A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos

Detecting breast lesion in videos is crucial for computer-aided diagnosis. Existing video-based breast lesion detection approaches typically perform temporal feature aggregation of deep backbone features based on the self-attention operation. We argue that such a strategy struggles to effectively perform deep feature aggregation and ignores the useful local information. To tackle these issues, we propose a spatial-temporal deformable attention based framework, named STNet. Our STNet introduces a spatial-temporal deformable attention module to perform local spatial-temporal feature fusion. The spatial-temporal deformable attention module enables deep feature aggregation in each stage of both encoder and decoder. To further accelerate the detection speed, we introduce an encoder feature shuffle strategy for multi-frame prediction during inference. In our encoder feature shuffle strategy, we share the backbone and encoder features, and shuffle encoder features for decoder to generate the predictions of multiple frames. The experiments on the public breast lesion ultrasound video dataset show that our STNet obtains a state-of-the-art detection performance, while operating twice as fast inference speed. The code and model are available at https://github.com/AlfredQin/STNet.Comment: Accepted by MICCAI 202

A consensus linkage map of the grass carp (Ctenopharyngodon idella) based on microsatellites and SNPs

<p>Abstract</p> <p>Background</p> <p>Grass carp (<it>Ctenopharyngodon idella</it>) belongs to the family Cyprinidae which includes more than 2000 fish species. It is one of the most important freshwater food fish species in world aquaculture. A linkage map is an essential framework for mapping traits of interest and is often the first step towards understanding genome evolution. The aim of this study is to construct a first generation genetic map of grass carp using microsatellites and SNPs to generate a new resource for mapping QTL for economically important traits and to conduct a comparative mapping analysis to shed new insights into the evolution of fish genomes.</p> <p>Results</p> <p>We constructed a first generation linkage map of grass carp with a mapping panel containing two F1 families including 192 progenies. Sixteen SNPs in genes and 263 microsatellite markers were mapped to twenty-four linkage groups (LGs). The number of LGs was corresponding to the haploid chromosome number of grass carp. The sex-specific map was 1149.4 and 888.8 cM long in females and males respectively whereas the sex-averaged map spanned 1176.1 cM. The average resolution of the map was 4.2 cM/locus. BLAST searches of sequences of mapped markers of grass carp against the whole genome sequence of zebrafish revealed substantial macrosynteny relationship and extensive colinearity of markers between grass carp and zebrafish.</p> <p>Conclusions</p> <p>The linkage map of grass carp presented here is the first linkage map of a food fish species based on co-dominant markers in the family Cyprinidae. This map provides a valuable resource for mapping phenotypic variations and serves as a reference to approach comparative genomics and understand the evolution of fish genomes and could be complementary to grass carp genome sequencing project.</p

Identifying vital nodes in recovering dynamical process of networked system

Vital nodes identification is the problem of identifying the most significant nodes in complex networks, which is crucial in understanding the property of the networks and has applications in various fields such as pandemic controlling and energy saving. Traditional methods mainly focus on some types of centrality indices, which have restricted application cases. To improve the flexibility of the process and enable simultaneous multiple nodes mining, a deep learning-based vital nodes identification algorithm is proposed in this study, where we train the influence score of each node by using a set of nodes to approximate the rest of the network via the graph convolutional network. Experiments are conducted with generated data to justify the effectiveness of the proposed algorithm. The experimental results show that the proposed method outperforms the traditional ways in adaptability and accuracy to recover the dynamical process of networked system under different classes of network structure

Experimental study on permeability and mechanical properties of coal under different pore pressure and confining pressure

With the continuous increase of coal mining depth, the response of coal mechanics and the mechanism of gas migration have become extremely complicated. In order to explore the coal damage evolution and gas seepage mechanism under the integrated operation of first extraction and subsequent mining in engineering, the K2 coal seam briquette sample of Chongqing Songzao Coal Mine was used as the research object. Using the triaxial servo seepage device of thermal-fluid-solid coupling of gas-bearing coal, the reduced pore pressure seepage test and the triaxial compression-seepage test were successively carried out on the same specimen. According to the elasto-plasticity theory, a statistical damage constitutive model that characterized the whole stress-strain relationship of coal was derived, and the permeability model of coal under consideration of damage was further constructed. The results of the research shown that, in the reduced pore pressure seepage test, the permeability of coal under constant external stress shown a trend of first rising gently and then rising sharply with the decrease of pore pressure. In this process, the change of coal permeability was affected by the competition between effective stress and gas desorption. In the process of the triaxial compression-seepage test, the characteristics of coal deformation stages under different confining stresses were basically similar. As the confining stress increased, the coal mechanics properties were strengthened. The coal permeability curve changed as a negative exponential function with the increasing axial strain . The damage variable curves and plastic strain curves shown a trend of first rising slowly and then rising sharply with the increase of axial strain, the damage evolution process was corresponded to the whole stress-strain curve of each stage of coal deformation and failure. The rationality of the constructed damage constitutive model and permeability model were verified by comparison with test data, which shown that the model can more accurately reflect the characteristics of coal deformation stages and the law of gas seepage

UXT at the crossroads of cell death, immunity and neurodegenerative diseases

The ubiquitous expressed transcript (UXT), a member of the prefoldin-like protein family, modulates regulated cell death (RCD) such as apoptosis and autophagy-mediated cell death through nuclear factor-κB (NF-κB), tumor necrosis factor-α (TNF-α), P53, P62, and methylation, and is involved in the regulation of cell metabolism, thereby affecting tumor progression. UXT also maintains immune homeostasis and reduces proteotoxicity in neuro-degenerative diseases through selective autophagy and molecular chaperones. Herein, we review and further elucidate the mechanisms by which UXT affects the regulation of cell death, maintenance of immune homeostasis, and neurodegenerative diseases and discuss the possible UXT involvement in the regulation of ferroptosis and immunogenic cell death, and targeting it to improve cancer treatment outcomes by regulating cell death and immune surveillance

Golgi Phosphoprotein 3 Promotes Wls Recycling and Wnt Secretion in Glioma Progression

Background/Aims: Golgi phosphoprotein 3 (GOLPH3) plays pro-malignancy roles in several types of cancer. However, the molecular mechanism underlying GOLPH3 promoting tumor progression remains poorly understood. Methods: The expression of GOLPH3 and Wntless (Wls) in glioma tissues was examined by western blotting and immunohistochemistry. EdU incorporation assay and colony formation assay was used to examine the cell growth ability. The effect of GOLPH3 on Wls recycling, Wnt secretion and β-catenin activity was detected using western blotting, immunofluorescence, RT-PCR, ELISA or luciferase assay. Results: The protein levels of GOLPH3 and Wls were upregulated and positively correlated with each other in human glioma tissues. The promoting effect of GOLPH3 on glioma cell proliferation was partially mediated by Wls. In addition, GOLPH3 interacted with Wls and GOLPH3 down-regulation drove Wls into lysosome for degradation, inhibiting its recycling to golgi and the plasma membrane. Importantly, GOLPH3 down-regulation inhibited Wnt2b secretion and decreased β-catenin level and transcription activity. Conclusions: This study provides a brand new evidence that GOLPH3 promotes glioma cell proliferation by facilitating Wls recycling and Wnt/β-catenin signaling. Our findings suggest a rationale for targeting the GOLPH3-Wls-Wnt axis as a promising therapeutic approach for glioblastoma

Study on Flow and Heat Transfer Characteristics of Porous Media in Engine Particulate Filters Based on Lattice Boltzmann Method

To investigate the laminar flow characteristics of porous media in the inner core of engine particulate filters, a two-dimensional lattice Boltzmann&ndash;Cellular Automata (LB&ndash;CA) probabilistic model is used to simulate the flow characteristics of porous media. The variation of dimensionless permeability of various numerical structures on pore scale with Reynolds number is analyzed, and the heat transfer as well as particle filtration are considered. The results show that the flow law of different structures obeys Darcy law under the condition of low Reynolds number (Re &lt; 1). The dimensionless permeability coefficient of the ordered structure is significantly higher than that of the disordered structure; however. the filtration efficiency of the ordered structure decreases. With the increase of Reynolds number, the heat transfer increases. Furthermore, it is found that the particle size has a great influence on the filtration efficiency

Random pore structure and REV scale flow analysis of engine particulate filter based on LBM

In this article, lattice Boltzmann method (LBM) is used to simulate the multi-scale flow characteristics of the engine particulate filter at the pore scale and the representative elementary volume (REV) scale, respectively. Four kinds of random wall-pore structures are considered, which are circular random structure, square random structure, isotropic quartet structure generation set (QSGS), and anisotropic QSGS, with difference analysis done. In terms of the REV scale, the influence of different inlet flow velocities and wall permeabilities on the flow in single channel is analyzed. The result indicates that the internal seepage laws of random structures constructed in this article and single channel are in accordance with Darcy’s law. Circular random structure has better permeability than square random structure. Isotropic QSGS has better fluidity than anisotropic one. The flow in single channel is similar to Poiseuille flow. The flow lines in the channel are complicated and a large number of vortices appear at the ends of channel with high inlet flow rate. With the increase of inlet velocity, the static pressure in channel gradually increases along the axial direction as well as the seepage velocity. The temperature field in the channel becomes more uniform as the flow velocity increases, and the higher temperature distribution appears on the wall of the porous media

A New Method for Feature Extraction and Classification of Single-Stranded DNA Based on Collaborative Filter

The traditional support vector machine algorithm is not enough to classify single-stranded DNA molecules, so this paper proposes an improved threshold extraction algorithm based on collaborative filter for the classification of single-stranded DNA. Firstly, according to the different characteristic curves of the blocking current signals formed by the four bases (A, T, C, and T) that make up DNA molecules crossing the nanopore, the collaborative filter feature extraction algorithm with improved threshold is proposed. Then, the feature information is reconstructed and sent to the SVM classifier for training. Finally, the unfiltered, collaborative filter, improved threshold collaborative filter, and Bessel filter data are, respectively, extracted and sent to the SVM classifier for classification and comparison research. The experimental results show that the improved collaborative filter algorithm has higher accuracy in single-stranded DNA molecular classification

Location-Aware Measurement for Cyber Mimic Defense: You Cannot Improve What You Cannot Measure

Cyber mimic defense is designed to ensure endogenous security, effectively countering unknown vulnerabilities and backdoors, thereby addressing a significant challenge in cyberspace. However, the immense scale of real-world networks and their intricate topology pose challenges for measuring the efficacy of cyber mimic defense. To capture and quantify defense performance within specific segments of these expansive networks, we embrace a partitioning approach that subdivides large networks into smaller regions. Metrics are then established within an objective space constructed on these smaller regions. This approach enables the establishment of several fine-grained metrics that offer a more nuanced measurement of cyber mimic defense deployed in complex networks. For example, the common-mode index is introduced to highlight shared vulnerabilities among diverse nodes, the transfer probability computes the likelihood of risk propagation among nodes, and the failure risk assesses the likelihood of cyber mimic defense technology failure within individual nodes or entire communities. Furthermore, we provide proof of the convergence of the transfer probability. A multitude of simulations are conducted to validate the reliability and applicability of the proposed metrics