MOTIVATION: Single-cell heterogeneity analysis faces significant challenges due to the high dimensionality, complexity, and noise inherent in scRNA-seq data, especially when aiming for precise cell type classification. Existing analytical methods often exhibit limited generalization ability and adaptability across different biological contexts, leading to biased identification of cell subpopulations and hindering a comprehensive understanding of diseases, therapeutic responses, and biological processes. RESULTS: To address these issues, we propose a novel method named scKD, which integrates a hybrid neighbourhood-enhanced comparative learning model with a self-knowledge distillation strategy. scKD enhances clustering accuracy and is capable of accurately identifying both major cell types and rare cell subtypes. Extensive evaluations on multiple real-world datasets demonstrate that scKD achieves superior performance in subpopulation identification, clustering stability, and robustness. These results suggest that scKD is a powerful and reliable tool for analyzing single-cell transcriptomic data, facilitating deeper insights into cellular heterogeneity. AVAILABILITY: All datasets used in this study are publicly available. Detailed information about all the single-cell datasets analyzed in this paper is provided in Supplementary Table 1. All datasets can be accessed at https://zenodo.org/records/15412380. The source code is available at https://github.com/A-qlh/sckd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The precise assessment of major depressive disorder (MDD) is of vital importance for clinical management. Electroencephalographic (EEG) is expected to provide an objective neurophysiological alternative for this purpose, such as the abnormal functional connectivity characteristics that are closely related to MDD. However, the current approaches using a single functional connectivity matrix cannot fully capture the information in EEG signals, and combining multiple matrices would introduces redundancy. This study proposes a Multi-head and Channel Twin Self-Attention Network (MCTSA-Net), a diagnosis model based on multi-feature fusion and self-attention mechanisms. Based on coupling methods and graph theory, it extracts different functional connectivity matrices and topological attributes from EEG signals, and the best combinations and fusion of these properties are selected, including Pearson correlation coefficient (PCC), phase locking value (PLV), strength feature (sf), and clustering coefficient (cc). Then, multi-head and channel selfattention mechanisms are incorporated to capture complex relationships between topological properties and frequency bands, automatically assigning appropriate weights for classification. The model achieves an accuracy of 98.53 % at the MODMA dataset, outperforming existing MDD diagnostic methods. Additionally, the SHAP analysis shows that the gamma band contributes the most significantly to the classification outcomes, further exploring the interpretability of the diagnosis framework.

Magnetic resonance imaging (MRI) is essential for studying neurological and psychiatric disorders, but its analysis is limited by sparse connectivity and individual variability. Most existing methods rely on pairwise Pearson correlation, which captures only direct interactions and overlooks indirect, mediated connections, leading to oversimplified brain networks. To address this, we propose Dynamic Graph Enhancement -Multi-level Attention Weighting (DGE-MAW), a data-centered approach that improves structural representation and diagnostic interpretability. It comprises two main modules: Dynamic Graph Enhancement dynamically updates the graph structure to identify indirect connections. By integrating intermediate nodes, nonlinear activations, and topological indicators, it mimics neural transmission pathways and reconstructs more informative brain networks, reducing information loss. Multi-level Attention Weighting introduces a hierarchical attention mechanism inspired by biological signal transmission, capturing individual, regional, and global semantics. This enables interpretable and adaptive integration of multimodal features. Experiments show that DGE-MAW significantly enhances data quality, interpretability, multimodal fusion, and diagnostic accuracy. On the ABIDE and ADHD-200 datasets, it outperforms state-of-the-art model-centric methods across various neuroimaging tasks. By modeling indirect connections and incorporating prior brain knowledge, it demonstrates the potential of MRI augmentation. Code and resources are available at: https://github.com/A3-247/DGE-MAW.

Functional connectivity changes detected via functional magnetic resonance imaging (fMRI) often precede clinical symptoms of neurological and psychiatric disorders, making them valuable biomarkers for early diagnosis and monitoring. This study introduces a multi-modal fusion framework that combines fMRI-derived connectivity with demographic data, using a multi-task learning strategy informed by medical domain knowledge. We propose a novel HemiSynap Polarity Attention mechanism to enhance feature extraction by modeling inter-hemispheric interactions and capturing polarityspecific (positive/negative) connectivity patterns. An adversarial multi-task training scheme aligns classification and regression objectives, promoting loss sharing and effective information fusion. The model was validated on three public datasets (ABIDE, ADHD-200, ADNI), showing notable improvements over state-of-the-art methods. Furthermore, we conducted statistical analyses of attention patterns across brain networks and regions, demonstrating the model's interpretability and offering insights into its clinical relevance for aiding diagnosis. The code is available at: https://github.com/A3-247/HPA-AT

Single-Track two-wheeled (STTW) robots, due to their unique kinematic structure, offer superior dynamic performance, making them highly suitable for real-world applications. However, research on drift control for STTW robots is limited due to complex dynamics, narrow feasible region and difficulty in collecting demonstration data for adopting data-driven solutions. In this paper, we explore the feasibility and effectiveness of applying model-free deep reinforcement learning (DRL) to two drift tasks: steady-state drifting and drift trajectory tracking. We trained a reinforcement learning controller based on novel reward functions particularly designed for STTW drifting control, which does not require demonstration data. Furthermore, we propose a sampling and verification mechanism that excludes drift targets violating action constraints and thus improve training performance. Simulation results show that the DRL-based solution can achieve consistent and generalizable drifting behaviors for STTW robots, paving the way toward their practical deployment in dynamic environments.

Deciphering the cell dynamics in complex biological systems is of great significance for understanding the mechanisms of life and facilitating disease treatment. Recent advances in single-cell sequencing technologies have enabled the measurement of single-cell characteristics over multiple time points. However, the integration and analysis of these dynamic single-cell data face many challenges and raise new demands for computational methodologies. In this review, we first elaborate these challenges in the context of experimental limitations, data features, and biological discoveries. Then, we provide an overview of the algorithmic advancements across four key tasks: inferring single-cell dynamics, dissecting dynamic mechanisms, predicting future cell fates, and integrating lineage tracing information to characterize cell dynamics. Finally, we discuss that the cutting-edge developments in biological technologies and artificial intelligence algorithms may greatly enhance our ability to explore complex life processes from a spatiotemporal systemic perspective.

The functional connectivity network which provides a perspective of interactions among all brain regions and reveals the functional organizational structure of the brain has gained considerable traction in the domain of diagnosis of neuropsychiatric disorders. To further extract lesion attributes within brain networks , Graph Neural Networks (GNNs) can achieve interpretable feature extraction and compression by simulating the structure and signal transmission in real brains. This has led to the prevalence of GNNs in utilizing non-invasive resting-state functional magnetic resonance imaging (rs-fMRI) data for brain graph construction and disease diagnosis. However, this computer-aided diagnostic (CAD) approach still needs to be further optimized in terms of data collection and model construction, and its diagnostic accuracy and clinical value are expected to be further improved. Therefore, this review conducts an in-depth and extensive investigation and analysis of GNNs in the diagnosis of neuro-psychiatric disorders. It presents the overall process of disease diagnosis, offering novel insights and methodologies to researchers in the field, thereby facilitating further improvement and optimization of related technologies.

EEG-based emotion recognition has advanced rapidly due to its objectivity and reliability, but individual differences present challenges: subject-specific models underperform on new subjects, and general models lack accuracy. While domain adaptation (DA) algorithms reduce distribution differences between source and target EEG domains, single-source methods struggle with knowledge transfer, and multi-source methods neglect source domain differences. To address this, we propose a collaborative learning and dynamic distributed adaptation algorithm (CL-DDA) for EEG emotion recognition. By dividing the model into two sub-networks, collaborative learning improves generalization. Additionally, local subdomain alignment addresses inter-subject emotion differences, while global domain alignment minimizes marginal distribution disparities. Our model achieved 90.08% and 77.55% accuracy on SEED and SEED-IV datasets, respectively, in cross-subject emotion recognition.

SUMMARY: Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663 857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME. AVAILABILITY AND IMPLEMENTATION: The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296.

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.

The inference of gene co-expression associations is one of the fundamental tasks for large-scale transcriptomic data analysis. Due to the high dimensionality and high noises in transcriptomic data, it is difficult to infer stable gene co-expression associations from single dataset. Meta-analysis of multisource data can effectively tackle this problem. We proposed Joint Embedding of multiple BIpartite Networks (JEBIN) to learn the low-dimensional consensus representation for genes by integrating multiple expression datasets. JEBIN infers gene co-expression associations in a nonlinear and global similarity manner and can integrate datasets with different distributions in linear time complexity with the gene and total sample size. The effectiveness and scalability of JEBIN were verified by simulation experiments, and its superiority over the commonly used integration methods was proved by three indexes on real biological datasets. Then, JEBIN was applied to study the gene co-expression patterns of hepatocellular carcinoma (HCC) based on multiple expression datasets of HCC and adjacent normal tissues, and further on latest HCC single-cell RNA-seq data. Results show that gene co-expressions are highly different between bulk and single-cell datasets. Finally, many differentially co-expressed ligand-receptor pairs were discovered by comparing HCC with adjacent normal data, providing candidate HCC targets for abnormal cell-cell communications.

Quantifying cell proportions, especially for rare cell types in some scenarios, is of great value in tracking signals associated with certain phenotypes or diseases. Although some methods have been proposed to infer cell proportions from multicomponent bulk data, they are substantially less effective for estimating the proportions of rare cell types which are highly sensitive to feature outliers and collinearity. Here we proposed a new deconvolution algorithm named ARIC to estimate cell type proportions from gene expression or DNA methylation data. ARIC employs a novel two-step marker selection strategy, including collinear feature elimination based on the component-wise condition number and adaptive removal of outlier markers. This strategy can systematically obtain effective markers for weighted $\upsilon$-support vector regression to ensure a robust and precise rare proportion prediction. We showed that ARIC can accurately estimate fractions in both DNA methylation and gene expression data from different experiments. We further applied ARIC to the survival prediction of ovarian cancer and the condition monitoring of chronic kidney disease, and the results demonstrate the high accuracy and robustness as well as clinical potentials of ARIC. Taken together, ARIC is a promising tool to solve the deconvolution problem of bulk data where rare components are of vital importance.

Molecular heterogeneities and complex microenvironments bring great challenges for cancer diagnosis and treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer cell heterogeneities and microenvironments at single-cell transcriptomic level. Here, we develop an R package named scCancer, which focuses on processing and analyzing scRNA-seq data for cancer research. Except basic data processing steps, this package takes several special considerations for cancer-specific features. Firstly, the package introduced comprehensive quality control metrics. Secondly, it used a data-driven machine learning algorithm to accurately identify major cancer microenvironment cell populations. Thirdly, it estimated a malignancy score to classify malignant (cancerous) and non-malignant cells. Then, it analyzed intra-tumor heterogeneities by key cellular phenotypes (such as cell cycle and stemness), gene signatures and cell-cell interactions. Besides, it provided multi-sample data integration analysis with different batch-effect correction strategies. Finally, user-friendly graphic reports were generated for all the analyses. By testing on 56 samples with 433 405 cells in total, we demonstrated its good performance. The package is available at: http://lifeome.net/software/sccancer/.

MOTIVATION: Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping. RESULTS: We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq. AVAILABILITY AND IMPLEMENTATION: http://github.com/QiuyuLian/CITE-sort. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

The alteration of DNA methylation landscape is a key epigenetic event in cancer. As the accumulation of large-scale genome-wide DNA methylation data from clinical samples, we are able to characterize the patterns of DNA methylation alterations for identifying candidate epigenetic markers and drivers. In this survey, we take hepatocellular carcinoma (HCC) as an example to show the basic steps of analyzing the DNA methylation patterns in cancer across multiple data sets. We collected three genome-wide DNA methylation data sets with ∼800 clinical samples and the corresponding gene expression data sets. First, by quantitatively analyzing two global methylation alterations, it is found that about 90% tumors acquire either genome-wide DNA hypo-methylation or CpG island methylator phenotype. Second, probe-level analysis identified 267, 228 and 197 hyper-methylated sites in promoter regions for the three data sets, respectively. These local hyper-methylated patterns are highly consistent: 84 sites (from 61 promoters) are hyper-methylated in all the three studied data sets, including many previously reported genes, such as CDKL2, TBX15 and NKX6-2. Then, these hyper-methylated sites were used as candidate markers to classify tumor and non-tumor samples. The classifiers based on only 10 selected probes can achieve high discriminative ability across different data sets. Finally, by integrative analyzing DNA methylation and gene expression data, we identified 222 candidate epigenetic drivers, which are enriched in inflammatory response and multiple metabolic pathways. A set of high-confidence candidates, including SFN, SPP1 and TKT, are significantly associated with patients' overall survivals. In summary, this study systematically characterized the DNA methylation alterations and their impacts on gene expressions in HCCs based on multiple data sets.

MOTIVATION: Molecule-based prediction of drug response is one major task of precision oncology. Recently, large-scale cancer genomic studies, such as The Cancer Genome Atlas (TCGA), provide the opportunity to evaluate the predictive utility of molecular data for clinical drug responses in multiple cancer types. RESULTS: Here, we first curated the drug treatment information from TCGA. Four chemotherapeutic drugs had more than 180 clinical response records. Then, we developed a computational framework to evaluate the molecule based predictions of clinical responses of the four drugs and to identify the corresponding molecular signatures. Results show that mRNA or miRNA expressions can predict drug responses significantly better than random classifiers in specific cancer types. A few signature genes are involved in drug response related pathways, such as DDB1 in DNA repair pathway and DLL4 in Notch signaling pathway. Finally, we applied the framework to predict responses across multiple cancer types and found that the prediction performances get improved for cisplatin based on miRNA expressions. Integrative analysis of clinical drug response data and molecular data offers opportunities for discovering predictive markers in cancer. This study provides a starting point to objectively evaluate the molecule-based predictions of clinical drug responses. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: MicroRNAs (miRNAs), a class of small regulatory RNAs, play important roles in cancer initiation, progression and therapy. MiRNAs are found to regulate diverse cancer-related processes by targeting a large set of oncogenic and tumor-suppressive genes. To establish a high-confidence reference resource for studying the miRNA-regulated target genes and cellular processes in cancer, we manually curated 2259 entries of cancer-related miRNA regulations with direct experimental evidence from ∼9000 abstracts, covering more than 300 miRNAs and 829 target genes across 25 cancer tissues. A web-based portal named oncomiRDB, which provides both graphical and text-based interfaces, was developed for easily browsing and searching all the annotations. It should be a useful resource for both the computational analysis and experimental study on miRNA regulatory networks and functions in cancer. AVAILABILITY AND IMPLEMENTATION: http://bioinfo.au.tsinghua.edu.cn/oncomirdb/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks. RESULTS: We proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145. CONCLUSIONS: Our network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.

Recently, with the explosive increase of automobiles in China, people park their cars on streets without following the rules and police has hard time to conduct law enforcement without introducing effective street parking system. To solve the problem, we propose a SPS (street parking system) based on wireless sensor networks. For accurately detecting a parking car, a parking algorithm based on state machine is also proposed. As the vehicle detection in SPS is absolutely critical, we use a Honeywell 3-axis magnetic sensor to detect vehicle. However, the magnetic sensor may be affected by the fluctuation of the outside temperature. To solve the problem, we introduce a moving drift factor. On the parking lot in Shenzhen Institutes of Advanced Technology (SIAT), 62 sensor devices are deployed to evaluate the performance of SPS. By running the system for several months, we observe the vehicle detection accurate rate of the SPS is nearly 99%. The proposed SPS is energy efficient. The end device can run about 7 years with one 2400mAh AA battery.

MOTIVATION: Tumor necrosis factor-alpha (TNF-α), a major inflammatory cytokine, is closely related to several cardiovascular pathological processes. However, its effects on the cell cycle of vascular endothelial cells (VECs) have been the subject of some controversy. To investigate the molecular mechanism underlying this process, we constructed time-course protein-protein interaction (PPI) networks of TNF-α induced regulation of cell cycle in VECs using microarray datasets and genome-wide PPI datasets. Then, we analyzed the topological properties of the responsive PPI networks and calculated the node degree and node betweenness centralization of each gene in the networks. We found that p21, p27 and cyclinD1, key genes of the G1/S checkpoint, are in the center of responsive PPI networks and their roles in PPI networks are significantly altered with induction of TNF-α. According to the following biological experiments, we proved that TNF-α can promote G(1)/S transition of cell cycle in VECs and facilitate the cell cycle activation induced by vascular endothelial growth factor. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: MicroRNAs are a class of endogenous small RNAs that play regulatory roles. Intergenic miRNAs are believed to be transcribed independently, but the transcriptional control of these crucial regulators is still poorly understood. RESULTS: In this work, phylogenetic footprinting is used to identify conserved cis-regulatory elements (CCEs) surrounding intergenic miRNAs in Drosophila. With a two-step strategy that takes advantage of both alignment-based and motif-based methods, we identified CCEs that are conserved across the 12 fly species. When compared with TRANSFAC database, these CCEs are significantly enriched in known transcription factor binding sites (TFBSs). Moreover, several TFs that play essential roles in Drosophila development (e.g. Adf-1, Abd-B, Sd, Prd, Ubx, Zen and En) are found to be preferentially regulating the miRNA genes. Further analysis revealed many over-represented cis-regulatory modules (CRMs) composed of multiple known TFBSs, motif pairs with significant distance constraints and a number of novel motifs, many of which preferentially occur near the transcription start site of protein-coding genes. Additionally, a number of putative miRNA-TF regulatory feedback loops were also detected. AVAILABILITY: Supplementary Material and the Perl scripts performing two-step phylogenetic footprinting are available at http://bioinfo.au.tsinghua.edu.cn/member/xwwang/mircisreg

BACKGROUND: MicroRNAs (miRNAs) are a class of endogenous regulatory small RNAs which play an important role in posttranscriptional regulations by targeting mRNAs for cleavage or translational repression. The base-pairing between the 5'-end of miRNA and the target mRNA 3'-UTRs is essential for the miRNA:mRNA recognition. Recent studies show that many seed matches in 3'-UTRs, which are fully complementary to miRNA 5'-ends, are highly conserved. Based on these features, a two-stage strategy can be implemented to achieve the de novo identification of miRNAs by requiring the complete base-pairing between the 5'-end of miRNA candidates and the potential seed matches in 3'-UTRs. RESULTS: We presented a new method, which combined multiple pairwise conservation information, to identify the frequently-occurred and conserved 7-mers in 3'-UTRs. A pairwise conservation score (PCS) was introduced to describe the conservation of all 7-mers in 3'-UTRs between any two Drosophila species. Using PCSs computed from 6 pairs of flies, we developed a support vector machine (SVM) classifier ensemble, named Cons-SVM and identified 689 conserved 7-mers including 63 seed matches covering 32 out of 38 known miRNA families in the reference dataset. In the second stage, we searched for 90 nt conserved stem-loop regions containing the complementary sequences to the identified 7-mers and used the previously published miRNA prediction software to analyze these stem-loops. We predicted 47 miRNA candidates in the genome-wide screen. CONCLUSION: Cons-SVM takes advantage of the independent evolutionary information from the 6 pairs of flies and shows high sensitivity in identifying seed matches in 3'-UTRs. Combining the multiple pairwise conservation information by the machine learning approach, we finally identified 47 miRNA candidates in D. melanogaster.

MOTIVATION: MicroRNAs (miRNA) are approximately 22 nt long non-coding RNAs that are derived from larger hairpin RNA precursors and play important regulatory roles in both animals and plants. The short length of the miRNA sequences and relatively low conservation of pre-miRNA sequences restrict the conventional sequence-alignment-based methods to finding only relatively close homologs. On the other hand, it has been reported that miRNA genes are more conserved in the secondary structure rather than in primary sequences. Therefore, secondary structural features should be more fully exploited in the homologue search for new miRNA genes. RESULTS: In this paper, we present a novel genome-wide computational approach to detect miRNAs in animals based on both sequence and structure alignment. Experiments show this approach has higher sensitivity and comparable specificity than other reported homologue searching methods. We applied this method on Anopheles gambiae and detected 59 new miRNA genes. AVAILABILITY: This program is available at http://bioinfo.au.tsinghua.edu.cn/miralign. SUPPLEMENTARY INFORMATION: Supplementary information is available at http://bioinfo.au.tsinghua.edu.cn/miralign/supplementary.htm.

The paper proposes a new human-computer interface for telerobotic applications in remote unstructured environments. The interface is based upon Augmented Reality (AR), concepts and integrates three basic aspects: (1) interactive perception, allowing on-demand generation of virtual worksite views for improved examination by the operator; (2) enhanced task representation, involving the 3D embedding of a virtual replica of the equipment into worksite views in order to allow better task planning and rehearsal; (3) haptic feedback to provide the operator with a realistic sense of equipment-worksite mechanical interactions during the planning phase. Experiments with this AR interface in the context of a mining drilling task are also described.

We present an easy, practical and efficient full body cloning methodology. This system utilizes photos taken from the front, side and back of a person in any given imaging environment without requiring a special background or a controlled illuminating condition. A seamless generic body specified in the VRML H‐Anim 1.1 format is used to generate an individualized virtual human. The system is composed of two major components: face‐cloning and body‐cloning. The face‐cloning component uses feature points on front and side images and then applies DFFD for shape modification. Next a fully automatic seamless texture mapping is generated for 360° coloring on a 3D polygonal model. The body‐cloning component has two steps: (i feature points specification, which enables automatic silhouette detection in an arbitrary background (ii two‐stage body modification by using feature points and body silhouette respectively. The final integrated human model has photo‐realistic animatable face, hands, feet and body. The result can be visualized in any VRML compliant browser.

We present computer vision techniques for building dressed human models using images. We develop an algorithm for three-dimensional body reconstruction and texture mapping using contour, stereo, and texture information from several images and deformable superquadrics as the model parts. We demonstrate a novel vision technique for analysis of cloth draping behavior. This technique allows for estimation of cloth model parameters, such as bending properties, but can also be used to estimate the contact points between the body and clothing in the range data of dressed humans. Combined with our body reconstruction algorithm and additional constraints on the articulation model, the detection of the garment-body contact points allows construction of a dressed human model in which even the geometry that was covered by clothing in the available data is reasonably well estimated.

In this paper we present a method for 3-D reconstruction of human bodies with application in CAD systems for garment design. The reconstruction scheme uses image information from several arbitrary views and deformable superquadrics as the models of the body parts. Two visual cues are used: occluding contours and stereo (possibly aided by projected patterns). Our preliminary experiments show that the reconstruction is more complete than in purely stereo or structured light based methods and more precise than the reconstruction from occluding contours only. From the reconstructed human body, the body measurements can be taken automatically, and used in garment design. We give an example of draping of virtual garment over the photo-realistic 3D model of the imaged human. One can easily envision the use of the described algorithms in the development of custom-fit garment retail software over the Internet, which would include the possibility of trying the garment on in virtual reality.