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3,200 | ATLANTIS: A benchmark for semantic segmentation of waterbody images | Vision-based semantic segmentation of waterbodies and nearby related objects provides important information for managing water resources and handling flooding emergency. However, the lack of large-scale labeled training and testing datasets for water-related categories prevents researchers from studying water-related issues in the computer vision field. To tackle this problem, we present ATLANTIS, a new benchmark for semantic segmentation of waterbodies and related objects. ATLANTIS consists of 5,195 images of waterbodies, as well as high quality pixel-level manual annotations of 56 classes of objects, including 17 classes of man-made objects, 18 classes of natural objects and 21 general classes. We analyze ATLANTIS in detail and evaluate several state-of-the-art semantic segmentation networks on our benchmark. In addition, a novel deep neural network, AQUANet, is developed for waterbody semantic segmentation by processing the aquatic and non-aquatic regions in two different paths. AQUANet also incorporates low-level feature modulation and cross-path modulation for enhancing feature representation. Experimental results show that the proposed AQUANet outperforms other state-of-the-art semantic segmentation networks on ATLANTIS. We claim that ATLANTIS is the largest waterbody image dataset for semantic segmentation providing a wide range of water and water-related classes and it will benefit researchers of both computer vision and water resources engineering. |
3,201 | Algorithm and Architecture Co-Design of Hardware-Oriented, Modified Diamond Search for Fast Motion Estimation in H.264/AVC | In this paper, we present a new hardware-oriented, modified diamond search (HMDS) algorithm, for fast integer pel, motion estimation in H. 264/AVC. We also present our co-designed, low power very large scale integration (VLSI) architecture for HMDS. The goal of HMDS is to enable the support of high quality video on low power mobile devices and low bit rate applications which typically use H.264/AVC baseline profile at levels 1-2. Our experiments use standard test sequences ranging from QCIF to high-definition 1280 x 720p video. The proposed VLSI architecture is prototyped as an field-programable gate array (FPGA)-based field programable system-on- chip. Our results show that HMDS on average has better rate-distortion performance and speedup, compared to previous state-of-the-art fast motion estimation algorithms, while its losses compared to full search motion estimation, are insignificant. Our prototyped architecture is more hardware-efficient than previous FPGA-based architectures in terms of power consumption, area, throughput, and memory utilization. We also show that its performance in terms of maximum frequency, minimum frequency, transistor count, and power consumption are comparable to that of state-of-the-art architectures implemented on application-specific integrated circuits. |
3,202 | Pixel-Based Approach for Generating Original and Imitating Evolutionary Art | We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the pixel-based method, a von Neumann neighbor topology-modified particle swarm optimization (PSO) is employed to the proposed method. The fitness functions of PSO are well prepared. Firstly, we come up with a set of aesthetic fitness functions. Next, the imitating fitness function is designed. Finally, the aesthetic fitness functions and the imitating fitness function are weighted into one single object function, which is used in the modified PSO. Both the original outputs and imitating outputs are shown. A questionnaire is designed to investigate the subjective aesthetic feeling of proposed evolutionary art, and the statistics are shown. |
3,203 | A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information | A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios.</p> |
3,204 | GraphSKT: Graph-Guided Structured Knowledge Transfer for Domain Adaptive Lesion Detection | Adversarial-based adaptation has dominated the area of domain adaptive detection over the past few years. Despite their general efficacy for various tasks, the learned representations may not capture the intrinsic topological structures of the whole images and thus are vulnerable to distributional shifts especially in real-world applications, such as geometric distortions across imaging devices in medical images. In this case, forcefully matching data distributions across domains cannot ensure precise knowledge transfer and are prone to result in the negative transfer. In this paper, we explore the problem of domain adaptive lesion detection from the perspective of relational reasoning, and propose a Graph-Structured Knowledge Transfer (GraphSKT) framework to perform hierarchical reasoning by modeling both the intra- and inter-domain topological structures. To be specific, we utilize cross-domain correspondence to mine meaningful foreground regions for representing graph nodes and explicitly endow each node with contextual information. Then, the intra- and inter-domain graphs are built on the top of instance-level features to achieve a high-level understanding of the lesion and whole medical image, and transfer the structured knowledge from source to target domains. The contextual and semantic information is propagated through graph nodes methodically, enhancing the expressive power of learned features for the lesion detection tasks. Extensive experiments on two types of challenging datasets demonstrate that the proposed GraphSKT significantly outperforms the state-of-the-art approaches for detection of polyps in colonoscopy images and of mass in mammographic images. |
3,205 | o-Quinodimethane Atropisomers: Enantioselective Synthesis and Stereospecific Transformation | o-Quinodimethanes have remarkable utility as reactive intermediates in Diels-Alder reactions, enabling significantly accelerated routes to complex polycyclic compounds. The discovery of different discrete precursors to thermally generate o-quinodimethanes thereby greatly augmented their availability and versatility. However, due to the required high temperatures and the immense reactivity of o-quinodimethanes, stereoselectivity to afford isomerically defined products still constitutes a critical challenge. Herein, we describe the accessibility of atropisomeric o-quinodimethanes, the enantioselective synthesis of their precursors, their remarkable configurational stability and the stereospecific transformation by the benzannulation of dienophiles. A catalyst-stereocontrolled [2+2+2] cycloaddition, the generation of o-quinodimethane atropisomers and ensuing stereospecific Diels-Alder reactions enabled enantioselectivities through these transient intermediates with of up to 96 : 4 e.r. |
3,206 | [Exploration and Thinking on Fine Management of Cost Accounting of Medical Consumables] | With the gradual advancement of The Reform Plan to Control High-value Medical Consumables published by the State Council, the reform policies such as purchase with quantity, charging consumables" zero bonus" were born, the operating pressure of medical institutions on medical consumables increased sharply, and the fine cost accounting management demands were improving. Due to the manage features of medical consumables, this will lead to the inaccurate and cross-cycle of cost accounting. In order to achieve the refined cost accounting management, the related business system and process adjustment are studied. |
3,207 | Automatic Object Tracking and Segmentation Using Unsupervised SiamMask | In this paper we address the basic limitation of SiamMask - the state of the art single object tracking and segmentation algorithm. SiamMask requires semi-supervision in that it needs a bounding box to be drawn manually around the object that has to be tracked. This is however not always possible or feasible, and slows down the pipeline even in the best case. We overcome this limitation by using state-of-the-art object detection algorithms: Detectron2 and YOLO to automatically detect the object and then track using SiamMask. The main purpose of this study is to devise an efficient technique for an end-to-end object detection and tracking, which can then be used in other applications like self-driving cars, etc. We compared different approaches using current state-of-the-art tools for time and detection efficiency. One of the secondary aim was to test how the two approaches perform on different types of datasets. We note that YOLO gives better and more meaningful detection of objects in the scene. However, Detectron2 gives a higher detection speed than YOLO, making the overall detection and tracking process faster. |
3,208 | Novel concepts on mechanisms underlying Hepatitis Delta virus persistence and related pathogenesis | Hepatitis Delta virus is the smallest known human virus, exploiting the HBV surface proteins (HBsAg) for the release of its progeny and de novo entry into hepatocytes. Ever growing evidence have highlighted the existence of multiple mechanisms underlying HDV persistence including integrated HBV-DNA as a source of HBsAg production and the capability of the HDV genome to propagate through cell proliferation, thus supporting a potential HDV persistence even in the absence of HBV. Chronic HDV-infection causes the most severe form of viral hepatitis, leading to the development of cirrhosis in 15% of cases within 1-2 years and in 50%-60% of cases within 5-10 years. The rates of hepatocellular carcinoma and hepatic decompensation are also 2-3-fold higher than for HBV mono-infection. There is the evidence that persistent viral replication plays a key role in triggering liver injury, suggesting the existence of direct viral cytopathic properties that can modulate, synergistically with immune-responses, the progression towards end-stage liver diseases. All these aspects can be further exacerbated by the extraordinary degree of viral genetic variability that can promote HDV evasion from immune responses and has enabled viral differentiation into genotypes and subgenotypes with potential different pathobiological properties. In this light, this review aims at providing comprehensive insights of mechanisms (with a focus on virological factors) underlying HDV persistence and pathogenesis, critical in shaping the clinical outcome of the infection. Dissecting these mechanisms is pivotal to optimize therapeutic strategies aimed at fully counteracting this fascinating and fearsome virus. |
3,209 | Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking | In recent years, discriminative correlation filter (DCF) based algorithms have significantly advanced the state of the art in visual object tracking. The key to the success of DCF is an efficient discriminative regression model trained with powerful multi-cue features, including both hand-crafted and deep neural network features. However, the tracking performance is hindered by their inability to respond adequately to abrupt target appearance variations. This issue is posed by the limited representation capability of fixed image features. In this work, we set out to rectify this shortcoming by proposing a complementary representation of a visual content. Specifically, we propose the use of a collaborative representation between successive frames to extract the dynamic appearance information from a target with rapid appearance changes, which results in suppressing the undesirable impact of the background. The resulting collaborative representation coefficients are combined with the original feature maps using a spatially regularised DCF framework for performance boosting. The experimental results on several benchmarking datasets demonstrate the effectiveness and robustness of the proposed method, as compared with a number of state-of-the-art tracking algorithms. |
3,210 | A comment on two-locus epistatic interaction models for genome-wide association studies | Detection of epistatic interactions in genome-wide association studies is a computationally hard problem. Many detection algorithms have been proposed and will continue to be. Most of those algorithms measure their predictive power by running on simulated data many times under various disease models. However, we find that there have been subtle differences in interpreting the meaning of existing disease models among the previous studies on detection of epistatic interactions. We elucidate those differences and suggest that future studies on epistatic interactions in GWAS state explicitly which versions/interpretations are employed. We also provide a way to facilitate setting parameters of disease models. |
3,211 | Virtual Adversarial Training-Based Deep Feature Aggregation Network From Dynamic Effective Connectivity for MCI Identification | Dynamic functional connectivity (dFC) network inferred from resting-state fMRI reveals macroscopic dynamic neural activity patterns for brain disease identification. However, dFC methods ignore the causal influence between the brain regions. Furthermore, due to the complex non-Euclidean structure of brain networks, advanced deep neural networks are difficult to be applied for learning high-dimensional representations from brain networks. In this paper, a group constrained Kalman filter (gKF) algorithm is proposed to construct dynamic effective connectivity (dEC), where the gKF provides a more comprehensive understanding of the directional interaction within the dynamic brain networks than the dFC methods. Then, a novel virtual adversarial training convolutional neural network (VAT-CNN) is employed to extract the local features of dEC. The VAT strategy improves the robustness of the model to adversarial perturbations, and therefore avoids the overfitting problem effectively. Finally, we propose the high-order connectivity weight-guided graph attention networks (cwGAT) to aggregate features of dEC. By injecting the weight information of high-order connectivity into the attention mechanism, the cwGAT provides more effective high-level feature representations than the conventional GAT. The high-level features generated from the cwGAT are applied for binary classification and multiclass classification tasks of mild cognitive impairment (MCI). Experimental results indicate that the proposed framework achieves the classification accuracy of 90.9%, 89.8%, and 82.7% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification respectively, outperforming the state-of-the-art methods significantly. |
3,212 | Asphalt production technology from engineering art to science | In the last third of the last century, important progress was made in developing the scientific basis for oxidized asphalt production technology. The classification of crude oils and mathematical model developed allow quantitatively predicting the basic parameters of asphalt production based on vacuum distillation and oxidation process using the reference properties of the crude oil. |
3,213 | Cube-Evo: A Query-Efficient Black-Box Attack on Video Classification System | The current progressive research in the domain of black-box adversarial attack enhances the reliability of deep neural network (DNN)-based video systems. Recent works mainly carry out black-box adversarial attacks on video systems by query-based parameter dimension reduction. However, the additional temporal dimension of video data leads to massive query consumption and low attack success rate. In this article, we embark on our efforts to design an effective adversarial attack on popular video classification systems. We deeply root the observations that the DNN-based systems are sensitive to adversarial perturbations with high frequency and reconstructed shape. Specifically, we propose a systematic attack pipeline Cube-Evo, aiming to reduce the search space dimension and obtain the effective adversarial perturbation via the optimal parameter group updating. We evaluate the proposed attack pipeline on two popular datasets: UCF101 and JESTER. Our attack pipeline reduces query consumption and achieves a high success rate on various DNN-based video classification systems. Compared with the state-of-the-art method Geo-Trap-Att, our pipeline averagely reduces 1.6x query consumption in untargeted attacks and 2.9x in targeted attacks. Besides, Cube-Evo improves 13% attack success rate on average, achieving new state-of-the-art results over diverse video classification systems. |
3,214 | Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images | Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs), resulting in data hungry models that learn independent features at each orientation. Allowing CNNs to be rotation-equivariant removes the necessity to learn this set of transformations from the data and instead frees up model capacity, allowing more discriminative features to be learned. This reduction in the number of required parameters also reduces the risk of overfitting. In this paper, we propose Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework. Each filter is defined as a linear combination of steerable basis filters, enabling exact rotation and decreasing the number of trainable parameters compared to standard filters. We also provide the first in-depth comparison of different rotation-equivariant CNNs for histology image analysis and demonstrate the advantage of encoding rotational symmetry into modern architectures. We show that DSF-CNNs achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of computational pathology: breast tumour classification, colon gland segmentation and multi-tissue nuclear segmentation. |
3,215 | CRISPRi screening identifies CASP8AP2 as an essential viability factor in lung cancer controlling tumor cell death via the AP-1 pathway | Since lung cancer remains the leading cause of cancer death globally, there is an urgent demand for novel therapeutic targets. We carried out a CRISPR interference (CRISPRi) loss-of-function screen for human lung adenocarcinoma (LUAD) targeting 2098 deregulated genes using a customized algorithm to comprehensively probe the functionality of every resolvable transcriptional start site (TSS). CASP8AP2 was identified as the only hit that significantly affected the viability of all eight screened LUAD cell lines while the viability of non-transformed lung cells was only moderately impacted. Knockdown (KD) of CASP8AP2 induced both autophagy and apoptotic cell death pathways. Systematic expression profiling linked the AP-1 transcription factor to the CASP8AP2 KD-induced cancer cell death. Furthermore, inhibition of AP-1 reverted the CASP8AP2 silencing-induced phenotype. Overall, the tailored CRISPRi screen profiled the impact of over 2000 genes on the survival of eight LUAD cell lines and identified the CASP8AP2 - AP-1 axis mediating lung cancer viability. |
3,216 | [Pandemic and value change?] | Recent sociological diagnoses suggest that profound social crises such as the COVID-19 pandemic challenge our value orientations and could change them even in the relatively short term. Based on this observation, we investigate whether significant shifts in value priorities according to the Shalom Schwartz scale took place in Austria in the period May 2020 to March/April 2021. The first two waves of the Values in Crisis panel study serve as data material. Two theoretical assumptions are central to the interpretation of the results: first, the thesis of a trend toward conservatism and second, the thesis of the effective power of political discourses in times of (re)emerging populism. The article also pays special attention to a methodological discussion of changes in the meaning of questionnaire items due to the COVID-19 pandemic.The empirical analyses confirm a clear stability of value orientations. Above all, the value of conformity has changed, becoming more important for a significant part of the population; at the same time, the desire for a hedonistic lifestyle lost some of its importance. Conformity became more important, particularly for voters of the governing political parties, while this trend was not apparent, especially among voters of the FPÖ. Since the observed shift in value priorities mainly concerns "pandemic-sensitive" value dimensions, the results suggest a short-term reaction to the crisis rather than a long-term change in values. |
3,217 | SYK coordinates neuroprotective microglial responses in neurodegenerative disease | Recent studies have begun to reveal critical roles for the brain's professional phagocytes, microglia, and their receptors in the control of neurotoxic amyloid beta (Aβ) and myelin debris accumulation in neurodegenerative disease. However, the critical intracellular molecules that orchestrate neuroprotective functions of microglia remain poorly understood. In our studies, we find that targeted deletion of SYK in microglia leads to exacerbated Aβ deposition, aggravated neuropathology, and cognitive defects in the 5xFAD mouse model of Alzheimer's disease (AD). Disruption of SYK signaling in this AD model was further shown to impede the development of disease-associated microglia (DAM), alter AKT/GSK3β-signaling, and restrict Aβ phagocytosis by microglia. Conversely, receptor-mediated activation of SYK limits Aβ load. We also found that SYK critically regulates microglial phagocytosis and DAM acquisition in demyelinating disease. Collectively, these results broaden our understanding of the key innate immune signaling molecules that instruct beneficial microglial functions in response to neurotoxic material. |
3,218 | Maximum Density Divergence for Domain Adaptation | Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence ("match" in ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations. |
3,219 | Modeling and Evaluation of Carbon-Nanotube-Based Integrated Power Inductor for On-Chip Switching Power Converters | This paper presents a nanotechnology-based high-power-density and low-power-loss on-chip power inductor for dc-dc switching power converters. This power inductor utilizes a composite of bundled multiwalled (concentric) carbon nanotubes (BMWCNTs) and Fe (iron) in order to achieve high performance in a small size. Titanium (Ti) is then added as a coating material on the BMWCNT-based power inductor. The BMWCNT-based power inductor with a single layer and three turns occupies an area of 200 mu m x 200 mu m. It exhibits an inductance of 206 nH, a quality factor of 427 at 20 MHz, and a dc rated current of 100 mA. The power inductor size and the performance characteristics are competitive with state-of-the-art power inductors. The design, analysis, modeling, and simulation results of the BMWCNT-based power inductor are presented and compared with state-of-the-art conventional power inductors from the literature. |
3,220 | Hybrid histidine kinase HisK2301 modulates carotenoid production to counteract cold-induced oxidative stress in Rhodosporidium kratochvilovae YM25235 under low temperature | Hybrid histidine kinases (HHKs) are major sensor proteins for fungi that contribute to stress tolerance. In the present work, we investigated the roles and mechanisms of the HHK HisK2301 in cold-adapted Rhodosporidium kratochvilovae strain YM25235. The HisK2301 deletion strain was constructed by homologous recombination method and arranged for multiple stress tests. We analysed the content of carotenoid using UV-Vis and HPLC. Relative transcript levels of genes phytoene desaturase (RKCrtI) and phytoene synthase and lycopene cyclase (RKCrtYB) were analysed by RT-qPCR. Intracellular reactive oxygen species (ROS) generation was measured using 2',7'-dichlorodihydrofluorescein diacetate (DCFH-DA). Our results clearly indicated that YM25235 produces γ-carotene, torulene, β-carotene and torularhodin, with the latter two components strongly related to adapt to cold. HisK2301 is crucial for YM25235 adaptation to different types of stress such as cold, salt, osmotic and oxidative stress. Growth at low temperature clearly induced oxidative stress in YM25235, as more ROS accumulated at cold. During cold stress, HisK2301 is suggested to sense cold-induced ROS signals and then promote carotenoid production partially by RKCrtI and RKCrtYB to scavenge excessive ROS production. Such an inducible protective system may confer YM25235 fast response and better adaptation to cold stress. To conclude, our findings give the first insight into the effect of HisK2301 on carotenoid biosynthesis and cold-induced oxidative stress in fungi under low temperature and suggest the potential use of the cold-adapted HHK HisK2301 in industrial production of carotenoid. |
3,221 | Self-Supervised Attention Mechanism for Pediatric Bone Age Assessment With Efficient Weak Annotation | Pediatric bone age assessment (BAA) is a common clinical practice to investigate endocrinology, genetic and growth disorders of children. Different specific bone parts are extracted as anatomical Regions of Interest (RoIs) during this task, since their morphological characters have important biological identification in skeletal maturity. Following this clinical prior knowledge, recently developed deep learning methods address BAA with an RoI-based attention mechanism, which segments or detects the discriminative RoIs for meticulous analysis. Great strides have been made, however, these methods strictly require large and precise RoIs annotations, which limits the real-world clinical value. To overcome the severe requirements on RoIs annotations, in this paper, we propose a novel self-supervised learning mechanism to effectively discover the informative RoIs without the need of extra knowledge and precise annotation-only image-level weak annotation is all we take. Our model, termed PEAR-Net for Part Extracting and Age Recognition Network, consists of one Part Extracting (PE) agent for discriminative RoIs discovering and one Age Recognition (AR) agent for age assessment. Without precise supervision, the PE agent is designed to discover and extract RoIs fully automatically. Then the proposed RoIs are fed into AR agent for feature learning and age recognition. Furthermore, we utilize the self-consistency of RoIs to optimize PE agent to understand the part relation and select the most useful RoIs. With this self-supervised design, the PE agent and AR agent can reinforce each other mutually. To the best of our knowledge, this is the first end-to-end bone age assessment method which can discover RoIs automatically with only image-level annotation. We conduct extensive experiments on the public RSNA 2017 dataset and achieve state-of-the-art performance with MAE 3.99 months. Project is available at http://imcc.ustc.edu.cn/project/ssambaa/. |
3,222 | Circadian clock-mediated nuclear receptors in cancer | Circadian system coordinates the daily periodicity of physiological and biochemical functions to adapt to environmental changes. Circadian disruption has been identified to increase the risk of cancer and promote cancer progression, but the underlying mechanism remains unclear. And further mechanistic understanding of the crosstalk between clock components and cancer is urgent to achieve clinical anticancer benefits from chronochemotherapy. Recent studies discover that several nuclear receptors regulating circadian clock, also play crucial roles in mediating multiple cancer processes. In this review, we aim to summarize the latest developments of clock-related nuclear receptors in cancer biology and dissect mechanistic insights into how nuclear receptors coordinate with circadian clock to regulate tumorigenesis and cancer treatment. A better understanding of circadian clock-related nuclear receptors in cancer could help prevent tumorigenesis and improve anticancer efficacy. |
3,223 | CARA: Connectivity-Aware Relay Algorithm for Multi-Robot Expeditions | The exploration of unknown environments is an essential application of multi-robot systems, particularly in critical missions, such as hazard detection and search and rescue. These missions share the need to reach full coverage of the explorable space in the shortest time possible. To minimize the completion time, robots in the fleet must be able to reliably exchange information about the environment with one another. One of the main methods to expand coverage is by placing relays. Existing relay-placement algorithms tend to either require prior knowledge of the environment, or they rely on maintaining specific distances between the relays and the rest of the robots. These approaches lack flexibility and adaptability to the environment. This paper introduces the "Connectivity-Aware Relay Algorithm" (CARA), a dynamic context-aware relay-placement algorithm that does not require any prior knowledge of the environment. We compare CARA against a state-of-the-art distance-based relay-placement algorithm. Our results demonstrate that CARA outperformed the state-of-the-art algorithm in terms of the time to completion by a factor of 10 as it placed, on average, half the number of relays. |
3,224 | Plasma-processed CoSn/RGO nanocomposite: A low-cost and sustainable counter electrode for dye-sensitized solar cells | The high cost of state-of-the-art Pt counter electrodes (CEs) hinders the large-scale applications of dye-sensitized solar cells (DSCs). The development of Pt-free catalysts while maintaining state-of-the-art catalytic activity for CE materials is one mean to reduce costs. Here, CoxSn1-x/reduced graphene oxide (RGO) (0 <= x <= 1) nanohybrids were synthesized and employed as inexpensive, stable, and earth-abundant CEs in DSCs. The synthesis was performed through the plasma-assisted reduction of the oxygen functional groups of the graphene oxide along with the immobilization of bimetallic nanoparticles (NPs) on the surface of RGO. The optimization of the composition of the alloy NPs for the highest efficiency of DSC yields the Co0.9Sn0.1/RGO nanocomposite. The highest device performance correlates well with the experimentally obtained lowest charge transfer resistance in conjunction with the highest electrocatalytic activity of the Co0.9Sn0.1/RGO CE. The DSC employed the synthesized CE showed good stability over long term operation. Both the developed CoSn/RGO nanohybrids and the strategy used for their synthesis are cost-effective. Our results provide economically implementable and green nanotechnology for efficient and stable DSCs required for commercialization. |
3,225 | Hybrid Joint Diagonalization Algorithms | This letter deals with a hybrid joint diagonalization problem considering both Hermitian and transpose congruence. Such problem can be encountered in certain noncircular signal analysis applications including blind source separation. We introduce new Jacobi-like algorithms using Givens or a combination of Givens and hyperbolic rotations. These algorithms are compared with state-of-the-art methods and their performance gain, especially in the high dimensional case, is assessed through simulation experiments including examples related to blind separation of noncircular sources. |
3,226 | The timed up and go test for lumbar degenerative disc disease | We report on the use and performance of an objective measure of functional impairment, the timed up and go (TUG) test, in clinical practice for patients with lumbar degenerative disc disease (DDD). We illustrate nine representative patients with lumbar DDD, who were selected from an ongoing prospective study, to report our clinical experience with the TUG test. In addition, a preliminary sample of 30 non-selected consecutive patients is presented. The following parameters were assessed preoperatively, and 3 days and 6 weeks postoperatively: back and leg pain using the visual analogue scale (VAS); functional impairment using the Oswestry disability index (ODI) and Roland-Morris disability index (RMDI); health-related quality of life using the EuroQol 5D (EQ5D) and Short-Form 12 (SF-12). The TUG test results improved by 2.6 and 5.4s after 3 days and 6 weeks compared to the baseline assessment. The mean VAS for back and leg pain decreased by 2.3 and 5.3, respectively, after 3 days, and by 2.7 and 4.6 after 6 weeks. The mean RMDI and ODI decreased by 3.4 and 23.3, respectively, after 3 days, and by 7.0 and 28.0 after 6 weeks. The mean EQ5D increased by 0.38 after 3 days and 0.358 after 6 weeks. The mean SF-12 mental component scale decreased by 0.2 after 3 days and increased by 5.6 after 6 weeks, whereas the mean SF-12 physical component scale increased by 6.4 after 3 days and by 9.8 after 6 weeks. The TUG test proved to be a useful, easy to use tool that could add a new, objective dimension to the armamentarium of clinical tests for the diagnosis and management of DDD. From our preliminary experience, we conclude that the TUG test accurately reflects a patient's objective functional impairment before and after surgery. |
3,227 | GAD: A Global-Aware Diversity-Based Template Matching Method | A novel similarity measurement metric, global-aware diversity (GAD), between a template and an image is proposed in this article, which can be efficiently utilized in a template matching method. Unlike the existing nearest neighbor field (NNF)-based methods, which use either the local or global diversity alone, GAD utilizes the global context to guide the local diversity. We verify that the global context is important for template matching task and can largely remove the background and outliers. The state-of-the-art NNF-based methods in general are not efficient enough as resolution grows. Thus, we also elaborately design a very efficient algorithm of GAD to reduce the complexity from . First, the GAD algorithm has been evaluated on two challenging benchmark datasets best-buddies similarity (BBS) and TinyTLP. Experiments show that the GAD algorithm achieves the state-of-the-art performance. As GAD does not impose any prior on unseen data, it is more insensitive to large rotation or deformation than the deformation-based algorithms. Besides, the GAD algorithm can run at 150 ms on average with high accuracy on the dataset of resolution 1280 x 780. |
3,228 | Distribution system monitoring for smart power grids with distributed generation using artificial neural networks | The increasing number of distributed generators connected to distribution grids requires a reliable monitoring of distribution grids. Economic considerations prevent a full observation of distribution grids with direct measurements. First approaches using a limited number of measurements to monitor distribution grids exist, some of which use artificial neural networks (ANN). The current ANN-based approaches, however, are limited to static topologies, only estimate voltage magnitudes, do not work properly when confronted with a high amount of distributed generation and often yield inaccurate results. These strong limitations have prevented a true applicability of ANN for distribution system monitoring. The objective of this paper is to overcome the limitations of existing approaches. We do that by presenting an ANN-based scheme, which advances the state-of-the-art in several ways: Our scheme can cope with a very low number of measurements, far less than is traditionally required by the state-of-the-art weighted least squares state estimation (WLS SE). It can estimate both voltage magnitudes and line loadings with high precision and includes different switching states as inputs. Our contribution consists of a method to generate useful training data by using a scenario generator and a number of hyperparameters that define the ANN architecture. Both can be used for different power grids even with a high amount of distributed generation. Simulations are performed with an elaborate evaluation approach on a real distribution grid and a CIGRE benchmark grid both with a high amount of distributed generation from photo-voltaics and wind energy converters. They demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art ANN schemes and WLS SE under normal operating conditions and different situations such as gross measurement errors when comparing voltage magnitude and line magnitude estimation errors. |
3,229 | Tracking and Pairing Vehicle Headlight in Night Scenes | Traffic surveillance is an important topic in computer vision and intelligent transportation systems and has intensively been studied in the past decades. However, most of the state-of-the-art methods concentrate on daytime traffic monitoring. In this paper, we propose a nighttime traffic surveillance system, which consists of headlight detection, headlight tracking and pairing, and camera calibration and vehicle speed estimation. First, a vehicle headlight is detected using a reflection intensity map and a reflection suppressed map based on the analysis of the light attenuation model. Second, the headlight is tracked and paired by utilizing a simple yet effective bidirectional reasoning algorithm. Finally, the trajectories of the vehicle's headlight are employed to calibrate the surveillance camera and estimate the vehicle's speed. Experimental results on typical sequences show that the proposed method can robustly detect, track, and pair the vehicle headlight in night scenes. Extensive quantitative evaluations and related comparisons demonstrate that the proposed method outperforms state-of-the-art methods. |
3,230 | CoN4-supported Co2N metal clusters for developing sensitive chemiluminescent immunochromatographic assays | In view of the optimal catalytic efficiency (∼100%), single-atom site catalysts are being widely exploited in a range of areas including organic synthesis, energy conversion, environmental remediation, biotherapy, etc. However, low loading ratio of the unitary active sites on single-atom site catalysts dramatically hinders the remarkable improvement of their catalytic activity. Hereby, a facile low-temperature reduction protocol was adopted for synthesizing CoN4-supported Co2N metal clusters on graphitic carbon nitride, which show the remarkably superior chemiluminescent (CL) catalytic capacity than some reported pure single-atom site catalysts. Nitrogen-encapsulated Co2N clusters coupled with isolated Co-N4 moieties (Co2N@Co-N4) endowed the synergetic catalysts with high Co content of 53.2 wt%. Through X-ray absorption spectroscopy, the synergetic active sites (Co2N@Co-N4) afforded the CoN4-supported Co2N clusters with the remarkable catalytic activity for accelerating the decomposition of H2O2 to produce extensive superoxide radical anion rather than singlet oxygen or hydroxyl radical. Therefore, the CoN4-supported Co2N clusters possessed the superb enhancement effect on luminol-H2O2 CL reaction by ∼22829 times. The CoN4-supported Co2N clusters were utilized as signal probes to establish a CL immunochromatographic assay (ICA) platform for quantitating mycotoxins. Herein, aflatoxin B1 was employed as a mode analyte and the limit of detection was as low as 0.33 pg mL-1 (3σ). As a proof-of-principle work, the developed ICA protocol was successfully employed on the detection of aflatoxin B1 spiked in Angelica dahurica and Ganoderma lucidum with acceptable recoveries of 84.0-107.0%. The ideal practicability of the work elucidates that CoN4-supported Co2N clusters showed a new perspective for developing the sensitive CL biosensing. |
3,231 | Off-chip bus power minimization using serialization with cache-based encoding | The data bus is a major component of high power consumption in small process high-performance systems and in systems-on-chip (SoC) design. This paper presents an analysis of different state-of-the-art techniques for reducing the power of off-chip memory bus interface, with proposing an approach overcoming some limitations existing in the state-of-art methods. More precisely, the paper introduces a serialization (S) method combined with cache-based encoding scheme, aiming at saving the optimal possible power for off-chip memory bus. Bus serialization reduces the number of transmission wires, while a Serialization-Widening (SW) approach reduces the bus capacitance and the number of transmission wires. Experimental results show that, for off-chip data bus, the serialization approach with cache-based encoding achieves 31% and 52% power reduction for single-core and multi-core applications, respectively, when using fixed voltage and frequency with 128 bits data bus. (C) 2016 Elsevier Ltd. All rights reserved. |
3,232 | D2C-Based Hybrid Network for Predicting Group Cohesion Scores | Group cohesiveness represents the bonding between members in a group. Indeed, a group with high cohesiveness may easily reach success in their task. Therefore, the most critical element that affects the success of a group is group cohesiveness, which is estimated by Group Cohesion Score (GCS). This study proposed an automatic GCS estimation system for the 7(th) Emotion Recognition in the Wild (EmotiW 2019) challenge in the task of the Group Cohesion Prediction. We proposed a multi-stream hybrid network based on scene-level, skeleton-level, UV coordinates-level, mid-fusion, and face-level, followed by late-fusion to combine these approaches. We also developed a joint training method called Discrete labels to Continuous scores (D2C), where discrete labels (categorical labels) directly participate in generating continuous scores. Our proposed method achieved 0.416 mean squared error on the testing set of the EmotiW 2019 dataset and became a state-of-the-art in this challenge. Furthermore, to confirm the ability of the proposed D2C method, we performed experiments on the AffectNet database and obtained relatively better results than state-of-the-art approaches. |
3,233 | Omalizumab as a corticosteroid-sparing agent in the treatment of bullous pemphigoid | Bullous pemphigoid (BP) is the most common autoimmune blistering skin disease, characterized by the development of autoantibodies against hemidesmosomal components BP180 and BP230. The mainstay of therapy is topical and systemic corticosteroids (CS) and immunosuppressors. As this pathology mainly involves the elderly, subjects often have numerous comorbidities that influence the clinical management. Omalizumab is a recombinant humanized monoclonal anti-IgE antibody which has recently emerged as a promising treatment for BP in patients for whom CS are contraindicated or conventional treatments have failed to control the disease. For this study, we selected five patients who presented with corticosteroid-dependent BP with a contraindication to the use of other immunosuppressive treatments. The objectives of our study were to evaluate the effectiveness of omalizumab in controlling BP and allowing to decrease the dosage of systemic CS, assessing the effects of omalizumab on the clinical manifestations and the titers of circulating anti-BP180 and BP230 antibodies, IgE and eosinophils. A reduction in the dose of systemic CS was possible in 100% of the patients and complete resolution of the clinical picture was seen in 100% for skin lesions and in 40% for pruritus. A reduction of circulating IgE was found in 40%, anti-BP180 and BP230 IgGs were decreased in 60% and eosinophils in 80%. |
3,234 | Emerging Technologies for Next Generation Remote Health Care and Assisted Living | Remote health care is currently one of the most promising solutions to ensure a high level of treatment outcome, cost-efficiency and sustainability of the healthcare systems worldwide. Even though research on remote health care can be traced back to the early days of the Internet, the recent COVID-19 has necessitated further improvement in existing health care systems with invigorated research on remote health care technologies. In this article we delve into the state-of-the-art research in latest technologies and technological paradigms that play a vital role in enabling the next generation remote health care and assisted living. First the need of using the latest technological developments in the domain of remote health care is briefly discussed. Then the most important technologies and technological paradigms that are crucial in enabling remote health care and assisted living are emphasised. Henceforth, a detailed survey of existing technologies, potential challenges in those technologies, and possible solutions is conducted. Finally, missing research gaps and important future research directions in each enabling technology are brought forth to motivate further research in remote health care. |
3,235 | CPiX: Real-Time Analytics Over Out-of-Order Data Streams by Incremental Sliding-Window Aggregation | Stream processing is used in various fields. In the field of big data, stream aggregation is a popular processing technique, but it suffers serious setbacks when the order of events (e.g., stream elements) occurring is different from the order of events arriving to the systems. Such data streams are called "non-FIFO steams". This phenomenon usually occurs in a distributed environment due to many factors, such as network disruptions, delays, etc. Many analyzing scenarios require efficient processing of such non-FIFO streams to meet various data processing requirements. This paper proposes an efficient scalable checkpoint-based bidirectional indexing approach, called CPiX, for faster real-time analysis over non-FIFO streams. CPiX maintains the partial aggregation results in an on-demand manner per checkpoint. CPiX needs less time and space than the state-of-the-art approach. Extensive experiments confirm that CPiX can deal with out-of-order streams very efficiently and is, on average, about 3.8 times faster than the state-of-the-art approach while consuming less memory. |
3,236 | Dynamic Image Quantization Using Leaky Integrate-and-Fire Neurons | This paper introduces a novel coding/decoding mechanism that mimics one of the most important properties of the human visual system: its ability to enhance the visual perception quality in time. In other words, the brain takes advantage of time to process and clarify the details of the visual scene. This characteristic is yet to be considered by the state-of-the-art quantization mechanisms that process the visual information regardless the duration of time it appears in the visual scene. We propose a compression architecture built of neuroscience models; it first uses the leaky integrate-and-fire (LIF) model to transform the visual stimulus into a spike train and then it combines two different kinds of spike interpretation mechanisms (SIM), the time-SIM and the rate-SIM for the encoding of the spike train. The time-SIM allows a high quality interpretation of the neural code and the rate-SIM allows a simple decoding mechanism by counting the spikes. For that reason, the proposed mechanisms is called Dual-SIM quantizer (Dual-SIMQ). We show that (i) the time-dependency of Dual-SIMQ automatically controls the reconstruction accuracy of the visual stimulus, (ii) the numerical comparison of Dual-SIMQ to the state-of-the-art shows that the performance of the proposed algorithm is similar to the uniform quantization schema while it approximates the optimal behavior of the non-uniform quantization schema and (iii) from the perceptual point of view the reconstruction quality using the Dual-SIMQ is higher than the state-of-the-art. |
3,237 | Identification of Differentially Expressed Genes in Chilling-Induced Potato (Solanum tuberosum L.); a Data Analysis Study | Cold stress, as chilling (<20 °C) or freezing (<0 °C), is one of the frequently exposed stresses in cultivated plants like potato. Under cold stress, plants differentially modulate their gene expression to develop a cold tolerance/acclimation. In the present study, we aimed to identify the overall gene expression profile of chilling-stressed (+4 °C) potato at four time points (4, 8, 12, and 48 h), with a particular emphasis on the genes related with transcription factors (TFs), phytohormones, lipid metabolism, signaling pathway, and photosynthesis. A total of 3504 differentially expressed genes (DEGs) were identified at four time points of chilling-induced potato, of which 1397 were found to be up-regulated while 2107 were down-regulated. Heatmap showed that genes were mainly up-regulated at 4-, 8-, and 12-h time points; however, at 48-h time point, they inclined to down-regulate. Seventy five up-regulated TF genes were identified from 37 different families/groups, including mainly from bHLH, WRKY, CCAAT-binding, HAP3, and bZIP families. Protein kinases and calcium were major signaling molecules in cold-induced signaling pathway. A collaborated regulation of phytohormones was observed in chilling-stressed potato. Lipid metabolisms were regulated in a way, highly probably, to change membrane composition to avoid cold damage and render in signaling. A down-regulated gene expression profile was observed in photosynthesis pathway, probably resulting from chilling-induced reduced enzyme activity or light-triggered ROSs damage. The findings of this study will be a valuable theoretical knowledge in terms of understanding the chilling-induced tolerance mechanisms in cultivated potato plants as well as in other Solanum species. |
3,238 | Randomized placebo-controlled crossover trial of memantine in children with epileptic encephalopathy | Memantine is an N-methyl-D-aspartate receptor antagonist, approved for dementia treatment. There is limited evidence of memantine showing benefit for paediatric neurodevelopmental phenotypes, but no randomized placebo-controlled trials in children with developmental and epileptic encephalopathy. In this randomized double-blind placebo-controlled crossover trial (Trial registration: https://clinicaltrials.gov/ct2/show/NCT03779672), patients with developmental and epileptic encephalopathy received memantine and placebo, each for a 6-week period separated by a 2-week washout phase. Electroencephalography, seizure diary, patient caregivers' global impression, serum inflammatory markers and neuropsychological evaluation were performed at baseline and after each treatment phase. The primary outcome measure was classification as a 'responder', defined as ≥2 of: >50% seizure frequency reduction, electroencephalography improvement, caregiver clinical impression improvement or clear neuropsychological testing improvement. Thirty-one patients (13 females) enrolled. Two patients withdrew prior to initiating medication and two (twins) had to be removed from analysis. Of the remaining 27 patients, nine (33%) were classified as responders to memantine versus two (7%) in the placebo group (P < 0.02). Electroencephalography improvement was seen in eight patients on memantine compared to two on placebo (P < 0.04). Seizure improvement was observed in eight patients on memantine and two on placebo (P < 0.04). Caregivers reported overall clinical improvement in 10 patients on memantine compared to seven on placebo (not significant). Statistical analysis of neuropsychological evaluation suggested improvements in symptoms of attention-deficit hyperactivity disorder and autism. Memantine is a safe and effective treatment for children with developmental and epileptic encephalopathy, having the potential to improve both seizure control and cognitive function. |
3,239 | Adaptive transitions for automation in cars, trucks, buses and motorcycles | Automated vehicles are entering the roads and automation is applied to cars, trucks, buses, and even motorcycles today. High automation foresees transitions during driving in both directions. The driver and rider state become a critical parameter since reliable automation allows safe intervention and transit control to the automation when manual driving is not performed safely anymore. When the control transits from automation to manual an appropriate driver state needs to be identified before releasing the automated control. The detection of driver states during manual and automated driving and an appropriate design of the human-machine interaction (HMI) are crucial steps to support these transitions. State-of-the-art systems do not take the driver state, personal preferences, and predictions of road conditions into account. The ADAS&ME project, funded by the H2020 Programme of the European Commission, proposes an innovative and fully adaptive HMI framework, able to support driver/rider state monitoring-based transitions in automated driving. The HMI framework is applied in the target vehicles: passenger car, truck, bus, and motorcycle, and in seven different use cases. |
3,240 | Estrogenic, androgenic, and glucocorticoid activities and major causative compounds in river waters from three Asian countries | Estrogen, androgen, and glucocorticoid receptors (ER, AR, and GR) agonist activities in river water samples from Chennai and Bangalore (India), Jakarta (Indonesia), and Hanoi (Vietnam) were evaluated using a panel of chemical-activated luciferase gene expression (CALUX) assays and were detected mainly in the dissolved phase. The ER agonist activity levels were 0.011-55 ng estradiol (E2)-equivalent/l, higher than the proposed effect-based trigger (EBT) value of 0.5 ng/l in most of the samples. The AR agonist activity levels were < 2.1-110 ng dihydrotestosterone (DHT)-equivalent/l, and all levels above the limit of quantification exceeded the EBT value of 3.4 ng/l. GR agonist activities were detected in only Bangalore and Hanoi samples at dexamethasone (Dex)-equivalent levels of < 16-150 ng/l and exceeded the EBT value of 100 ng/l in only two Bangalore samples. Major compounds contributing to the ER, AR, and GR agonist activities were identified for water samples from Bangalore and Hanoi, which had substantially higher activities in all assays, by using a combination of fractionation, CALUX measurement, and non-target and target chemical analysis. The results for pooled samples showed that the major ER agonists were the endogenous estrogens E2 and estriol, and the major GR agonists were the synthetic glucocorticoids Dex and clobetasol propionate. The only AR agonist identified in major androgenic water extract fractions was DHT, but several unidentified compounds with the same molecular formulae as endogenous androgens were also found. |
3,241 | Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning | Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms. |
3,242 | D-galactose protects the intestine from ionizing radiation-induced injury by altering the gut microbiome | This article aims to investigate the protection of the intestine from ionizing radiation-induced injury by using D-galactose (D-gal) to alter the gut microbiome. In addition, this observation opens up further lines of research to further increase therapeutic potentials. Male C57BL/6 mice were exposed to 7.5 Gy of total body irradiation (TBI) or 13 Gy of total abdominal irradiation (TAI) in this study. After adjustment, D-gal was intraperitoneally injected into mice at a dose of 750 mg/kg/day. Survival rates, body weights, histological experiments and the level of the inflammatory factor IL-1β were observed after TBI to investigate radiation injury in mice. Feces were collected from mice for 16S high-throughput sequencing after TAI. Furthermore, fecal microorganism transplantation (FMT) was performed to confirm the effect of D-gal on radiation injury recovery. Intraperitoneally administered D-gal significantly increased the survival of irradiated mice by altering the gut microbiota structure. Furthermore, the fecal microbiota transplanted from D-gal-treated mice protected against radiation injury and improved the survival rate of recipient mice. Taken together, D-gal accelerates gut recovery following radiation injury by promoting the growth of specific microorganisms, especially those in the class Erysipelotrichia. The study discovered that D-gal-induced changes in the microbiota protect against radiation-induced intestinal injury. Erysipelotrichia and its metabolites are a promising therapeutic option for post-radiation intestinal regeneration. |
3,243 | Prehistoric pictographs of Finland: Symbolism and territoriality | Transformation of landscape with symbolic art does not only mark a space as sacred. Such demarcations also serve as symbols of other social functions, such as the delineation of territories and exchange of information. As enduring monuments to the past, they symbolise a territory of time; as part of a cultural landscape, they symbolise a territory of space. A distributional study of evidence of symbolic territoriality in the landscapes of three rock art sites from the Neolithic - Early Metal periods in Finland, this work utilises GIS spatial analysis to locate prehistoric dwelling sites and sacred sites (red ochre burials, cairn burials, cremation burials, and cup-stones) within 5 and 10 km catchment zones of the pictographs sites. The results are interpreted with explanatory models from Information Exchange Theory and territorial analysis. (c) 2017 Elsevier Ltd and INQUA. All rights reserved. |
3,244 | Research on Art Teaching Practice Supported by Virtual Reality (VR) Technology in the Primary Schools | Nowadays, teaching and learning methods are constantly changing with the development and popularization of information technology. Many teaching activities are exploring the integration of virtual technology. However, the specific effects of VR are challenging to verify. In this paper, "teaching in VR environment" and "traditional teaching" were designed to carry out a series of teaching comparison practices between two groups of a primary school. By analyzing the experimental data of the experimental group and the control group, the research found that it is easier to enter mental flow in virtual reality, and the introduction of virtual reality technology is positively correlated with learning engagement. What is more, compared with traditional teaching and learning methods, virtual reality technology and related software can help individuals give full play to their creativity. |
3,245 | An innovative computational approach based on a particle swarm strategy for adaptive phased-arrays control | In this paper a new approach to the control of phased arrays is presented and assessed. Starting from the adaptive array theory, a particle swarm strategy is used to tune the phase coefficients of the array in order to adaptively minimize/avoid the effects of interfering signals at the receiver. To show the effectiveness of the proposed approach, a selected set of numerical examples, concerned. with linear as well as planar arrays, is presented. Furthermore, to evaluate the advantages of the particle swarm optimizer (PSO)-based strategy over state-of-art methods, a comparative study is carried out by analyzing the performance of the method in terms of both the signal-to-interference-plus-noise-ratio and resulting beam pattern. The achieved results, even though preliminary, seem to confirm that the PSO-based approach satisfactorily works and it generally outperforms previously proposed/state-of-art phase-only adaptive control strategies. |
3,246 | Accurate Modeling of the Microwave Treatment of Works of Art | The microwave heating treatment is a useful methodology and the disinfestation of works of art can also benefit from this approach. However, even if the microwave treatment is able to eliminate the pests that could damage the works of arts, it may nevertheless present some unexpected effects such as the presence of highly heated areas (hot spots) or areas with poor radiation due to particular shapes. To overcome this issue, we developed a mathematical model allowing predicting and monitoring tasks about the heating process. The prediction model has been developed into a software solution able to predict the distribution of heating power in objects to be treated, even of complex shapes, in order to define the exposure conditions, the time necessary to the processing, the power to be transmitted in the chamber and any repair or protection to cover the most sensitive areas. It can also predict the behaviour of irradiation in the presence of other entities such as nails or pests. The data to be provided for performing a simulation are: the geometry of the object, the shape of the infesting agent and their dielectric characteristics. As a result, we obtain the distribution of heating power and a software tool able to model and predict activities for cultural heritage treatments. |
3,247 | EFFECTS OF ECOLOGICAL ENVIRONMENT INTEGRATED ART EDUCATION ON STUDENTS' LEARNING OUTCOME AND ENVIRONMENTAL BEHAVIOUR | Ecological environment is the basis of social development. The destruction of natural environment and ecology, on the contrary, would restrict social development. Environmental issues therefore have become the common concerns globally. To promote the knowledge and perception of environmental problems through education and to understand the correlations among living environment, resources, and humans are urgent for educators. Art is originated from life and is integrated into life, and life is the source of culture. Integrating ecological environment into art teaching allows students being close to the environment, reflecting the correlation of 'human-environment-society', potentially acquiring sense of accomplishment and potential inspiration, as well as developing sound personality. Taking a university in Guilin, Guangxi Zhuang autonomous region, as the research object, total 196 students are preceded the ecological environment integrated art education experiment in this study. The 15-week experimental teaching is preceded for 3 h per week (total 45 h). According to the results, suggestions are proposed, expecting to promote students' learning interests in art and emphasis on environmental conservation to achieve the effect of ecological art education and cultivate students' responsible environmental behaviour. |
3,248 | Efficient large-scale face clustering using an online Mixture of Gaussians | In recent years, the number of applications demanding real-time face clustering algorithms has increased, especially for security and surveillance purposes. However, state-of-the-art face clustering methods are offline, they need to repeat the whole clustering process every time new data arrives, and thus, they are not suitable for real-time applications. On the other hand, online clustering methods are highly dependent on the order and the size of the data, and they are less accurate than offline methods. To overcome these limitations, we present an online gaussian mixture-based clustering method (OGMC). The key idea of this method is the proposal that an identity can be represented by more than just one distribution or cluster. Using feature vectors extracted from the incoming faces, OGMC generates clusters that may be connected to others depending on their proximity and their robustness, and updates their connections every time their parameters are updated. With this approach, we reduce the dependency of the clustering process on the order and the size of the data and we are able to deal with complex data distributions. Experimental results show that OGMC outperforms state-of-the-art clustering methods on large-scale face clustering benchmarks not only in accuracy, but also in efficiency and scalability. |
3,249 | Does new-type urbanization curb haze pollution? A case study from China | The rapid urbanization process has led to a high concentration of population and economic activities in urban space, thus leading to severe environmental pollution. The concept of new-type urbanization has been proposed in China to combat the pollution associated with urbanization. This study analyzes the interaction effect of new-type urbanization with land, industry, and technology on haze pollution, using Chinese provincial-level panel data, and employs a STIRPAT model with interaction terms for empirical testing. The results find that new-type urbanization can significantly reduce the national haze pollution level; meanwhile, the optimization of intensive urban land use level, industrial structure, and technological innovation can interact with it to promote haze reduction, and there is regional heterogeneity. The improvement of intensive urban land use and industrial structure in the central region will significantly enhance the haze reduction effect of new-type urbanization, while the improvement of technological innovation in the west will instead weaken its haze reduction effect, and the interaction in the eastern region is not significant. This research provides a theoretical basis for better implementation of new-type urbanization construction and effective promotion of green and sustainable urban development. |
3,250 | Multi-Modal Dictionary Learning for Image Separation With Application in Art Investigation | In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front-and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component captures features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single-and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data-taken from digital acquisition of the Ghent Altarpiece (1432)-confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation. |
3,251 | STATE-OF-THE-ART CAPABILITIES OF COMPUTER GRAPHICS IN MUSEUM LIGHTING MODELLING | Computer graphics and technologies have currently come a long way from engineering methods of environment and space parameter calculation. In the spheres of light engineering and lighting design, it is time to switch to modelling of illumination using mainly graphic lighting effects and imagery rather than digital values. Such transfer is impossible without trying to form the methodology and recommendations for 3D modelling of illumination. The authors of this article attempt to describe the key approaches to 3D modelling of museum illumination using contemporary software. The operating lighting installation of one of the halls of the Pushkin State Museum of Fine Arts is used as an example. Based on the achieved results of the research, we tried to describe the methodology and to provide recommendations for quality design and modelling of illumination in any exhibition spaces. The described methodology may be useful both for lighting engineers, architects, designers, and for curators and museum employees. The former may use the methodology for technical approach and implementation of museum illumination while the latter may use it to find a common language with the former and to compile more accurate terms of reference for them. |
3,252 | Research on the development trend and application of digital media art in graphic design education | The rapid development of new media technology has a significant impact on the traditional graphic design art. It not only promotes the rapid development of graphic design field, but also accelerates the transformation and upgrading of the whole visual communication design education concept. The traditional teaching concept of graphic design cannot meet the needs of students, but the education of graphic design under digital information can improve the satisfaction of students with gorgeous visual effects. Therefore, it is of great significance to study the reform of modern graphic design education mode in the digital media environment. This paper first analyzes the development of digital media art and graphic design education, then studies the relationship between graphic design teaching and mobile digital media, and finally discusses the value of digital media art in graphic design teaching and the significance of reform. |
3,253 | Progression of the pluripotent epiblast depends upon the NMD factor UPF2 | Nonsense-mediated RNA decay (NMD) is a highly conserved RNA turnover pathway that degrades RNAs harboring in-frame stop codons in specific contexts. Loss of NMD factors leads to embryonic lethality in organisms spanning the phylogenetic scale, but the mechanism remains unknown. Here, we report that the core NMD factor, UPF2, is required for expansion of epiblast cells within the inner cell mass of mice in vivo. We identify NMD target mRNAs in mouse blastocysts - both canonical and alternatively processed mRNAs - including those encoding cell cycle arrest and apoptosis factors, raising the possibility that NMD is essential for embryonic cell proliferation and survival. In support, the inner cell mass of Upf2-null blastocysts rapidly regresses with outgrowth and is incompetent for embryonic stem cell derivation in vitro. In addition, we uncovered concordant temporal- and lineage-specific regulation of NMD factors and mRNA targets, indicative of a shift in NMD magnitude during peri-implantation development. Together, our results reveal developmental and molecular functions of the NMD pathway in the early embryo. |
3,254 | Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment | Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, i.e., bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively. |
3,255 | Elimination by Linear Association: An Effective and Efficient Static Mirror Adaptive Random Testing | Adaptive random testing (ART) is a software testing method which combines randomness with even distribution of test cases within the input domain of a program with the aim of improving the effectiveness of random testing (RT). It was established right from the onset that, ART is considerably less efficient compared to RT due to the overhead cost involved in filtering randomly generated test cases in order to achieve the even spread objective. Again, it has been observed that over-concentration on achieving better effectiveness at the expense of efficiency will make ART advantage over RT a superficial one. Besides, the ART is close to its theoretical bound in terms of effectiveness. Various algorithms have therefore emerged that seeks to minimize the efficiency deficit incurred by the ART. One of such strategies is mirror adaptive random testing (MART). Unfortunately, the MART's performance is generally unstable due to the lack of diversity in mirror generated test cases. The culprit has been identified as the mirroring functions used in place of complex ART computations. In this paper, we present elimination (E) by linear association (E-MART) as a solution to the problem of the MART that guarantees diversity in all dimension(s) of mirror test cases. By partitioning the source domain into multiple subdomains, we systematically isolate mirror partitions which are linearly associated with the source domains. The source domain is then iteratively partitioned whiles forgetting strategy is applied to select test cases. The simulations and experimental studies conducted indicate that the EMART has a more stable performance compared to the MART and compares favorably in terms of efficiency by reducing the quadratic time of the MART to linear. |
3,256 | Automated Recovery of Compressedly Observed Sparse Signals From Smooth Background | We propose a Bayesian based algorithm to recover sparse signals from compressed noisy measurements in the presence of a smooth background component. This problem is closely related to robust principal component analysis and compressive sensing, and is found in a number of practical areas. The proposed algorithm adopts a hierarchical Bayesian framework for modeling, and employs approximate inference to estimate the unknowns. Numerical examples demonstrate the effectiveness of the proposed algorithm and its advantage over the current state-of-the-art solutions. |
3,257 | Tabdoc Approach: An Information Fusion Method to Implement Semantic Interoperability Between IoT Devices and Users | Services generated by various Internet of Things (IoT) devices are in heterogeneous contexts, hindering users from efficient consumption of those services and hence development of IoT. This paper addresses the problems in user-device interaction. A user-device interoperability framework, Tabdoc approach, is hereby established to implement semantic interoperability between IoT devices and users. In this approach, a divide-and-conquer strategy is used; a semantic document representation method for message modeling is devised to guarantee consistent message understanding among different contexts, and a semantic extraction algorithm and a semantic interpretation algorithm are proposed to implement automatic cross-context message processing. The experimental results show promising performance improvements over state-of-the-art methods. |
3,258 | Pseudomonas aeruginosa Pangenome: Core and Accessory Genes of a Highly Resourceful Opportunistic Pathogen | In this chapter, we leverage a novel approach to assess the seamless population structure of Pseudomonas aeruginosa, using the full repertoire of genomes sequenced to date (GenBank, April 6, 2020). In order to assess the set of core functions that represents the species as well as the differences in these core functions among the phylogroups observed in the population structure analysis, we performed pangenome analyses at the species level and at the phylogroup level. The existence of the phylogroups described in the population structure analyses was supported by their different profiles of antibiotic-resistant determinants. Finally, we utilized a presence/absence matrix of protein families from the entire species to evaluate if P. aeruginosa phylogroups can be differentiated according to their accessory genomic content. Our analysis shows that the core genome of P. aeruginosa is approximately 62% of the average gene content for the species, and it is highly enriched with pathways related to the metabolism of carbohydrates and amino acids as well as cellular processes and cell maintenance. The analysis of the accessory genome of P. aeruginosa performed in this chapter confirmed not only the existence of the three phylogroups previously described in the population structure analysis, but also of 29 genetic substructures (subgroups) within the main phylogroups. Our work illustrates the utility of populations genomics pipelines to better understand highly complex bacterial species such as P. aeruginosa. |
3,259 | Experimental comparison of state-of-the-art methods for digital optimum filter synthesis with arbitrary constraints and noise | We present the experimental application and comparison of two methods for the synthesis of digital filters, which represent the state-of-the-art of optimum digital processing of shaped signals with arbitrary constraints in time and frequency domain, and any kind of stationary noise power spectral density, The methods are implemented in experimental measurement setups, and optimum filters are synthesized with regard to assigned constraints (e.g., finite duration, flat top, peaking time, zero area, etc.) and by taking into account the real environmental noise or disturbance present in the system, identified from datasets of simple signal experimental acquisitions. Implementation issues are detailed and basic design rules for digital signal processors based on these techniques are derived. |
3,260 | Direct Analysis of Soil Composition for Source Apportionment by Laser Ablation Single-Particle Aerosol Mass Spectrometry | Soil has always been the most complex biomaterial on the planet. The rapid determination of the components in the soil and their original source is a prerequisite for soil quality, environmental, and human health risk assessments. In this study, the chemical compositions and source apportionment of surface soil samples collected from five sites in Shanghai, China, were successfully investigated using a laboratory-developed laser ablation single-particle aerosol mass spectrometry (LA-SPAMS) instrument combined with an adaptive resonance theory-based neural network algorithm (ART-2a) data-processing method for the first time. In total, more than 35,000 particles, ranging from 200 to 2000 nm, were sized, and around 15-20% of the particles were chemically analyzed by LA-SPAMS to generate both positive and negative mass spectra. The results show that there are significant differences in particle size distribution among the five samples, with peaks of various sizes and different profiles, while all five soil samples contain crustal elements, heavy metals, organic and inorganic components, and so forth. The chemical composition of each sample varied considerably, so different classes of SPAMS particle classes were identified, which were later grouped into seven general categories: EC-rich (containing elemental carbon), secondary components, organic nitrogen, crust, HM (containing heavy metal), PAH (containing polycyclic aromatic hydrocarbons), and NaK-rich particles, based on the dominant marked ions. The composition analysis and source apportionment showed that soil components in different areas have been affected by the local environment, such as local industrial emissions and automobile exhaust, which are usually characterized by varying degrees of mixing between the crust and environmental aerosols. In combination with the ART-2a method, LA-SPAMS enables rapid and direct analysis of soil samples based on real-time single-particle measurements, which will help in understanding the distribution, transport, and fate of the soil components, thus providing new insights into soil-quality assessment. Moreover, the established LA-SPAMS can also be practically applied to other daily inspection tasks, such as rocks, minerals, metals, ceramics, polymers, and other solid materials for ingredient analysis and quality evaluation. |
3,261 | Investigation of Asymptomatic Infection of Phellinus noxius in Herbaceous Plants | The white-rot fungus Phellinus noxius is known to cause brown root rot disease (BRRD) in woody trees and shrubs. To understand the pathogenicity of P. noxius in herbaceous plants, we investigated 23 herbaceous weed and turfgrass species in 32 BRRD-infested sites in Taiwan and/or tested them by artificial inoculation. In the field survey, P. noxius was isolated from seven symptomless herbaceous species (i.e., Typhonium blumei, Paspalum conjugatum, Paspalum distichum, Oplismenus compositus, Bidens pilosa, Digitaria ciliaris, and Zoysia matrella). Potted plant inoculation assays suggested that P. noxius is able to infect Artemisia princeps, O. compositus, and Z. matrella but not Axonopus compressus, Eremochloa ophiuroides, Ophiopogon japonicus, or Cynodon dactylon. A. princeps plants wilted within 2 weeks postinoculation, but inoculated O. compositus and Z. matrella were asymptomatic, and P. noxius could be isolated from only inoculated sites. The colonization of P. noxius in the cortex and vascular cylinder of roots was visualized by paraffin sectioning and trypan blue staining of juvenile seedlings grown on water agar. To evaluate the effect of replantation for the remediation of BRRD-infested sites, P. noxius-inoculated wood strips were buried in soil with or without vegetation. After 4 weeks, P. noxius could be detected only in the bare soil group. For the control of BRRD, the herbaceous hosts should be removed around the diseased trees/stumps and non-host turfgrasses (e.g., A. compressus, E. ophiuroides, O. japonicus, or C. dactylon) planted to accelerate the degradation of P. noxius. |
3,262 | Retrograde urethrogram - a novel approach to diagnosing a posterior urethral polyp in a neonate | We present a case of antenatally detected fetal megacystis caused by an obstructing posterior urethral polyp. Antenatal and postnatal ultrasounds showed bladder wall thickening and bilateral hydroureteronephrosis, most marked antenatally. A working diagnosis of posterior urethral valves was therefore made. However, further postnatal assessment with a micturating cystourethrogram (MCUG) combined with a retrograde urethrogram identified a pedunculated urethral polyp as the cause. The addition of a retrograde urethrogram as an adjunct to the MCUG in the diagnosis of posterior urethral polyp has not previously been reported, and in this case provided diagnostic confidence of this rare condition, allowing for definitive surgical planning. |
3,263 | LMOT: Efficient Light-Weight Detection and Tracking in Crowds | Multi-object tracking is a vital component in various robotics and computer vision applications. However, existing multi-object tracking techniques trade off computation runtime for tracking accuracy leading to challenges in deploying such pipelines in real-time applications. This paper introduces a novel real-time model, LMOT, i.e., Light-weight Multi-Object Tracker, that performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-the-art trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state-of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model. |
3,264 | Social isolation induces succinate dehydrogenase dysfunction in anxious mice | We have previously reported social isolation induces anxiety-like behavior, cognitive decline, and reduction in brain ATP levels in mice. These changes were ameliorated by treatment with dihydromyricetin (DHM), a compound that positively modulates γ-aminobutyric A (GABAA) receptor. To gain further insight into the subcellular mechanisms underlying these changes, we utilized a social isolation-induced anxiety mouse model and investigated changes in mitochondrial oxidative capacity via the electron transport chain. We found that 4 weeks of social isolation decreased ATP levels by 43% and succinate dehydrogenase capacity by 52% of the control, while daily DHM (2 mg/kg oral) administration restored succinate dehydrogenase capacity. These results suggest that social isolation decreased mitochondrial capacity to generate ATP. DHM can be developed to be a therapeutic against anxiety and mitochondrial stress. |
3,265 | Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning | This paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). Specifically, the proposed algorithm utilizes the correntropy induced metric (CIM) for defining a similarity measure, a node insertion criterion, and an edge creation criterion. Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. Moreover, generated topological networks cannot represent the distribution of data. In contrast, the proposed algorithm realizes a stable computation and reduces the number of parameters compared to existing algorithms. Furthermore, improving the ability to express the data structure more appropriately by the topological network, a mechanism that adaptively controls the node insertion criterion is introduced to the proposed algorithm. The experimental results showed that the proposed algorithm has superior performance with respect to the self-organizing and the classification abilities compared with the state-of-the-art topological clustering algorithms. |
3,266 | Compressed Holistic ConvNet Representations for Detecting Loop Closures in Dynamic Environments | Detecting loop closures in dynamic environments is a severe challenge for the simultaneous localization and mapping (SLAM) system. Convolutional neural networks (ConvNet) could provide high-level and abstract representations extracted directly from images as image descriptors. Some novel ConvNet-based methods have been presented. In dynamic environments, they perform better than the state-of-the-art methods which use hand-crafted features. In this paper, (1) We proposed a flexible loop closure detection workflow based on the holistic representations; (2) In this workflow, a post-processing method is applied to the raw holistic ConvNet representations for redundant information compression; (3) In addition, a compression ratio is introduced in (2) to determine how much information will be retained depending on background change and moving objects. We evaluated our workflow in four open datasets. The experimental results demonstrate that the proposed workflow performs better than many state-of-the-art methods and ConvNet-based approaches. |
3,267 | Teaching of practice innovation of new media interactive for art design training in college | The traditional education and teaching methods have limitations in professional talent training. With the rapid development of science and technology, the new media environment based on digital technology and featuring strong interactive communication and influence provides new ideas and paths for the cultivation of artistic design talents in colleges and universities. This study puts forward the innovation of the new media interactive teaching concept and mode in the process of training innovation in practice, through the analysis of the new media environment education in colleges and universities teaching use present situation and problem of university art and design professional talent training, the use of new media interactive and immediacy, mass and participatory, multimedia and hypertext, individual and social characteristics, puts forward the innovation of the new media interactive teaching concept and mode in the process of training innovation in practice. This research plays an important role in promoting the quality of art and design talents training in colleges and universities. |
3,268 | Research on image recognition of Wushu action based on remote sensing image and embedded system | The cultural connotation of martial arts inherited the country's culture and played an important role in promoting. Martial arts culture and inspire the national spirit in the strong penetration of the East's strong culture and the collision of Western culture Western. Martial arts education is related to martial arts. Penetration of the spread and martial arts culture in primary and secondary schools has imminent. To foster a national spirit and carry forward has played a very important role. Method of comparing literature, cultural studies, the use of the modern significance of education, and the forward martial arts research. After development for over fifty years, human beings are now the atmosphere. To obtain a large-scale data set accuracy and for marine-based, can use different light and microwave sensors. Frequency and spatial resolution of the data acquisition range per minute at a time from one month, microwave, centimeter-scale from kilometers from the visible light, and the range of the electromagnetic spectrum covers wavelengths Data progress from the point of view of remote sensing sensor ethnic significant space and time of lives to expand understanding of the human nature of the environment, to get the data on a global scale, more and more digital Earth were able to provide the resources. The development and trends of remote sensing satellites around the world have been introduced. |
3,269 | MiR-493-5p inhibits Th9 cell differentiation in allergic asthma by targeting FOXO1 | The role of micro RNAs (miRNAs) in asthma remains unclear. In this study, we examined the role of miRNA in targeting FOXO1 in asthma. Results showed that miR-493-5p was one of the differentially expressed miRNAs in the PBMCs of asthmatic children, and was also associated with Th cell differentiation. The miR-493-5p expression decreased significantly in the OVA-induced asthma mice than the control groups. The miR-493-5p mimic inhibited the expression of the IL-9, IRF4 and FOXO1, while the inhibitor restored these effects. Moreover, the Dual-Luciferase analysis results showed FOXO1 as a novel valid target of miR-493-5p. According to the rescue experiment, miR-493-5p inhibited Th9 cell differentiation by targeting FOXO1. Then the exosomes in association with the pathogenesis of asthma was identified. Various inflammatory cells implicated in asthmatic processes including B and T lymphocytes, DCs, mast cells, and epithelial cells can release exosomes. Our results demonstrated that the DC-derived exosomes can inhibit Th9 cell differentiation through miR-493-5p, thus DC-derived exosomal miR-493-5p/FOXO1/Th9 may serve as a potential therapeutic target in the development of asthma. |
3,270 | Generating Natural Language Descriptions From Tables | This paper proposes a neural generative architecture, namely NLDT, to generate a natural language short text describing a table which has formal structure and valuable information. Specifically, the architecture maps fields and values of a table to continuous vectors and then generates a natural language description by leveraging the semantics of a table. The NLDT architecture adopts a two-level neural model to make the most of the structure of a table to fully express the relationship between contents. To deal with the problem of out-of-vocabulary, this paper develops a simple and fast word-conversion method that replaces rare words appearing in texts with common field information in tables and directly replicates contents from table to the output sequence according to the field information. Besides, this paper adds the concept of theme to adapt the NLDT architecture to open domain and improves beam search algorithm to strengthen the results in the inference stage. On the WEATHERGOV dataset, the NLDT architecture improves the state-of-the-art BLEU-4 score from 61.01 to 62.89 and the current state-of-the-art F1 score from 73.21 to 78. On the WIKIBIO and WIKITABLE datasets, the NLDT architecture achieves a BLEU-4 score of 45.77 and 38.71 respectively which also outperform the state-of-the-art approaches. Furthermore, this paper introduces a Chinese dataset WIKIBIOCN including 33,244 biographies with corresponding tables. On the WIKIBIOCN dataset, the NLDT architecture achieves a BLEU-4 score of 38.87 and fairly good manual evaluation. |
3,271 | Scaling and optimization of MOS optical modulators in nanometer SOI waveguides | In this paper, a very accurate model of optical modulators in silicon-on-insulator technology is developed and validated using experimental results reported in literature. Using an optimized nanometer MOS structure, a significant bandwidth increase (around 45%), length decrease (around four times), and power consumption reduction (three times) with respect to the state-of-the-art have been obtained. |
3,272 | Prospective Metabolomic Studies in Precision Medicine: The AKRIBEA Project | For a long time, conventional medicine has analysed biomolecules to diagnose diseases. Yet, this approach has proven valid only for a limited number of metabolites and often through a bijective relationship with the disease (i.e. glucose relationship with diabetes), ultimately offering incomplete diagnostic value. Nowadays, precision medicine emerges as an option to improve the prevention and/or treatment of numerous pathologies, focusing on the molecular mechanisms, acting in a patient-specific dimension, and leveraging multiple contributing factors such as genetic, environmental, or lifestyle. Metabolomics grasps the required subcellular complexity while being sensitive to all these factors, which results in a most suitable technique for precision medicine. The aim of this chapter is to describe how NMR-based metabolomics can be integrated in the design of a precision medicine strategy, using the Precision Medicine Initiative of the Basque Country (the AKRIBEA project) as a case study. To that end, we will illustrate the procedures to be followed when conducting an NMR-based metabolomics study with a large cohort of individuals, emphasizing the critical points. The chapter will conclude with the discussion of some relevant biomedical applications. |
3,273 | Exosomes derived from bone marrow mesenchymal stem cells inhibit human aortic vascular smooth muscle cells calcification via the miR-15a/15b/16/NFATc3/OCN axis | Cardiovascular events among patients with chronic kidney disease (CKD) are associated with vascular calcification (VC). Nevertheless, the process of vascular calcification is complicated. A mechanism of VC is cellular osteogenic transdifferentiation. The mechanism through which bone marrow mesenchymal stem cell-derived exosomes (BMSC-Exo) relieve VC is unknown. For the purpose of this study, we used human aortic vascular smooth muscle cells (HA-VSMCs) stimulated by high phosphate to investigate how BMSC-Exo works. Cell calcification was detected by Alizarin red S staining, AKP activity analysis, and the Ca2+ concentration test. The dual-luciferase reporter gene assays were utilized to confirm the targeting link between miR-15a-5p, miR-15b-5p, and miR-16-5p (miR-15a/15b/16) and nuclear factors of activated T cells 3 (NFATc3). The expression of osteogenic transdifferentiation biomarkers was detected using Western blot and RT-qPCR. Based on our findings, miR-15a/15b/16 plays a crucial role in BMSC-Exo's inhibitory effects on calcification and osteogenic transdifferentiation. We then confirmed that miR-15a/15b/16 specifically target the 3'UTR of NFATc3 mRNA and that three miRNAs are more effective than one miRNA. Moreover, we found that down-regulation of NFATc3 could inhibit osteocalcin (OCN) expression, thereby inhibiting the osteogenic transdifferentiation and calcification of HA-VSMCs. This study found that BMSC-Exo plays a role in calcification inhibition by transferring miR-15a/15b/16 and inhibiting their common target gene NFATc3, which down-regulates OCN expression and thus inhibits HA-VSMC osteogenic transdifferentiation. This study identifies a novel target for therapeutic therapy of CKD-VC. |
3,274 | Optimal Index Assignment for Multiple Description Scalar Quantization With Translated Lattice Codebooks | We design a K-description scalar quantizer, whose construction is based on a structure of translated scalar lattices and a lattice in K-1 dimensional space. The use of translated lattices provides a performance advantage by exploiting a so-called staggering gain. The use of the K-1 dimensional lattice facilitates analytic insight into the performance and significantly speeds up the computation of the index assignment compared to state-of-the-art methods. Using a common decoding method, the proposed index assignment is proven to be optimal for the K-description case. It is shown that the optimal index assignment is not unique. This is illustrated for the two-description case, where a periodic index assignment is selected from possible optimal assignments and described in detail. The performance of the proposed quantizer accurately matches theoretic analysis over the full range of operational redundancies. Moreover, the quantizer outperforms the state-of-the-art MD scheme as the redundancy among the description increases. |
3,275 | Differing associations between sex determination and sex-linked inversions in two ecotypes of Littorina saxatilis | Sexual antagonism is a common hypothesis for driving the evolution of sex chromosomes, whereby recombination suppression is favored between sexually antagonistic loci and the sex-determining locus to maintain beneficial combinations of alleles. This results in the formation of a sex-determining region. Chromosomal inversions may contribute to recombination suppression but their precise role in sex chromosome evolution remains unclear. Because local adaptation is frequently facilitated through the suppression of recombination between adaptive loci by chromosomal inversions, there is potential for inversions that cover sex-determining regions to be involved in local adaptation as well, particularly if habitat variation creates environment-dependent sexual antagonism. With these processes in mind, we investigated sex determination in a well-studied example of local adaptation within a species: the intertidal snail, Littorina saxatilis. Using SNP data from a Swedish hybrid zone, we find novel evidence for a female-heterogametic sex determination system that is restricted to one ecotype. Our results suggest that four putative chromosomal inversions, two previously described and two newly discovered, span the putative sex chromosome pair. We determine their differing associations with sex, which suggest distinct strata of differing ages. The same inversions are found in the second ecotype but do not show any sex association. The striking disparity in inversion-sex associations between ecotypes that are connected by gene flow across a habitat transition that is just a few meters wide indicates a difference in selective regime that has produced a distinct barrier to the spread of the newly discovered sex-determining region between ecotypes. Such sex chromosome-environment interactions have not previously been uncovered in L. saxatilis and are known in few other organisms. A combination of both sex-specific selection and divergent natural selection is required to explain these highly unusual patterns. |
3,276 | A Study on the Indoor Environment of the Main Building of the National Museum of Western Art, in Japan, for the Development of a Retrofit Scheme | The National Museum of Western Art is the only work of Le Corbusier in Japan and 50 years have already passed since its construction. In order to maintain the museum's value as a cultural asset, there is an urgent need to draft a thorough retrofit plan both to maintain the building's function as an art museum and to restore Le Corbusier's original design concept. This study, which carried out an indoor environmental investigation for the purpose of developing such a retrofit plan in the main building, confirms the current airflow characteristics on the basis of a survey of factors such as the flow rates of the air conditioning system, internal pressures, and the age of air distribution. Because the exhaust flow rate was greater than the supply of fresh air into the building, there was concern about air infiltration. It was found that the age of air was highest in the exhibition space, comparatively lower in the entrance hall, and lowest in the restaurant. The adjacent wall air velocities varied greatly according to the locations and the highest values were around 0.7 m/s. In order to separate the exhibition space from the entrance hall to avoid possible disturbance from outdoors, glass partitions that were not part of Le Corbusier's original design were added after the building was completed. CFD predictions were carried out to evaluate the air environment under the conditions of the original design using variable air conditioning zoning plans. Analysis indicated that if the original design were restored, using the present air conditioning zoning, there would be an increased risk of moisture condensation on the glass surface of the entrance hall. It was concluded, however, that this risk would be significantly reduced by subdividing the air conditioning zone and changing the air conditioning controls in each subdivided area. |
3,277 | One-Domain-One-Input: Adaptive Random Testing by Orthogonal Recursive Bisection With Restriction | One goal of software testing may be the identification or generation of a series of test cases that can detect a fault with as few test executions as possible. Motivated by insights from research into failure-causing regions of input domains, the even-spreading (even distribution) of tests across the input domain has been identified as a useful heuristic to more quickly find failures. This finding has encouraged a shift in focus from traditional random testing (RT) to its enhancement, adaptive random testing (ART), which retains the randomness of test input selection, but also attempts to maintain a more evenly distributed spread of test inputs across the input domain. Given that there are different ways to achieve the even distribution, several different ART methods and approaches have been proposed. This paper presents a new ART method, called ART by orthogonal recursive bisection (ART-ORB), which explores the advantages of repeated geometric bisection of the input domain, combined with restriction regions, to evenly spread test inputs. Experimental results show a better performance in terms of fewer test executions than RT to find failures. Compared with other ART methods, ART-ORB has comparable performance (in terms of required test executions), but incurs lower test input selection overheads, especially in higher dimensional input space. It is recommended that ART-ORB can be used in testing situations involving expensive test input execution. |
3,278 | New test structure to monitor contact-to-poly leakage in sub-90 nm CMOS technologies | The high leakage or even direct short between contact and gate is a serious problem after the feature sizes are shrunk to 65-nm technology and beyond. However, there is no suitable test structure to effectively monitor the leakage current between them. We have designed a new test structure which can eliminate the drawbacks of existing test structures and effectively monitor the leakage current between contact and gate electrode in state-of-the-art CMOS process technology. |
3,279 | Improving patch-based scene text script identification with ensembles of conjoined networks | This paper focuses on the problem of script identification in scene text images. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed aspect ratio as in the typical use of holistic CNN classifiers, we propose here a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme. Our experiments with this learning procedure demonstrate state-of-the-art results in two public script identification datasets. In addition, we propose a new public benchmark dataset for the evaluation of multi-lingual scene text end-to-end reading systems. Experiments done in this dataset demonstrate the key role of script identification in a complete end-to-end system that combines our script identification method with a previously published text detector and an off-the-shelf OCR engine. (C) 2017 Elsevier Ltd. All rights reserved. |
3,280 | Neuro-fuzzy ART-based document management system: application to mail distribution and digital libraries | A new document management system is proposed in this paper. Its kernel is based on a new set of neuro-fuzzy systems of the ART family: FasArt and RFasArt. The first one, FasArt, is used to support a simple Optical Character Recognition (OCR) that inherits fine properties of ART architectures. such as fast and incremental learning, stability and modularity. On the other hand, RFasArt is a new recurrent version of FasArt which efficiently exploits contextual information in the task of logical labeling. The proposed system is extensively tested in two real-world applications. i.e. E-mail of printed business letter and digital library of scientific papers. Experimental results show logical labeling and OCR rates over 90%. The proposed system is better compared to a previous system proposed by the group, where instead of using contextual information in an integrated way. a postprocessing Viterbi-based model was employed. (C) 2002 Elsevier Science Ltd. All rights reserved. |
3,281 | Fine mapping and marker development for the wheat leaf rust resistance gene Lr32 | Wheat leaf rust is caused by the fungal pathogen Puccinia triticina and is one of the wheat diseases of concern globally. Among the known leaf rust resistance genes (Lr) genes, Lr32 is a broadly effective gene derived from the diploid species Aegilops tauschii coss. accession RL5497-1 and has been genetically mapped to chromosome arm 3DS. However, Lr32 resistance has not been utilized in current cultivars in part due to the lack of modern, predictive DNA markers. The goals of this study were to fine map the Lr32 region and develop SNP-based kompetitive allele-specific polymerase chain reaction markers. The genomic analysis was conducted by using doubled haploid and F2-derived mapping populations. For marker development, a 90K wheat chip array, 35K and 820K Axiom R SNPs, A. tauschii pseudomolecules v4.0 and International Wheat Genome Sequencing Consortium ReqSeq v2.1 reference genomes were used. Total 28 kompetitive allele-specific polymerase chain reaction and 2 simple sequence repeat markers were developed. The Lr32 region was fine mapped between kompetitive allele-specific polymerase chain reaction markers Kwh142 and Kwh355 that flanked 34-35 Mb of the diploid and hexaploid reference genomes. Leaf rust resistance mapped as a Mendelian trait that cosegregated with 20 markers, recombination restriction limited the further resolution of the Lr32 region. A total of 10-11 candidate genes associated with disease resistance were identified between the flanking regions on both reference genomes, with the majority belonging to the nucleotide-binding domain and leucine-rich repeat gene family. The validation analysis selected 2 kompetitive allele-specific polymerase chain reaction markers, Kwh147 and Kwh722, for marker-assisted selection. The presence of Lr32 along with other Lr genes such as Lr67 and Lr34 would increase the resistance in future wheat breeding lines and have a high impact on controlling wheat leaf rust. |
3,282 | Genetic factors contribute to medication-induced QT prolongation: A review | QT prolongation is a heart rhythm condition that impacts the lives of many people and when severe can be life-threatening. QT prolongation has been linked to variations in several genes, but it can also arise in the course of treatments with medications such as certain antipsychotics and antidepressants. However, it is unclear whether the risk of medication-induced QT prolongation (MIQTP) depends on specific genetic vulnerability. Here, we review the available literature on the interplay between genetic risk and medication exposure in the context of psychiatric treatment. A review was conducted on the genetic contribution to MIQTP in psychiatric patients. A literature search was conducted on the PubMed platform with 8 papers meeting criteria for review. A total of 3,838 patients from 8 studies meeting criteria for a psychotic or mood disorder were included in this review. All studies found evidence for the genetic contribution to MIQTP. The specific genes identified in these studies included the NOS1AP, ABCB1, KCNH2, SLC22A23, EPB41L4A, LEP, CACNA1C, CERKL, SLCO3A1, BRUNOL4, NRG3, NUBPL, PALLD, NDRG4 and PLN genes. The findings highlight both the importance of monitoring heart parameters in psychiatry and the possible role for genetic profiling to increase the treatment safety. |
3,283 | Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors | In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation-maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification. |
3,284 | A-DSP: An Adaptive Join Algorithm for Dynamic Data Stream on Cloud System | The join operations, including both equi and non-equi joins, are essential to the complex data analytics in the big data era. However, they are not inherently supported by existing DSPEs (Distributed Stream Processing Engines). The state-of-the-art join solutions on DSPEs rely on either complicated routing strategies or resource-inefficient processing structures, which are susceptible to dynamic workload, especially when the DSPEs face various join predicate operations and skewed data distribution. In this paper, we propose a new cost-effective stream join framework, named A-DSP (Adaptive Dimensional Space Processing), which enhances the adaptability of real-time join model and minimizes the resource used over the dynamic workloads. Our proposal includes: 1) a join model generation algorithm devised to adaptively switch between different join schemes so as to minimize the number of processing task required; 2) a load-balancing mechanism which maximizes the processing throughput; and 3) a lightweight algorithm designed for cutting down unnecessary migration cost. Extensive experiments are conducted to compare our proposal against state-of-the-art solutions on both benchmark and real-world workloads. The experimental results verify the effectiveness of our method, especially on reducing the operational cost under pay-as-you-go pricing scheme. |
3,285 | Positron emission tomography in autoimmune encephalitis: Clinical implications and future directions | 18 F-fluoro-deoxyglucose position emission tomography (18 F-FDG-PET) has been proven as a sensitive and reliable tool for diagnosis of autoimmune encephalitis (AE). More attention was paid to this kind of imaging because of the shortage of MRI, EEG, and CSF findings. FDG-PET has been assessed in a few small studies and case reports showing apparent abnormalities in cases where MRI does not. Here, we summarized the patterns (specific or not) in AE with different antibodies detected and the clinical outlook for the wide application of FDG-PET considering some limitations. Specific patterns based on antibody subtypes and clinical symptoms were critical for identifying suspicious AE, the most common of which was the anteroposterior gradient in anti- N -methyl- d -aspartate receptor (NMDAR) encephalitis and the medial temporal lobe hypermetabolism in limbic encephalitis. And the dynamic changes of metabolic presentations in different phases provided us the potential to inspect the evolution of AE and predict the functional outcomes. Except for the visual assessment, quantitative analysis was recently reported in some voxel-based studies of regions of interest, which suggested some clues of the future evaluation of metabolic abnormalities. Large prospective studies need to be conducted controlling the time from symptom onset to examination with the same standard of FDG-PET scanning. |
3,286 | Structural and Genomic Evolution of RRNPPA Systems and Their Pheromone Signaling | In Firmicutes, important processes such as competence development, sporulation, virulence, and biofilm formation are regulated by cytoplasmic quorum sensing (QS) receptors of the RRNPPA family using peptide-based communication. Although these systems regulate important processes in a variety of bacteria, their origin and diversification are poorly understood. Here, we integrate structural, genomic, and phylogenetic evidence to shed light on RRNPPA protein origin and diversification. The family is constituted by seven different subfamilies with different domain architectures and functions. Among these, three were found in Lactobacillales (Rgg, ComR, and PrgX) and four in Bacillales (AimR, NprR, PlcR, and Rap). The patterns of presence and the phylogeny of these proteins show that subfamilies diversified a long time ago, resulting in key structural and functional differences. The concordance between the distribution of subfamilies and the bacterial phylogeny was somewhat unexpected, since many of the subfamilies are very abundant in mobile genetic elements, such as phages, plasmids, and phage-plasmids. The existence of diverse propeptide architectures raises intriguing questions about their export and maturation. It also suggests the existence of diverse roles for the RRNPPA systems. Some systems encode multiple pheromones on the same propeptide or multiple similar propeptides, suggesting that they act as "chatterers." Many others lack pheromone genes and may be "eavesdroppers." Interestingly, AimR systems without associated propeptide genes were particularly abundant in chromosomal regions not classed as prophages, suggesting that either the bacterium or other mobile elements are eavesdropping on phage activity. IMPORTANCE Quorum sensing (QS) is a mechanism of bacterial communication, coordinating important decisions depending on bacterial population. QS regulates important processes not only in bacterial behavior but also in genetic mobile elements and host-guest interactions. In Firmicutes, the most important family of QS receptors is the RRNPPA family. Despite the importance of such systems in microbiology, we know little about RRNPPA origin and diversification. In this work, the combination of sequence analysis and structural biology allowed us to identify a very large number of novel systems but also to class of them in functional families and thereby study of their origin and functional diversification. Moreover, peptide pheromone analysis revealed new and intriguing mechanisms of communication, such as "eavesdropper" systems which only listen for the pheromone and "chatterers" that take control of the communication in their microenvironment. |
3,287 | Prevalence and prediction of dropout during depression treatment in routine outpatient care: an observational study | Efficacious treatments are available for major depressive disorder (MDD), but treatment dropout is common and decreases their effectiveness. However, knowledge about prevalence of treatment dropout and its risk factors in routine care is limited. The objective of this study was to determine the prevalence of and risk factors for dropout in a large outpatient sample. In this retrospective cohort analysis, routinely collected data from 2235 outpatients with MDD who had a diagnostic work-up between 2014 and 2016 were examined. Dropout was defined as treatment termination without achieving remission before the fourth session within six months after its start. Total and item scores on the Dutch Measure for Quantification of Treatment Resistance in Depression (DM-TRD) at baseline, and demographic variables were analyzed for their association with dropout using logistic regression and elastic net analyses. Data of 987 subjects who started routine outpatient depression treatment were included in the analyses of which 143 (14.5%) dropped out. Higher DM-TRD-scores were predictive for lower dropout odds [OR = 0.78, 95% CI = (0.70-0.86), p < 0.001]. The elastic net analysis revealed several clinical variables predictive for dropout. Higher SES, higher depression severity, comorbid personality pathology and a comorbid anxiety disorder were significantly associated with less dropout in the sample. In this observational study, treatment dropout was relatively low. The DM-TRD, an easy-to-use clinical instrument, revealed several variables associated with less dropout. When applied in daily practice and combined with demographical information, this instrument may help to reduce dropout and increase treatment effectiveness. |
3,288 | Salient Object Detection: A Benchmark | We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted three years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for the state-of-the-art models, provide useful hints toward constructing more challenging large-scale data sets and better saliency models. Finally, we propose probable solutions for tackling several open problems, such as evaluation scores and data set bias, which also suggest future research directions in the rapidly growing field of salient object detection. |
3,289 | Consumer opinion on social policy approaches to promoting positive body image: Airbrushed media images and disclaimer labels | Disclaimer labels on airbrushed media images have generated political attention and advocacy as a social policy approach to promoting positive body image. Experimental research suggests that labelling is ineffective and consumers' viewpoints have been overlooked. A mixed-method study explored British consumers' ( N = 1555, aged 11-78 years) opinions on body image and social policy approaches. Thematic analysis indicated scepticism about the effectiveness of labelling images. Quantitatively, adults, although not adolescents, reported that labelling was unlikely to improve body image. Appearance diversity in media and reorienting social norms from appearance to function and health were perceived as effective strategies. Social policy and research implications are discussed. |
3,290 | Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking | This work proposes a novel approach for motion-robust diffusion-weighted (DW) brain MRI reconstruction through tracking temporal head motion using slice-to-volume registration. The slice-level motion is estimated through a filtering approach that allows tracking the head motion during the scan and correcting for out-of-plane inconsistency in the acquired images. Diffusion-sensitized image slices are registered to a base volume sequentially over time in the acquisition order where an outlier-robust Kalman filter, coupled with slice-to-volume registration, estimates head motion parameters. Diffusion gradient directions are corrected for the aligned DWI slices based on the computed rotation parameters and the diffusion tensors are directly estimated from the corrected data at each voxel using weighted linear least squares. The method was evaluated in DWI scans of adult volunteers who deliberately moved during scans as well as clinical DWI of 28 neonates and children with different types of motion. Experimental results showed marked improvements in DWI reconstruction using the proposed method compared to the state-of-the-art DWI analysis based on volume-to-volume registration. This approach can be readily used to retrieve information from motion-corrupted DW imaging data. |
3,291 | Comparison of the biodegradability of the grey fraction of municipal solid waste of Barcelona in mesophilic and thermophilic conditions | The results of the start-up of two digesters in mesophilic and thermophilic conditions, together with its steady results at several organic loading rates are described. A kinetic study is also carried out which allows one to estimate the ultimate methane production, together with the first-order kinetic constant. Operation at thermophilic temperature yields better results as it allows a more loaded reactor and the methane production is slightly higher. |
3,292 | Computer-aided design of the RF-cavity for a high-power S-band klystron | This article describes the computer-aided design of the RF-cavity for a S-band klystron operating at 2856 MHz. State-of-the-art electromagnetic simulation tools SUPERFISH, CST Microwave studio, HFSS and MAGIC have been used for cavity design. After finalising the geometrical details of the cavity through simulation, it has been fabricated and characterised through cold testing. Detailed results of the computer-aided simulation and cold measurements are presented in this article. |
3,293 | Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss | Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed model is a variant of fully-residual convolutional autoencoder and generative adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled k-space data. In addition, our solution leveragesa chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate-the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a 256 x 256 image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases. |
3,294 | Taxonomy for Evaluation of Distributed Control Strategies for Distributed Finergy Resources | Distributed control strategies applied to power distribution control problems are meant to offer robust and scalable integration of distributed energy resources (DERs). However, the term "distributed control" is often loosely applied to a variety of very different control strategies. In particular there is a lack of discrimination between aspects related to communication topology, physical distribution of components, and associated control objectives. This has lead to a lack of objective criteria for performance comparison and general quality assessment of state of the art distributed control solutions. For such comparison, a classification is required that is consistent across the different aspects mentioned above. This paper develops systematic categories of control strategies that accounts for communication, control and physical distribution aspects of the problem, and provides a set of criteria that can be assessed for these categories. The proposed taxonomy is applied to the state of the art as part of a review of existing work on distributed control of DER. Finally, we demonstrate the applicability and usefulness of the proposed classification to researchers and system designers. |
3,295 | Adaptive Multi-Objective Evolutionary Algorithms for Overtime Planning in Software Projects | Software engineering and development is well-known to suffer from unplanned overtime, which causes stress and illness in engineers and can lead to poor quality software with higher defects. Recently, we introduced a multi-objective decision support approach to help balance project risks and duration against overtime, so that software engineers can better plan overtime. This approach was empirically evaluated on six real world software projects and compared against state-of-the-art evolutionary approaches and currently used overtime strategies. The results showed that our proposal comfortably outperformed all the benchmarks considered. This paper extends our previous work by investigating adaptive multi-objective approaches to meta-heuristic operator selection, thereby extending and (as the results show) improving algorithmic performance. We also extended our empirical study to include two new real world software projects, thereby enhancing the scientific evidence for the technical performance claims made in the paper. Our new results, over all eight projects studied, showed that our adaptive algorithm outperforms the considered state of the art multi-objective approaches in 93 percent of the experiments (with large effect size). The results also confirm that our approach significantly outperforms current overtime planning practices in 100 percent of the experiments (with large effect size). |
3,296 | Nuclear organization of the rock hyrax (Procavia capensis) amygdaloid complex | The current study details the nuclear organization of the rock hyrax amygdaloid complex using both Nissl and myelin stains, along with a range of immunohistochemical stains. The rock hyrax appears to be the least derived of the Afrotherians, a group with a huge range of body phenotypes, life histories and specialized behaviours, brain sizes, and ecological niches. In this sense, the rock hyrax represents a species where the organization of the amygdaloid complex may be reflective of that in stem Eutherian mammals. Our analysis indicates that the nuclear organization of the rock hyrax amygdaloid complex is indeed very similar to that in other mammals studied, with four major nuclear groupings (the deep or basolateral group; the superficial or cortical-like or corticomedial group; the centromedial group; and the other amygdaloid nuclei) being observed, which is typical of Eutherian mammals. Moreover, each of these groupings is composed of several nuclei, the vast majority of which were readily identified in the rock hyrax. Small nuclei identified in rodents and primates were absent in the superficial and centromedial groups, seemingly involved with olfaction. A novel shell-like nucleus of the accessory basal nuclear cluster was observed in the rock hyrax, again, likely to be involved in olfaction. The current study underlines the conserved nature of nuclear parcellation in the Eutherian mammal amygdaloid complex and indicates that across most species, the flow of information processing related to species-specific affective-laden stimuli and the resultant physiological and behavioural outcomes are likely to be similar across species. |
3,297 | Dual-Wavelength Bit Input Optical RAM With Three SOA-XGM Switches | We demonstrate a novel all-optical static RAM cell that exploits wavelength diversity in the incoming optical streams towards reducing the number of active elements. The circuit requires only three semiconductor optical amplifiers-cross gain modulation gates for successful read/write operation, yielding a 25% reduction in power consumption compared to state-of-the-art configurations. Proof-of-concept experimental verification is presented at 8 Mb/s using fiber-interconnected off-the-shelf bulk components. |
3,298 | EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes | Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical assessment and treatment planning for patients is to process magnetoencephalography (MEG) data to identify epileptogenic zones. As a widely accepted biomarker of epileptic foci, epileptic MEG spikes need to be precisely detected. Given that the visual inspection of spikes is time consuming, an automatic and efficient system with adequate accuracy for spike detection is valuable in clinical practice. However, current approaches for MEG spike autodetection are dependent on hand-engineered features. Here, we propose a novel multiview Epileptic MEG Spikes detection algorithm based on a deep learning Network (EMS-Net) to accurately and efficiently recognize the spike events from MEG raw data. The results of the leave-k-subject-out validation tests for multiple datasets (i.e., balanced and realistic datasets) showed that EMS-Net achieved state-of-the-art classification performance (i.e., accuracy: 91.82% - 99.89%; precision: 91.90% - 99.45%; sensitivity: 91.61% - 99.53%; specificity: 91.60% - 99.96%; f1 score: 91.70% - 99.48%; and area under the curve: 0.9688 - 0.9998). |
3,299 | Three new cytotoxic annonaceous acetogenins from the seeds of Annona squamosa | Three new annonaceous acetogenins, annotemoyin L (1), annotemoyin Y (2) and annotemoyin X, (3) were isolated from the seeds of Annona squamosa Linn. Their structures were ascertained by chemical methods and spectral data. The cytotoxic activities of compounds against three multidrug-resistant cancer cell lines were evaluated, and compound 3 exerted strong cytotoxicity against SMMC 7721/ADR (IC50 0.163 μM), A549/T (IC50 0.064 μM) and MCF-7/ADR (IC50 0.057 μM). |
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