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Mutations in the gene encoding the transient receptor potential vanilloid member 4 (TRPV4), a Ca2+ permeable nonselective cation channel, cause TRPV4‐related disorders. TRPV4 is widely expressed in the brain; however, the pathogenesis underlying TRPV4‐mediated Ca2+ deregulation in neurodevelopment remains unresolved and an effective therapeutic strategy remains to be established. These issues were addressed by isolating mutant dental pulp stem cells from a tooth donated by a child diagnosed with metatropic dysplasia with neurodevelopmental comorbidities caused by a gain‐of‐function TRPV4 mutation, c.1855C > T (p.L619F). The mutation was repaired using CRISPR/Cas9 to generate corrected isogenic stem cells. These stem cells were differentiated into dopaminergic neurons and the pharmacological effects of folic acid were examined. In mutant neurons, constitutively elevated cytosolic Ca2+ augmented AKT‐mediated α‐synuclein (α‐syn) induction, resulting in mitochondrial Ca2+ accumulation and dysfunction. The TRPV4 antagonist, AKT inhibitor, or α‐syn knockdown, normalizes the mitochondrial Ca2+ levels in mutant neurons, suggesting the importance of mutant TRPV4/Ca2+/AKT‐induced α‐syn in mitochondrial Ca2+ accumulation. Folic acid was effective in normalizing mitochondrial Ca2+ levels via the transcriptional repression of α‐syn and improving mitochondrial reactive oxygen species levels, adenosine triphosphate synthesis, and neurite outgrowth of mutant neurons. This study provides new insights into the neuropathological mechanisms underlying TRPV4‐related disorders and related therapeutic strategies. [ABSTRACT FROM AUTHOR]

A novel mechanism for skeleton-based action recognition is proposed in this paper by enhancing and fusing diverse skeleton features from distinct levels. Graph convolutional neural networks (GCNs) have been proven to be efficient in skeleton-based action recognition. However, most graph convolutional networks tend to capture and fuse discriminative information from different forms of data in spatial neighborhoods. In that case, the deeper interactions among different forms of data as well as the extraction of information in the temporal and channel dimensions are limited. To tackle the issue, we propose the ternary adaptive graph convolution (TAGC) module to capture spatiotemporal information by graph convolution. A novel skeleton information, called parallax information, is explored from original joints or bones with little computation to further improve the performance of action recognition. In addition, in order to make better use of multiple streams, multi-stream feature fusion (MSFF) is proposed to mine deeper-level hybrid features supplementing the original streams. And a graph-based ternary enhance (GTE) module is proposed to further refine the extracted discriminative features. Finally, the proposed multi-stream ternary enhanced graph convolutional network (MS-TEGCN) achieves the state-of-the-art results through extensive experiments on three challenging datasets for skeleton-based action recognition, NTU-60, NTU-120 and Kinetics-Skeleton. [ABSTRACT FROM AUTHOR]

Beach recovery describes the processes by which there is a natural restoration of beach material and coastal morphology following storm events, and these processes are common across the globe. However, the effects of beach recovery on salinity distribution and solute transport in unconfined coastal aquifers are poorly understood. This study examined the changes in salinity distribution in tidally influenced aquifers in response to beach recovery, based on numerical modeling. The extent and location of the upper saline plume and saltwater wedge were found to vary with the beach recovery. The variations in salinity distribution directly changed the particle travel times in the aquifers. Compared with the erosion profile after the storm (storm profile), an increase of up to 743% of the particle travel time in the intertidal zone was observed when the beach recovered to a berm (silting) profile. The berm profile increased the residence time and peak concentration of the land-sourced solute plume in the beach aquifer compared with the storm profile. The berm profile also enhanced the aquifer–ocean mass exchange, resulting in increased intertidal saltwater infiltration and submarine groundwater discharge. On the other hand, the storm profile can generate much higher solute efflux than the berm profile. The storm profile is more favorable in diluting the land-sourced conservative solute and shortening its residence time in an aquifer. [ABSTRACT FROM AUTHOR]

The stable continuation of trajectories among different targets has always been the key to the tracking performance of multi-object tracking (MOT) tasks. If features of the target are aggregated and classified simply, the discriminant features of the target will be ignored. This will affect the robustness of the trajectory generated by the model. Meanwhile, many popular models are keen to execute detection and feature extraction tasks in parallel. But these two tasks will conflict with each other when optimized respectively. Therefore, we propose our tracker MOTFR to solve the above problems. In this paper, we propose a Locally Shared Information Decoupling Module (LSIDM) to reduce task optimization conflicts while ensuring the necessary information sharing. Meanwhile, a feature recoding module for deep extraction of identity discriminative features is proposed, which is called the Feature Purification Module (FPM). By combining LSIDM and FPM modules, the model utilizes the discriminative appearance features to guide the optimization of detection and further improves the performance of our model. To solve the problem of targets disappearing due to various abnormal occlusion, a Short-term Trajectory Online Complement Strategy (STOCS) is proposed to realize the trajectories continuation of these targets in the tracking stage. Through sufficient experiments, we demonstrate the superior performance of our MOTFR, which guarantees high-quality detection while achieving the stability of the target trajectory. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of bolt positioning for industrial robots, this paper studies the bolt positioning system of industrial robots combined with multieye vision technology. This paper introduces three reconstruction algorithms ART, SIRT, and SART from the perspective of theory and implementation. Moreover, the projection matrix calculated with 0, 1 weighting, length weighting, and linear interpolation weighting is used for three reconstruction algorithms to carry out reconstruction experiments. In addition, this paper combines the actual working conditions of bolt positioning to construct the system of this paper and conducts system simulation research combined with the working conditions of the robot. The research shows that the bolt positioning system based on the multieye vision industrial robot proposed in this paper has a good performance in bolt positioning. [ABSTRACT FROM AUTHOR]

Unsupervised person re-identification (ReID) is a task that aims to retrieve pedestrians across different cameras from unlabeled data. Existing methods rely on clustering to generate pseudo-labels, but they are inevitably noisy. Although pseudo-label refinement approaches have been presented, the essentiality of patch contours is ignored. The tendency analysis of retrieval between global and patch features has not been well investigated. In this paper, we propose a Patch-based Tendency Camera Multi-Constraint Learning (PTCML) model for unsupervised person ReID. First, to explore the tendentious retrieval of global and patch features, we design a Ranking Tendency Similarity (RTS) score by gauging the distribution discrepancy of distance changes. Second, based on RTS score, we propose a Tendency-based Mutual Complementation (TMC) loss to improve the quality of global and patch pseudo-labels. Third, to resist camera variations, we propose an Adaptive Camera Multi-Constraint (ACM) loss to optimize recognition results with camera distribution constraint and instance constraint simultaneously. Finally, numerous experiments on Market-1501 and MSMT17 demonstrate that our method can significantly surpass the state-of-the-art performance. • We design RTS score to mine the complementary tendency of increased distance between global and patch features. • Based on RTS score, TMC loss is proposed to refine the pseudo-labels. • We propose ACM loss to apply camera distribution and instance constraints simultaneously. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) person re-identification (ReID) faces enormous challenges due to the severe shift between the source and target domains, as well as the dramatic variations within the target domain. In this paper, to address these issues, we propose a multi-loss gap minimization learning (MGML) approach for UDA person ReID. Firstly, we introduce the part model to learn discriminative patch features and design a Patch-based Part Ignoring (PPI) loss to select reliable instances for the efficient learning of the part model. Then, given the gap that typically occurs because of the inter-domain shift and intra-domain variations, a Gap-based Minimum Camera Discrepancy (G-MCD) loss is proposed. Specifically, in terms of the inter-domain, we propose to leverage the tracklet and camera information to label each distance vector, and accordingly align pair-wise distance distributions to bridge the inter-domain gap. As for the intra-domain, to alleviate the biased search, we propose to perform the mined neighborhood for intra-camera and inter-camera separately to optimize matching results by exploring neighborhood relations more deeply. Finally, experimental results on three challenging datasets demonstrate that applying our method to unlabeled target domain outperforms current UDA methods for person ReID. [ABSTRACT FROM AUTHOR]

This JAMA Patient Page describes solar eclipses and how to view them safely. [ABSTRACT FROM AUTHOR]

In the domain of image fusion, integrating infrared and visible images provides a more complete scene description by merging the unique strengths of each modality. Existing methods struggle with handling the differences between modalities, which is caused by the inherent entanglement of scene-common information and modality-specific information within each modality. In response, we propose MIRFuse, a model for infrared and visible image fusion based on disentanglement representation via mutual information regularization. The process of disentanglement, in which scene-common information and modality-specific information are separated, forms the basis for identifying both shared and exclusive features. First, mutual information maximization is used as consistency constraint, enabling scene-common encoders to effectively extract shared features. Second, the Hilbert–Schmidt independence criterion is employed as heterogeneity constraint, promoting modality-specific encoders to extract exclusive features. Finally, both shared and exclusive features are identified and combined using various fusion strategies to produce a fused image. The resulting fused image provides a comprehensive representation of the entire scene, allowing for more effective utilization of information from multiple modalities. Our experiments have validated the advanced nature and effectiveness of our method. [ABSTRACT FROM AUTHOR]

Due to different retinal fundus image acquisition devices having various imaging principles, domain shift often occurs between different datasets. Hence, a segmentation network well-trained on one dataset (i.e., source domain) usually obtains very poor performance on another dataset (i.e., target domain), which results in us having to annotate the new dataset (target domain) to train the segmentation network again. However, annotating a new dataset is usually time-consuming and laborious. To address this problem, we proposed a novel unsupervised domain adaptation method for optic disc and cup segmentation. To be specific, we first utilized a domain adaptation method based on self-ensembling to effectively align the features of the source domain and target domain. Then, we designed a novel backbone network (MBU-Net) to make full use of the mask and boundary information to improve the segmentation performance of self-ensembling. Finally, we proposed an output-level adversarial domain adaptation (OADA) to address the domain shift problem of the structured output space in self-ensembling. In experiments, we test our proposed method on three different target domain datasets including Target Domain 1 (RIM-ONE_r3 dataset), Target Domain 2 (Drishti-GS dataset) and Target Domain 3 (REFUGE dataset). The experimental results demonstrate that our proposed method outperforms the compared state-of-the-art methods in the optic disc and cup segmentation tasks. [ABSTRACT FROM AUTHOR]

Background: The mixed-lineage leukemia (MLL) gene is located on chromosome 11q23. The MLL gene can be rearranged to generate partial tandem duplications (MLL-PTD), which occurs in about 5-10% of acute myeloid leukemia (AML) with a normal karyotype and in 5-6% of myelodysplastic syndrome (MDS) patients. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is currently one of the curative therapies available for AML and MDS with excess blasts (MDS-EB). However, how the prognosis of patients with high levels of MLL-PTD after allo-HSCT, and whether MLL-PTD could be used as a reliable indicator for minimal residual disease (MRD) monitoring in transplant patients remains unknown. Our study purposed to analyze the dynamic changes of MLL-PTD peri-transplantation and the best threshold for predicting relapse after transplantation.Methods: We retrospectively collected the clinical data of 48 patients with MLL-PTD AML or MDS-EB who underwent allo-HSCT in Peking University People's Hospital. The MLL-PTD was examined by real-time quantitative polymerase chain reaction (RQ-PCR) at the diagnosis, before transplantation and the fixed time points after transplantation. Detectable MLL-PTD/ABL > 0.08% was defined as MLL-PTD positive in this study.Results: The 48 patients included 33 AML patients and 15 MDS-EB patients. The median follow-up time was 26(0.7-56) months after HSCT. In AML patients, 7 patients (21.2%) died of treatment-related mortality (TRM), 6 patients (18.2%) underwent hematological relapse and died ultimately. Of the 15 patients with MDS-EB, 2 patients (13.3%) died of infection. The 3-year cumulative incidence of relapse (CIR), overall survival (OS), disease-free survival (DFS) and TRM were 13.7 ± 5.2, 67.8 ± 6.9, 68.1 ± 6.8 and 20.3% ± 6.1%, respectively. ROC curve showed that post-transplant MLL-PTD ≥ 1.0% was the optimal cut-off value for predicting hematological relapse after allo-HSCT. There was statistical difference between post-transplant MLL-PTD ≥ 1.0% and MLL-PTD < 1.0% groups (3-year CIR: 75% ± 15.3% vs. 0%, P < 0.001; 3-year OS: 25.0 ± 15.3% vs. 80.7% ± 6.6%, P < 0.001; 3-year DFS: 25.0 ± 15.3% vs. 80.7 ± 6.6%, P < 0.001; 3-year TRM: 0 vs. 19.3 ± 6.6%, P = 0.277). However, whether MLL-PTD ≥ 1% or MLL-PTD < 1% before transplantation has no significant difference on the prognosis.Conclusions: Our study indicated that MLL-PTD had a certain stability and could effectively reflect the change of tumor burden. The expression level of MLL-PTD after transplantation can serve as an effective indicator for predicting relapse. [ABSTRACT FROM AUTHOR]

Clipping on learning rates in Adam leads to an effective stochastic algorithm—AdaBound. In spite of its effectiveness in practice, convergence analysis of AdaBound has not been fully explored, especially for non-convex optimization. To this end, we address the convergence of the last individual output of AdaBound for non-convex stochastic optimization problems, which is called individual convergence. We prove that, with the iteration of the AdaBound, the cost function converges to a finite value and the corresponding gradient converges to zero. The novelty of this proof is that the convergence conditions on the bound functions and momentum factors are much more relaxed than the existing results, especially when we remove the monotonicity and convergence of the bound functions, and only keep their boundedness. The momentum factors can be fixed to be constant, without the restriction of monotonically decreasing. This provides a new perspective on understanding the bound functions and momentum factors of AdaBound. At last, numerical experiments are provided to corroborate our theory and show that the convergence of AdaBound extends to more general bound functions. [ABSTRACT FROM AUTHOR]

A novel method for skeleton-based action recognition by fusing multi-level spatial features and multi-level temporal features is proposed in this article. Recently, Graph Convolutional Network (GCN) for skeleton-based action recognition has attracted the eyes of many researchers and has a great performance in the field of action recognition. But most of them focus on changing architecture of single-stream network and only use simple methods like average fusion to fuse different forms of skeleton data. In this article, we shift the focus to the problem that insufficient interactions between the different forms of features for that networks are unable to fully capture efficient information from skeleton data. To tackle this problem, we propose a multi-stream network called Symmetrical Enhanced Fusion Network (SEFN). The network is composed of a spatial stream, a temporal stream and a fusion stream. The spatial stream extracts spatial features from skeleton data by GCN. The temporal stream is able to extract temporal features from skeleton data with the help of the embedded Motion Sequence Calculation Algorithm. The fusion stream provides an early fusion method and extra fusion information for the whole network. It gathers multi-level features from two feature extractions and fuses them with the Multi-perspective Attention Fusion Module (MPAFM) we propose. The MPAFM enables different forms of data to enhance each other and can strengthen feature extractions. In the final, we generalize the skeleton data from joint data to bone data and evaluate our network in three large-scale benchmarks: NTU-RGBD, NTU-RGBD 120 and Kinetics-Skeleton. Experiment results demonstrate that our method achieves competitive performance. [ABSTRACT FROM AUTHOR]

Recent studies have shown that approximately 50% of patients with chronic myeloid leukemia (CML) receiving tyrosine kinase inhibitor (TKI) therapy with a sustained deep molecular response (DMR) (BCR-ABL1IS ≤ 0.01%) can achieve treatment-free remission (TFR, stopping TKI without relapse) and that prior interferon (IFN)-α therapy and higher NK cell counts at and after TKI discontinuation are associated with TFR. We recently reported that post-TKI discontinuation of IFN-α therapy could prevent molecular relapse (MR, BCR-ABL1IS > 0.1%). Here, we evaluated whether NK cells are associated with MR and investigated the effects of post-TKI discontinuation IFN-α therapy on lymphocyte subsets. A total of 34 patients measuring blood lymphocyte subclasses were included. In the 22 patients who did not receive IFN-α therapy, at 1 month after TKI discontinuation, the nonrelapsed patients showed a significantly higher proportion and count of NK cells than the relapsed patients. In particular, the proportion and count of CD56dim NK cells were significantly higher in the nonrelapsed patients than in the relapsed patients. In the 12 patients who received IFN-α therapy, the level of CD56bright NK cells increased significantly after 3 and 6 months of IFN-α therapy. In summary, NK cells, in particular CD56dim NK cells, were associated with MR after TKI discontinuation in patients with CML. Additionally, IFN-α therapy gradually increased the level of CD56bright NK cells in patients with CML. [ABSTRACT FROM AUTHOR]

Isotropic materials are required to adhere to various mechanical principles due to their limited thermal stability. For instance, it is essential for Poisson's ratio to be within the range of −1 to 0.5, and the longitudinal wave velocity must exceed the transverse wave velocity. Nevertheless, perfect crystals, as anisotropic materials, have the ability to defy conventional rules. Through the integration of high-throughput processes and first-principles calculations, a comprehensive exploration of known materials was conducted, resulting in the establishment of a database featuring an extreme anisotropic mechanism. This included the identification of abnormal Poisson's ratios (with the directional Poisson's ratio ranging from −3.00 to 3.67), the discovery of extreme negative linear compressibility, the determination of the upper and lower limits of the sound velocity, and other associated properties. Several materials with abnormal Poisson's ratios (<−1 or >0.5) were listed, and their peculiar mechanical behavior, wherein the volume decreased counterintuitively with uniaxial tension, was discussed. Finally, this study focused on the velocities of longitudinal and transverse waves, with specific emphasis on materials exhibiting transverse wave velocities that exceeded the longitudinal wave velocities. [ABSTRACT FROM AUTHOR]

Liver cancer is one of the leading causes of cancer deaths in Asia and Africa. It is caused by the Hepatocellular carcinoma (HCC) in almost 90% of all cases. HCC is a malignant tumor and the most common histological type of the primary liver cancers. The detection and evaluation of viable tumor regions in HCC present an important clinical significance since it is a key step to assess response of chemoradiotherapy and tumor cell proportion in genetic tests. Recent advances in computer vision, digital pathology and microscopy imaging enable automatic histopathology image analysis for cancer diagnosis. In this paper, we present a multi-resolution deep learning model HistoCAE for viable tumor segmentation in whole-slide liver histopathology images. We propose convolutional autoencoder (CAE) based framework with a customized reconstruction loss function for image reconstruction, followed by a classification module to classify each image patch as tumor versus non-tumor. The resulting patch-based prediction results are spatially combined to generate the final segmentation result for each WSI. Additionally, the spatially organized encoded feature map derived from small image patches is used to compress the gigapixel whole-slide images. Our proposed model presents superior performance to other benchmark models with extensive experiments, suggesting its efficacy for viable tumor area segmentation with liver whole-slide images. [ABSTRACT FROM AUTHOR]

Currently, the discriminative correlation filter‐based trackers have achieved higher tracking accuracy. However, visual tracking still faces challenges in terms of heavy occlusion, scale variation and so on. In this study, the authors intend to solve heavy occlusion by introducing collaborative model into classifier‐box. Firstly, they introduce complex colour features into correlation filter tracker to improve the effect of the tracker. Secondly, they introduce a multi‐scale method into their tracker to ease the scale problem. Thirdly, in order to solve the heavy occlusion in the tracking process, they adopt the locally weighted distance and classifier‐box. Their algorithm achieves distance precision rates of 81.7 and 77.4% on OTB2013 dataset and OTB2015 dataset, respectively. Their contribution focuses on solving heavy occlusion by using colour features, locally weighted distance and classifier‐box. The experimental results on OTB2013 and OTB2015 datasets demonstrate their algorithm to perform better than state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Different types of devices are used in our daily lives, and each type has its own advantages and disadvantages. Cross-device interaction that involves multiple devices can potentially overcome each device's inherent disadvantages. Without a toolkit, it is time consuming to develop cross-device interaction. Focusing on a horizontal large display (called a tabletop), this paper proposes a generic toolkit for prototyping tabletop-centric cross-device applications that involve a large display and multiple smartphones. In our toolkit, a user uses a smartphone as a look-through lens for browsing and selecting objects on a tabletop, and remotely manipulates the selected object with multimodal feedback. Based on the above interaction style, our toolkit formalizes the development of cross-device interaction as defining a mobile interface on each tabletop interface object. Since our toolkit encapsulates the interface distribution and synchronization through message passing, interface developers can focus on developing cross-device interaction by designing a mobile interface in response to a selected tabletop interface object. In order to demonstrate the versatility and design space of our toolkit, six design issues, i.e., data transfer, personalization, user interface composition, authentication, localized & private feedback, and input expressiveness, were discussed through a set of sample applications. [ABSTRACT FROM AUTHOR]

Methyl decanoate (MD), methyl-9-decanoate (MD9D), and n-heptane (H) as alternative blends of biodiesel (B) were used to build a detailed chemical kinetic mechanism containing 3,324 components and 11,053 elementary reactions. This condition verifies that the ignition delay time of the detailed mechanism in the experiment conditions is reasonable. MD and MD9D will produce methyl-2-palmitate (MP2D) and finally be dehydrogenated as CH 2 O. Furthermore, R4 (O + H 2 O→OH + OH) and R228 (CH 2 CHO + O 2 →CH 2 O + CO + OH) are the key reactions, which will influence the ignition delay and heat release. According to the simulation result, the rate of BH (the volume ratio of the biodiesel/n-heptane mixture is fixed at 20%/80%) in constant volume bomb (Cetane Ignition Delay 510, CID 510) is the highest. However, with the development of ethanol, the rates decreased. The reactors of BHE5 (BHE5 refers to a blend of 5% ethanol and 95% biodiesel/n-heptane) have the highest rate among the ethanol blends. In addition, the reaction rate of the intermediate substance of ketohydroperoxide (KHP) in a modified cooperative fuel research engine (CFR) during combustion decreased with ethanol addition. However, the KHP rate of BHE15 (BHE15 refers to a blend of 15% ethanol and 85% biodiesel/n-heptane) and BHE20 (BHE20 refers to a blend of 20% ethanol and 80% biodiesel/n-heptane) is similar, causing the closed onset of low-temperature heat release. The rate of CH 2 O and MP2D of BH is the highest over the others in CID 510. The rate of CH 2 O and MP2D of BHE5 is lower than that of BHE10 (BHE10 refers to a blend of 10% ethanol and 90% biodiesel/n-heptane), BHE15, and BHE20. • A chemical kinetic mechanism of ethanol/biodiesel/n-Heptane. • R228 is the key reaction that affects the low-temperature combustion process. • R4 will influence the ignition delay and heat release. [ABSTRACT FROM AUTHOR]

Background. Diabetic nephropathy (DN) is an important cause of end-stage renal disease and is recognized as a public health problem worldwide. However, there have been no nationwide surveys of DN prevalence in China. This study is aimed at estimating the pooled prevalence of DN among patients with type 2 diabetes in China. Methods. Published studies on the prevalence of DN among patients with type 2 diabetes published from January 1980 to October 2019 were systematically reviewed using PubMed, Embase, Google Scholar, Chinese Wanfang databases, and Chinese National Knowledge Infrastructure. The pooled prevalence of DN was estimated with the random effects model using R software. Prevalence estimates were also stratified by study design, methodological approach, and study population characteristics. Results. Thirty studies with a total of 79,364 participants were included in our study. The overall pooled prevalence of DN was 21.8% [95% confidence interval (CI): 18.5-25.4%]. Subgroup analysis found that the prevalence of DN varied significantly according to different DM and DN diagnostic criteria (P < 0.05); the pooling estimate was the highest in the west region of 41.3%, followed by that in the east region of China with 22.3%, northeast region with 20.7%, and central region with 15.6% (P < 0.05), and was higher in the male-dominated studies 27.7%, compared with the female-dominated studies 17.6% (P < 0.05). Conclusions. The prevalence of DN is high in Chinese patients with type 2 diabetes and shows geographic and gender variation. These data indicate that national strategies aimed at primary and secondary prevention of DN and screening programs for DN are urgently needed to reduce the risk and burden of DN in China. [ABSTRACT FROM AUTHOR]

Discriminative correlation filter‐based tracking algorithms have recently shown impressive performance on benchmark data sets. However, visual tracking is still a challenging task in the case of partial occlusions, irregular deformations and so on. In this study, the authors intend to solve these issues by introducing the adaptive collaborative model into part‐based tracking. First, instead of a simple linear superposition, the collaborative strategy they proposed combines the template model and colour‐based model adaptively and relies on the strengths of both to promote the accuracy. Second, we utilise the voting strategy to figure out the final object position from reliable parts, and the motion information is used in evaluation for reliable parts to enable the tracker to be robust in various situations. Third, the authors utilise a discriminative multi‐scale estimate method to solve the problem of scale variations. Finally, they introduce a dimensionality reduction method to limit the computational complexity of the tracker. Abundant experiments demonstrate that the tracker performs superiorly against several advanced algorithms on both the Online Tracking Benchmark (OTB) 2013 and OTB2015 data sets while maintaining the high frame rates. [ABSTRACT FROM AUTHOR]

Highlights • Isothermal: detection was carried on at a temperature between 37 and 42 °C. • Rapid: 1 h to obtain results from plant DNA extraction to amplification. • Simple: portable tube scanner, pipette and magnet. • Sensitivity: enough to identify real sample. • Doubleplex: simultaneous assay of aggressive L. maculans and non-aggressive species L. biglobosa. Abstract The fungus Leptosphaeria maculans leading to Phoma stem canker (blackleg) of Brassica napus (oilseed rape, canola) produces the phytotoxin sirodesmin PL, which is responsible for major yield losses of oilseed rape worldwide. Polymerase chain reaction (PCR) remains the gold standard diagnostic tool for L. maculans , but the required expensive equipment and long time make it inappropriate for fast field test. Herein, a portable system for rapid assaying L. maculans and L. biglobosa is designed around recombinase polymerase amplification (RPA) with fluorescent probe as the signal indicator, which allowed the real-time assay of amplification performed on a portable device between 37 and 42 °C. The time needed to observe the positive reaction results is controlled within 30 min. The proposed assay system is a good choice for on-site disease screening of oilseed rape plant where rapid detection is valuable, including port quarantine, agriculture quality testing, and pathogen spreading control. [ABSTRACT FROM AUTHOR]

Most existing multi-object tracking models only focus on the spatial information generated by image-level input while ignoring the necessary temporal information. The temporal motion information between consecutive frames can effectively reflect the target's motion status, which is essential to improving the performance in dealing with occlusion and motion blur of the model. We propose MISTracker to realize the motion information supplement to the original tracking model. Specifically, we divide multi-scale feature maps into two categories from the perspective of space and channel information. Meanwhile, the spatial-level frame differences processing (SFDP) module and the channel-level frame differences processing (CFDP) module are proposed to deal with these differences between continuous frames, respectively. The SFDP processes the differences from the perspective of spatial information and supplements the motion information through the perception of pixel-level information changes in the feature maps. The CFDP processes the differences from the perspective of channels and enhances the information of motion-sensitive channels through the overall pixel differences of different channels. Eventually, temporal and motion information are complementary to each other after upsampling fusion. The whole process is realized by simple convolution, which reduces the computational force as much as possible and enhances the tracking performance of the model. [ABSTRACT FROM AUTHOR]

• The new analytical solution accurately predicts watertable fluctuations. • Vertical flow and dynamic effective porosity impact watertable wave propagation. • Increasing beach slope weakens vertical flow and dynamic effective porosity effects. Interactions between the tide and sloping sea boundary make watertable fluctuations in coastal unconfined aquifers complicated. Based on a perturbation method, we derived a new analytical solution to predict watertable fluctuations for coastal unconfined aquifers with a sloping sea boundary. Following validation with a numerical model, the analytical solution was used to explore the effects of the vertical flow (in the saturated zone) and dynamic effective porosity on watertable fluctuations. Results show that the new analytical solution accurately predicts watertable fluctuations for coastal unconfined aquifers with a sloping sea boundary. Compared with sand coastal unconfined aquifers, both vertical flow and dynamic effective porosity effects on watertable fluctuations are more pronounced for loam coastal unconfined aquifers. Vertical flow has a minor influence on the fluctuation amplitude while it significantly decreases the phase lag of the watertable fluctuation at a given location. In contrast to vertical flow, accounting for the dynamic effective porosity not only decreases the phase lag, but also significantly amplifies the fluctuation amplitude for a given location, which enables watertable wave propagation further inland. Increasing the beach slope weakens the effects of the vertical flow and dynamic effective porosity on watertable fluctuations. Furthermore, including either the vertical flow or dynamic effective porosity effects leads to a lower watertable overheight. These results highlight the importance of vertical flow and dynamic effective porosity effects in models of watertable fluctuations. [ABSTRACT FROM AUTHOR]

Nowadays, graph convolutional networks are widely used in skeleton-based action recognition. However, these methods ignore the difference between main participant and subordinate participant, as well as the consistency and causality reasoning in human–human interactive actions. In this paper, we construct a novel Participants-based Synchronous Optimization Network (PSONet). Firstly, we construct main participant branch, subordination participant branch and relative movements branch for the individual and interactive information of participants. Secondly, in the training process, Participants-based Synchronous Response (PSR) loss is constructed to optimize our network. Online mutual response mechanism in PSR regulates the consistency and captures the causality between the main participant action and the overall interactive action. Joint cross-entropy loss in PSR is used to constrain the action instances with individual and interactive action information. Finally, Representative Temporal Enhanced (RTE) block is proposed to complement representative temporal aggregation features and enhance the spatial modeling of representative temporal frames. Experiments have been conducted on the NTU RGB+D 60 dataset and the NTU RGB+D 120 dataset, which have verified that PSONet outperforms state-of-the-art methods. • We propose a solution for weak interactive action processing in two-branch structure. • We propose an interactive action recognition network and a participants-based loss. • Our proposed network is robust and performs well on large-scale action datasets. [ABSTRACT FROM AUTHOR]

Highlights • A modified VG model was validated on a wide range of soil textures from sand to silt clay loam. • The modified VG model is more accurate and flexible than the original VG model. • New analytical expressions for the wetting front pressure and characteristic length are derived. Abstract Proper soil hydraulic functions of the water retention curve (the WRC) and relative hydraulic conductivity (the RHC) are essential for studies of unsaturated groundwater flow in soils. Various mathematical expressions of these functions have been proposed, including the widely used van Genuchten-Mualem model combining the van Genuchten formula for the WRC and the Mualem formula for the RHC. Here we validate a model based on the van Genuchten function for the both WRC and RHC (termed the MVG model), against published experimental data covering a wide range of soil textures from sand to silt clay loam. The results show that the MVG model is more accurate and flexible than existing soil hydraulic functions, especially for predicting the RHC. The fitting of the model to the data also reveals a relationship of model parameters between the MVG-WRC and MVG-RHC functions. Both functions can be integrated and differentiated to attain simple analytical expressions for characteristic parameters of soil water flow, for example, the wetting front pressure ψ f used in the Green and Ampt model for ponded infiltration analysis and the characteristic length L c as a key factor in determining various flow processes (e.g., drainage or evaporation) in unsaturated soils. [ABSTRACT FROM AUTHOR]

The emergence of low-cost depth cameras creates potential for RGB-D based human action recognition. However, most of the existing RGB-D based approaches simply concatenate original heterogeneous features without discovering the latent relations among different modalities. We propose a discriminative common structure learning (DCSL) model for human action recognition from RGB-D sequences. Specifically, we extract deep learning-based features and hand-crafted features from multimodal data (skeleton, depth, and RGB). In particular, we propose a deep architecture based on 3-D convolutional neural network to automatically extract deep spatiotemporal features from raw sequences. The proposed DCSL model utilizes a generalized version of collective matrix factorization to learn shared features among different modalities. To perform supervised learning and preserve intermodal similarity, we formulate a graph regularization term by considering both label information and similar geometric structure of multimodal data, which intends to improve the discriminative power of shared features. Moreover, we solve the objective function using an iterative optimization algorithm. Then, an improved collaborative representation classifier is employed to perform computationally efficient action recognition. Experimental results on four action datasets demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights • Our CMFL model jointly learns shared-specific features and action classifiers. • The proposed RSTPF features extract dynamic local patterns around each human joint. • The CMFL model enables the features to be optimized for classification. • The CMFL performs well even if one or two modalities are missing in the testing stage. • A max-margin framework is introduced to fuse skeleton, depth and RGB data. Abstract The emergence of cost-effective depth sensors opens up a new dimension for RGB-D based human action recognition. In this paper, we propose a collaborative multimodal feature learning (CMFL) model for human action recognition from RGB-D sequences. Specifically, we propose a robust spatio-temporal pyramid feature (RSTPF) to capture dynamic local patterns around each human joint. The proposed CMFL model fuses multimodal data (skeleton, depth and RGB), and learns action classifiers using the fused features. The original low-level feature matrices are factorized to learn shared features and modality-specific features under a supervised fashion. The shared features describe the common structures among the three modalities while the modality-specific features capture intrinsic information of each modality. We formulate shared-specific features mining and action classifiers learning in a unified max-margin framework, and solve the formulation using an iterative optimization algorithm. Experimental results on four action datasets demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Graphical abstract (a) Joint statistic matrix color-coded by TPs, FPs, FNs and TNs, which were obtained from the comparison between the connective matrices derived from tract-tracing database and dMRI data. The FVE brain map, the optimal brain map (out of the three brain maps in the present work) for global connectome, was used to construct connective matrices. The dMRI tractography connective matrix was constructed under the optimal tractography parameters (QA=0.2, Angle=70°) and binarized by the connective strength threshold corresponding to the maximal Youden index. Those parameters also apply to (b)–(i); (b) dMRI tractography fibers corresponding to the TP connections in (a). Fibers corresponding to the same connection have the same color; (c) The connective matrix color-coded by the average local C values on the dMRI fiber tracts corresponding to the TP connections. Pink block highlights those connecting brain sites in frontal lobe; (d) Top 1% connections in term of their large average local C values. Color blocks highlight some of these connections, and (e)-(i) show the corresponding dMRI fibers as well as the brain sites they connect. Dashed blocks indicate that those connections can find their symmetry counterparts in regions the solid blocks highlight. Interpretations of those blocks are found in the text. Highlights • Construction of a large-scale dMRI connectome was evaluated by gold-standard data: neuron tracing data and myelin stain data. • The presented framework is open for more data modalities and tractography models to be easily integrated in this framework. • With optimized tractography parameters and scales, we could substantially trust dMRI with more confidence. Abstract Accurate assessment of connectional anatomy of primate brains can be an important avenue to better understand the structural and functional organization of brains. To this end, numerous connectome projects have been initiated to create a comprehensive map of the connectional anatomy over a large spatial expanse. Tractography based on diffusion MRI (dMRI) data has been used as a tool by many connectome projects in that it is widely used to visualize axonal pathways and reveal microstructural features on living brains. However, the measures obtained from dMRI are indirect inference of microstructures. This intrinsic limitation reduces the reliability of dMRI in constructing connectomes for brains. In this work, we proposed a framework to increase the accuracy of constructing a dMRI-based connectome on macaque brains by integrating meso-scale connective information from tract-tracing data and micro-scale axonal orientation information from myelin stain data. Our results suggest that this integrative framework could advance the mapping accuracy of dMRI based connections and axonal pathways, and demonstrate the prospect of the proposed framework in constructing a large-scale connectome on living primate brains. [ABSTRACT FROM AUTHOR]

ABSTRACT Jing, L.; Kong, J.; Wang Q., and Yao Y.T., 2018. An improved contaminant source identification method for sudden water pollution accident in coaster estuaries, China. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 946–950. Coconut Creek (Florida), ISSN 0749-0208. This paper presents a method for quickly and accurately identifying contaminant source in estuary region, characterized by decoupling solving releasing time, location and density of source problems, respectively. The method firstly gets the rough source releasing time by analyzing the typical double peak phenomenon in tidal estuary region, and then presents the rough position of source by using Lagrange tracing scheme. The rough position makes up the deficiency of priori information in traditional Genetic Algorithm (GA). And then, according to the correlation between the measured and calculated concentration at measuring point, a high resolution mass transportation and an optimization models are operated repeatedly and alternatively. In order to increase the efficiency of searching optimal parameters, the Genetic Algorithm is improved by introducing a weighting factor based on the precision improvement trend. Such an optimization model can effectively reduce the calculation burden when parameters are increased in demand. This model has been successfully applied in an accident case in Quanzhou Bay of China. Simulated results confirm the model's merits in reasonably identifying relevant unknown parameters. The convergence of present model is more efficient in searching for the optimal parameters with less iteration times nearly half the traditional Genetic Algorithm. This model is high efficiency and has great practical significance in dealing with emergent water pollution in estuary and coastal areas. [ABSTRACT FROM AUTHOR]

ABSTRACT Pan, M.J.; Kong, J.; Luo, Z.Y., and Lv, Z.J., 2018. Influence of typhoon path on salt water intrusion in the Modaomen Waterway, China. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 936–940. Coconut Creek (Florida), ISSN 0749-0208. Three-dimensional baroclinic model was applied in the Modaomen Waterway to analyze the effect of typhoon path on salt water intrusion. Taking typhoon ‘Nesat’ (the west-path typhoon) and ‘Usagi’ (the east-path typhoon) as examples, two opposite dynamic effects on saltwater transport and stratification were discovered. Influenced by typhoon remote wind, the east-path typhoon leads to the set-down of coastal sea level, which increases the oceanward advective flux. While the west-path typhoon results in the set-up of coastal sea level that promotes the oceanward advective flux landward and causes more serious salt water intrusion. Influenced by typhoon local wind, the west-path typhoon with the up-estuary local wind tends to reduce stratification. However, the east-path typhoon with the down-estuary local wind is likely to enhance stratification under moderate wind, but reduce stratification when the wind stress increases. Besides, it should be mentioned that with respect to time-varying salinity value, a bimodal characteristic appeared during the process of typhoon Nesat. The salt flux decomposition and the numerical experiments were adopted to investigate this mechanism underlying the formation. Results show that the first peak was caused by periodic salt water intrusion in the control of steady shear salt flux during neap-spring cycle with low river discharge, while the latter peak resulted from the oceanward advective flux suddenly turning to landward in the control of sea level setup. [ABSTRACT FROM AUTHOR]

Human action recognition is a challenging task in machine learning and pattern recognition. This paper presents an action recognition framework based on depth sequences. An effective feature descriptor named depth motion maps pyramid (DMMP) inspired by DMMs is developed. First, a series of DMMs with temporal scales are constructed to effectively capture spatial -- temporal motion patterns of human actions. Then these DMMs are fused to obtain the final descriptor named DMMs pyramid. Second, we propose a discriminative collaborative representation classifier (DCRC), where an extra constraint on the collaborative coefficient is imposed to provide prior knowledge for the representation coefficient. In addition, we apply DCRC to encode the obtained features and recognize the human actions. The proposed framework is evaluated on MSR three-dimensional (3-D) action datasets, MSR hand gesture dataset, UTD-MHAD, and MSR daily Activity3D dataset, respectively. The experimental results indicate the effectiveness of our proposed method for human action recognition. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.3.033027] [ABSTRACT FROM AUTHOR]

Cucumber Mosaic Virus (CMV) has a small RNA genome that encodes a limited number of proteins, but can infect many plant species, including Arabidopsis thaliana and Nicotiana benthamiana. Virus proteins thus have multiple means of conferring their pathogenicity during the infection process. However, the pathogenic mechanism of CMV remains unclear. Here we discovered that the expression of the CMV movement protein (MP) in A. thaliana and N. benthamiana can suppress reactive oxygen species (ROS) production triggered by multiple pathogen-associated molecular patterns (PAMPs), such as bacteria-derived peptide flg22, elf18, and fungal-derived chitin. Transgenic Arabidopsis plants expressing the MP were compromised in flg22-induced immune activation and were more susceptible to Pseudomonas syringae pv. tomato ( Pst ) DC3000 hrcC - strain infection. Further analysis revealed that flg22-induced resistance gene expression was also compromised in MP transgenic plants. The CMV MP protein was previously reported to function in cell-to-cell movement processes, and our findings offer a new molecular mechanism for the CMV MP protein in suppression of host PAMP-triggered immune responses. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the effects of the initial MLL-partial tandem duplication (PTD) expression levels on clinical outcomes in 36 MLL-PTD-positive acute myeloid leukemia (AML) patients between 2014 and 2016. ROC curves showed 1.0% MLL-PTD as the optimal diagnostic cutoff for complete remission (CR). Nineteen and 17 cases had MLL-PTD <1.0% (low-level group) and ≥1.0% (high-level group), respectively. The FAB type distribution (M2 incidence, 100% vs. 53%, p = .003) and double-CEBPA-mutation incidence (37% vs. 0%, p = .008) significantly differed between the groups, as did the CR rates after the first (78.9% vs. 35.3%, p = .008) and second chemotherapies (84.2% vs. 47.1%, p = .001). High MLL-PTD level was the only independent factor affecting the CR rate (odds ratio = 0.16, p = .024). The 24-month overall survival was significantly lower in the high-level group (52.6% vs. 29.4%, p = .043). In conclusion, AML patients with high initial MLL-PTD levels have lower induction CR and survival rates. [ABSTRACT FROM AUTHOR]

Jing, L.; Kong, J.; Wang Q., and Yao Y.T., 2018. An improved contaminant source identification method for sudden water pollution accident in coaster estuaries, China. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 946–950. Coconut Creek (Florida), ISSN 0749-0208. This paper presents a method for quickly and accurately identifying contaminant source in estuary region, characterized by decoupling solving releasing time, location and density of source problems, respectively. The method firstly gets the rough source releasing time by analyzing the typical double peak phenomenon in tidal estuary region, and then presents the rough position of source by using Lagrange tracing scheme. The rough position makes up the deficiency of priori information in traditional Genetic Algorithm (GA). And then, according to the correlation between the measured and calculated concentration at measuring point, a high resolution mass transportation and an optimization models are operated repeatedly and alternatively. In order to increase the efficiency of searching optimal parameters, the Genetic Algorithm is improved by introducing a weighting factor based on the precision improvement trend. Such an optimization model can effectively reduce the calculation burden when parameters are increased in demand. This model has been successfully applied in an accident case in Quanzhou Bay of China. Simulated results confirm the model's merits in reasonably identifying relevant unknown parameters. The convergence of present model is more efficient in searching for the optimal parameters with less iteration times nearly half the traditional Genetic Algorithm. This model is high efficiency and has great practical significance in dealing with emergent water pollution in estuary and coastal areas. [ABSTRACT FROM AUTHOR]

Pan, M.J.; Kong, J.; Luo, Z.Y., and Lv, Z.J., 2018. Influence of typhoon path on salt water intrusion in the Modaomen Waterway, China. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 936–940. Coconut Creek (Florida), ISSN 0749-0208. Three-dimensional baroclinic model was applied in the Modaomen Waterway to analyze the effect of typhoon path on salt water intrusion. Taking typhoon 'Nesat' (the west-path typhoon) and 'Usagi' (the east-path typhoon) as examples, two opposite dynamic effects on saltwater transport and stratification were discovered. Influenced by typhoon remote wind, the east-path typhoon leads to the set-down of coastal sea level, which increases the oceanward advective flux. While the west-path typhoon results in the set-up of coastal sea level that promotes the oceanward advective flux landward and causes more serious salt water intrusion. Influenced by typhoon local wind, the west-path typhoon with the up-estuary local wind tends to reduce stratification. However, the east-path typhoon with the down-estuary local wind is likely to enhance stratification under moderate wind, but reduce stratification when the wind stress increases. Besides, it should be mentioned that with respect to time-varying salinity value, a bimodal characteristic appeared during the process of typhoon Nesat. The salt flux decomposition and the numerical experiments were adopted to investigate this mechanism underlying the formation. Results show that the first peak was caused by periodic salt water intrusion in the control of steady shear salt flux during neap-spring cycle with low river discharge, while the latter peak resulted from the oceanward advective flux suddenly turning to landward in the control of sea level setup. [ABSTRACT FROM AUTHOR]

Innovative methods and new technologies have significantly improved the quality of our daily life. However, disabled people, for example those that cannot use their arms and legs anymore, often cannot benefit from these developments, since they cannot use their hands to interact with traditional interaction methods (such as mouse or keyboard) to communicate with a computer system. A brain-computer interface (BCI) system allows such a disabled person to control an external device via brain waves. Past research mostly dealt with static interfaces, which limit users to a stationary location. However, since we are living in a world that is highly mobile, this paper evaluates a speller interface on a mobile phone used in a moving condition. The spelling experiments were conducted with 14 able-bodied subjects using visual flashes as the stimulus to spell 47 alphanumeric characters (38 letters and 9 numbers). This data was then used for the classification experiments. In par- ticular, two research directions are pursued. The first investigates the impact of different classification algorithms, and the second direction looks at the channel configuration, i.e., which channels are most beneficial in terms of achieving the highest classification accuracy. The evaluation results indicate that the Bayesian Linear Discriminant Analysis algorithm achieves the best accuracy. Also, the findings of the investigation on the channel configuration, which can potentially reduce the amount of data processing on a mobile device with limited computing capacity, is especially useful in mobile BCIs. [ABSTRACT FROM AUTHOR]

• The ADJST realizes users' anomalous electricity consumption behaviors detection. • The MS-GCN achieves extracting spatial features and preventing feature coupling. • The MS-TCN module reduces the loss of time sequence information in long sequences. • Enhance the model's stability and generalization by joint spatio-temporal learning. The use of electric energy is an integral part of people's daily life. Anomaly detection of electricity consumption data, as a classification problem, has always been a hot research topic of scholars. Anomaly detection can not only reduce energy waste, but also prevent small problems from becoming overwhelming problems. At present, most anomaly detection algorithms mainly focus on the time series information of electricity consumption data, while ignoring the spatial feature of electricity consumption data. To fill this research gap, the paper proposes an Anomaly Detection based on Joint Spatio-Temporal learning (ADJST) method for building electricity consumption. First, a Multi-Scale Graph Convolutional Network (MS-GCN) is proposed to learn the spatial features of building electricity consumption data. Specifically, two types of graphs are constructed to extract short-term correlation features and long-term regularity features of building electricity consumption data. Second, a Multi-Scale Temporal Convolutional Network (MS-TCN) is proposed to learn the temporal features of building electricity consumption data. Adopt a multi-scale vanilla convolution structure to extract multi-scale time series information from building electricity consumption data. Third, the combination of temporal features and spatial features detects anomalous electricity consumption of marked users. Final, taken the user electricity consumption data collected by the State Grid Corporation's smart meter as examples, compared with a variety of classical anomaly detection algorithms, the results of F1-score and AUC of the proposed method are 0.935 and 0.977 respectively, which proves the superiority of the method. The model shows good stability in dealing with extreme imbalance of data, and is proved to be generalized by experiments and can be transferred to other datasets. [ABSTRACT FROM AUTHOR]

• A new method (RC-EnKF) is proposed to identify contaminant source jointly. • Decoupling Technique is introduced to relieve measuring error negative influence. • RC-EnKF is more efficient and accurate for identifying source than traditional EnKF. To reduce the adverse influence of sudden water pollution accidents, it is essential to estimate the unknown contaminant source information (normally including the source location, initial release time, and total release mass) as soon as possible. The ensemble Kalman filter (EnKF) has been proven to be an effective algorithm for such an inverse problem. This paper proposes a new method based on EnKF to identify contaminant source information. The method we called RC-EnKF uses the relation coefficient of concentration instead of timely concentration as a state variable in the assimilation process. The advantage lies in the release source mass that can be decoupled from the parameter group of unknown contaminant information to improve the assimilation speed and reduce interference with the accuracy of assimilation. Two categories of cases are employed for validating the applicability and testing the performance compared with the traditional EnKF method in detail. Sensitivity analyses are carried out with different observation errors, number of observation sites, number of ensemble realizations, and model grid size. The results demonstrate that with the uncertain error of observation data, the RC-EnKF works nicely and shows superiority to the traditional EnKF method reflected in the strong immunity to interference from observation data errors and elevated efficiency with the requirement of fewer observation sites, ensemble realizations, and model grids. It illustrates that the RC-EnKF is a more efficient and robust method for estimating unknown contaminant source information. [ABSTRACT FROM AUTHOR]

• Macropores can affect the salinity distributionand lead to longer paths for inland solute transport. • Macropores significantly altered the NO 3 − discharge and transformation processes. • The deep burial dissolved organic matter (DB-DOM) promoted a higher NO 3 − removal efficiency. Coastal ecosystems are sensitive to the input of chemicals originating from submarine groundwater discharge (SGD). The mixing zone between freshwater and saltwater in beach aquifers promotes biogeochemical transformations affecting nutrient fluxes into the coastal ocean. Macropores affect groundwater flow, leading to a more complicated mixing process and greatly promoting nitrogen transformations. Laboratory-scale experiments and numerical modeling were employed in this study to investigate how macropores affect freshwater-saltwater mixing and terrestrial solute transport. A numerical investigation is presented demonstrating the influence of macropores on the transport and transformation of nitrate (NO 3 −) in a homogeneous unconfined nearshore aquifer under spring-neap tide action using COMSOL. The results indicate that macropores modify the groundwater discharge pathway of terrestrial NO 3 − by changing nearshore groundwater flow dynamics. The denitrification zone expanded from the low tide mark to the high tide mark, extended downward to the base of the upper saline plume (USP), and expanded with increasing macropore depth and quantity. Macropores increased NO 3 − removal by creating larger mixing zones, which increased with the macropore depth and quantity. Lower freshwater levels enhanced denitrification and weakened the impact of macropores on denitrification. The deep burial dissolved organic matter (DB-DOM) simulation results demonstrated that dissolved organic matter (DOM) could stimulate denitrifiers to increase the denitrification zone by fully consuming O 2 along longer flow paths, resulting in up to 85% higher NO 3 − removal. The findings provide notable implications for management design engineering to protect coastal ecosystems and decrease NO 3 − fluxes into the ocean. [ABSTRACT FROM AUTHOR]

Resumen A partir de una muestra de recién graduados del área de Beijing, se examinan cómo afectan a las perspectivas de empleo y a los salarios iniciales el prestigio de la universidad (como ≪señal≫ de competencia y calificación), la especialidad y el sexo. Las estimaciones con un modelo paramétrico de supervivencia y un modelo de selección de Heckman muestran que los graduados de universidades prestigiosas encuentran trabajo más rápidamente y reciben salarios iniciales más altos que los de otras universidades, así como los graduados en ingeniería y empresariales, mientras que los de humanidades y ciencias sociales obtienen salarios iniciales más altos. Las mujeres encuentran trabajo más rápido, pero ganan menos que los hombres. [ABSTRACT FROM AUTHOR]

Aims The Notch signalling pathway is involved in normal development as well as tumorigenesis. However, it is unclear whether Notch activation is related to diverse clinicopathological factors in papillary thyroid carcinoma ( PTC). Methods and results We examined the relationship between clinicopathological factors and the expression of activated Notch1 and Hey1, which are indicators of Notch signalling pathway activation, in 109 PTC cases. Activated Notch1 showed strong, moderate and weak expression in 23, 48 and 36 cases, respectively. Its expression was related significantly to histopathological variants ( P = 0.007), lymph node metastasis ( P = 0.016), BRAF mutation ( P = 0.036) and extent of surgery ( P = 0.014). Hey1 immunostaining could be divided into two groups: positive and negative, with 26 and 83 cases, respectively. Its expression was related significantly to histopathological variants ( P = 0.026), extrathyroidal extension ( P = 0.005), BRAF mutation ( P = 0.048) and recurrence or soft tissue metastasis ( P = 0.000). Multivariate analysis revealed that tumour size (>1 cm), Hey1 immunoreactivity and the presence of lymph node metastasis were associated significantly with recurrence or soft tissue metastasis (odds ratio = 7.38, 4.28 and 12.00, respectively). Conclusions Thus, we found that activation of Notch signalling was correlated significantly with clinicopathological parameters. Therefore, Notch signalling could be a useful prognostic marker in patients with PTC. [ABSTRACT FROM AUTHOR]

• A new expression is proposed for dynamic effective porosity • Dynamic effective porosity effects are included in the Boussinesq equation • The modified Boussinesq equation correctly predicts watertable fluctuations • The new theory predicts no standing waves for high-frequency watertable fluctuations Watertable fluctuations are a characteristic feature of coastal unconfined aquifers. They interact with the vadose zone creating a dynamic effective porosity, for which a new (empirical) expression is proposed based on a dimensionless parameter related to the fluctuation frequency. After comparing with both experimental data and numerical simulations, the new expression is implemented into a modified Boussinesq equation, allowing for examination of the effects of the dynamic effective porosity on watertable fluctuations. The dispersion relation arising from the modified Boussinesq equation predicts laboratory experimental data accurately, highlighting the importance of the dynamic effective porosity in modeling watertable fluctuations in coastal unconfined aquifers. This in turn confirms the applicability of the real-valued expression of the dynamic effective porosity. An outcome is that the phase lag between the total moisture (above the watertable) and watertable height measured in laboratory experiments using vertical soil columns (1D systems) can be ignored when predicting watertable fluctuations in coastal unconfined aquifers (2D systems). The dynamic effective porosity is always smaller than the soil porosity, and so by comparison there is a reduction in vertical water exchange between the saturated and vadose zones. Consequently, watertable waves can propagate further landward without standing wave behaviors. [ABSTRACT FROM AUTHOR]

Collaborative representation has been successfully applied to visual tracking to powerfully use all the PCA basis vectors in the target subspace for object representation. However, collaborative representation always exists redundant features that may affect the performance of visual tracking. In this paper, a visual tracking algorithm is proposed by solving a generalized ℓ p -regularized (0≤p≤1) problem within a Bayesian inference framework for the reduction of redundant features. To efficiently solve the minimization problem of ℓ p -regularization, the Generalization of Soft-threshold (GST) operator is applied in the framework of iterative Accelerated Proximal Gradient (APG) approach. Moreover, the GST operator can also provide a unified framework to observe the effects of different sparsity for visual tracking. To show the feasibility of ℓ p -regularizer, we choose the representative ℓ 0.5 -norm as the regularizer for the target coefficient and adjust the corresponding sparsity to be appropriate. Furthermore, we also introduce an extra ℓ 0 -regularized tracker to observe the effect of excessive sparsity in a unified framework. Experimental results on several challenging sequences demonstrate that the proposed tracker leads to a more favorable performance in terms of accuracy measures including the overlap ratio and center location error, respectively. [ABSTRACT FROM AUTHOR]

Kong, J.; Pan, M.; Shen, C.; Hua, G., and Zhao, H., 2016. Analysis of the morphological changes and related sediment transport mechanisms of the Baisha Shoal in the Qiongzhou Strait, China. The morphological changes and evolutionary mechanism of the Baisha Shoal in the Qiongzhou Strait were investigated using various methods, including bathymetric chart comparisons, geomodeling, hydrodynamic modeling, and hydrologic statistical analysis. Water depths were extracted from digitized charts to explore the evolution of the Baisha Shoal and to quantitatively estimate the changes in the shoal area within the 5-m isobath. The grain-size trend analysis (GSTA) model was introduced to explain the features of bed sediment distribution and to analyze the sources of the sediment. Tidal flow models and wave models were adopted to simulate the tidal flows and wave fields around the Baisha Shoal and to reveal the sediment transport dynamics. Further comparisons based on a Mann-Kendall trend analysis and remote-sensing image data from different years were used to discuss the reason for the recent evolution of the Baisha Shoal. The results show that the formation of the Baisha Shoal was caused by the combined effects of reciprocating flow in the eastern and western directions and waves from the NE direction. However, in recent years, the Baisha Shoal has demonstrated an unsteady geomorphological state and is transitioning from a deposition-dominated state to an erosion-dominated state. Erosion has become most significant since 2004, as reflected in the shoal area within the 5-m isobath, which is decreasing by 0.732 km2/y. The analysis demonstrated that a reduction in sediment load and increased sand mining are the two most significant factors that are progressively altering the shoal morphology. If these issues cannot be resolved, the strong dynamic surroundings will aggravate the erosion of the Baisha Shoal. This study on the Baisha Shoal highlights the necessity of combining different approaches to better understand coastal evolutionary features, dynamic mechanisms, and evolutionary causes, as well as to guide future coastal protection engineering design. [ABSTRACT FROM AUTHOR]

Summary Above a shallow unconfined aquifer, a considerable amount of water is stored in the vadose zone. Through water exchange with the underlying unconfined aquifer, the vadose zone affects the groundwater table dynamics and overall behavior of the aquifer. In this paper, we examine tide-induced groundwater table fluctuations in unconfined aquifers influenced by vadose zone of finite thickness. Under the condition of small aquifer thickness ( D ) compared with the groundwater wavelength ( L ) (i.e., μ 2 = D / L 2 ≪ 1 ) and small boundary oscillation amplitude ( a ) (i.e., ε = a / D ≪ 1 ) (where μ 2 and ε are two parameters), an approximate analytical solution was derived to quantify systematically the vadose zone effects, with a particular consideration of capping by the ground surface, i.e., the upper boundary of the vadose zone. Depending on the extent to which the capillary rise is truncated by the ground surface, the vadose zone enhances the groundwater table fluctuations in an unconfined aquifer. However, the mean groundwater table height and exchange between surface water and groundwater are reduced due to the presence of the vadose zone. These effects are intensified with increased capillary rise, but weakened as the vadose zone thickens. This study provides a criterion for assessing the importance of vadose zone in modulating the response of unconfined aquifers to low-frequency forcing oscillations such as tides. [ABSTRACT FROM AUTHOR]

Motivation: Deep learning attained excellent results in digital pathology recently. A challenge with its use is that high quality, representative training datasets are required to build robust models. Data annotation in the domain is labor intensive and demands substantial time commitment from expert pathologists. Active learning (AL) is a strategy to minimize annotation. The goal is to select samples from the pool of unlabeled data for annotation that improves model accuracy. However, AL is a very compute demanding approach. The benefits for model learning may vary according to the strategy used, and it may be hard for a domain specialist to fine tune the solution without an integrated interface. Results: We developed a framework that includes a friendly user interface along with run-time optimizations to reduce annotation and execution time in AL in digital pathology. Our solution implements several AL strategies along with our diversity-aware data acquisition (DADA) acquisition function, which enforces data diversity to improve the prediction performance of a model. In this work, we employed a model simplification strategy [Network Auto-Reduction (NAR)] that significantly improves AL execution time when coupled with DADA. NAR produces less compute demanding models, which replace the target models during the AL process to reduce processing demands. An evaluation with a tumor-infiltrating lymphocytes classification application shows that: (i) DADA attains superior performance compared to state-of-the-art AL strategies for different convolutional neural networks (CNNs), (ii) NAR improves the AL execution time by up to 4.3×, and (iii) target models trained with patches/data selected by the NAR reduced versions achieve similar or superior classification quality to using target CNNs for data selection. Availability and implementation: Source code: https://github.com/alsmeirelles/DADA. [ABSTRACT FROM AUTHOR]

The launching of Microsoft Kinect with skeleton tracking technique opens up new potentials for skeleton based human action recognition. However, the 3D human skeletons, generated via skeleton tracking from the depth map sequences, are generally very noisy and unreliable. In this paper, we introduce a robust informative joints based human action recognition method. Inspired by the instinct of the human vision system, we analyze the mean contributions of human joints for each action class via differential entropy of the joint locations. There is significant difference between most of the actions, and the contribution ratio is highly in accordance with common sense. We present a novel approach named skeleton context to measure similarity between postures and exploit it for action recognition. The similarity is calculated by extracting the multi-scale pairwise position distribution for each informative joint. Then feature sets are evaluated in a bag-of-words scheme using a linear CRFs. We report experimental results and validate the method on two public action dataset. Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the intra-class variation. [ABSTRACT FROM AUTHOR]

Asymmetric division of stem cells is a highly conserved and tightly regulated process by which a single stem cell produces two daughter cells and simultaneously directs the differential fate of both: one retains its stem cell identity while the other becomes specialized and loses stem cell properties. Coordinating these events requires control over numerous intra- and extracellular biological processes and signaling networks. In the initial stages, critical events include the compartmentalization of fate determining proteins within the mother cell and their subsequent passage to the appropriate daughter cell. Disturbance of these events results in an altered dynamic of self-renewing and differentiation within the cell population, which is highly relevant to the growth and progression of cancer. Other critical events include proper asymmetric spindle assembly, extrinsic regulation through micro-environmental cues, and noncanonical signaling networks that impact cell division and fate determination. In this review, we discuss mechanisms that maintain the delicate balance of asymmetric cell division in normal tissues and describe the current understanding how some of these mechanisms are deregulated in cancer. [ABSTRACT FROM AUTHOR]