The evolution of Artificial Intelligence (AI) has reached a critical point, where agentic AI systems demonstrate strong capabilities in goal formulation and planning but remain difficult to deploy in real-world settings due to their limited grounding in physical environments. These limitations arise from the challenges of partial observability, actuation uncertainty, and strict resource constraints that characterize the physical world. This survey argues that the Artificial Intelligence of Things (AIoT) provides the necessary foundation to embed agentic intelligence into such environments by enabling continuous interaction between sensing, reasoning, and action. We analyze the synergy between goal-driven agentic AI and distributed AIoT infrastructures and present a unified taxonomy of AIoT-enabled agentic architectures, highlighting trade-offs across centralized, edge-native, and hybrid deployment models. The survey further examines key enabling technologies, including edge intelligence, semantic communication, digital twins, and trust mechanisms, and discusses how they integrate into cognitive control loops. Through representative applications in smart cities, industrial automation, healthcare, and energy systems, we show how this convergence moves automation beyond rule-based behavior toward context-aware autonomy. Finally, we identify open challenges related to long-horizon safety, resource-aware intelligence, and ethical governance, and outline research directions toward robust, trustworthy, and socially embedded autonomous systems.

Abstract Single-molecule Real-time Isoform Sequencing (Iso-seq) of transcriptomes by PacBio can generate very long and accurate reads, thus providing an ideal platform for full-length transcriptome analysis.A number of computational tools have been developed for long-read sequencing data. However, integrated computational frameworks for analyzing Iso-seq data are still lacking. We present a Toolkit for Analyzing full-length GEne Transcripts (TAGET) for Iso-seq. Starting from polished high-quality transcripts (circular consensus sequences or CCSs), TAGET first aligns transcripts to the reference genome by integrating alignment results from long and short reads and further improves splice site predictions using a Convolutional Neural Network (CNN). TAGET then annotates transcripts by comparing with reference isoform databases and classifies transcripts into seven classes. Finally, TAGET estimates gene or isoform expressions and performs differential expression gene (DEG) and differential isoform usage (DIU) analysis. We evaluate the performance of TAGET using a public Iso-seq dataset and newly sequenced Iso-seq datasets from tumor patients. TAGET gives significantly more precise novel splice site prediction and enables more accurate novel isoform and gene fusion discoveries, as validated by experimental validations and comparisons with RNA-seq data. We identify and experimentally validate a differential isoform usage gene ECM1, and further show that its isoform ECM1b may be a tumor-suppressor in laryngocarcinoma. Our results demonstrate that TAGET provides a valuable computational toolkit and can be applied to many full-length transcriptome studies.

A modality gap exists between infrared and visible images, presenting challenges for image fusion. Despite the modality heterogeneity, both types of images inherently capture the same scene, suggesting the presence of common information. Effectively extracting shared features while distinguishing modality-specific ones is pivotal for bridging this gap and achieving superior fusion outcomes. To address this, we propose the S hared-S pecific F usion Net work (S2F-Net). The S2F-Net introduces a three-branch feature extractor, which retains two branches for extracting features from each modality, innovatively creating an additional branch dedicated to facilitating the separation of shared features from modality-specific ones. This facilitates guiding the fusion of cross-modal information to generate efficient fusion features, ensuring the effective integration of complementary information from different modalities. To achieve feature fusion and image reconstruction, we propose two fusion modules: the Cross-modality Attention-Guided Fusion Module (CAGFM) and the Multi-Level Fusion Module (MLFM). The former utilizes shared and specific features by employing cross-modality channel attention, enabling effective integration of information across modalities. The latter facilitates feature interaction across different levels. Additionally, to effectively disentangle shared and specific features, we introduce the shared-specific learning module. Extensive experiments conducted on open-source datasets validate the superior performance of our proposed method.

MOTIVATION: Whole-genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias. RESULTS: We show that the replication bias is widespread using ∼200 bacterial WGS data. We develop CNV-BAC (CNV-Bacteria) that can properly normalize the replication bias and other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms. AVAILABILITY AND IMPLEMENTATION: CNV-BAC is available at https://github.com/XiDsLab/CNV-BAC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Structural variation (SV) is an important class of genomic variations in human genomes. A number of SV detection algorithms based on high-throughput sequencing data have been developed, but they have various and often limited level of sensitivity, specificity and breakpoint resolution. Furthermore, since overlaps between predictions of algorithms are low, SV detection based on multiple algorithms, an often-used strategy in real applications, has little effect in improving the performance of SV detection. RESULTS: We develop a computational tool called SVmine for further mining of SV predictions from multiple tools to improve the performance of SV detection. SVmine refines SV predictions by performing local realignment and assess quality of SV predictions based on likelihoods of the realignments. The local realignment is performed against a set of sequences constructed from the reference sequence near the candidate SV by incorporating nearby single nucleotide variations, insertions and deletions. A sandwich alignment algorithm is further used to improve the accuracy of breakpoint positions. We evaluate SVmine on a set of simulated data and real data and find that SVmine has superior sensitivity, specificity and breakpoint estimation accuracy. We also find that SVmine can significantly improve overlaps of SV predictions from other algorithms. AVAILABILITY AND IMPLEMENTATION: SVmine is available at https://github.com/xyc0813/SVmine. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Structural variations (SVs) are wide-spread in human genomes and may have important implications in disease-related and evolutionary studies. High-throughput sequencing (HTS) has become a major platform for SV detection and simulation serves as a powerful and cost-effective approach for benchmarking SV detection algorithms. Accurate performance assessment by simulation requires the simulator capable of generating simulation data with all important features of real data, such GC biases in HTS data and various complexities in tumor data. However, no available package has systematically addressed all issues in data simulation for SV benchmarking. RESULTS: Pysim-sv is a package for simulating HTS data to evaluate performance of SV detection algorithms. Pysim-sv can introduce a wide spectrum of germline and somatic genomic variations. The package contains functionalities to simulate tumor data with aneuploidy and heterogeneous subclones, which is very useful in assessing algorithm performance in tumor studies. Furthermore, Pysim-sv can introduce GC-bias, the most important and prevalent bias in HTS data, in the simulated HTS data. CONCLUSIONS: Pysim-sv provides an unbiased toolkit for evaluating HTS-based SV detection algorithms.