multidimensional study of factors influencing sustainable construction adoption in Yemen: insights for implementing sustainable practices | Despite political volatility, Yemen's construction sector is gradually progressing to satisfy the country's housing needs. Most construction projects, however, employ traditional construction methods and have yet to be able to adopt sustainable construction, which is economically feasible, socially responsible, and environmentally beneficial. To support sustainable construction, this study analyzes various factors that might drive sustainable practices in construction projects in Yemen. These drivers are discovered from the literature and validated by experts using grey Delphi. Forty-four drivers were approved across three dimensions: economic, social, and environmental. These drivers are assessed using grey AHP. The economic factor is determined to be the most crucial in the adoption of sustainable construction. Competitiveness, improved well-being, and improved indoor environmental quality are rated as the top economic, social, and environmental dimensions, respectively. Overall, competitiveness is acknowledged as the most important driver for the implementation of sustainable practices in Yemen's construction projects. The study's findings were discussed with the experts who were involved in the evaluation. The findings were agreed upon, and it was underlined that a proactive approach from both construction project participants and public authorities can increase the competitiveness of sustainable construction. Additional policies to promote competitiveness of sustainable construction are also advocated. |
5,690 | The Synergistic Effect of Urban Canyon Geometries and Greenery on Outdoor Thermal Comfort in Humid Subtropical Climates | Understanding the synergistic effect of multiple parameters is helpful to urban planners trying to design sustainable cities through a holistic approach. The objective of this research was to investigate how the street aspect ratio (HW), street orientation (AO), and greenery parameters, such as leaf area density (LAD) and aspect ratio of trees (ART), could affect the microclimate and outdoor thermal comfort of street canyons in a central business district under the local climate conditions of Chongqing city. To achieve this goal, a series of single- and multi-parameter simulations which followed an orthogonal design of experiments (ODOE) were conducted. The physiological equivalent temperature (PET) was adopted to assess the results of microclimate simulations for different urban models. The main findings are as follows: 1) The aspect ratio and orientation of urban canyons and ART play significant roles in influencing microclimate variables at the pedestrian level. 2) There is an inverse relationship between the street aspect ratio and T-mrt, and likewise for ART; the highest wind velocity was obtained when the aspect ratio of canyons was 2 and 3, which consequently developed the channeling phenomenon (when the domain wind is prevailing with street direction). 3) The East-West streets and canyons with an HW = 0.5 incur the warmest thermal conditions and longest extreme discomfort durations. 4) Results for the PET and meteorological parameters exhibit less significant variation obtained from different values of LAD than those observed in the other three parameters. |
5,691 | An introduction to block cipher cryptanalysis | Since the introduction of the Data Encryption Standard (DES) in the mid-1970s, block ciphers have played an ever-increasing role in cryptology. Because of the growing number of practical applications relying on their security, block ciphers have received, and are still receiving, a substantial amount of attention from academic cryptanalysts. This has led, over the last decades, to the development of several general techniques to analyze the security of block ciphers. This paper reviews the fundamental principles behind today's state of the art in block cipher cryptanalysis. |
5,692 | AED-Net: A Single Image Dehazing | In the past decade, significant research effort has been directed toward developing single-image dehazing algorithms. Despite this effort, dehazing continues to present a challenge, particularly in complex real-world cases. Indeed, it is an ill-posed problem because scene transmission depends on unknown and nonhomogeneous depth information. This paper proposes a novel end-to-end adaptive enhancement dehazing network (AED-Net) to recover clean scenes from hazy images. We evaluate it quantitatively and qualitatively against several state-of-the-art methods on three commonly used dehazing benchmark datasets as well as hazy real-world images. Moreover, we evaluated it against the top-scoring methods of the Codalab NTIRE 2021 competition based on the dehazing challenge dataset. Extensive computer simulations demonstrated that AED-Net outperforms state-of-the-art single-image haze removal algorithms in terms of PSNR, SSIM, and other key metrics. Furthermore, it improves image texture, detail edges, boosts image contrast and color fidelity. Finally, AED-Net is more effective under complex real-world conditions. |
5,693 | A new approach for multiclass motor imagery recognition using pattern image features generated from common spatial patterns | This paper presents a new method that can classify multiple motor imageries and can be implemented in a realistic application because of its low computation time. The method proposes the use of pattern images, generated with the common spatial pattern (CSP) technique. The paper also suggests a new algorithm to determine the best frequency bands for optimal discrimination among the diverse motor imageries to classify. The pattern images and the state images, which represent the mental state of the user in a specific segment of time, are used to compute cross-correlation coefficients. Feature vectors, including characteristics obtained with CSP, and the mean and variance of the correlation coefficients were employed to design binary classifiers with support vector machines. In addition, the work includes a real-time simulation involving a sliding window technique. The proposed method was evaluated in four datasets: IVa, IVb and V from BCI Competition III and another provided by the software BCILAB, which compared with other state-of-the-art methods. The results that overcome surpassed the methods in these competitions and other state-of-the-art methods mentioned in this paper. The method also presents short computation time, robustness between subjects and capability to classify between multiple mental states. |
5,694 | Recent advances in mesoporous silica nanoparticle-based targeted drug-delivery systems for cancer therapy | Targeted drug-delivery systems are a growing research topic in tumor treatment. In recent years, mesoporous silica nanoparticles (MSNs) have been extensively studied and applied in noninvasive and biocompatible drug-delivery systems for tumor therapy due to their outstanding advantages, which include high surface area, large pore volume, tunable pore size, easy surface modification and stable framework. The advances in the application of MSNs for anticancer drug targeting are covered and highlighted in this review, and the challenges and prospects of MSN-based targeted drug-delivery systems are discussed. This review provides new insights for researchers interested in targeted drug-delivery systems against cancer. |
5,695 | Astersaponin I from Aster koraiensis is a natural viral fusion blocker that inhibits the infection of SARS-CoV-2 variants and syncytium formation | The continuous emergence of SARS-CoV-2 variants prolongs COVID-19 pandemic. Although SARS-CoV-2 vaccines and therapeutics are currently available, there is still a need for development of safe and effective drugs against SARS-CoV-2 and also for preparedness for the next pandemic. Here, we discover that astersaponin I (AI), a triterpenoid saponin in Aster koraiensis inhibits SARS-CoV-2 entry pathways at the plasma membrane and within the endosomal compartments mainly by increasing cholesterol content in the plasma membrane and interfering with the fusion of SARS-CoV-2 envelope with the host cell membrane. Moreover, we find that this functional property of AI as a fusion blocker enables it to inhibit the infection with SARS-CoV-2 variants including the Alpha, Beta, Delta, and Omicron with a similar efficacy, and the formation of syncytium, a multinucleated cells driven by SARS-CoV-2 spike protein-mediated cell-to-cell fusion. Finally, we claim that the triterpene backbone as well as the attached hydrophilic sugar moieties of AI are structurally important for its inhibitory activity against the membrane fusion event. Overall, this study demonstrates that AI is a natural viral fusion inhibitor and proposes that it can be a broad-spectrum antiviral agent against current COVID-19 pandemic and future outbreaks of novel viral pathogens. |
5,696 | Development of ZnO nanoflowers-assisted DNAzyme-based electrochemical platform for invertase and glucose oxidase-dominated biosensing | The investigation on invertase (INV) and glucose oxidase (GOx)-dominated biological process offers a new opportunity for the development of clinical diagnosis and prognostic treatment. Herein, a ZnO nanoflowers (ZnONFs)-assisted DNAzyme-based electrochemical platform for INV- and GOx-dominated biosensing is proposed by the change of pH in microenvironment. In this strategy, INV can usually catalyze the dissolution of sucrose to generate glucose, and glucose is then consumed by GOx to produce H2O2 and gluconic acid, in which ZnONFs can be effectively etched into free Zn2+ ions. Subsequently, the released Zn2+ ions have a shearing action for Zn2+-specific DNAzyme, thus triggering hybridization chain reaction along with the imbedding of methylene blue. The excellent electrochemical signals illustrate the method can be employed well for testing sucrose, INV and GOx with a low detection limit (0.019 μM, 0.047 mU/mL and 0.012 mU/mL, respectively). Finally, a series of basic and advanced logic gates (YES, AND, INHIBIT, and AND-AND-INHIBIT) in the biological process are constructed with different logic inputs, providing a valuable platform for the establishment of advanced molecular devices for bioanalysis and clinical diagnostics. |
5,697 | A fast arbitrary factor video resizing algorithm | We present a new algorithm for resizing video frames in the discrete cosine transform (DCT) space. We demonstrate that a frame resizing operation can be represented as multiplication by fixed matrices and propose a computation scheme which is applicable to any DCT-based compression method. The proposed approach is general enough to accommodate resizing operations with arbitrary factors conforming to the syntax of 16 x 16 macroblocks. The approach is shown to possess significant computational gain over the faster known state of the art algorithms while achieving similar picture quality. |
5,698 | Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification | Vibroarthrography (VAG) is a non-invasive and potentially widely available method supporting the joint diagnosis process. This research was conducted using VAG signals classified to five different condition classes: three stages of chondromalacia patellae, osteoarthritis, and control group (healthy knee joint). Ten new spectral features were proposed, distinguishing not only neighboring classes, but every class combination. Additionally, Frequency Range Maps were proposed as the frequency feature extraction visualization method. The results were compared to state-of-the-art frequency features using the Bhattacharyya coefficient and the set of ten different classification algorithms. All methods evaluating proposed features indicated the superiority of the new features compared to the state-of-the-art. In terms of Bhattacharyya coefficient, newly proposed features proved to be over 25% better, and the classification accuracy was on average 9% better. |
5,699 | Angiotensin II Regulates Mitochondrial mTOR Pathway Activity Dependent on Acyl-CoA Synthetase 4 in Adrenocortical Cells | Two well-known protein complexes in mammalian cells, mTOR type 1 and type 2 (mTORC1/2) are involved in several cellular processes such as protein synthesis, cell proliferation, and commonly dysregulated in cancer. An acyl-CoA synthetase type 4 (ACSL4) is one of the most recently mTORC1/2 regulators described, in breast cancer cells. The expression of ACSL4 is hormone-regulated in adrenocortical cells and required for steroid biosynthesis. mTORC1/2 have been reported to be crucial in the proliferation of human adrenocortical tumor cells H295R and interestingly reported at several subcellular locations, which has brought cell biology to the vanguard of the mTOR signaling field. In the present work, we study the regulation of mTORC1/2 activation by angiotensin II (Ang II)-the trophic hormone for adrenocortical cells-the subcellular localization of mTORC1/2 signaling proteins and the role of ACSL4 in the regulation of this pathway, in H295R cells. Ang II promotes activation by phosphorylation of mTORC1/2 pathway proteins in a time-dependent manner. Mitochondrial pools of ribosomal protein S6, protein kinase B (Akt) in threonine 308, and serine 473 and Rictor are phosphorylated and activated. Glycogen synthase kinase type 3 (GSK3) is phosphorylated and inactivated in mitochondria, favoring mTORC1 activation. Epidermal growth factor, a classic mTORC1/2 activator, promoted unique activation kinetics of mTORC1/2 pathway, except for Akt phosphorylation. Here, we demonstrate that ACSL4 is necessary for mTORC1/2 effectors phosphorylation and H295R proliferation, triggered by Ang II. Ang II promotes activation of mitochondrial mTORC1/2 signaling proteins, through ACSL4, with a direct effect on adrenocortical cellular proliferation. |
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