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Multi-object tracking (MOT) is an important problem in computer vision which has a wide range of applications. Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency. However, we find that the two tasks tend to compete with each other which need to be carefully addressed. In particular, previous works usually treat re-ID as a secondary task whose accuracy is heavily affected by the primary detection task. As a result, the network is biased to the primary detection task which is not fair to the re-ID task. To solve the problem, we present a simple yet effective approach termed as FairMOT based on the anchor-free object detection architecture CenterNet. Note that it is not a naive combination of CenterNet and re-ID. Instead, we present a bunch of detailed designs which are critical to achieve good tracking results by thorough empirical studies. The resulting approach achieves high accuracy for both detection and tracking. The approach outperforms the state-of-the-art methods by a large margin on several public datasets. The source code and pre-trained models are released at https://github.com/ifzhang/FairMOT.

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.

Alzheimer's disease (AD) is the most common type of senile dementia, characterized by neurofibrillary tangle and amyloid plaque in brain pathology. Major efforts in AD drug were devoted to the interference with the production and accumulation of amyloid-β peptide (Aβ), which plays a causal role in the pathogenesis of AD. Aβ is generated from amyloid precursor protein (APP), by consecutive cleavage by β-secretase and γ-secretase. Therefore, β-secretase and γ-secretase inhibition have been the focus for AD drug discovery efforts for amyloid reduction. Here, we review β-secretase inhibitors and γ-secretase inhibitors/modulators, and their efficacies in clinical trials. In addition, we discussed the novel concept of specifically targeting the γ-secretase substrate APP. Targeting amyloidogenic processing of APP is still a fundamentally sound strategy to develop disease-modifying AD therapies and recent advance in γ-secretase/APP complex structure provides new opportunities in designing selective inhibitors/modulators for AD.

Background Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA. Results CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs. Conclusions This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.

Amyloid is an insoluble fibrous protein and its mis-aggregation can lead to some diseases, such as Alzheimer’s disease and Creutzfeldt–Jakob’s disease. Therefore, the identification of amyloid is essential for the discovery and understanding of disease. We established a novel predictor called RFAmy based on random forest to identify amyloid, and it employed SVMProt 188-D feature extraction method based on protein composition and physicochemical properties and pse-in-one feature extraction method based on amino acid composition, autocorrelation pseudo acid composition, profile-based features and predicted structures features. In the ten-fold cross-validation test, RFAmy’s overall accuracy was 89.19% and F-measure was 0.891. Results were obtained by comparison experiments with other feature, classifiers, and existing methods. This shows the effectiveness of RFAmy in predicting amyloid protein. The RFAmy proposed in this paper can be accessed through the URL http://server.malab.cn/RFAmyloid/.

Background Due to the ability of circRNA to bind with corresponding RBPs and play a critical role in gene regulation and disease prevention, numerous identification algorithms have been developed. Nevertheless, most of the current mainstream methods primarily capture one-dimensional sequence features through various descriptors, while neglecting the effective extraction of secondary structure features. Moreover, as the number of introduced descriptors increases, the issues of sparsity and ineffective representation also rise, causing a significant burden on computational models and leaving room for improvement in predictive performance. Results Based on this, we focused on capturing the features of secondary structure in sequences and developed a new architecture called CRBPSA, which is based on a sequence-structure attention mechanism. Firstly, a base-pairing matrix is generated by calculating the matching probability between each base, with a Gaussian function introduced as a weight to construct the secondary structure. Then, a Structure_Transformer is employed to extract base-pairing information and spatial positional dependencies, enabling the identification of binding sites through deeper feature extraction. Experimental results using the same set of hyperparameters on 37 circRNA datasets, totaling 671,952 samples, show that the CRBPSA algorithm achieves an average AUC of 99.93%, surpassing all existing prediction methods. Conclusions CRBPSA is a lightweight and efficient prediction tool for circRNA-RBP, which can capture structural features of sequences with minimal computational resources and accurately predict protein-binding sites. This tool facilitates a deeper understanding of the biological processes and mechanisms underlying circRNA and protein interactions.

Global warming is one of the greatest threats to the social development of human beings. It is a typical example of global climate change, and has profoundly affected human production and life in various aspects. As the foundation of human existence, agricultural production is particularly vulnerable to climate change, which has altered environmental factors such as temperature, precipitation, and wind speed, and affected crop growth cycles, the frequency of extreme weather events, and the occurrence patterns of pests and diseases directly or indirectly, ultimately influencing crop yield and quality. This article reviews the latest research progress in this field, summarizes the impact of global climate change on agricultural production as well as the feedback mechanisms of agricultural activities on climate change, and proposes strategies for agricultural production to cope with global climate change. This paper aims to provide a scientific basis and suggestions for ensuring the sustainable development of agricultural production.

High levels of ultrafine particles (UFPs; diameter of less than 50 nm) are frequently produced from new particle formation under urban conditions, with profound implications on human health, weather, and climate. However, the fundamental mechanisms of new particle formation remain elusive, and few experimental studies have realistically replicated the relevant atmospheric conditions. Previous experimental studies simulated oxidation of one compound or a mixture of a few compounds, and extrapolation of the laboratory results to chemically complex air was uncertain. Here, we show striking formation of UFPs in urban air from combining ambient and chamber measurements. By capturing the ambient conditions (i.e., temperature, relative humidity, sunlight, and the types and abundances of chemical species), we elucidate the roles of existing particles, photochemistry, and synergy of multipollutants in new particle formation. Aerosol nucleation in urban air is limited by existing particles but negligibly by nitrogen oxides. Photooxidation of vehicular exhaust yields abundant precursors, and organics, rather than sulfuric acid or base species, dominate formation of UFPs under urban conditions. Recognition of this source of UFPs is essential to assessing their impacts and developing mitigation policies. Our results imply that reduction of primary particles or removal of existing particles without simultaneously limiting organics from automobile emissions is ineffective and can even exacerbate this problem.

There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods of Chinese herbal medicine identification must meet higher standards of accuracy. With the rapid development of bioinformatics, methods relying on bioinformatics strategies offer advantages with respect to the speed and accuracy of the identification of Chinese herbal medicine ingredients. This article reviews the applicability and limitations of biochip and DNA barcoding technology in the identification of Chinese herbal medicines. Furthermore, the future development of the two technologies of interest is discussed.

UDP-glycosyltransferases (UGTs) were widely distributed in plants and played crucial roles in mediating glycosylation reactions associated with metabolic pathways. Although the UGT gene family has been characterized in numerous plant species, a systematic analysis in Populus trichocarpa still requires further refinement. In this study, 204 PtrUGT genes were identified through genome-wide analysis, revealing significant variations in protein length, molecular weight, and isoelectric point. Chromosomal mapping revealed an uneven distribution across all 19 chromosomes, with chr16 exhibiting the highest gene density. Furthermore, tandem duplication events were identified as the primary drivers of gene family expansion. Synteny analysis of P. trichocarpa identified 266 paralogous PtrUGT gene pairs, with significant enrichment on Chr16, which were highly conserved among closely related woody plants. Phylogenetic classification grouped the PtrUGTs into 19 distinct subgroups (A-S), with subgroup-specific motif conservation and gene structures. Promoter analysis uncovered abundant cis -regulatory elements associated with light, methyl jasmonate, abscisic acid, and stress responses, indicating functional diversification among the PtrUGT genes. Both RNA-seq and quantitative real-time PCR (qRT-PCR) analyses revealed tissue-specific expression patterns and stress-responsive regulation, with certain PtrUGTs showing significant induction under drought, salt stress, or insect herbivory stress. Subcellular localization analysis revealed that the stress-responsive PtrUGT198 was present in both the nucleus and the cytoplasm. This study provides a systematic characterization of the PtrUGT family, offering valuable insights for identifying genes related to stress resistance and facilitating molecular breeding strategies in poplar.