Saliency guided Siamese attention network for infrared ship target tracking

Due to the lack of discriminative features in infrared images, most of existing trackers cannot separate a target from its background. There are some studies on generating discriminative features where feature fusion and attention are applied to enhance targets. However, the saliency information and information interaction which assist in locating the targets is ignored. To improve the accuracy of infrared ship target tracking, we propose a saliency guided Siamese attention network (SGSiamAttn). The main contribution is to design a saliency prediction network that obtains the saliency map of a search region and followed by a saliency enhancement network to highlight the target. With the saliency information, our network is able to perceive the entire target, which improves the discriminative ability and the tracking accuracy. Meanwhile, a local-to-global correlation module is applied before the saliency prediction network, aiming to refine the correlation map while suppressing non-target interferences. We also impose a shared cross-correlation module on the region proposal network. By sharing the correlation map in the classification and regression branches, it enhances information interaction between the two tasks and reduces the computational cost. As there are limited number of infrared ship tracking datasets publicly available, we construct a new infrared ship dataset (ISD) which includes 16 different types of ships and 7,872 video frames with manual annotations. The experimental results on ISD and other three public datasets, namely VOT-TIR2015, PTB-TIR, and LSOTB-TIR, demonstrate that our tracker achieves superior performance in terms of accuracy, expected average overlap, success, and precision

Human sex hormone-binding globulin gene expression- multiple promoters and complex alternative splicing

Human sex hormone-binding globulin (SHBG) regulates free sex steroid concentrations in plasma and modulates rapid, membrane based steroid signaling. SHBG is encoded by an eight exon-long transcript whose expression is regulated by a downstream promoter (PL). The SHBG gene was previously shown to express a second major transcript of unknown function, derived from an upstream promoter (PT), and two minor transcripts. We report that transcriptional expression of the human SHBG gene is far more complex than previously described. PL and PT direct the expression of at least six independent transcripts each, resulting from alternative splicing of exons 4, 5, 6, and/or 7. We mapped two transcriptional start sites downstream of PL and PT, and present evidence for a third SHBG gene promoter (PN) within the neighboring FXR2 gene; PN regulates the expression of at least seven independent SHBG gene transcripts, each possessing a novel, 164-nt first exon (1N). Transcriptional expression patterns were generated for human prostate, breast, testis, liver, and brain, and the LNCaP, MCF-7, and HepG2 cell lines. Each expresses the SHBG transcript, albeit in varying abundance. Alternative splicing was more pronounced in the cancer cell lines. PL- PT- and PN-derived transcripts were most abundant in liver, testis, and prostate, respectively. Initial findings reveal the existence of a smaller immunoreactive SHBG species in LNCaP, MCF-7, and HepG2 cells. These results extend our understanding of human SHBG gene transcription, and raise new and important questions regarding the role of novel alternatively spliced transcripts, their function in hormonally responsive tissues including the breast and prostate, and the role that aberrant SHBG gene expression may play in cancer

A Two-branch Edge Guided Lightweight Network for infrared image saliency detection

In the dynamic landscape of saliency detection, convolutional neural networks have emerged as catalysts for innovation, but remain largely tailored for RGB imagery, falling short in the context of infrared images, particularly in memory-restricted environments. These existing approaches tend to overlook the wealth of contour information vital for a nuanced analysis of infrared images. Addressing this notable gap, we introduce the novel Two-branch Edge Guided Lightweight Network (TBENet), designed explicitly for the robust analysis of infrared image saliency detection. The main contributions of this paper are as follows. First, we formulate the saliency detection task as two subtasks, contour enhancement and foreground segmentation. Therefore, the TBENet is divided into two specialized branches: a contour prediction branch for extracting target contour and a saliency map generation branch for separating the foreground from the background. The first branch employs an encoder–decoder architecture to meticulously delineate object contours, serving as a guiding blueprint for the second branch. This latter segment adeptly integrates spatial and semantic data, creating a precise saliency map that is refined further by an innovative edge-weighted contour loss function. Second, to enhance feature integration capabilities, we propose depthwise multi-scale and multi-cue modules, facilitating sophisticated feature aggregation. Third, a high-level linear bottleneck module is devised to ensure the extraction of rich semantic information, and by replacing the standard convolution with the depthwise convolution, it is beneficial to reduce model complexity. Additional, we reduce the number of channels of the feature maps from each stage of the decoder to further enhance the lightweight of the model. Last, we construct a novel infrared ship dataset Small-IRShip to train and evaluate our proposed model. Experimental results on the homemade dataset Small-IRShip and two publicly available datasets, namely RGB-T and IRSTD-1k, demonstrate TBENet’s superior performance over state-of-the-art methods, affirming its effectiveness in harnessing edge information and incorporating advanced feature integration strategies

Aptamers, the Nucleic Acid Antibodies, in Cancer Therapy

The arrival of the monoclonal antibody (mAb) technology in the 1970s brought with it the hope of conquering cancers to the medical community. However, mAbs, on the whole, did not achieve the expected wonder in cancer therapy although they do have demonstrated successfulness in the treatment of a few types of cancers. In 1990, another technology of making biomolecules capable of specific binding appeared. This technique, systematic evolution of ligands by exponential enrichment (SELEX), can make aptamers, single-stranded DNAs or RNAs that bind targets with high specificity and affinity. Aptamers have some advantages over mAbs in therapeutic uses particularly because they have little or no immunogenicity, which means the feasibility of repeated use and fewer side effects. In this review, the general properties of the aptamer, the advantages and limitations of aptamers, the principle and procedure of aptamer production with SELEX, particularly the undergoing studies in aptamers for cancer therapy, and selected anticancer aptamers that have entered clinical trials or are under active investigations are summarized

Experimental research on compressive strength deterioration of coal seam floor sandstone under the action of acidic mine drainage

Abstract In sulphur-coal symbiotic coal seams, after the mining of sulphide iron ore, when the coal resources are mined, the mine water accumulated in the roadway mining area will have a certain impact on the stability of the surrounding rock of the coal seam roadway. Taking the floor sandstone of sulfur coal symbiotic coal seam as the research object, the roof fissure water with pH values of 7.48, 4.81 and 2.62 was used as the experimental solution. 10 experimental schemes were designed to measure the compressive strength of the samples under the action of AMD, and the hydrochemical analysis of AMD was conducted. The pore structures of the samples before and after the action of AMD were analyzed. Based on the hydrochemistry and pore structure, the deterioration mechanism of compressive strength of the coal seam floor sandstone under the action of AMD was explained. The results indicated that the compressive strength of the samples decreased with the increasing action time of AMD. The compressive strength decreased with the increment of the porosity. The concentration of H+ ion in AMD was relatively small. Na2O in albite dissolved and reacted with water, leading to an increase in the concentration of Na+ ion. Soluble substances such as MgCl2 and CaSO4 in the pore structure dissolved, leading to an increase in the concentration of Ca2+ and Mg2+ ions. The dissolution of soluble substances and the physical–chemical reactions between solutions and minerals were the essential causes of the continuous deterioration of the compressive strength of the coal seam floor sandstone. The results of this study can provide a theoretical basis for the deterioration of the mechanical properties of the peripheral rock in the roadway of the sulphur coal seam, and can also provide a certain engineering reference for the sulphur coal seam roadway

Identifying the Non-Traditional Safety Risk Paths of Employees from Chinese International Construction Companies in Africa

In recent years, more and more construction enterprises are expanding into overseas markets, especially in underdeveloped regions such as Africa. Compared to domestic construction projects, international construction projects have been faced with more uncertainties and increased levels of safety risks to the employees in the context of political turmoil, racism, and religious conflict in the host country. This study aims to answer what risk factors contribute to the threat to the safety of overseas employees and how safety risk factors interact, using employees from Chinese international construction companies (CICCs) in Africa as an example. A total of 39 safety risk factors were selected by literature review and case study based on Heinrich’s Domino Theory of Accident Causation. To identify the critical safety risk sources and significant risk paths, a questionnaire survey was conducted among 208 professionals who have participated in construction projects in Africa. Using structural equation modeling (SEM), a total of twelve critical risk paths and five controllable risk sources were identified. The improper behaviors of the CICCs and their employees were shown to have the largest impact on the safety of Chinese employees, through the mediating effect of the criminal offense. This study provides some insights into safety risk management in international construction projects. Meanwhile, the quantitative approach proposed can also be used by other international companies or governments in identifying the safety risk paths of their overseas workers involved in international construction projects.Real Estate ManagementHousing System

A Two-branch Edge Guided Lightweight Network for infrared image saliency detection

In the dynamic landscape of saliency detection, convolutional neural networks have emerged as catalysts for innovation, but remain largely tailored for RGB imagery, falling short in the context of infrared images, particularly in memory-restricted environments. These existing approaches tend to overlook the wealth of contour information vital for a nuanced analysis of infrared images. Addressing this notable gap, we introduce the novel Two-branch Edge Guided Lightweight Network (TBENet), designed explicitly for the robust analysis of infrared image saliency detection. The main contributions of this paper are as follows. First, we formulate the saliency detection task as two subtasks, contour enhancement and foreground segmentation. Therefore, the TBENet is divided into two specialized branches: a contour prediction branch for extracting target contour and a saliency map generation branch for separating the foreground from the background. The first branch employs an encoder–decoder architecture to meticulously delineate object contours, serving as a guiding blueprint for the second branch. This latter segment adeptly integrates spatial and semantic data, creating a precise saliency map that is refined further by an innovative edge-weighted contour loss function. Second, to enhance feature integration capabilities, we propose depthwise multi-scale and multi-cue modules, facilitating sophisticated feature aggregation. Third, a high-level linear bottleneck module is devised to ensure the extraction of rich semantic information, and by replacing the standard convolution with the depthwise convolution, it is beneficial to reduce model complexity. Additional, we reduce the number of channels of the feature maps from each stage of the decoder to further enhance the lightweight of the model. Last, we construct a novel infrared ship dataset Small-IRShip to train and evaluate our proposed model. Experimental results on the homemade dataset Small-IRShip and two publicly available datasets, namely RGB-T and IRSTD-1k, demonstrate TBENet’s superior performance over state-of-the-art methods, affirming its effectiveness in harnessing edge information and incorporating advanced feature integration strategies