bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genomic Diversity Illuminates the Environmental Adaptation ofDrosophila suzukii
Authors: Feng Siyuan , DeGrey Samuel P. , Guédot Christelle , Schoville Sean D. , Pool John E. 
Published: July 2023

AbstractBiological invasions carry substantial practical and scientific importance, and represent natural evolutionary experiments on contemporary timescales. Here, we investigated genomic diversity and environmental adaptation of the crop pestDrosophila suzukiiusing whole-genome sequencing data and environmental metadata for 29 population samples from its native and invasive range. Through a multifaceted analysis of this population genomic data, we increase our understanding of theD. suzukiigenome, its diversity and its evolution, and we identify an appropriate genotype-environment association pipeline for our data set. Using this approach, we detect genetic signals of local adaptation associated with nine distinct environmental factors related to altitude, wind speed, precipitation, temperature, and human land use. We uncover unique functional signatures for each environmental variable, such as a prevalence of cuticular genes associated with annual precipitation. We also infer biological commonalities in the adaptation to diverse selective pressures, particularly in terms of the apparent contribution of nervous system evolution to enriched processes (ranging from neuron development to circadian behavior) and to top genes associated with all nine environmental variables. Our findings therefore depict a finer-scale adaptive landscape underlying the rapid invasion success of this agronomically important species.
Host specificity shapes fish viromes across lakes on an isolated remote island
Authors: Grimwood Rebecca M. , Fortune-Kelly Grace , Holmes Edward C. , Ingram Travis , Geoghegan Jemma L. 
Published: July 2023

AbstractFish viromes often provide insights into the origin and evolution of viruses affecting tetrapods, including those associated with significant human diseases. However, despite fish being the most diverse vertebrate group, their viruses are still understudied. We investigated the viromes of fish on Chatham Island (Rēkohu), a geographically isolated island housing 9% of New Zealand’s threatened endemic fish species. Using metatranscriptomics, we analyzed samples from seven host species across 16 waterbodies. We identified 19 fish viruses, including 16 novel species, expanding families such as theCoronaviridae, Hantaviridae, Poxviridae, and the recently proposedTosoviridaefamily. Surprisingly, virome composition was not influenced by ecological factors measured, and smelt (Retropinna retropinna) viromes were consistent across lakes despite differences in host life history, seawater influence, and community richness. Overall, fish viromes across Rēkohu were highly diverse and revealed a long history of codivergence between host and virus despite their unique and geographically isolated ecosystem.
Reproducing Left Ventricular Twist by Mimicking Myocardial Fiber Orientation Using 3D Bioprinting-assisted Tissue Assembly
Authors: Hwang Dong Gyu , Choi Hwanyong , Yong Uijung , Kim Donghwan , Kang Wonok , Park Sung-Min , Jang Jinah 
Published: July 2023

AbstractBackgroundLeft ventricular twist, an opposite rotation of the apex and base, is caused by myocardial fiber orientation, a unique structural feature of the myocardium, and contributes to the effective ejection fraction of the native heart. Reproducing this structural-functional relationship in anin vitroheart model remains challenging due to the lack of synchrony between layers when mimicking the fiber orientations of each layer.MethodsWe employed a hierarchical approach for creating multilayered and multiaxial fibers in a chamber-like structure, as follows: 3D bioprinting-assisted tissue assembly, fabrication of uniaxially aligned engineered heart tissue as a building block, and assembly of them to create a myocardial fiber orientation in a chamber-like structure.ResultThe EHT module confirmed uniaxial alignment and cardiac functions such as contractility and electrophysiological properties. By fabricating the assembly platform by 3D bioprinting, it is possible to guide building blocks in various directions as intended, confirming the versatility of this method. The assembly platform allows structural and functional synchrony of assembled tissues while controlling and maintaining predefined cellular alignment. Furthermore, various shapes and sizes of EHT modules and assembly platform were fabricated for mimicking myocardial fiber orientation in a chamber-like structure. The resulting structure exhibited three layers and three orientations representing myocardial fiber orientation. Moreover, the left ventricular twist was confirmed by measuring basal and apical rotations.ConclusionsRecapitulation of the microscale structure of the left ventricle enabled the identification of information not discernible from the existing macroscale structure. This understanding of the structure-function relationship of the heart can provide insights into the mechanisms underlying cardiac structure, function, and related diseases. Furthermore, the versatility of the 3D bioprinting-assisted tissue assembly allows for the creation of organs and tissue collections with complex structural and functional features by fabricating and assembling modules that meet the specific requirements of target tissues and organs.
A novelin silicoscaffold-hopping method for drug repositioning in rare and intractable diseases
Authors: Tanabe Mao , Sakate Ryuichi , Nakabayashi Jun , Tsumura Kyosuke , Ohira Shino , Iwato Kaoru , Kimura Tomonori 
Published: July 2023

AbstractIn the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase (SYK) inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new “compound-target” information provided DR candidates that could be utilized for future drug development.
Exposure to constant artificial light alters honey bee sleep rhythms and disrupts sleep
Authors: Kim Ashley Y. , Velazquez Aura , Saavedra Belen , Smarr Benjamin , Nieh James C. 
Published: July 2023

ABSTRACTArtificial light at night (ALAN) is known to create changes in animal behavior in some invertebrates and vertebrates and can result in decreased fitness. ALAN effects have not been studied in European honey bees (Apis mellifera), an important pollinator. Colonies can be exposed to ALAN in swarm clusters, when bees cluster outside the nest on hot days and evenings, and, in limited cases, when they build nests in the open. Forager bees maintained in incubated cages were subjected to constant light or dark and observed with infrared cameras. The bees maintained a regular sleep pattern for three days but showed a shift on the fourth day in the presence of continuous light. Bees under constant light demonstrated a 24.05-hour rhythm, compared to a 23.12-hour rhythm in the dark. After 95 hours, the light-exposed bees slept significantly less and experienced significantly more disturbances from their peers. They also preferred to sleep in the lower portion of the cages, which had lower light intensity. These findings suggest that ALAN can disrupt honey bees’ sleep patterns, which may have implications for their behavior and overall colony health.
Multi-omic integration of microbiome data for identifying disease-associated modules
Authors: Muller Efrat , Shiryan Itamar , Borenstein Elhanan 
Published: July 2023

AbstractThe human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiome’s role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions.Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms.Here, we address this challenge by introducingMintTea –an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease.Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuringPeptostreptococcusandGemellaspecies and several fecal amino acids, in agreement with these species’ reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features.Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.
An ancient split of germline and somatic stem cell lineages in Hydra
Authors: Nishimiya-Fujisawa Chiemi , Petersen Hendrik , Koubková-Yu Tracy Chih-Ting , Noda Chiyo , Shigenobu Shuji , Bageritz Josephine , Fujisawa Toshitaka , Simakov Oleg , Kobayashi Satoru , Holstein Thomas W. 
Published: July 2023

AbstractIn many animals, germ cell segregation occurs during early embryogenesis to protect the genome, but its origin in basal metazoans is controversial. Here, we show in the freshwater polypHydraby clonal analysis and transgenic animals that interstitial stem cells comprise two separate stem cell populations, i.e., germline and multipotent somatic stem cells. We isolated genetically labelled stem cells for a global transcriptome study and discovered a broad set of germline-specific/enriched genes includingPrdm9, Pax5, Dmrt1. In an alternative splicing analysis, we identified many genes with germline-specific isoforms; among them, male-specific isoforms ofDmrt1andSnf5. The somatic interstitial stem cell lineage was characterized by numerous neuronal control genes likeNeurog. But all stem cells inHydraalso share a core of stemness genes that has its roots in unicellular eukaryotes. This suggests an evolutionary scenario in which, at the emergence of animal multicellularity, there was an early split into a stable germline and different somatic stem cell lineages.
Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis
Authors: Akshay Akshay , Katoch Mitali , Shekarchizadeh Navid , Abedi Masoud , Sharma Ankush , Burkhard Fiona C. , Adam Rosalyn M. , Monastyrskaya Katia , Gheinani Ali Hashemi 
Published: July 2023

AbstractBackgroundMachine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.ResultsTo address these challenges, we have developed a novel tool calledMachine Learning Made Easy(MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme’s feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.ConclusionMLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available athttps://github.com/FunctionalUrology/MLme.Key PointsMLme is a novel tool that simplifies machine learning (ML) for researchers by integrating Data Exploration, AutoML, CustomML, and Visualization functionalities.MLme improves efficiency and productivity by streamlining the ML workflow and eliminating the need for extensive coding efforts.Rigorous testing on diverse datasets demonstrates MLme’s promising performance in classification problems.MLme provides intuitive interfaces for data exploration, automated ML, customizable ML pipelines, and result visualization.Future developments aim to expand MLme’s capabilities to include support for unsupervised learning, regression, hyperparameter tuning, and integration of user-defined algorithms.
Dynamic ventral disc contraction is necessary forGiardiaattachment and host pathology
Authors: Nosala C , Hagen KD , Guest SL , Hilton NA , Müller A , Laue M , Klotz C. , Aebischer A , Dawson SC 
Published: July 2023

AbstractGiardia lambliais a common parasitic protist that infects the small intestine and causes giardiasis, resulting in diarrhea, vomiting, weight loss, and malabsorption. Giardiasis leads to cellular damage, including loss of microvilli, disruption of tight junctions, impaired barrier function, enzyme inhibition, malabsorption, and apoptosis. In the host, motileGiardiatrophozoites attach to the duodenal microvilli using a unique microtubule organelle called the ventral disc. Despite early observations of disc-shaped depressions in microvilli after parasite detachment, little is known about disc-mediated attachment mechanisms and there little direct evidence showing that parasite attachment causes cellular damage. However, advancements inin vitroorganoid models of infection and genetic tools have opened new possibilities for studying molecular mechanisms of attachment and the impact of attachment on the host. Through high-resolution live imaging and a novel disc mutant, we provide direct evidence for disc contraction during attachment, resolving the long-standing controversy of its existence. Specifically, we identify three types of disc movements that characterize contraction, which in combination result in a decrease in disc diameter and volume. Additionally, we investigate the consequences of attachment and disc contractility using an attachment mutant that has abnormal disc architecture. In a human organoid model, we demonstrate that this mutant has a limited ability to break down the epithelial barrier as compared to wild type. Based on this direct evidence, we propose a model of attachment that incorporates disc contraction to generates the forces required for the observed “grasping” of trophozoites on the host epithelium. Overall, this work highlights the importance of disc contractility in establishing and maintaining parasite attachment, leading to intestinal barrier breakdown.
A pre-trained large generative model for translating single-cell transcriptome to proteome
Authors: Liu Linjing , Li Wei , Wong Ka-Chun , Yang Fan , Yao Jianhua 
Published: July 2023

AbstractProteins are crucial for life, and measuring their abundance at the single-cell level can facilitate a high-resolution understanding of biological mechanisms in cellular processes and disease progression. However, current single-cell proteomic technologies face challenges such as limited coverage, throughput, and sensitivity, as well as batch effects, high costs, and stringent experimental operations. Drawing inspiration from the translation procedure of both natural language processing (NLP) and the genetic central dogma, we propose a pre-trained, large generative model named scTranslator (single-cell translator). scTranslator is align-free and capable of generating multi-omics data by inferring the missing single-cell proteome based on the transcriptome. Systematic benchmarking confirms the accuracy, stability, and flexibility of scTranslator across various quantification techniques, cell types, and conditions. Furthermore, scTranslator has demonstrated its superiority in assisting various downstream analyses and applications, including gene/protein interaction inference, gene pseudo-knockout, cell clustering, batch correction, and cell origin recognition on pan-cancer data.
Unconscious manipulation of conceptual representations with decoded neurofeedback impacts search behaviour
Authors: Margolles Pedro , Elosegi Patxi , Mei Ning , Soto David 
Published: July 2023

AbstractThe necessity of conscious awareness in human learning has been a long-standing topic in psychology and neuroscience. Previous research on non-conscious associative learning is limited by the low signal-to-noise ratio of the subliminal stimulus, and the evidence remains controversial, including failures to replicate. Using functional MRI decoded neurofeedback (fMRI-DecNef) we guided participants from both sexes to generate neural patterns akin to those observed when visually perceiving real-world entities (e.g., dogs). Importantly, participants remained unaware of the actual content represented by these patterns. We utilized an associative DecNef approach to imbue perceptual meaning (e.g., dogs) into Japanese hiragana characters that held no inherent meaning for our participants, bypassing a conscious link between the characters and the dogs concept. Despite their lack of awareness regarding the neurofeedback objective, participants successfully learned to activate the target perceptual representations in the bilateral fusiform. The behavioural significance of our training was evaluated in a visual search task. DecNef and control participants searched for dogs or scissors targets that were pre-cued by the hiragana used during DecNef training or by a control hiragana. The DecNef hiragana did not prime search for its associated target but, strikingly, participants were impaired at searching for the targeted perceptual category. Hence, conscious awareness may function to support higher-order associative learning. Meanwhile, lower-level forms of re-learning, modification, or plasticity in existing neural representations can occur unconsciously, with behavioural consequences outside the original training context. The work also provides an account of DecNef effects in terms of neural representational drift.Significance StatementThis study examined the role of conscious awareness in human learning by using fMRI-DecNef. These techniques enabled participants to self-regulate their brain activity to align with the perceptual representations generated by a real-world entity (i.e., dogs), without awareness of the content they represented. We demonstrated that established brain conceptual representations can be unconsciously modified, influencing visual search behaviour for the targeted perceptual content through the neural representational drift mechanism. Nonetheless, our research suggests that conscious awareness plays a role in more advanced forms of associative learning. Further, this study offers methodological insights for improving DecNef protocols and suggests potential for personalized interventions, including guidance to correct maladaptive conceptual representations.
Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile
Authors: Bryant Patrick , Noé Frank 
Published: July 2023

AbstractStructure prediction of protein complexes has improved significantly with AlphaFold2 and AlphaFold-multimer (AFM), but only 60% of dimers are accurately predicted. A way to improve the predictions is to inject noise to generate more diverse predictions. However, thousands of predictions are needed to obtain a few that are accurate in difficult cases. Here, we learn a bias to the MSA representation that improves the predictions by performing gradient descent through the AFM network. We effectively denoise the MSA profile, similar to how a blurry image would be sharpened. We demonstrate the performance on seven difficult targets from CASP15 and increase the average MMscore to 0.76 compared to 0.63 with AFM. We evaluate the procedure on 334 protein complexes where AFM fails and demonstrate an increased success rate (MMscore>0.75) of 8% on these hard targets. Our protocol, AFProfile, provides a way to direct predictions towards a defined target function guided by the MSA. We expect gradient descent over the MSA to be useful for different tasks, such as generating alternative conformations. AFProfile is freely available at:https://github.com/patrickbryant1/AFProfile
A Basal Ganglia Model for understanding Working Memory Functions in Healthy and Parkinson’s Conditions
Authors: C Vigneswaran , Nair Sandeep Sathyanandan , Chakravarthy V. Srinivasa 
Published: July 2023

ABSTRACTWorking memory is considered as the scratchpad to write, read, and process information to perform cognitive tasks. Basal Ganglia (BG) and Prefrontal Cortex are two important parts of the brain that are involved in working memory functions and both the structures receive projections from dopaminergic nuclei. In this modelling study, we specifically focus on modelling the working memory functions of the BG, the working memory deficits in Parkinson’s disease conditions, and the impact of dopamine deficiency on different kinds of working memory functions. Though there are many experimental and modelling studies of working memory properties, there is a paucity of models of the BG that provide insights into the contributions of the BG in working memory functions. The proposed model of the BG is a unified model that can explain the working memory functions of the BG over a wide variety of tasks in normal and Parkinson’s disease conditions.