bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long distance runners in the marine realm: New insights into genetic diversity, kin relationships and social fidelity of Indian Ocean male sperm whales
Authors: Girardet Justine , Sarano Francois , Richard Gaëtan , Tixier Paul , Guinet Christophe , Alexander Alana , Sarano Véronique , Vitry Hugues , Preud’homme Axel , Heuzey René , Garcia-Cegarra Ana M. , Adam Olivier , Madon Bénédicte , Jung Jean-Luc 
Published: April 2021

AbstractBackgroundAdult male sperm whales (Physeter macrocephalus) are long distance runners of the marine realm, feeding in high latitudes and mating in tropical and subtropical waters where stable social groups of females and immatures live. Several areas of uncertainty still limit our understanding of their social and breeding behaviour, in particular concerning the potential existence of geographical and/or social fidelities.In this study, using underwater observation and sloughed-skin sampling, we looked for male social fidelity to a specific matrilineal sperm whale group near Mauritius. In addition, we captured a wider picture of kin relationships and genetic diversity of male sperm whales in the Indian Ocean thanks to biopsies of eight unique individuals taken in a feeding ground near the Kerguelen and Crozet Archipelagos (Southern Indian Ocean).ResultsTwenty-six adult male sperm whales, of which 13 were sampled, were identified when socializing with adult females and immatures off Mauritius. Long-term underwater observation recorded several noteworthy social interactions between adult males and adult females and/or immatures. We identified seven possible male recaptures over different years (three by direct observation, and four at the gametic level), which supports a certain level of male social fidelity. Several first- and second-degree kin relationships were highlighted between members of the social unit and adult males, confirming that some of the adult males observed in Mauritian waters are reproductive. Male social philopatry to their natal group can be excluded, as none of the males sampled shared the haplotype characteristic of the matrilineal social group. Mitochondrial DNA control region haplotype and nucleotide diversities calculated over the 21 total male sperm whales sampled were similar to values found by others in the Indian Ocean.ConclusionsOur study strongly supports the existence of some levels of male sperm whale social fidelity, not directed to their social group of birth, in the Indian Ocean. Males sampled in breeding and feeding grounds are linked by kin relationships. Our results support a model of male mediated gene flow occurring at the level of the whole Indian Ocean, likely interconnected with large-scale geographical fidelity to ocean basin, and a small-scale social fidelity to matrilineal social groups.
PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes
Authors: Carpenter Charlie M. , Zhang Weiming , Gillenwater Lucas , Severn Cameron , Ghosh Tusharkanti , Bowler Russel , Kechris Katerina , Ghosh Debashis 
Published: April 2021

AbstractHigh-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the “-omics” family. For this work, we focus on subsets that interact with one another and represent these “pathways” as graphs. Observed pathways often have disjoint components, i.e. nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway which notably lessens testing power. In this paper we propose thePathwayIntegratedRegression-basedKernelAssociationTest (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or “smoothed” graph into a score test, PaIRKAT is capable of providing more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.Author SummaryPaIRKAT is a tool for improving testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. Our paper proposes PaIRKAT, a method for including known biological connections between high dimensional variables by representing them as edges of ‘graphs’ or ‘networks.’ It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or ‘smoothing’ approach for managing this issue. We demonstrate the benefits of this approach through simulation studies and an application to the metabolomic data from the COPDGene study.
The role of microbial diversity in the formation of soil organic matter quality and persistence
Authors: Domeignoz-Horta Luiz A. , Shinfuku Melissa , Junier Pilar , Poirier Simon , Verrecchia Eric , Sebag David , DeAngelis Kristen M 
Published: April 2021

AbstractThe largest terrestrial carbon sink on earth is soil carbon stocks. As the climate changes, the rate at which the Earth’s climate warms depends in part on the persistence of soil organic carbon. Microbial turnover forms the backbone of soil organic matter (SOM) formation and it has been recently proposed that SOM molecular complexity is a key driver of stability. Despite this, the links between microbial diversity, chemical complexity and biogeochemical nature of soil organic matter remain missing. Here we used a model soil system to test the hypothesis that more diverse microbial communities generate more stable soil organic matter. We inoculated microbial communities of varying diversities into an model soil matrix amended with simple carbon, and measured the thermal stability of the resultant soil organic matter. Using a novel data analysis approach with Rock-Eval®ramped thermal analysis, we found that microbial community diversity drives the chemical fingerprint of soil organic matter. Bacteria-only and low diversity communities lead to less chemically-diverse and more thermally-labile soil carbon pools than highly diverse communities. Our results provide direct evidence for a link between microbial diversity, molecular complexity and SOM stability. This evidence demonstrates the benefits of managing soils for maximum biological diversity as a means of building persistent SOM stocks.ClassificationBiological Sciences: Ecology
Reconciling contrast invariance and non-linear computation in cortical circuits
Authors: Timón L. Bernáez , Ekelmans P. , Konrad S. , Nold A. , Tchumatchenko T. 
Published: April 2021

AbstractNetwork selectivity for orientation is invariant to changes in the stimulus contrast in the primary visual cortex. Similarly, the selectivity for odor identity is invariant to changes in odorant concentration in the piriform cortex. Interestingly, invariant network selectivity appears robust to local changes in synaptic strength induced by synaptic plasticity, even though: i) synaptic plasticity can potentiate or depress connections between neurons in a feature-dependent manner, and ii) in networks with balanced excitation and inhibition, synaptic plasticity is a determinant for the network non-linearity. In this study, we investigate whether network contrast invariance is consistent with a variety of synaptic states and connectivities in balanced networks. By using mean-field models and spiking network simulations, we show how the synaptic state controls the non-linearity in the network response to contrast and how it can lead to the emergence of contrast-invariant or contrast-dependent selectivity. Different forms of synaptic plasticity sharpen or broaden the network selectivity, while others do not affect it. Our results explain how the physiology of individual synapses is linked to contrast-invariant selectivity at the network level.
Cooperation enhances structural stability in mutualistic systems
Authors: Portillo José R. , Soler-Toscano Fernando , Langa José A. 
Published: April 2021

AbstractDynamical systems on graphs allow to describe multiple phenomena from different areas of Science. In particular, many complex systems in Ecology are studied by this approach. In this paper we analize the mathematical framework for the study of the structural stability of each stationary point, feasible or not, introducing a generalization for this concept, defined asGlobal Structural Stability. This approach would fit with the proper mathematical concept of structural stability, in which we find a full description of the complex dynamics on the phase space due to nonlinear dynamics. This fact can be analyzed as an informational field grounded in a global attractor whose structure can be completely characterized. These attractors are stable under perturbation and suppose the minimal structurally stable sets. We also study in detail, mathematically and computationally, the zones characterizing different levels of biodiversity in bipartite graphs describing mutualistic antagonistic systems of population dynamics. In particular, we investigate the dependence of the region of maximal biodiversity of a system on its connectivity matrix. On the other hand, as the network topology does not completely determine the robustness of the dynamics of a complex network, we study the correlation between structural stability and several graph measures. A systematic study on synthetic and biological graphs is presented, including 10 mutualistic networks of plants and seed-dispersal and 1000 random synthetic networks. We compare the role of centrality measures and modularity, concluding the importance of just cooperation strength among nodes when describing areas of maximal biodiversity. Indeed, we show that cooperation parameters are the central role for biodiversity while other measures act as secondary supporting functions.Author summaryWe introduce the concept of Global Structural Stability as a proper mathematical concept to fully understand biodiversity in some ecological systems. Our concept retakes the definitions in the classical works of R. Thom [1] and Andronov-Pontryagin [2]. Moreover, there exists a close relation between the structure of a complex network, described as a graph, and its associated dynamics. Mutualistic networks introduce cooperation links between two groups of species, as plant and pollinators or seed-dispersal. The understanding of organizational aspects leading to maximizing biodiversity is one of the more important research areas in Theoretical and Applied Ecology. In this work we introduce a systematic study on different graph measures in order to identify optimal organization for maximal biodiversity (defined as structural stability). Our results conclude that, for mutualistic systems, the strength in cooperation parameters are the core fact, i.e., cooperation is the real fact optimizing biodiversity among other possible structural configurations.
Sequential defects in cardiac lineage commitment and maturation cause hypoplastic left heart syndrome
Authors: Krane Markus , Dreßen Martina , Santamaria Gianluca , My Ilaria , Schneider Christine M. , Dorn Tatjana , Laue Svenja , Mastantuono Elisa , Berutti Riccardo , Rawat Hilansi , Gilsbach Ralf , Schneider Pedro , Lahm Harald , Schwarz Sascha , Doppler Stefanie A. , Paige Sharon , Puluca Nazan , Doll Sophia , Neb Irina , Brade Thomas , Zhang Zhong , Abou-Ajram Claudia , Northoff Bernd , Holdt Lesca M. , Sudhop Stefanie , Sahara Makoto , Goedel Alexander , Dendorfer Andreas , Tjong Fleur V.Y. , Rijlaarsdam Maria E. , Cleuziou Julie , Lang Nora , Kupatt Christian , Bezzina Connie , Lange Rüdiger , Bowles Neil E. , Mann Matthias , Gelb Bruce , Crotti Lia , Hein Lutz , Meitinger Thomas , Wu Sean , Sinnecker Daniel , Gruber Peter J. , Laugwitz Karl-Ludwig , Moretti Alessandra 
Published: April 2021

ABSTRACTBackgroundComplex molecular programs in specific cell lineages govern human heart development. Hypoplastic left heart syndrome (HLHS) is the most common and severe manifestation within the spectrum of left ventricular outflow tract obstruction defects occurring in association with ventricular hypoplasia. The pathogenesis of HLHS is unknown, but hemodynamic disturbances are assumed to play a prominent role.MethodsTo identify perturbations in gene programs controlling ventricular muscle lineage development in HLHS, we performed: i) whole-exome sequencing of 87 HLHS parent-offspring trios, ii) nuclear transcriptomics of cardiomyocytes from ventricles of 4 patients with HLHS and 15 controls at different stages of heart development, iii) single cell RNA sequencing and iv) 3D modeling in iPSCs from 3 patients with HLHS and 3 controls.ResultsGene set enrichment and protein network analyses of damagingde-novomutations and dysregulated genes from ventricles of patients with HLHS suggested alterations in specific gene programs and cellular processes critical during fetal ventricular cardiogenesis, including cell-cycle and cardiomyocyte maturation. Single-cell and 3D modeling with iPSCs demonstrated intrinsic defects in the cell-cycle/UPR/autophagy hub resulting in disrupted differentiation of early cardiac progenitor lineages leading to defective cardiomyocyte-subtype differentiation/maturation in HLHS. Additionally, premature cell-cycle exit of ventricular cardiomyocytes from HLHS patients prevented normal tissue responses to developmental signals for growth leading to multinucleation/polyploidy, accumulation of DNA damage, and exacerbated apoptosis, all potential drivers of left ventricular hypoplasia in absence of hemodynamic cues.ConclusionsOur results highlight that despite genetic heterogeneity in HLHS, many mutations converge on sequential cellular processes primarily driving cardiac myogenesis, suggesting novel therapeutic approaches.
Persistent Activity during Working Memory from Front to Back
Authors: Curtis Clayton E. , Sprague Thomas C. 
Published: April 2021

AbstractWorking memory (WM) extends the duration over which information is available for processing. Given its importance in supporting a wide-array of high level cognitive abilities, uncovering the neural mechanisms that underlie WM has been a primary goal of neuroscience research over the past century. Here, we critically review what we consider the two major ‘arcs’ of inquiry, with a specific focus on findings that were theoretically transformative. For the first arc, we briefly review classic studies that led to the canonical WM theory that cast the prefrontal cortex (PFC) as a central player utilizing persistent activity of neurons as a mechanism for memory storage. We then consider recent challenges to the theory regarding the role of persistent neural activity. The second arc, which evolved over the last decade, stemmed from sophisticated computational neuroimaging approaches enabling researchers to decode the contents of WM from the patterns of neural activity in many parts of the brain including early visual cortex. We summarize key findings from these studies, their implications for WM theory, and finally the challenges these findings pose. A comprehensive theory of WM will require a unification of these two ‘arcs’ of research.
Spatial analysis of tumor infiltrating lymphocytes based on deep learning using histopathology image to predict progression-free survival in colorectal cancer
Authors: Xu Hongming , Cha Yoon Jin , Clemenceau Jean R. , Choi Jinhwan , Lee Sung Hak , Kang Jeonghyun , Hwang Tae Hyun 
Published: April 2021

AbstractPurposeThis study aimed to explore the prognostic impact of spatial distribution of tumor infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC).MethodsThe prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n=180) and validated in the TCGA cohort (n=268). Concurrently, two experienced pathologists manually measured TILs at the most invasive margin as 0-3 by the Klintrup-Mäkinen (KM) grading method and compared to DL approaches. Interobserver agreement for TILs was measured using Cohen’s kappa coefficient.ResultsOn multivariate analysis of spatial TILs features derived by DL approaches and clinicopathological variables including tumor stage, Microsatellite instability, and KRAS mutations, TILs densities within 200 μm of the invasive margin (f_im200) was remained as the most significant prognostic factor for progression-free survival (PFS) (HR 0.004 [95% CI, 0.0001-0.1502],p=.002) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031, [95% CI 0.001-0.645],p=.024). Interobserver agreement of manual KM grading based on Cohen’s kappa coefficient was insignificant in the Yonsei (κ=.109) and the TCGA (κ=.121), respectively. The survival analysis based on KM grading showed statistically significant different PFS from the TCGA cohort, but not the Yonsei cohort.ConclusionsAutomatic quantification of TILs at the invasive margin based on DL approaches showed a prognostic utility to predict PFS, and could provide robust and reproducible TILs density measurement in patients with CRC.Data and Code AvailabilitySource code and data used for this study is available at the following link:https://github.com/hwanglab/TILs_Analysis
Data-driven precision nutrition improves clinical outcomes and risk scores for IBS, depression, anxiety, and T2D
Authors: Connell Janelle , Toma Ryan , Ho Cleo Han-Chen , Shen Nan , Moura Pedro , Le Tiep , Patridge Eric , Antoine Grant , Keiser Hilary , Julian Cristina , Naidoo Uma , Tanton Damon , Banavar Guruduth , Vuyisich Momchilo 
Published: April 2021

AbstractMany studies have shown that foods and nutritional ingredients play an important role in healthy human homeostasis, either directly or via the microbiome. We have developed an objective, integrated, and automated approach to personalized food and supplement recommendations that is powered by artificial intelligence and individualized molecular data from the gut microbiome, the human host, and their interactions. The process starts with a clinically validated transcriptomic analysis of a person’s stool (and some cases also blood) sample, from which the following molecular data are computed using clinically validated bioinformatics: 1. Quantitative strain-level taxonomic classification of all microorganisms, 2. Gene expression level for microbial genes, 3. Gene expression level for all human genes. These molecular data are converted into personalized nutritional recommendations (foods and supplements) using algorithms derived from clinical research studies and domain knowledge. We describe an application of our precision nutrition technology platform to human populations with irritable bowel syndrome (IBS), depression, anxiety, and type 2 diabetes (T2D). In these pilot interventional studies, our precision nutrition program achieved significant improvements in clinical outcomes of IBS (39% for severe IBS), depression (31% for severe depression), anxiety (31% for severe anxiety), and the risk score for T2D (>30% reduction relative to the control arm). These data support the integration of data-driven precision nutrition into the standard of care.
Dissecting the co-transcriptome landscape of plants and microbiota members
Authors: Nobori Tatsuya , Cao Yu , Entila Frederickson , Dahms Eik , Tsuda Yayoi , Garrido-Oter Ruben , Tsuda Kenichi 
Published: April 2021

AbstractInteractions between plants and neighboring microbial species are fundamental elements that collectively determine the structure and function of the plant microbiota. However, the molecular basis of such interactions is poorly characterized. Here, we monocolonized Arabidopsis leaves with nine plant- associated bacteria from all major phyla of the plant microbiota and profiled co- transcriptomes of plants and bacteria. We detected both common and distinct co- transcriptome signatures among plant-commensal pairs. In planta responses of commensals were similar to those of a disarmed pathogen characterized by the suppression of genes involved in general metabolism in contrast to a virulent pathogen. We identified genes that are enriched in the genome of plant-associated bacteria and induced in planta, which may be instrumental for bacterial adaptation to the host environment and niche separation. This study provides insights into how plants discriminate among bacterial strains and lays the foundation for in- depth mechanistic dissection of plant-microbiota interactions.
Scaling between stomatal size and density in forest plants
Authors: Liu Congcong , Muir Christopher D. , Li Ying , Xu Li , Li Mingxu , Zhang Jiahui , de Boer Hugo Jan , Sack Lawren , Han Xingguo , Yu Guirui , He Nianpeng 
Published: April 2021

AbstractThe size and density of stomatal pores limit the maximum rate of leaf carbon gain and water loss (gmax) in land plants. The limits ofgmaxdue to anatomy, and its constraint by the negative correlation of stomatal size and density at broad phylogenetic scales, has been unclear and controversial. The prevailing hypothesis posits that adaptation to highergmaxis typically constrained by geometry and/or an economic need to reduce the allocation of epidermal area to stomata (stomatal-area minimization), and this would require the evolution of greater numbers of smaller stomata. Another view, supported by the data, is that across plant diversity, epidermal area allocated to guard cells versus other cells can be optimized without major trade-offs, and highergmaxwould typically be achieved with a higher allocation of epidermal area to stomata (stomatal-area increase). We tested these hypotheses by comparing their predictions for the structure of the covariance of stomatal size and density across species, applying macroevolutionary models and phylogenetic regression to data for 2408 species of angiosperms, gymnosperms, and ferns from forests worldwide. The observed stomatal size-density scaling and covariance supported the stomatal-area increase hypothesis for highgmax. A highergmaxinvolves construction costs and maintenance costs that should be considered in models assessing optimal stomatal conductance for predictions of water use, photosynthesis, and water-use efficiency as influences on crop productivity or in Earth System models.