bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated simulation-based membrane-protein refinement into cryo-EM data
Authors: Yvonnesdotter Linnea , Rovšnik Urška , Blau Christian , Lycksell Marie , Howard Rebecca J. , Lindahl Erik 
Published: November 2022

I.ABSTRACTThe resolution revolution has increasingly enabled single-particle cryogenic electron microscopy (cryo-EM) reconstructions of previously inaccessible systems, including membrane proteins – a category that constitutes a disproportionate share of drug targets. We present a protocol for using density-guided molecular dynamics simulations to automatically refine atomistic models into membrane-protein cryo-EM maps. Using adaptive-force density-guided simulations as implemented in the GROMACS molecular dynamics package, we show how automated model refinement of a membrane protein is achieved without the need to manually tune the fitting force ad hoc. We also present selection criteria to choose the best fit model which balances stereochemistry and goodness-of-fit. The proposed protocol was used to refine models into a new cryo-EM density of the membrane protein maltoporin, either in a lipid bilayer or detergent micelle, and we found that results do not substantially differ from fitting in solution. Fitted structures satisfied classical model-quality metrics and improved the quality and the model-to-map correlation of the X-ray starting structure. Additionally, the density-guided fitting in combination with generalized orientation-dependent all-atom potential (GOAP) was used to correct the pixel-size estimation of the experimental cryo-EM density map. This work demonstrates the applicability of a straightforward automated approach to fitting membrane-protein cryo-EM densities. Such computational approaches promise to facilitate rapid refinement of proteins under different conditions or with various ligands present, including targets in the highly relevant superfamily of membrane proteins.II.STATEMENT OF SIGNIFICANCECryo-EM is an increasingly critical method of structure determination. As data collection and model generation become more efficient, iteratively fitting an experimental density can still require considerable time and expertise. Membrane proteins are particularly important targets in pharmacology and bioengineering, but can present distinctive challenges to data quality and modeling. Here, we tested a new tool to drive density fitting with molecular dynamics simulations, in context of a new structure of the membrane protein maltoporin. Fitting performed well in detergent, lipids, or solution, offering simpler options for fully automated simulation protocols. We were also able to apply fitting to adjust the microscope’s pixel size. The approach described here should be applicable to rapid, accurate refinement of a variety of membrane-protein structures.
Rapid evolution of flower phenology and clonality in restored populations of multiple grassland species
Authors: Bucharova Anna , Conrady Malte , Klein-Raufhake Theresa , Schultz Franziska , Hölzel Norbert 
Published: November 2022

AbstractRestoration of terrestrial ecosystems often requires re-introduction of plants. In restored sites, the plants often face environment that differs from the one in natural populations. This which can affect plant traits, reduce performance and impose novel selection pressures. As a response, restored populations might rapidly evolve and adapt to the novel conditions. This may enhance population survival and contribute to restoration success, but has been rarely tested so far. Here, we focused on populations of three grassland species restored 20 years ago (Galium wirtgenii, Inula salicinaandCentaurea jacea) by the transfer of green hay, and compared them with populations that were source of the hay. We measured plants both in-situ, and in common garden under control and three stress conditions.In-situ, restored and natural populations differed in flowering phenology in two out of the three species. In the common garden, plants of the restored population flowered earlier (inGalium) or showed increased plasticity of clonal propagation in response to clipping (Inula). Both these traits suggest rapid adaptation to the contrasting mowing regimes in restored in comparison to the natural donor sites. InCentaurea, we detected no differentiation, neither in-situ, nor in the common garden. Rapid evolution in two out of three species indicates that evolution in restoration may be rather common, yet not ubiquitous across species.
Reversing gut microbiome-driven adipose tissue inflammation alleviates metabolic syndrome
Authors: Newman N. K. , Zhang Y. , Padiadpu J. , Miranda C. L. , Magana A. A. , Wong C.P. , Hioki K. A. , Pederson J. W. , Li Z. , Gurung M. , Bruce A. M. , Brown K , Bobe G. , Sharpton T. J. , Shulzhenko N. , Maier C. S. , Stevens J. F. , Gombart A. F. , Morgun A. 
Published: November 2022

AbstractThe gut microbiota contributes to macrophage-mediated inflammation in adipose tissue with consumption of an obesogenic diet, thus driving the development of metabolic syndrome. There is a need to identify and develop interventions that abrogate this condition. The hops-derived prenylated flavonoid xanthohumol (XN) and its semi-synthetic derivative tetrahydroxanthohumol (TXN) attenuate high-fat diet-induced obesity, hepatosteatosis and metabolic syndrome in C57Bl/6J mice. This coincides with a decrease in pro-inflammatory gene expression in the gut and adipose tissue, together with alterations in the gut microbiota and bile acid composition. In this study, we integrated and interrogated multi-omics data from different organs with fecal 16S sequences and systemic metabolic phenotypic data using a transkingdom network analysis. By incorporating cell type information from single cell RNA-seq data, we discovered TXN attenuates macrophage inflammatory processes in adipose tissue. TXN treatment also reversed levels of inflammation-inducing microbes, such asOscillibacter valericigenes, that lead to adverse metabolic phenotypes. Furthermore,in vitrovalidation in macrophage cell lines andin vivomouse supplementation showed addition ofO. valericigenessupernatant induced the expression of metabolic macrophage signature genes that are downregulated by TXNin vivo. Our findings establish an important mechanism by which TXN mitigates adverse phenotypic outcomes from diet-induced obesity and metabolic syndrome. It primarily reduces the abundance of pro-inflammatory gut microbes that can otherwise promote macrophage-associated inflammation in adipose tissue.
Small immunological clocks identified by Deep Learning and Gradient Boosting
Authors: Kalyakulina Alena , Yusipov Igor , Kondakova Elena , Bacalini Maria Giulia , Franceschi Claudio , Vedunova Maria , Ivanchenko Mikhail 
Published: November 2022

AbstractBackgroundThe aging process affects all systems of the human body, and the observed increase in inflammatory components affecting the immune system in old age can lead to the development of age-associated diseases and systemic inflammation.ResultsWe propose a small clock model SImAge based on a limited number of immunological biomarkers. To regress the chronological age from cytokine data, we first use a baseline Elastic Net model, gradient-boosted decision trees models, and several deep neural network architectures. For the full dataset of 46 immunological parameters, DANet, SAINT, FT-Transformer and TabNet models showed the best results for the test dataset. Dimensionality reduction of these models with SHAP values revealed the 10 most age-associated immunological parameters, taken to construct the SImAge small immunological clock. The best result of the SImAge model shown by the FT-Transformer deep neural network model has mean absolute error of 6.94 years and Pearsonρ= 0.939 on the independent test dataset. Explainable artificial intelligence methods allow for explaining the model solution for each individual participant.ConclusionsWe developed an approach to construct a model of immunological age based on just 10 immunological parameters, coined SImAge, for which the FT-Transformer deep neural network model had proved to be the best choice. The model shows competitive results compared to the published studies on immunological profiles, and takes a smaller number of features as an input. Neural network architectures outperformed gradient-boosted decision trees, and can be recommended in the further analysis of immunological profiles.
Heart cockle shells transmit sunlight to photosymbiotic algae using bundled fiber optic cables and condensing lenses
Authors: McCoy Dakota E. , Burns Dale H. , Klopfer Elissa , Herndon Liam K. , Ogunlade Babatunde , Dionne Jennifer A. , Johnsen Sönke 
Published: November 2022

AbstractMany animals convergently evolved photosynthetic symbioses. In bivalves, giant clams (Cardiidae: Tridacninae) gape open to irradiate their symbionts, but heart cockles (Cardiidae: Fraginae) stay closed because sunlight passes through transparent windows in their shells. Here, we show that heart cockles (Corculum cardissaand spp.) use biophotonic adaptations to transmit sunlight for photosynthesis. Heart cockles transmit 11-62% of photosynthetically active radiation (mean=31%) but only 5-28% of potentially harmful UV radiation (mean=14%) to their symbionts. Beneath each window, microlenses condense light to penetrate more deeply into the symbiont-rich tissue. Within each window, aragonite forms narrow fibrous prisms perpendicular to the surface. These bundled “fiber optic cables’’ project images through the shell with a resolution of >100 lines/mm. Parameter sweeps show that the aragonite fibers’ size (∼1µm diameter), morphology (long fibers rather than plates), and orientation (along the optical c-axis) transmit more light than many other possible designs. Heart cockle shell windows are thus: (i) the first instance of fiber optic cable bundles in an organism to our knowledge; (ii) a second evolution, with epidermal cells in angiosperm plants, of condensing lenses for photosynthesis; and (iii) a photonic system that efficiently transmits useful light while protecting photosymbionts from UV radiation.
Influence of anthropogenic light on puma road crossing movement
Authors: Abelson Eric S. , Sikich Jeff A. , Riley Seth P.D. , Blumstein Daniel T. 
Published: November 2022

AbstractWildlife may be attracted or repelled by anthropogenic stimuli – by understanding these responses we gain powerful tools to modify their behavior. Anthropogenic lighting is ubiquitous but little is known about how it influences carnivores. We focused on pumas (Puma concolor) living around Los Angeles, California for which vehicular collision is an important source of mortality. We used GPS collars to track their movement around roads and combined this with ground-level and remotely sensed data of human generated nighttime light to see how that influenced activity around roads. There are multiple landscape level scales, and metrics of light relevant at each scale, that influence the perceptual landscape of wildlife and thus these different scales must be considered. We found that, at a broad spatial scale, pumas crossed roads more frequently in areas with low levels of anthropogenic light (controlling for distance to urbanization). However, while pumas crossed roads in darker parts of the landscape at a broad-landscape scale, we did not find a statistically significant relationship between puma eye-level light intensity and predicted road crossing locations.Regarding pumas and nighttime light, it appears that having dark swaths of land, at a broad spatial scale, is important. This is important for puma conservation and road design as little is currently known about wildlife movement response to nighttime light and the ideal placement for mitigation structures. Ultimately, anthropogenic light at night is a landscape aspect that needs to be better understood and integrated into conservation as the human footprint increases.
Deep model predictive control of gene expression in thousands of single cells
Authors: Lugagne Jean-Baptiste , Blassick Caroline M. , Dunlop Mary J. 
Published: November 2022

AbstractGene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system inEscherichia colicells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the framework’s ability to link expression patterns to dynamic functional outcomes by controlling expression of thetetAantibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput.
GGSDT: A unified signal detection framework for confidence data analysis
Authors: Miyoshi Kiyofumi , Nishida Shin’ya 
Published: November 2022

AbstractHuman decision behavior entails a graded awareness of its certainty, known as a feeling of confidence. Until now, considerable interest has been paid to behavioral and computational dissociations of decision and confidence, which has raised an urgent need for measurement frameworks that can quantify the efficiency of confidence rating relative to decision accuracy (metacognitive efficiency). As a unique addition to such frameworks, we have developed a new signal detection theory paradigm utilizing the generalized gaussian distribution (GGSDT). This framework evaluates the observer’s internal standard deviation ratio and metacognitive efficiency through the scale and shape parameters respectively. The shape parameter quantifies the kurtosis of internal distributions and can practically be understood in reference to the proportion of the gaussian ideal observer’s confidence being disrupted with random guessing (metacognitive lapse rate). This interpretation holds largely irrespective of the contaminating effects of decision accuracy or operating characteristic asymmetry. Thus, the GGSDT enables hitherto unexplored research protocols (e.g., direct comparison of yes/no versus forced-choice metacognitive efficiency), expected to find applications in various fields of behavioral science. This paper provides a detailed walkthrough of the GGSDT analysis with an accompanying R package (ggsdt).
Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
Authors: Hekselman Idan , Vital Assaf , Ziv-Agam Maya , Kerber Lior , Yeger-Lotem Esti 
Published: November 2022

ABSTRACTHereditary diseases manifest clinically in certain tissues, however their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in 1,113 hereditary diseases. Overall, we identified 110 cell types affected by 714 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechanisms and cellular vulnerability.
Mixed Models Quantify Annual Volume Change; Linear Regression Determines Thalamic Volume as the Best Subcortical Structure Volume Predictor in Alzheimer’s Disease and Aging
Authors: Leger Charles S. , Herbert Monique , Dale Stevens W. , DeSouza Joseph F. ,   Alzheimer’s Disease Neuroimaging Initiative
Published: November 2022

AbstractBackgroundThalamus-hippocampus-putamen and thalamus-cerebellar interconnections are dense. The extent this connectivity is paralleled by each structure’s volume impact on another is unquantified in Alzheimer’s disease (AD). Mixed model quantification of annual volume change in AD is scarce and absent inclusive of the cerebellum, hippocampus, putamen and lateral ventricles and thalamus. Among these structures, autopsy evidence of early-stage AD seems largely but not entirely restricted to the hippocampus and thalamus.ObjectiveVariation in annual volume related to time and baseline age was assessed for the hippocampus, putamen, cerebellum, lateral ventricles and thalamus. Which subcortical structure’s volume had the largest explanatory effect of volume variation in other subcortical structures was also determined.MethodThe intraclass correlation coefficient was used to assess test-retest reliability of structure automated segmentation. Linear regression (N= 45) determined which structure’s volume most impacted volume of other structures. Finally, mixed models (N= 36; 108 data points) quantified annual structure volume change from baseline to 24-months.ResultsHigh test-retest reliability was indicated by a mean ICC score of .989 (SD= .012). Thalamic volume consistently had the greatest explanatory effect of hippocampal, putamen, cerebellar and lateral ventricular volume. The group variable proxy for AD significantly contributed to the best-fitting hippocampal linear regression model, hippocampal and thalamic longitudinal mixed models, and approached significance in the longitudinal lateral ventricular mixed model. Mixed models determined time (1 year) had a negative effect on hippocampal, cerebellar and thalamic volume, no effect on putamen volume, and a positive effect on lateral ventricular volume. Baseline age had a negative effect on hippocampal and thalamic volume, no effect on cerebellar or putamen volume and a positive effect on lateral ventricular volume.InterpretationLinear regression determined thalamic volume as a virtual centralized index of hippocampal, cerebellar, putamen, and lateral ventricular volume. Relative to linear regression, longitudinal mixed models had greater sensitivity to detect contribution of early AD, or potential AD pathology (MCI), via the group variable not just to volume reduction in the hippocampus but also in the thalamus.
Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations
Authors: Lauterbur M. Elise , Cavassim Maria Izabel A. , Gladstein Ariella L. , Gower Graham , Pope Nathaniel S. , Tsambos Georgia , Adrion Jeff , Belsare Saurabh , Biddanda Arjun , Caudill Victoria , Cury Jean , Echevarria Ignacio , Haller Benjamin C. , Hasan Ahmed R. , Huang Xin , Iasi Leonardo Nicola Martin , Noskova Ekaterina , Obšteter Jana , Pavinato Vitor Antonio Corrêa , Pearson Alice , Peede David , Perez Manolo F. , Rodrigues Murillo F. , Smith Chris C. R. , Spence Jeffrey P. , Teterina Anastasia , Tittes Silas , Unneberg Per , Vazquez Juan Manuel , Waples Ryan K. , Wohns Anthony Wilder , Wong Yan , Baumdicker Franz , Cartwright Reed A. , Gorjanc Gregor , Gutenkunst Ryan N. , Kelleher Jerome , Kern Andrew D. , Ragsdale Aaron P. , Ralph Peter L. , Schrider Daniel R. , Gronau Ilan 
Published: November 2022

AbstractSimulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed frameworkstdpopsimseeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version ofstdpopsimfocused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release ofstdpopsim(version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements tostdpopsimaim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.
Spatial structure imposes sex-specific costs but does not reduce interlocus sexual conflict
Authors: Halder Subhasish , Kar Shramana , Sethi Simran , Tewari Swadha , Verma Tanya , Nandy Bodhisatta 
Published: November 2022

AbstractSpatial structure is a common feature of all naturally occurring populations. Theoretically, spatial structuring of a habitat could modulate the intensity of Interlocus sexual conflict (ISC) in a population, possibly by modulating intersexual encounter rate. We tested this theory using laboratory populations ofDrosophila melanogasterby measuring male induced fitness decline in females in three-patch habitat systems under two alternative habitat types – structured-interconnected and unstructured. Our results on reproductive and survival costs in females suggested significant costs due to (a) male presence (i.e., ISC) and (b) living on structured habitat. However, there was only a weak evidence supporting the theory of modulation of ISC by habitat structuring only. Through a follow up experiment, we further showed that the effect of habitat on ISC is also robust to the alteration of female conditions. Therefore, it appears that spatial structuring per se is unlikely to modulate ISC, but can impose additional survival costs. We further showed such survival cost could be sex-specific possibly reflecting female biased spontaneous dispersal.