bluuebunny/crossref_metadata_2025_split huggingface dataset:
Standardizing marine habitat modelling practices to enhance inter-comparability across biological observations
Authors: Schickele Alexandre , Clerc Corentin , Benedetti Fabio , De Angelis Daniele , Hofmann-Elizondo Urs , Münnich Matthias , Irisson Jean-Olivier , Vogt Meike 
Published: September 2024

ABSTRACTIn recent years, the volume of accessible marine pelagic observations has increased exponentially and now incorporates a wealth of new data types, including information derived from metagenomics and quantitative imaging. This calls for standardized modelling protocol across taxonomically harmonized observations, to better predict biogeographic patterns in space and time, and thus investigate marine ecosystem structure and functioning on a macroecological scale. In this context, we introduce CEPHALOPOD (Comprehensive Ensemble Pipeline for Habitat modelling Across Large-scale Ocean Pelagic Observation Datasets), a standardized and flexible framework to perform multi-species marine habitat modelling across data types and data sources. We built this new framework on observational data from federating initiatives such as AtlantECO, OBIS, GBIF, associated with already existing statistical and machine learning methods that enable to extract and model information from heterogeneous, scarce, and biased field observations. Here, we first document our statistical ensemble modelling approach and then assess its strength and limitations with a virtual ecologist approach. We show how our framework performs in reproducing a range of distributions from biased field samples. Then, we illustrate its performance and comparability across data types by investigating the global diversity patterns of coccolithophores from both abundance and metagenomic data. Our modelling framework serves as a foundation for the consistent generation of Essential Biodiversity and Ocean Variables (EBVs and EOVs) and carries the potential to significantly advance our comprehension of biodiversity and marine ecosystems functioning. Finally, it provides an unprecedented opportunity to foster collaborations in the field of marine science, sustainable ecological practices, and, ultimately, contribute to the preservation of global marine biodiversity.
Ongoing activation of visual cortex and superior colliculus in therd10mouse model of retinitis pigmentosa
Authors: Rüland Thomas , Doerenkamp Kerstin , Graff Peter Severin , Wetz Sophie , Jain Anoushka , Nabbefeld Gerion , Gehlen Jana , Gilissen Sara RJ , Arckens Lutgarde , Musall Simon , Müller Frank , Kampa Björn M. 
Published: September 2024

AbstractEfforts in vision restoration have been focused on a condition called Retinitis Pigmentosa, where photoreceptors in the retina degenerate while the rest of the visual pathway remain mostly intact. Retinal implants that replace the phototransduction process by stimulating retinal ganglion cells have shown promising but limited results in patients so far. Apart from technical limitations, cross-modal plasticity of visual areas might contribute to this problem. We therefore investigated if the primary visual cortex (V1) of therd10mouse model for retinal degeneration became more sensitive to auditory or tactile sensory inputs, potentially hindering retinal stimulation. After reaching complete blindness confirmed by the lack of optomotor responses, activity in visual cortex and superior colliculus (SC) was recorded using Neuropixels probes. While we could not find any significant differences in tactile or auditory responses compared to wildtype mice, the local field potential revealed distinct oscillatory events (0.5 – 6 Hz) in V1 and SC resembling previously observed aberrant activity in the retina of rd10 mice. Further absence of cross-modal plasticity was confirmed by a lacking increase inzif268expression in V1 after tactile stimulation. We therefore propose that aberrant retinal activity is transmitted to higher visual areas where it prevents cross-modal changes.
ERVcancer: a web resource designed for querying activation of human endogenous retroviruses across major cancer types
Authors: Lei Xiaoyun , Mao Song , Li Yinshuang , Huang Shi , Li Jinchen , Du Wei , Kuang Chunmei , Yuan Kai 
Published: September 2024

AbstractHuman endogenous retroviruses (HERVs) compose approximately 8% of the human genome, co-opted into the dynamic regulatory network of cellular potency in early embryonic development. In recent studies, resurgent HERVs’ transcriptional activity has been frequently observed in many types of human cancers, suggesting their potential functions in the occurrence and progression of malignancy. However, a web resource dedicated to querying the relationship between activation of HERVs and cancer development is lacking. Here, we have constructed a database to explore the sequence information, expression profiles, survival prognosis, and genetic interactions of HERVs in diverse cancer types. Our database currently incorporates RNA sequencing (RNA-seq) data of 580 HERVs across 16246 samples, comprising 151 early embryonic data from the Gene Expression Omnibus (GEO) database, 8051 human adult tissues’ data from the Genotype-Tissue Expression (GTEx) project, 932 cancer cell lines’ data from the Cancer Cell Line Encyclopedia (CCLE) project, 6478 tumoral and 634 normal tissue samples’ data from The Cancer Genome Atlas (TCGA) project. The primary goal is to provide an easily accessible and user-friendly database for professionals in the fields of bioinformatics, pathology, pharmacology, and related areas, enabling them to efficiently screen the activity of HERVs of interest in normal and cancerous tissues and evaluate the clinical relevance. The ERVcancer database is available athttp://kyuanlab.com/ervcancer/.
Sociability genetically separable from social hierarchy in amniotes
Authors: Lin Xin , Dai Guangyi , Zhou Sumei , Li Yangyang , Pan Yi-Hsuan , Li Haipeng 
Published: September 2024

SummaryThe emergence of social structure and organization is essential for the evolution of amniotes, including human beings. Sociability and social hierarchy are two key features to form a social group. However, it remains unknown whether sociability and social hierarchy are genetically separable. In this study, we examined the social hierarchy, social and social novelty preference of PAS1 (placental-accelerated sequence 1) knock-out and knock-in mice. PAS1 is a social enhancer that modulates social hierarchy. We found that PAS1 knock-out mice lack social hierarchy while wallaby/chicken PAS1 knock-in mice establish stable social ranks. Moreover, social and social novelty preference was observed in all PAS1-mutated mice. PAS1 knock-in mice have stronger preference to interact with other mice than wild-type mice (C57BL/6). No aggressive alteration was found in PAS1-mutated mice. Overall, our results showed that PAS1 is an indispensable regulatory element in the formation of social hierarchy while PAS1 regulates one of pathways modulating sociability. Therefore, sociability is genetically separable from social hierarchy in amniotes, providing insights into how social structure and organization evolved.HighlightsPAS1, a social enhancer, is indispensable for amniotes to establish social hierarchy.PAS1 modulates sociability in amniotes.Sociability and social hierarchy are regulated differently in amniotes.The frequency of PAS1 allele was not rare when social hierarchy first appeared.
β-Glucan Reprograms Neutrophils to Induce Disease Tolerance Against Influenza A Virus
Authors: Khan Nargis , Chevre Raphael , Sun Sarah , Sadeghi Mina , Pernet Erwan , Herrero Andrea , Grant Alexander , Downey Jeffrey , Barreiro Luis B. , Yipp Bryan G , Soehnlein Oliver , Divangahi Maziar 
Published: September 2024

ABSTRACTDisease tolerance is an evolutionarily conserved host defence strategy that preserves tissue integrity and physiology without affecting pathogen load. Unlike host resistance, the mechanisms underlying disease tolerance remain poorly understood. In the present study, we investigated whether an adjuvant (β-glucan) can reprogram innate immunity to provide protection against Influenza A virus (IAV) infection. Here we observe that β-glucan treatment reduced the morbidity and mortality against IAV infection, independent of host resistance (viral load). Increased survival of β-glucan treated mice against IAV is associated with the accumulation of neutrophils via RoRγt+T cells in the lung tissue. Using gain-and-loss-of-function approaches, we demonstrate that β- glucan reprogrammed neutrophils are essential for promoting disease tolerance, limiting pulmonary tissue damage, and enhancing survival against IAV infection. β-glucan treatment promotes granulopoiesis in a type 1 interferon-dependent manner that leads to the generation of a unique subset of neutrophils, which are less mature with higher mitochondrial mass utilizing mitochondrial oxidative (OXPHOS) metabolism. Collectively, our data indicate that β-glucan reprograms hematopoietic stem cells (HSCs) to generate neutrophils with a novel “regulatory” function, which is required for promoting disease tolerance and maintaining lung tissue integrity against viral infection.
Mesoscale Explorer - Visual Exploration of Large-Scale Molecular Models
Authors: Rose Alexander , Sehnal David , Goodsell David S. , Autin Ludovic 
Published: September 2024

AbstractThe advent of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), coupled with computational modeling, has enabled the creation of integrative 3D models of viruses, bacteria, and cellular organelles. These models, composed of thousands of macromolecules and billions of atoms, have historically posed significant challenges for manipulation and visualization without specialized molecular graphics tools and hardware. With the recent advancements in GPU rendering power and web browser capabilities, it is now feasible to render interactively large molecular scenes directly on the web. In this work, we introduceMesoscale Explorer, a web application built using theMol*framework, dedicated to the visualization of large-scale molecular models ranging from viruses to cell organelles.Mesoscale Explorerprovides unprecedented access and insight into the molecular fabric of life, enhancing perception, streamlining exploration, and simplifying visualization of diverse data types, showcasing the intricate details of these models with unparalleled clarity.Statement:Mesoscale Explorerleverages advanced GPU rendering and web technologies to facilitate and democratize the interactive 3D visualization of large-scale molecular models from viruses to cellular organelles composed of millions of atoms.Mesoscale Explorerenables broader exploration and deeper understanding of the complex structure of these large molecular landscapes.
Reversible and effective cell cycle synchronization method for studying stage-specific investigations
Authors: Chen Yu-Lin , Reddy Syon , Suzuki Aussie 
Published: September 2024

AbstractThe cell cycle is a crucial process for cell proliferation, differentiation, and development. Numerous genes and proteins play pivotal roles at specific cell cycle stages to regulate these events precisely. Studying the stage-specific functions of the cell cycle requires accumulating cell populations at the desired cell cycle stage. Cell synchronization, achieved through the use of cell cycle kinase and protein inhibitors, is often employed for this purpose. However, suboptimal concentrations of these inhibitors can result in reduced efficiency, irreversibility, and undesirable cell cycle defects. In this study, we have optimized effective and reversible techniques to synchronize the cell cycle at each stage in human RPE1 cells, utilizing both fixed high-precision cell cycle identification methods and high-temporal live-cell imaging. These reproducible synchronization methods are invaluable for investigating the regulatory mechanisms specific to each cell cycle stage.
KegAlign: Optimizing pairwise alignments with diagonal partitioning
Authors: Gulhan A. Burak , Burhans Richard , Harris Robert , Kandemir Mahmut , Haeussler Maximilian , Nekrutenko Anton 
Published: September 2024

AbstractOur ability to generate sequencing data and assemble it into high quality complete genomes has rapidly advanced in recent years. These data promise to advance our understanding of organismal biology and answer longstanding evolutionary questions. Multiple genome alignment is a key tool in this quest. It is also the area which is lagging: today we can generate genomes faster than we can construct and update multiple alignments containing them. The bottleneck is in considerable computational time required to generate accurate pairwise alignments between divergent genomes, an unavoidable precursor to multiple alignments. This step is typically performed with lastZ, a very sensitive and yet equally slow tool. Here we describe an optimized GPU-enabled pairwise aligner KegAlign. It incorporates a new parallelization strategy, diagonal partitioning, with the latest features of modern GPUs. With KegAlign a typical human/mouse alignment can be computed in under 6 hours on a machine containing a single NVidia A100 GPU and 80 CPU cores without the need for any pre-partitioning of input sequences: a ∼150× improvement over lastZ. While other pairwise aligners can complete this task in a fraction of that time, none achieves the sensitivity of KegAlign’s main alignment engine, lastZ, and thus may not be suitable for comparing divergent genomes. In addition to providing the source code and a Conda package for KegAlign we also provide a Galaxy workflow that can be readily used by anyone.
Histamine interferes with the early visual processing in mice
Authors: Tripodi Matteo , Asari Hiroki 
Published: September 2024

AbstractSensory processing is dynamically modulated by an animal’s behavior and internal states. Growing evidence suggests that such modulation starts from early stages, even at the retina, but the underlying mechanisms remain elusive. Combining pharmacological and chemogenetic tools with single-unit extracellular recordings in awake head-fixed mice, here we identified that the visual responses of retinal ganglion cells and the lateral geniculate nucleus were both made weaker and slower by histaminergic projections from the tuberomammillary nucleus of the posterior hypothalamus. The observed changes in the visual responses were, however, not directly linked with histaminergic modulation of pupil dynamics or locomotion behavior. Our computational modelling analysis instead suggests neuronal circuit effects, such as gain modulation via the histamine H1 receptors in the retina. As nocturnal animals, facilitation of visual processing at low histamine level is ethologically beneficial for mice to respond faster to visual threats when animals are less active during daytime.
Discovery of interpretable patterning rules by integrating mechanistic modeling and deep learning
Authors: Lu Jia , Luo Nan , Liu Sizhe , Sahu Kinshuk , Maddamsetti Rohan , Baig Yasa , You Lingchong 
Published: September 2024

AbstractPredictive programming of self-organized pattern formation using living cells is challenging in major part due to the difficulty in navigating through the high-dimensional design space effectively. The emergence and characteristics of patterns are highly sensitive to both system and environmental parameters. Often, the optimal conditions able to generate patterns represent a small fraction of the possible design space. Furthermore, the experimental generation and quantification of patterns is typically labor intensive and low throughput, making it impractical to optimize pattern formation solely based on trials and errors. To this end, simulations using a well-formulated mechanistic model can facilitate the identification of optimal experimental conditions for pattern formation. However, even a moderately complex system can make these simulations computationally prohibitive when applied to a large parameter space. In this study, we demonstrate how integrating mechanistic modeling with machine learning can significantly accelerate the exploration of design space for patterning circuits and aid in deriving human-interpretable design rules. We apply this strategy to program self-organized ring patterns inPseudomonas aeruginosausing a synthetic gene circuit. Our approach involved training a neural network with simulated data to predict pattern formation 10 million times faster than the mechanistic model. This neural network was then used to predict pattern formation across a vast array of parameter combinations, far exceeding the size of the training dataset and what was computationally feasible using the mechanistic model alone. By doing so, we identified many parameter combinations able to generate desirable patterns, which still represent an extremely small fraction of explored parametric space. We next used the mechanistic model to validate top candidates and identify coarse-grained rules for patterning. We experimentally demonstrated the generation and control of patterning guided by the learned rules. Our work highlights the effectiveness in integrating mechanistic modeling and machine learning for rational engineering of complex dynamics in living cells.
Scattering-Based Super-Resolution Optical Fluctuation Imaging
Authors: Yudovich Shimon , Posnjak Gregor , Shani Lior , Teblum Eti , Liedl Tim , Enderlein Jörg , Weiss Shimon 
Published: September 2024

AbstractSuper-resolution optical imaging has become a prominent tool in life and material sciences, allowing one to decipher structures at increasingly greater spatial detail. Among the utilized techniques in this field, super-resolution optical fluctuation imaging (SOFI) has proved to be a valuable approach. A major advantage of SOFI is its less restrictive requirements for generating super-resolved images of neighboring nanostructures or molecules, as it only assumes that the detected fluctuating light from neighboring emitters is statistically uncorrelated, but not necessarily separated in time. While most optical super-resolution microscopies depend on signals obtained from fluorescence, they are limited by photobleaching and phototoxicity. An alternative source for optical signals can be acquired by detecting the light scattered from molecules or nanoparticles. However, the application of coherent scattering-based imaging modalities for super-resolution imaging has been considerably limited compared to fluorescence-based modalities. Here, we develop scattering-based super-resolution optical fluctuation imaging (sSOFI), where we utilize the rotation of anisotropic particles as a source of fluctuating optical signals. We discuss the differences in the application of SOFI algorithms for coherent and incoherent imaging modalities, and utilize interference microscopy to demonstrate super-resolution imaging of rotating nanoparticle dimers. We present a theoretical analysis of the relevant model systems, and discuss the possible effects of cusp artifacts and electrodynamic coupling between nearby nano-scatterers. Finally, we apply sSOFI as a label-free novelty filter that highlights regions with higher activity of biomolecules and demonstrate its use by imaging membrane protrusions of live cells. Overall, the development of optical super-resolution approaches for coherent scattering-based imaging modalities, as described here, could potentially allow for the investigation of biological processes at temporal resolutions and acquisition durations previously inaccessible in fluorescence-based imaging.
Domestic freezer storage: a solution for preserving stool microbiota integrity for at-home collection
Authors: Momo Cabrera Paula , Bokulich Nicholas A. , Zimmermann Petra 
Published: September 2024

AbstractThe gut microbiome is crucial for host health. Early childhood is a critical period for the development of a healthy gut microbiome, but it is particularly sensitive to external influences. Recent research has focused on using advanced techniques like shotgun metagenome sequencing to identify key microbial signatures and disruptions linked to disease. For accurate microbiome analysis, samples need to be collected and stored under specific conditions to preserve microbial integrity and composition, with -80°C storage considered the gold standard for stabilization.This study investigates the effect of domestic freezer storage on the microbial composition of stool samples from 20 children under 4 years with the use of shotgun metagenomic sequencing. Fresh stool samples were aliquoted into sterile tubes, with one aliquot stored at 4°C and analyzed within 24 hours, while others were frozen in domestic freezers (below -18°C) and analyzed after 1 week, 2 months, and 6 months. Assessments of contig assembly quality, microbial diversity, and antimicrobial resistance genes revealed no significant degradation or variation in microbial composition.ImportanceMost previous studies on sample storage have used amplicon sequencing, which limits relevance to metagenome sequencing, in which contig quality and functional gene detection are additional concerns. Moreover, the effects of domestic freezer storage for at-home stool collection on microbiome profiles, contig quality, and antimicrobial resistance gene profiles have not been tested previously.Our findings suggest that stool samples stored in domestic freezers for up to six months maintain the integrity of metagenomic data. These findings indicate that domestic freezer storage does not compromise the integrity or reproducibility of metagenomic data, offering a reliable and accessible alternative for temporary sample storage. This approach enhances the feasibility of large-scale at-home stool collection and citizen science projects, even those focused on the more easily perturbed early life microbiome. This advancement enables more inclusive research into the gut microbiome, enhancing our understanding of its role in human health.
Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization
Authors: Sendi Mohammad S. E. , Cole Eric R. , Piallat Brigitte , Ellis Charles A. , Eggers Thomas E. , Laxpati Nealen G. , Mahmoudi Babak , Gutekunst Claire-Anne , Devergnas Annaelle , Mayberg Helen , Gross Robert E. , Calhoun Vince D. 
Published: September 2024

AbstractBrain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments. We validated this framework through three experiments: (1) in silico modeling with synthetic data from a Parkinson’s disease model, (2) in silico modeling with real data from a non-human primate, and (3) in vivo modeling with a real-time rat optogenetic stimulation experiment. In each experiment, we compared AL models to RS models, using various query strategies and stimulation parameters (amplitude, frequency, pulse width). AL models consistently outperformed RS models, achieving lower error on unseen test data in silico (p<0.0056,N=1,000) and in vivo (p=0.0036,N=20). This approach represents a significant advancement in brain stimulation, potentially improving both research and clinical applications by making them more efficient and effective. Our findings suggest that AL can substantially reduce the cost and time required for developing personalized brain stimulation therapies, paving the way for more effective and accessible treatments for brain disorders.