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Polygala tenuifolia Wild is an ancient traditional Chinese medicine. Its main component, tenuifolin (TEN), has been proven to improve cognitive impairment caused by neurodegenerative diseases and ovariectomy. However, there was hardly any pharmacological research about TEN and its potential gender differences. Considering the reduction of TEN on learning and memory dysfunction in ovariectomized animals, therefore, we focused on the impact of TEN in different mice genders in the current study. Spontaneous alternation behavior (SAB), light-dark discrimination, and Morris water maze (MWM) tests were used to evaluate the mice's learning and memory abilities. The field excitatory postsynaptic potential (fEPSP) of the hippocampal CA1 region was recorded using an electrophysiological method, and the morphology of the dendritic structure was examined using Golgi staining. In the behavioral experiments, TEN improved the correct rate in female mice in the SAB test, the correct rate in the light-dark discrimination test, and the number of crossing platforms in the MWM test. Additionally, TEN reduced the latency of female mice rather than male mice in light-dark discrimination and MWM tests. Moreover, TEN could significantly increase the slope of fEPSP in hippocampal Schaffer-CA1 and enhance the total length and the number of intersections of dendrites in the hippocampal CA1 area in female mice but not in male mice. Collectively, the results of the current study showed that TEN improved learning and memory by regulating long-term potentiation (LTP) and dendritic structure of hippocampal CA1 area in female mice but not in males. These findings would help to explore the improvement mechanism of TEN on cognition and expand the knowledge of the potential therapeutic value of TEN in the treatment of cognitive impairment. • Tenuifolin enhanced learning and memory abilities with a more positive effect on female mice than on male mice. • Tenuifolin improved long-term potentiation and dendritic structure in female mice but not in male mice. • Tenuifolin showed noticeable gender differences in pharmacodynamics. [ABSTRACT FROM AUTHOR]

The stability evaluation of deep foundation pit slopes is crucial, including the solution of the factor of safety and the location of the critical slip surface. Prestressed anchor cables and underground diaphragm walls are commonly used to reinforce pit slopes. By means of the dimensional increase technique and the global analysis approach of slope stability, a nonlinear optimization problem is defined for the stability analysis of pit slopes. In the optimization problem, the ordinates of discrete points on the slip surface and the factor of safety are both the decision variables, the objective function is the factor of security, and the constraints are the equilibrium equations and the convexity of the slip surface. Because the objective function is linear and the constraint functions are polynomials of degree three at most, the optimization problem is a classical optimization one that conventional optimization techniques can solve without recourse to modern optimization techniques such as the AI technique. Examples suggest that the proposed procedure is far more efficient and stable than the optimization models based on the methods of slides. At last, applications are demonstrated to ongoing deep foundation pit slopes situated at Tong Zhou – Beijing sub-center. [ABSTRACT FROM AUTHOR]

The Tonle Sap Lake (TSL) is a flood-pulse system. It is the largest natural lake in South-East Asia and constitutes one of the largest fisheries over the world, supporting the livelihood of million peoples. Nonetheless, the Mekong River Basin is changing rapidly due to accelerating water infrastructure development (hydropower, irrigation, flood control, and water supply) and climate change, bringing considerable modifications to the annual flood-pulse of the TSL. Such modifications are expected to have strong impacts on fish biodiversity and abundance. This paper aims to characterize the spatio-temporal variations of fish taxonomic composition and to highlights the underlying determinants of these variations. For this purpose, we used data collected from a community catch monitoring program conducted at six sites during 141 weeks, covering two full hydrological cycles. For each week, we estimated beta diversity as the total variance of the site-by-species community matrix and partitioned it into Local Contribution to Beta Diversity (LCBD) and Species Contribution to Beta Diversity (SCBD). We then performed multiple linear regressions to determine whether species richness, species abundances and water level explained the temporal variation in the contribution of site and species to beta diversity. Our results indicate strong temporal variation of beta diversity due to differential contributions of sites and species to the spatial variation of fish taxonomic composition. We further found that the direction, the shape and the relative effect of species richness, abundances and water level on temporal variation in LCBD and SCBD values greatly varied among sites, thus suggesting spatial variation in the processes leading to temporal variation in community composition. Overall, our results suggest that fish taxonomic composition is not homogeneously distributed over space and time and is likely to be impacted in the future if the flood-pulse dynamic of the system is altered by human activities. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common malignant tumors among female. How to effectively discriminate the category of the cancers using the key features/factors is very important in the diagnosis and prediction. In this paper, Jointly Sparse Discriminant Analysis (JSDA) is proposed to explore the key factors in breast cancer and extract the key features for improving the accuracy in diagnosis and prediction. JSDA introduces the jointly sparse regular term (i.e. L 2 , 1 -norms term) to the criterion. A convergent iterative algorithm is designed to solve the optimization problem. It is shown that the proposed JSDA algorithm not only can learn the jointly sparse discriminant vectors to explore the key factors of the breast cancer in cancer pathologic diagnosis, but also can improve the diagnosis accuracy compared with the classical feature extraction and discriminant analysis algorithm. Experimental results on breast cancer datasets indicate that JSDA outperforms some well-known subspace learning algorithms in prediction accuracy, not matter they are non-sparse or sparse, particularly in the cases of small sample sizes. Data analysis shows that the key factors of the breast cancer explored by the JSDA are consistent with the practical experience. [ABSTRACT FROM AUTHOR]

The oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), namely, so-called oxygen electrode reactions, are two fundamental half-cell reactions in the energy storage and conversion devices, e.g., zinc–air batteries and fuel cells. However, the oxygen electrode reactions suffer from sluggish kinetics, large overpotential and complicated reaction paths, and thus require efficient and stable electrocatalysts. Transition-metal-based layered double hydroxides (LDHs) and their derivatives have displayed excellent catalytic performance, suggesting a major contribution to accelerate electrochemical reactions. The rational regulation of electronic structure, defects, and coordination environment of active sites via various functionalized strategies, including tuning the chemical composition, structural architecture, and topotactic transformation process of LDHs precursors, has a great influence on the resulting electrocatalytic behavior. In addition, an in-depth understanding of the structural performance and chemical-composition-performance relationships of LDHs-based electrocatalysts can promote further rational design and optimization of high-performance electrocatalysts. Finally, prospects for the design of efficient and stable LDHs-based materials, for mass-production and large-scale application in practice, are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Application of cell-–biomaterial systems in regenerative medicine can be facilitated by their successful low temperature preservation. Vitrification, which avoids ice crystal formation by amorphous solidification, is an emerging approach to cryopreservation. Developing vitrification strategy, effective cryopreservation of alginate–fibrin beads with porcine mesenchymal stromal cells has been achieved in this study. The cell–biomaterial constructs were pre-cultured for 20 days before cryopreservation, allowing for cell proliferation and construct stabilization. Ethylene glycol (EG) was employed as the basic cryoprotectant for two equilibration solutions. Successful cryopreservation of the constructs was achieved using vitrification solution composed of penetrating (EG MW 62Da) and non-penetrating (sucrose MW 342Da) cryoprotectants. Stepwise procedure of introduction to and removal of cryoprotectants was brief; direct plunging into liquid nitrogen was applied. Cell viability, evaluated by combining live/death staining and confocal laser microscopy, was similar for both control and vitrified cells in the beads. No detectable damage of microstructure of cryopreserved beads was found as shown by scanning electron microscopy. Both osteogenically induced control and vitrified cells in the constructs were equally capable of mineral production and deposition. There was no statistically significant difference in metabolic activity and proliferation between both groups during the entire culture period. Our study leads to the conclusion that the developed cryopreservation protocol allowed to maintain the integrity of the beads while preserving the ability of the pig bone marrow derived mesenchymal stromal cells to proliferate and subsequently differentiate; demonstrating that vitrification is a promising approach for cryopreser-vation of “ready-to-use” cell–biomaterial constructs. [Copyright &y& Elsevier]

When a tunnel passes through weak and fragmented surrounding rock, excavation disturbance can lead to significant deformation. To mitigate this, advanced support measures like the umbrella arch are necessary. This study investigates the effectiveness of the umbrella arch in controlling deformation of the surrounding rock, focusing on the super shallow-buried spillway section of the Shenzhen East Transit Expressway in China. Both numerical modeling and field monitoring were used to assess the support effect of the umbrella arch. The mechanism of the support was analyzed, revealing that the umbrella arch primarily prevents collapse of the surrounding rock by creating a small compression area. It is recommended that a starting segment with the same length as the tunnel diameter be reserved for the construction of long umbrella arches to maximize their supporting effect. The proposed numerical model was calibrated using field monitoring data, and the mechanical responses of an 80-m-long umbrella arch during construction were thoroughly analyzed. The research results presented in this paper can serve as a reference for the design of umbrella arches in similar projects in the future. [ABSTRACT FROM AUTHOR]

Bifurcated tunnels have two major engineering problems of large span and small interval simultaneously, and they pose higher demands on design and construction technology. The Liantang tunnel has a maximum span of 30.1 m, a minimum interval of only 1.05 m and a single excavation face; the existing construction method cannot meet the demand. Based on these features, this paper proposes a reverse expanding excavation method and a stepwise controlled blasting excavation method. By numerical calculation and field testing, the stability and mechanical characteristics of the surrounding rock and primary support using the two methods were researched. The results show that the reverse expanding excavation method, by excavating the pilot tunnel first and then reversely excavating the remaining part, achieves stepwise excavation of the super-large-span tunnel, provides mechanised working conditions and avoids multiple conversions of the excavation method. The stepwise controlled blasting excavation method, by grouting and reinforcing the shared rock first, and then controlled blasting of the later tunnel in steps, ensures the stability of the shared rock, reduces the construction influence of the later tunnel on the existing tunnel and omits the construction of a mid-partition. This study can provide a reference for future similar projects. [ABSTRACT FROM AUTHOR]

Hash learning is a hot topic since it can save storage space and perform fast retrieval. One of the most representative hashing methods is Supervised Discrete Hashing (SDH). However, there exist several problems in SDH. First, the potential of sparse feature extraction has been overlooked in the SDH-based methods. Second, SDH is incapable of preventing large information loss between the binary codes and the features. Third, the discrete cyclic coordinate descent (DCC) method is time-consuming. To address the aforementioned issues, we propose a novel regression model for hash learning named Jointly Sparse Fast Hashing (JSFH). By designing an orthogonal transformation, we introduce the jointly sparse regression into hash learning. The proposed method successfully integrates the feature extraction and hash learning into a unified framework, the original data and the label information can be comprehensively utilized such that the binary code generated by our method can be more discriminative with less information loss. Furthermore, we adopt an iterative algorithm for optimization, the optimization problem has closed-form solutions for each variable such that the designed iterative algorithm is highly efficient compared to the classic supervised hashing methods. The experimental results on four large-scale datasets demonstrate its superior performance. • The jointly sparse feature extraction with orthogonal constraint is designed for hash learning. • We design an iterative algorithm to optimize the model with closed-form solutions with less information loss. • The proposed JSFH is evaluated on four large-scale datasets. [ABSTRACT FROM AUTHOR]

The phenomena of progressive failures are very common and important in geotechnical engineering. In this paper, a reliable prediction model is proposed to interpret the progressive failure phenomenon of roof collapse in deep tunnels using the functional catastrophe theory. The progressive collapse mechanisms and collapsing block shapes of deep circular tunnels under conditions of plane strain are investigated. The analytical solutions for the shape curves of the collapsing blocks of circular tunnels are derived based on the nonlinear power-law failure criterion considering variable dilatancy angle and detaching velocity. Moreover, criterions with variable dilatancy angle on progressive failure occurrence for deep tunnels are obtained. Then, the analytical predictions obtained in this paper are compared with experimental testing results, which indicate that the impacts of variable detaching velocity on the shape curves of the several continuous collapsing blocks should be considered to obtain more consistent prediction results with the corresponding experimental testing results. [ABSTRACT FROM AUTHOR]

Due to the noises captured in ultrasound device and image reconstruction process, the edges of thyroid nodule are usually not distinctive and it is very difficult for existing approaches to well segment them in ultrasound images. While deep neural networks like U-Net have been successfully applied in many medical image segmentation tasks, their segmentation performances on ultrasound images are still not satisfactory. To address this issue, we propose in this paper a boundary field regression branch to provide useful boundary information to help improve the segmentation performance of existing networks. Without requirement of additional labeling costs, our approach firstly generates boundary field heatmap from available segmentation masks, which are then used as a supervision to train the regression branch. As a general architecture, our branch can be integrated with all encoder-decoder like segmentation networks. A dataset consisting of 3169 images from 2004 patients is used for experiments. We integrate our branch with U-Net, Attention U-Net, U-Net++ and DeepLabv3+; consistent improvements of Dice metrics were observed. The memory and computation costs required by adding our branch are marginal as well. [ABSTRACT FROM AUTHOR]

• We propose a self-supervised FSB, which can be easily extended to any CNN architectures. • We design an GLCM loss to minimize the differences among input images. • Our FSB achieves about 6% accuracy improvement for testing datasets. With the advance of deep learning technology, convolutional neural network (CNN) has been wildly used and achieved the state-of-the-art performances in the area of medical image classification. However, most existing medical image classification methods conduct their experiments on only one public dataset. When applying a well-trained model to a different dataset selected from different sources, the model usually shows large performance degradation and needs to be fine-tuned before it can be applied to the new dataset. The goal of this work is trying to solve the cross-domain image classification problem without using data from target domain. In this work, we designed a self-supervised plug-and-play feature-standardization-block (FSB) which consisting of image normalization (INB), contrast enhancement (CEB) and boundary detection blocks (BDB), to extract cross-domain robust feature maps for deep learning framework, and applied the network for chest x-ray-based lung diseases classification. Three classic deep networks, i.e. VGG, Xception and DenseNet and four chest x-ray lung diseases datasets were employed for evaluating the performance. The experimental result showed that when employing feature-standardization-block, all three networks showed better domain adaption performance. The image normalization, contrast enhancement and boundary detection blocks achieved in average 2%, 2% and 5% accuracy improvement, respectively. By combining all three blocks, feature-standardization-block achieved in average 6% accuracy improvement. [ABSTRACT FROM AUTHOR]

Ridge regression is an important method in feature extraction and has been extended in many different versions. Robust discriminant regression aims to solve the Small Sample Size Problem (SSSP) in ridge regression form by designing a novel regression model and imposing on L 2 , 1 -norm as the main metric instead of the regularized term. However, when the dimensions of data are very high, RDR will be faced with the problem of the curse of dimensionality and high memory space cost. The computation cost of RDR will be extremely high in the iterative procedures. To address this problem, we propose an improved method called Two-dimensional jointly sparse RDR (2DJSRDR) for image-based feature extraction. Unlike previous vector-based methods which stretch the data into a high-dimensional vector as input, the proposed 2DJSRDR uses the two-dimensional image matrix directly as the computational unit so that the drawbacks in RDR can be naturally avoided. Besides, we also introduce L 2 , 1 -norm as regularization term to obtain jointly sparse projections for feature selection, which is helpful to improve the performance of the model. Experiments on some benchmark datasets demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Sparse representation based classification (SRC) has become a popular methodology in face recognition in recent years. One widely used manner is to enforce minimum l 1 -norm on coding coefficient vector, which is considered as an unsupervised sparsity constraint and usually requires high computational cost. On the other hand, supervised sparsity representation based method (SSR) realizes sparse representation classification with higher efficiency by multiple phases of representing a probe. Nevertheless, since previous SSR methods only deal with Gaussian noise, they cannot satisfy empirical face recognition application which faces wide variations. In this paper, we propose a robust supervised sparse representation (RSSR) model, which uses two-phase of robust representation to compute a sparse coding vector. Huber loss is employed as the fidelity term in the linear representation, which improves the competitiveness of correct class in the first phase. Then training samples with weak competitiveness are removed by supervised way. In the second phase, the competitiveness of correct class is further boosted by Huber loss. We compare the RSSR with other state-of-the-art methods under different conditions, including illumination variations, gesture changes, expressions, corruptions, and occlusions. Comprehensive experiments on four open databases demonstrate the robustness of RSSR and competitive performance is obtained in dealing with face images with occlusion or not. [ABSTRACT FROM AUTHOR]

Abstract As a famous linear manifold learning method, orthogonal neighborhood preserving projections (ONPP) is able to provide a set of orthogonal projections for dimensionality reduction. However, a problem of ONPP is that it takes the L 2 -norm as the basic measurement and therefore tends to be sensitive to the outliers or the variations of the data. Aiming at strengthening the robustness of the conventional method ONPP, in this paper, a robust and sparse dimensionality reduction method based on linear reconstruction, called Robust Jointly Sparse Embedding (RJSE), is proposed by introducing L 2, 1 -norm as the basic measurement and regularization term. We design a simple iterative algorithm to obtain the optimal solution of the proposed robust and sparse dimensionality reduction model. Experiments on four benchmark data sets demonstrate the competitive performance of the proposed method compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A multiview method, GRMDA, is proposed for extracting the robust and sparse features, which reconstructs within-class scatter term and between-class scatter term in multiview scenario using L 2 , 1 norm to enhance robustness. • We conduct the analysis of small-class problem and adopt another criterion inspired by MMC to solve this problem. In terms of overfitting problem, the L 2 , 1 norm is also imposed on the regularization term for extracting sparse features. • The paper presents an iterative algorithm to compute optimal linear transform for each view. The poof of convergence is provided, and the computational complexity is analyzed. Several experiments were conducted to evaluate the performance of GRMDA Multiview data leads to the demand for classifying samples from various views, and the large gap between different views makes the classification task challenging. Recently, researchers have extended linear discriminant analysis (LDA) to multi-view scenarios. However, the extended methods are generally associated with the small-class problem, that is, the projection size is limited by the number of classes. In addition, they are sensitive to variations in images or outliers. To solve these problems, this study proposes a generalized robust multiview discriminant analysis (GRMDA) to obtain a linear transform for each view and for learning multiview jointly sparse discriminant common subspace. GRMDA aims to achieve both maximal between-class and minimal within-class variation for data from multiple views in a common space. Instead of formulating the ratio trace problem, we reformulate GRMDA inspired by maximum margin criterion (MMC) to address the small-class problem. Moreover, the proposed method achieves stronger robustness by reconstructing the within-class and between-class scatter terms from the definition of L 2 , 1 norm. Furthermore, GRMDA ensures joint sparsity using the L 2 , 1 norm-based regularization term. Additionally, we present an iterative algorithm, convergence proof, and complexity analysis. Experiments on six popular databases, that is, COIL100, USPS/MNIST, Extended Yale Face B, AR, BBCSport, and multiple feature datasets, were conducted to evaluate the performance of GRMDA against the state-of-the-art multiview methods. The experimental results demonstrate that the proposed method can achieve a significant performance with strong robustness and fast convergence. [ABSTRACT FROM AUTHOR]

[Display omitted] For deep-buried shield tunnels in dry cohesionless soils, it is critical to determine the support pressure acting on a tunnel face due to the significant soil arching effect during excavation. In this paper, a new analytical model of the multi-layer parabolic bearing arch is proposed to predict the limit support pressure based on the limit equilibrium method and the wedge model. The model consists of the multi-layer parabolic bearing arch, the friction arch and the wedge. According to the different buried depth of tunnel, the tunnel state is divided into shallow buried zone, transition zone and deep buried zone. By assuming the multi-layer parabolic soil arch as a three-hinged structural arch with reasonable arch axis, the load transfer expression of multi-layer parabolic soil arch is derived in transition zone and deep buried zone respectively. Then, the rationality and effectiveness of the proposed model were verified by comparing with model tests, existing theoretical methods and numerical model. Finally, the effects from the internal friction angle φ of soil on the inclination angle β , tunnel state and limit support pressure were also discussed. It is shown that with the increase of internal friction angle φ , the boundaries in between the shallow zone and the deep buried zone declines linearly, the inclination angle β increases linearly and the limit support pressure decreases nonlinearly. This study provides a significant reference for predicting the value of limit support pressure acting on the tunnel face in dry cohesionless soils. [ABSTRACT FROM AUTHOR]

The effects of environmental seasonality on food web structure have been notoriously understudied in empirical ecology. Here, we focus on seasonal changes in one key attribute of a food web, consumer trophic position. We ask whether fishes inhabiting tropical river–floodplain ecosystems behave as seasonal omnivores, by shifting their trophic positions in relation to the annual flood pulse, or whether they feed at the same trophic position all year, as much empirical work implicitly assumes. Using dietary data from the Tonle Sap Lake, Cambodia, and a literature review, we find evidence that some fishes, especially small piscivores, increased consumption of invertebrates and/or plant material during the wet season, as predicted. However, nitrogen stable isotope (δ15N) data for 26 Tonle Sap fishes, spanning a broader range of functional groups, uncovered high variation in seasonal trophic position responses among species (0 to ±0.52 trophic positions). Based on these findings, species respond to the flood pulse differently. Diverse behavioral responses to seasonality, underpinned by spatiotemporal variation at multiple scales, could be central for rerouting matter and energy flow in these dynamic ecosystems. Seasonally flexible foraging behaviors warrant further study given their potential influence on food web dynamics in a range of fluctuating environments. [ABSTRACT FROM AUTHOR]

The functional status of CD4+T cells is a critical determinant of antitumor immunity. Polyfunctional CD4+T cells possess the ability to concomitantly produce multiple Th1-type cytokines, exhibiting a functional attribute desirable for cancer immunotherapy. However, the mechanisms by which these cells are induced are neither defined nor it is clear if these cells can be used therapeutically to treat cancer. Here, we report that CD4+T cells exposed to exogenous IL-7 during antigenic stimulation can acquire a polyfunctional phenotype, characterized by their ability to simultaneously express IFNγ, IL-2, TNFα and granzyme B. This IL-7-driven polyfunctional phenotype was associated with increased histone acetylation in the promoters of the effector genes, indicative of increased chromatin accessibility. Moreover, forced expression of a constitutively active (CA) form of STAT5 recapitulated IL-7 in inducing CD4+T-cell polyfunctionality. Conversely, the expression of a dominant negative (DN) form of STAT5 abolished the ability of IL-7 to induce polyfunctional CD4+T cells. Thesein-vitro-generated polyfunctional CD4+T cells can traffic to tumor and expand intratumorally in response to immunization. Importantly, adoptive transfer of polyfunctional CD4+T cells following lymphodepletive chemotherapy was able to eradicate large established tumors. This beneficial outcome was associated with the occurrence of antigen epitope spreading, activation of the endogenous CD8+T cells and persistence of donor CD4+T cells exhibiting memory stem cell attributes. These findings indicate that IL-7 signaling can impart polyfunctionality and stemness potential to CD4+T cells, revealing a previously unknown property of IL-7 that can be exploited in adoptive T-cell immunotherapy. [ABSTRACT FROM AUTHOR]