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We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challenges not encountered in current video segmentation datasets. Specifically, we need to ensure both short- and long-term consistency of pixel-level annotations as objects undergo transformative interactions, e.g. an onion is peeled, diced and cooked - where we aim to obtain accurate pixel-level annotations of the peel, onion pieces, chopping board, knife, pan, as well as the acting hands. VISOR introduces an annotation pipeline, AI-powered in parts, for scalability and quality. In total, we publicly release 272K manual semantic masks of 257 object classes, 9.9M interpolated dense masks, 67K hand-object relations, covering 36 hours of 179 untrimmed videos. Along with the annotations, we introduce three challenges in video object segmentation, interaction understanding and long-term reasoning. For data, code and leaderboards: http://epic-kitchens.github.io/VISOR 

Abstract SummaryNetwork biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementationNot applicable. 

Abstract Higher-order genome organization and its variation in different cellular conditions remain poorly understood. Recent high-coverage genome-wide chromatin interaction mapping using Hi-C has revealed spatial segregation of chromosomes in the human genome into distinct subcompartments. However, subcompartment annotation, which requires Hi-C data with high sequencing coverage, is currently only available in the GM12878 cell line, making it impractical to compare subcompartment patterns across cell types. Here we develop a computational approach, SNIPER (Subcompartment iNference using Imputed Probabilistic ExpRessions), based on denoising autoencoder and multilayer perceptron classifier to infer subcompartments using typical Hi-C datasets with moderate coverage. SNIPER accurately reveals subcompartments using moderate coverage Hi-C datasets and outperforms an existing method that uses epigenomic features in GM12878. We apply SNIPER to eight additional cell lines and find that chromosomal regions with conserved and cell-type specific subcompartment annotations have different patterns of functional genomic features. SNIPER enables the identification of subcompartments without high-coverage Hi-C data and provides insights into the function and mechanisms of spatial genome organization variation across cell types. 

ABSTRACT We present haplotype-resolved reference genomes and comparative analyses of six ape species, namely: chimpanzee, bonobo, gorilla, Bornean orangutan, Sumatran orangutan, and siamang. We achieve chromosome-level contiguity with unparalleled sequence accuracy (<1 error in 500,000 base pairs), completely sequencing 215 gapless chromosomes telomere-to-telomere. We resolve challenging regions, such as the major histocompatibility complex and immunoglobulin loci, providing more in-depth evolutionary insights. Comparative analyses, including human, allow us to investigate the evolution and diversity of regions previously uncharacterized or incompletely studied without bias from mapping to the human reference. This includes newly minted gene families within lineage-specific segmental duplications, centromeric DNA, acrocentric chromosomes, and subterminal heterochromatin. This resource should serve as a definitive baseline for all future evolutionary studies of humans and our closest living ape relatives. 

As the continuation of Fungal Diversity Notes series, the current paper is the 16th contribution to this series. A total of 103 taxa from seven classes in Ascomycota and Basidiomycota are included here. Of these 101 taxa, four new genera, 89 new species, one new combination, one new name and six new records are described in detail along with information of hosts and geographic distributions. The four genera newly introduced are Ascoglobospora, Atheliella, Rufoboletus and Tenuimyces. Newly described species are Akanthomyces xixiuensis, Agaricus agharkarii, A. albostipitatus, Amphisphaeria guttulata, Ascoglobospora marina, Astrothelium peudostraminicolor, Athelia naviculispora, Atheliella conifericola, Athelopsis subglaucina, Aureoboletus minimus, A. nanlingensis, Autophagomyces incertus, Beltrania liliiferae, Beltraniella jiangxiensis, Botryobasidium coniferarum, Calocybella sribuabanensis, Calonarius caesiofulvus, C. nobilis, C. pacificus, C. pulcher, C. subcorrosus, Cortinarius flaureifolius, C. floridaensis, C. subiodes, Crustomyces juniperi, C. scytinostromoides, Cystostereum subsirmaurense, Dimorphomyces seemanii, Fulvoderma microporum, Ginnsia laricicola, Gomphus zamorinorum, Halobyssothecium sichuanense, Hemileccinum duriusculum, Henningsomyces hengduanensis, Hygronarius californicus, Kneiffiella pseudoabdita, K. pseudoalutacea, Laboulbenia bifida, L. tschirnhausii, L. tuberculata, Lambertella dipterocarpacearum, Laxitextum subrubrum, Lyomyces austro-occidentalis, L. crystallina, L. guttulatus, L. niveus, L. tasmanicus, Marasmius centrocinnamomeus, M. ferrugineodiscus, Megasporoporia tamilnaduensis, Meruliopsis crystallina, Metuloidea imbricata, Moniliophthora atlantica, Mystinarius ochrobrunneus, Neomycoleptodiscus alishanense, Nigrograna kunmingensis, Paracremonium aquaticum, Parahelicomyces dictyosporus, Peniophorella sidera, P. subreticulata, Phlegmacium fennicum, P. pallidocaeruleum, Pholiota betulicola, P. subcaespitosa, Pleurotheciella hyalospora, Pleurothecium aseptatum, Resupinatus porrigens, Russula chlorina, R. chrysea, R. cruenta, R. haematina, R. luteocarpa, R. sanguinolenta, Synnemellisia punensis, Tenuimyces bambusicola, Thaxterogaster americanoporphyropus, T. obscurovibratilis, Thermoascus endophyticus, Trechispora alba, T. perminispora, T. subfarinacea, T. tuberculata, Tremella sairandhriana, Tropicoporus natarajaniae, T. subramaniae, Usnea kriegeriana, Wolfiporiella macrospora and Xylodon muchuanensis. Rufoboletus hainanensis is newly transferred from Butyriboletus, while a new name Russula albocarpa is proposed for Russula leucocarpa G.J. Li & Chun Y. Deng an illegitimate later homonym of Russula leucocarpa (T. Lebel) T. Lebel. The new geographic distribution regions are recorded for Agaricus bambusetorum, Bipolaris heliconiae, Crinipellis trichialis, Leucocoprinus cretaceus, Halobyssothecium cangshanense and Parasola setulosa. Corresponding to morphological characters, phylogenetic evidence is also utilized to place the above-mentioned taxa in appropriate taxonomic positions. The current morphological and phylogenetic data is helpful for further clarification of species diversity and exploration of evolutionary relationships in the related fungal groups. 

Abstract MotivationNeural networks have been widely used to analyze high-throughput microscopy images. However, the performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Highly relevant to the goal of automated cell phenotyping from microscopy image data is rotation invariance. Here we consider the application of two schemes for encoding rotation equivariance and invariance in a convolutional neural network, namely, the group-equivariant CNN (G-CNN), and a new architecture with simple, efficient conic convolution, for classifying microscopy images. We additionally integrate the 2D-discrete-Fourier transform (2D-DFT) as an effective means for encoding global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). ResultsWe evaluated the efficacy of CFNet and G-CNN as compared to a standard CNN for several different image classification tasks, including simulated and real microscopy images of subcellular protein localization, and demonstrated improved performance. We believe CFNet has the potential to improve many high-throughput microscopy image analysis applications. Availability and implementationSource code of CFNet is available at: https://github.com/bchidest/CFNet. Supplementary informationSupplementary data are available at Bioinformatics online.