You-Only-Look-Once (YOLO) represents the state-of-the-art in object detection models. With the emergence of various applications utilizing small domain-specific datasets and limited computing resources for extensive model training and deployment, there is an increasing demand for customized lightweight YOLO architectures. In this paper, we propose a general NeuroEvolution-based method, termed YOLO-Light, designed to automatically create lightweight variants of YOLO architectures tailored to object detection tasks across diverse scenarios. For a given task, YOLO-Light first initializes a population of minimal YOLO architectures and subsequently evolves these models within a novel parallel-chain evolutionary space. This process employs a diversity-protecting evolutionary search strategy until some architectures meet the expected performance standards. During evolution, YOLO-Light incorporates a dynamic evolution regulation mechanism to adjust the evolutionary configuration, thereby enhancing efficiency based on the current evolutionary state. We applied YOLO-Light to generate lightweight YOLOv5, YOLOv8, and YOLOv10 architectures for object detection on the Roboflow 100 small dataset collection, which comprises 100 diverse datasets spanning 7 distinct imagery domains, with a total of 224,714 images and 829 classes. Our experiments focused on 20 datasets ranging from 105 to 8,992 images and 1 to 53 classes. The experimental results show that YOLO-Light reduced the number of parameters by 54–95%, while maintaining or improving mean Average Precision (mAP) compared to standard YOLO architectures. These results demonstrate the effectiveness of YOLO-Light in generating lightweight, task-specific YOLO architectures for resource-constrained object detection tasks. The code repository of YOLO-Light is available on GitHub at https://github.com/BruceShine/YOLO-Light.

The advancement of remote photoplethys-mography (rPPG) technology depends on the availability of comprehensive datasets. However, the reliance on facial features for rPPG signal acquisition poses significant privacy concerns, hindering the development of open-access datasets. This work establishes privacy protection principles for rPPG datasets and introduces the secure anonymization and encryption framework (SAEF) to address these challenges while preserving rPPG data integrity. SAEF first identifies privacy-sensitive facial regions for removal through importance and necessity analysis. The irreversible removal of these regions has an insignificant impact on signal quality, with anR-value deviation of less than 0.06 for BVP extraction and a mean absolute error (MAE) deviation of less than 0.05 for heart rate (HR) calculation. Additionally, SAEF introduces a high efficiency cascade key encryption method (CKEM), achieving encryption in 5.54 × 10−5seconds per frame, which is over three orders of magnitude faster than other methods, and reducing approximate point correlation (APC) values to below 0.005, approaching complete randomness. These advancements significantly improve real-time video encryption performance and security. Finally, SAEF serves as a preprocessing tool for generating volunteer-friendly, open-access rPPG datasets.

Aging is a complex and heterogeneous biological process at cellular, tissue, and individual levels. Despite extensive effort in scientific research, a comprehensive understanding of aging mechanisms remains lacking. This study analyzed aging-related gene networks, using single-cell RNA sequencing data from >15 000 cells. We constructed a gene correlation network, integrating gene expressions into the weights of network edges, and ranked gene importance using a random walk model to generate a gene importance matrix. This unsupervised method improved the clustering performance of cell types. To further quantify the complexity of gene networks during aging, we introduced network structural entropy. The findings of our study reveal that the overall network structural entropy increases in the aged cells compared to the young cells. However, network entropy changes varied greatly within different cell subtypes. Specifically, the network structural entropy among various cell types may increase, remain unchanged, or decrease. This wide range of changes may be closely related to their individual functions, highlighting the cellular heterogeneity and potential key network reconfigurations. Analyzing gene network entropy provides insights into the molecular mechanisms behind aging. This study offers new scientific evidence and theoretical support for understanding the changes in cell functions during aging.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

Lane detection is a special task in autonomous driving. Its most prominent inherent feature is to learn the imagination of severely occluded objects. Traditional CNN-based networks learning the imagination tend to perform poorly. In this work, we propose a novel architecture, called Cycle_accumulation-Transformer (CaT), which is the first structure to handle the lane detection by fusing CNN and Transformer. In particular, Cycle_accumulation structure and Transformer structure complement each other, and they adopt the four-direction cyclic accumulation process of “up to down”, “down to up”, “left to right” and “right to left” in the convolutional mode and the self-attention mechanism of “QKV” to fuse global information respectively. Our method is based on pixel-level semantic segmentation with high detection accuracy while meeting real-time requirements. Moreover, our proposed method achieves state-of-the-art results on the Tusimple and also achieves competitive results on the CULane.

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

Long noncoding RNA (lncRNA) is a kind of noncoding RNA with a length of more than 200 nucleotide units. Numerous research studies have proven that although lncRNAs cannot be directly translated into proteins, lncRNAs still play an important role in human growth processes by interacting with proteins. Since traditional biological experiments often require a lot of time and material costs to explore potential lncRNA-protein interactions (LPI), several computational models have been proposed for this task. In this study, we introduce a novel deep learning method known as combined graph auto-encoders (LPICGAE) to predict potential human LPIs. First, we apply a variational graph auto-encoder to learn the low dimensional representations from the high-dimensional features of lncRNAs and proteins. Then the graph auto-encoder is used to reconstruct the adjacency matrix for inferring potential interactions between lncRNAs and proteins. Finally, we minimize the loss of the two processes alternately to gain the final predicted interaction matrix. The result in 5-fold cross-validation experiments illustrates that our method achieves an average area under receiver operating characteristic curve of 0.974 and an average accuracy of 0.985, which is better than those of existing six state-of-the-art computational methods. We believe that LPICGAE can help researchers to gain more potential relationships between lncRNAs and proteins effectively.

Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale boundary-aware transformer, XBound-Former, to simultaneously address the variation and boundary problems of skin lesion segmentation. XBound-Former is a purely attention-based network and catches boundary knowledge via three specially designed learners. First, we propose an implicit boundary learner (im-Bound) to constrain the network attention on the points with noticeable boundary variation, enhancing the local context modeling while maintaining the global context. Second, we propose an explicit boundary learner (ex-Bound) to extract the boundary knowledge at multiple scales and convert it into embeddings explicitly. Third, based on the learned multi-scale boundary embeddings, we propose a cross-scale boundary learner (X-Bound) to simultaneously address the problem of ambiguous and multi-scale boundaries by using learned boundary embedding from one scale to guide the boundary-aware attention on the other scales. We evaluate the model on two skin lesion datasets and one polyp lesion dataset, where our model consistently outperforms other convolution- and transformer-based models, especially on the boundary-wise metrics. All resources could be found in https://github.com/jcwang123/xboundformer.

Long non-coding RNA (lncRNA) and microRNA (miRNA) are two typical types of non-coding RNAs (ncRNAs), their interaction plays an important regulatory role in many biological processes. Exploring the interactions between unknown lncRNA and miRNA can help us better understand the functional expression between lncRNA and miRNA. At present, the interactions between lncRNA and miRNA are mainly obtained through biological experiments, but such experiments are often time-consuming and labor-intensive, it is necessary to design a computational method that can predict the interactions between lncRNA and miRNA. In this paper, we propose a method based on graph convolutional neural (GCN) network and conditional random field (CRF) for predicting human lncRNA-miRNA interactions, named GCNCRF. First, we construct a heterogeneous network using the known interactions of lncRNA and miRNA in the LncRNASNP2 database, the lncRNA/miRNA integration similarity network, and the lncRNA/miRNA feature matrix. Second, the initial embedding of nodes is obtained using a GCN network. A CRF set in the GCN hidden layer can update the obtained preliminary embeddings so that similar nodes have similar embeddings. At the same time, an attention mechanism is added to the CRF layer to reassign weights to nodes to better grasp the feature information of important nodes and ignore some nodes with less influence. Finally, the final embedding is decoded and scored through the decoding layer. Through a 5-fold cross-validation experiment, GCNCRF has an area under the receiver operating characteristic curve value of 0.947 on the main dataset, which has higher prediction accuracy than the other six state-of-the-art methods.

MOTIVATION: Transcriptional surges generated by two-component systems (TCSs) have been observed experimentally in various bacteria. Suppression of the transcriptional surge may reduce the activity, virulence and drug resistance of bacteria. In order to investigate the general mechanisms, we use a PhoP/PhoQ TCS as a model system to derive a comprehensive mathematical modeling that governs the surge. PhoP is a response regulator, which serves as a transcription factor under a phosphorylation-dependent modulation by PhoQ, a histidine kinase. RESULTS: Our model reveals two major signaling pathways to modulate the phosphorylated PhoP (P-PhoP) level, one of which promotes the generation of P-PhoP, while the other depresses the level of P-PhoP. The competition between the P-PhoP-promoting and the P-PhoP-depressing pathways determines the generation of the P-PhoP surge. Furthermore, besides PhoQ, PhoP is also a bifunctional modulator that contributes to the dynamic control of P-PhoP state, leading to a biphasic regulation of the surge by the gene feedback loop. In summary, the mechanisms derived from the PhoP/PhoQ system for the transcriptional surges provide a better understanding on such a sophisticated signal transduction system and aid to develop new antimicrobial strategies targeting TCSs. AVAILABILITY AND IMPLEMENTATION: https://github.com/jianweishuai/TCS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.