bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental evidence for the impact of soil viruses on carbon cycling during surface plant litter decomposition
Authors: Albright Michaeline B.N. , Gallegos-Graves La Verne , Feeser Kelli L. , Montoya Kyana , Emerson Joanne B. , Shakya Migun , Dunbar John 
Published: October 2021

AbstractTo date, the potential impact of viral communities on biogeochemical cycles in soil has largely been inferred from indirect evidence, such as virus-driven changes in microbial abundances, viral auxiliary metabolic genes, and correlations with soil physiochemical properties. To more directly test the impact of soil viruses on carbon cycling during plant litter decomposition, we added concentrated viral community suspensions to complex litter decomposer communities in 40-day microcosm experiments. Microbial communities from two New Mexico alpine soils, Pajarito (PJ) and Santa Fe (SF), were inoculated onto grass litter on sand, and three treatments were applied in triplicate to each set of microcosms: addition of buffer (no added virus), addition of live virus (+virus), or killed virus (+killed-virus) fractions extracted from the same soil. Significant differences in respiration were observed between the +virus and +killed-virus treatments in the PJ, but not the SF microcosms. Bacterial and fungal community composition differed significantly by treatment in both PJ and SF microcosms. Combining data across both soils, viral addition altered links between bacterial and fungal diversity, dissolved organic carbon and total nitrogen. Overall, we demonstrate that increasing viral pressure in complex microbial communities can impact terrestrial biogeochemical cycling but is context-dependent.
Constructing benchmark test sets for biological sequence analysis using independent set algorithms
Authors: Petti Samantha N. , Eddy Sean R. 
Published: October 2021

AbstractStatistical inference and machine learning methods are benchmarked on test data independent of the data used to train the method. Biological sequence families are highly non-independent because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.
Decoding Estimates of Curvilinear Self-Motion from Neural Signals in a Model of Primate MSTd
Authors: Layton Oliver W. , Powell Nathaniel , Steinmetz Scott T , Fajen Brett R 
Published: October 2021

AbstractSelf-motion produces characteristic patterns of optic flow on the eye of the mobile observer. Movement along linear, straight paths without eye movements yields motion that radiates from the direction of travel (heading). The observer experiences more complex motion patterns while moving along more general curvilinear (e.g. circular) paths, the appearance of which depends on the radius of the curved path (path curvature) and the direction of gaze. Neurons in brain area MSTd of primate visual cortex exhibit tuning to radial motion patterns and have been linked with linear heading perception. MSTd also contains neurons that exhibit tuning to spirals, but their function is not well understood. We investigated in a computational model whether MSTd, through its diverse pattern tuning, could support estimation of a broader range of self-motion parameters from optic flow than has been previously demonstrated. We used deep learning to decode these parameters from signals produced by neurons tuned to radial expansion, spiral, ground flow, and other patterns in a mechanistic neural model of MSTd. Specifically, we found that we could accurately decode the clockwise/counterclockwise sign of curvilinear path and the gaze direction relative to the path tangent from spiral cells; heading from radial cells; and the curvature (radius) of the curvilinear path from activation produced by both radial and spiral populations. We demonstrate accurate decoding of these linear and curvilinear self-motion parameters in both synthetic and naturalistic videos of simulated self-motion. Estimates remained stable over time, while also rapidly adapting to dynamic changes in the observer’s curvilinear self-motion. Our findings suggest that specific populations of neurons in MSTd could effectively signal important aspects of the observer’s linear and curvilinear self-motion.Author SummaryHow do we perceive our self-motion as we move through the world? Substantial evidence indicates that brain area MSTd contains neurons that signal the direction of travel during movement along straight paths. We wondered whether MSTd neurons could also estimate more general self-motion along curved paths. We tested this idea by using deep learning to decode signals produced by a neural model of MSTd. The system accurately decoded parameters that specify the observer’s self-motion along straight and curved paths in videos of synthetic and naturalistic scenes rendered in the Unreal game engine. Our findings suggest that MSTd could jointly signal self-motion along straight and curved paths.
Assessing sequence-based protein-protein interaction predictors for use in therapeutic peptide engineering
Authors: Charih François , Biggar Kyle K. , Green James R. 
Published: October 2021

ABSTRACTEngineering peptides to achieve a desired therapeutic effect through the inhibition of a specific target activity or protein interaction is a non-trivial task. Few of the existing in silico peptide design algorithms generate target-specific peptides. Instead, many methods produce peptides that achieve a desired effect through an unknown mechanism. In contrast with resource-intensive high-throughput experiments, in silico screening is a cost-effective alternative that can prune the space of candidates when engineering target-specific peptides. Using a set of FDA-approved peptides we curated specifically for this task, we assess the applicability of several sequence-based protein-protein interaction predictors as a screening tool within the context of peptide therapeutic engineering. We show that similarity-based protein-protein interaction predictors are more suitable for this purpose than the state-of-the-art deep learning methods publicly available at the time of writing. We also show that this approach is mostly useful when designing new peptides against targets for which naturally-occurring interactors are already known, and that deploying it for de novo peptide engineering tasks may require gathering additional target-specific training data. Taken together, this work offers evidence that supports the use of similarity-based protein-protein interaction predictors for peptide therapeutic engineering, especially peptide analogs.
Investigating the genetic architecture of eye colour in a Canadian cohort
Authors: Lona-Durazo Frida , Thakur Rohit , Pairo-Castineira Erola , Funderburk Karen , Zhang Tongwu , Kovacs Michael A. , Choi Jiyeon , Jackson Ian J. , Brown Kevin M. , Parra Esteban J. 
Published: October 2021

AbstractThe main factors that determine eye colour are the amount of melanin concentrated in iris melanocytes, as well as the shape and distribution of melanosomes. Eye colour is highly variable in populations with European ancestry, in which eye colour categories cover a continuum of low to high quantities of melanin accumulated in the iris. A few polymorphisms in the HERC2/OCA2 locus in chromosome 15 have the largest effect on eye colour in these populations, although there is evidence of other variants in the locus and across the genome also influencing eye colour. To improve our understanding of the genetic loci determining eye colour, we performed a meta-analysis of genome-wide association studies in a Canadian cohort of European ancestry (N= 5,641) and investigated putative causal variants. Our fine-mapping results indicate that there are several candidate causal signals in the HERC2/OCA2 region, whereas other significant loci in the genome likely harbour a single causal signal (TYR, TYRP1, IRF4, SLC24A4). Furthermore, a short subset of the associated eye colour regions was colocalized with the gene expression or methylation profiles of cultured melanocytes (HERC2, OCA2), and transcriptome-wide association studies highlighted the expression of two genes associated with eye colour: SLC24A4 and OCA2. Finally, genetic correlations of eye and hair colour from the same cohort suggest high pleiotropy at the genome level, but locus-level evidence hints at several differences in the genetic architecture of both traits. Overall, we provide a better picture of how polymorphisms modulate eye colour variation, particularly in the HERC2/OCA2 locus, which may be a consequence of specific molecular processes in the iris melanocytes.Author SummaryEye colour differences among humans are the result of different amounts of melanin produced, as well as due to differences in the shape and distribution of the organelles in charge of producing melanin. Eye colour is a highly heritable trait, where several genes across the genome are involved in the process, but we currently do not fully understand which are the causal variants and how they modulate eye colour variation. By performing genome-wide association studies of eye colour across Canadian individuals of European ancestry, we identify several candidate causal signals in and near the gene OCA2, and one candidate signal in other genes, such as TYR, TYRP1, IRF4 and SLC24A4. Furthermore, we provide insights about how significant loci may modulate eye colour variation by testing for shared signals with polymorphisms associated with the expression of genes and DNA methylation. Overall, we provide a better picture of the genetic architecture of eye colour and the molecular mechanisms contributing to its variation.
The kinesin KIF4 mediates HBV/HDV entry through regulation of surface NTCP localization and can be targeted by RXR agonistsin vitro
Authors: Gad Sameh A. , Sugiyama Masaya , Tsuge Masataka , Wakae Kosho , Fukano Kento , Oshima Mizuki , Sureau Camille , Watanabe Noriyuki , Kato Takanobu , Murayama Asako , Li Yingfang , Shoji Ikuo , Shimotohno Kunitada , Chayama Kazuaki , Muramatsu Masamichi , Wakita Takaji , Nozaki Tomoyoshi , Aly Hussein H. 
Published: October 2021

AbstractIntracellular transport via microtubule-based dynein and kinesin family motors plays a key role in viral reproduction and transmission. We show here that Kinesin Family Member 4 (KIF4) plays an important role in HBV/HDV infection. We intended to explore host factors impacting the HBV life cycle that can be therapeutically addressed using siRNA library transfection and HBV/NLuc (HBV/NL) reporter virus infection in HepG2-hNTCP C4 cells. KIF4 silencing resulted in a 3-fold reduction in luciferase activity following HBV/NL infection and suppressed both wild-type HBV and HDV infection. Transient KIF4 depletion reduced surface and raised intracellular NTCP (HBV/HDV entry receptor) levels, according to both cellular fractionation and immunofluorescence analysis (IF). Overexpression of wild-type KIF4 but not ATPase-null KIF4 regains the surface localization of NTCP in these cells. Furthermore, immunofluorescence (IF) revealed KIF4 and NTCP colocalization across microtubule filaments, and a co-immunoprecipitation study showed that KIF4 physically binds to NTCP. KIF4 expression is regulated by FOXM1. Interestingly, we discovered that RXR agonists (Bexarotene, and Alitretinoin) down-regulated KIF4 expression via FOXM1-mediated suppression, resulting in a substantial decrease in HBV-Pre-S1 protein attachment to HepG2-hNTCP cell surface and subsequent suppression of HBV infection in HepG2-hNTCP and primary human hepatocytes (PXB) (Bexarotene, IC501.89 ± 0.98 μM). Overall, our findings show that human KIF4 is a critical regulator of NTCP surface transport and localization, which is required for NTCP to function as a receptor for HBV/HDV entry. Furthermore, small molecules that suppress or alleviate KIF4 expression would be potential antiviral candidates that target HBV and HDV entry phases.Author SummaryUnderstanding HBV/HDV entry machinery and the mechanism by which NTCP (HBV/HDV entry receptor) surface expression is regulated is crucial to develop antiviral entry inhibitors. We found that NTCP surface transport is mainly controlled by the motor kinesin KIF4. Surprisingly, KIF4 was negatively regulated by RXR receptors through FOXM1-mediated suppressionThis study not only mechanistically correlated the role of RXR receptors in regulating HBV/HDV entry but also suggested a novel approach to develop therapeutic rexinoids for preventing HBV and/or HDV infections in important clinical situations, such as in patients undergoing liver transplantation or those who are at a high risk of HBV infection and unresponsive to HBV vaccination.
Understanding the relationship between dispersal and range size
Authors: Alzate Adriana , Onstein Renske E. 
Published: October 2021

AbstractUnderstanding what drives the vast variability in species range size is still an outstanding question. Among the several processes potentially affecting species ranges, dispersal is one of the most prominent hypothesized predictors. However, the theoretical expectation of a positive dispersal-range size relationship has received mixed empirical support. Here, we synthesized results from 84 studies to investigate in which context dispersal is most important in driving species range size. We found that dispersal traits – proxies for dispersal ability – explain range sizes more often in temperate and subtropical regions than in tropical regions, when considering multiple components of dispersal, and when investigating a large number of species to capture dispersal and range size variation. In plants, positive effects of dispersal on range size were less often detected when examining broad taxonomic levels. In animals, dispersal is more important for range size increase in ectotherms than in endotherms. Our synthesis emphasizes the importance of considering different aspects of the dispersal process -departure, transfer, settlement-, niche aspects and evolutionary components, like time for range expansion and past geological-environmental dynamics. We therefore call for a more integrative view of the dispersal process and its causal relationship with range size.
Characterization of morpho-functional traits in mesophotic corals reveals optimized light capture and photosynthesis
Authors: Kramer Netanel , Guan Jiaao , Chen Shaochen , Wangpraseurt Daniel , Loya Yossi 
Published: October 2021

AbstractThe morphology and skeleton architecture of photosynthetic corals modulates the light capture and functioning of the coral-algal symbiosis on shallow-water corals. Since corals can thrive on mesophotic reefs under extreme light-limited conditions, we hypothesized that microskeletal coral features optimize light capture under low-light environments. Using micro-computed tomography scanning, we conducted a comprehensive three-dimensional (3D) assessment of small-scale skeleton morphology of the depth-generalist coral Stylophora pistillata collected from shallow (5 m) and mesophotic (45 m) depths. We detected a high phenotypic diversity between depths, resulting in two distinct morphotypes, with calyx diameter, theca height, and corallite marginal spacing contributing to most of the variation between depths. To determine whether such depth-specific morphotypes affect coral light capture and photosynthesis on the corallite-scale, we developed 3D simulations of light propagation based on photosynthesis-irradiance parameters. We found that corals associated with shallow morphotypes dissipated excess light through self-shading microskeletal features; while mesophotic morphotypes facilitated enhanced light absorption and photosynthesis under low-light conditions. We conclude that the mesophotic coral architecture provides a greater ability to trap solar energy and efficiently exploit the limited light conditions, and suggest that morphological modifications play a key role in the photoadaptation response to low-light.
Coral assemblages at higher latitudes favour short-term potential over long-term performance
Authors: Cant James , Reimer James D. , Sommer Brigitte , Cook Katie M. , Kim Sun W. , Sims Carrie A. , Mezaki Takuma , O’Flaherty Cliodhna , Brooks Maxime , Malcolm Hamish A. , Pandolfi John M. , Salguero-Gómez Roberto , Beger Maria 
Published: October 2021

AbstractThe persistent exposure of coral communities to more variable abiotic regimes is assumed to augment their resilience to future climatic variability. Yet, while the determinants of coral population resilience across species remain unknown, we are unable to predict the winners and losers across reef ecosystems exposed to increasingly variable conditions. Using annual surveys of 3171 coral individuals across Australia and Japan (2017-2019), we explore spatial variation across the short- and long-term dynamics of competitive, stress-tolerant, and weedy assemblages to evaluate how thermal variability mediates the structural composition of coral communities. We illustrate how, by promoting short-term potential over long-term performance, coral assemblages can reduce their vulnerability to stochastic environments. However, compared to stress-tolerant, and weedy assemblages, competitive coral taxa display a reduced capacity for elevating their short-term potential. Accordingly, future climatic shifts threaten the structural complexity of coral assemblages in variable environments, emulating the degradation expected across global tropical reefs.
Recombinant Fsh and Lh therapy for spawning induction of previtellogenic and early spermatogenic arrested teleost, the flathead grey mullet (Mugil cephalus)
Authors: Ramos-Júdez Sandra , Giménez Ignacio , Gumbau-Pous Josep , Arnold-Cruañes Lucas Stephen , Estévez Alicia , Duncan Neil 
Published: October 2021

AbstractWith the expansion and diversification of global aquaculture, efforts continue to develop new bio-technologies for assisted reproduction in species that present reproductive dysfunctions. Flathead grey mullet (Mugil cephalus) held in intensive conditions in the Mediterranean region, display a severe reproductive dysfunction, where males do not produce fluent milt and females are arrested at previtellogenesis or early stages of vitellogenesis. In the present study, weekly injections of species-specific single-chain recombinant gonadotropins (rGths); follicle stimulating hormone (rFsh) (6 to 12 μg kg-1 doses) and luteinizing hormone (rLh) (2.5 to 24 μg kg-1 doses) were administered to induce vitellogenesis, from previtellogenesis / early vitellogenesis to the completion of vitellogenic growth in females and enhance spermatogenesis to produce adequate volumes of sperm from non-fluent males. During the experiment, all treated females (n = 21) developed oocytes in late vitellogenesis with 603 ± 8 μm diameter and all treated males produced fluent sperm. To induce oocyte maturation, ovulation and spawning, females were treated with either (i) a priming dose of 30 μg kg-1 of rLh and a resolving dose of 40 mg kg-1 of progesterone (P4), (ii) priming and resolving doses of 30 μg kg-1 of rLh, or (iii) priming and resolving doses of 40 mg kg-1 of P4 given 24:05 ± 0:40 h apart. Females were placed in spawning tanks with rGth treated males that had fluent sperm. Spontaneous spawns of fertilised eggs were obtained after inducing with rLh + P4 or rLh + rLh (priming and resolving injections) with a spawning success of the 85% (8 of 9 females) and 100% (n = 6), respectively. The eggs collected from the tanks presented 64 ± 22% fertilization with embryo development and 57 ± 24 % hatching. The treatment P4 + P4 had a lower ovulation success (50 % - 3 of 6 females) and spawning success (17 %) with no fertilised eggs. Success was independent of the initial gonadal stage of females. In comparison, control females did not show any advance in gonadal development from initial stages and control males did not produce fluent sperm. The present results confirm the possibility of controlling oogenesis from previtellogenesis to the completion of maturation and fertilised tank spawning using exclusively rFsh and rLh in a teleost species.
SHAPR predicts 3D cell shapes from 2D microscopic images
Authors: Waibel Dominik J. E. , Kiermeyer Niklas , Atwell Scott , Sadafi Ario , Meier Matthias , Marr Carsten 
Published: October 2021

SummaryReconstruction of shapes and sizes of three-dimensional (3D) objects from two-dimensional (2D) information is an intensely studied subject in computer vision. We here consider the level of single cells and nuclei and present a neural network-based SHApe PRediction autoencoder. For proof-of-concept, SHAPR reconstructs 3D shapes of red blood cells from single view 2D confocal microscopy images more accurately than naïve stereological models and significantly increases the feature-based prediction of red blood cell types from F1 = 79.0% to F1 = 87.4%. Applied to 2D images containing spheroidal aggregates of densely grown human induced pluripotent stem cells, we find that SHAPR learns fundamental shape properties of cell nuclei and allows for prediction-based morphometry. Reducing imaging time and data storage, SHAPR will help to optimize and up-scale image-based high-throughput applications for biomedicine.
Multi-omics and deep learning provide a multifaceted view of cancer
Authors: Uyar Bora , Ronen Jonathan , Franke Vedran , Gargiulo Gaetano , Akalin Altuna 
Published: October 2021

AbstractCancer is a complex disease with a large financial and healthcare burden on society. One hallmark of the disease is the uncontrolled growth and proliferation of malignant cells. Unlike Mendelian diseases which may be explained by a few genomic loci, a deeper molecular and mechanistic understanding of the development of cancer is needed. Such an endeavor requires the integration of tens of thousands of molecular features across multiple layers of information encoded in the cells. In practical terms, this implies integration of multi omics information from the genome, transcriptome, epigenome, proteome, metabolome, and even micro-environmental factors such as the microbiome. Finding mechanistic insights and biomarkers in such a high dimensional space is a challenging task. Therefore, efficient machine learning techniques are needed to reduce the dimensionality of the data while simultaneously discovering complex but meaningful biomarkers. These markers then can lead to testable hypotheses in research and clinical applications. In this study, we applied advanced deep learning methods to uncover multi-omic fingerprints that are associated with a wide range of clinical and molecular features of tumor samples. Using these fingerprints, we can accurately classify different cancer types, and their subtypes. Non-linear multi-omic fingerprints can uncover clinical features associated with patient survival and response to treatment, ranging from chemotherapy to immunotherapy. In addition, multi-omic fingerprints may be deconvoluted into a meaningful subset of genes and genomic alterations to support clinically relevant decisions.Graphical Abstract