bluuebunny/crossref_metadata_2025_split huggingface dataset:
Socioeconomic inequalities in blood pressure: co-ordinated analysis of 147,775 participants from repeated birth cohort and cross-sectional datasets, 1989 to 2016
Authors: Bann David , Fluharty Meg , Hardy Rebecca , Scholes Shaun 
Published: December 2019

ObjectiveTo evaluate whether socioeconomic inequalities in blood pressure (BP) have changed from 1989 to 2016—a period in which average BP levels declined and the detection and treatment of high BP increased.DesignRepeated observational studies.Setting3 nationally representative British birth cohort studies—born in 1946, 1958, and 1970—and 21 nationally representative cross-sectional studies (Health Survey for England 1994 to 2016, HSE).Participants147,775 participants with BP outcomes at age 42-46 years (cohorts) or 25 years and over (HSE).Main outcome measureAbsolute differences in systolic BP (SBP) by educational attainment (cohorts and HSE) and early life social class (cohorts).ResultsIn both datasets, lower education was associated with higher SBP, with similar absolute magnitudes of inequality across the studied period. Differences in SBP by education (Slope Index of Inequality) based on HSE data were 3.0mmHg (95% CI: 1.8, 4.2) in 1994 and 4.3mmHg (2.3, 6.3) in 2016. Findings were similar for diastolic BP (DBP) and survey-defined hypertension. Cohort data suggested that disadvantage in early and adult life had cumulative independent associations with BP: cohort-pooled differences in SBP were 4.9mmHg (3.7, 6.1) in a score combining early life social class and own education, yet were 3.4mmHg (2.4, 4.4) for education alone. In both datasets, inequalities were found across the SBP distribution—below and above hypertension thresholds—yet were larger at the upper tail; in HSE, median SBP differences were 2.8mmHg (1.7, 3.9) yet 5.6mmHg (4.9, 6.4) at the 90th quantile.ConclusionSocioeconomic inequalities in BP have persisted from 1989 to 2016, despite improved detection and treatment of high BP. To achieve future reductions in BP inequalities, policies addressing the wider structural determinants of high BP levels are likely required—targeting detection and treatment alone is unlikely to be sufficient.
Release of human TFIIB from actively transcribing complexes is triggered upon synthesis of 7 nt and 9 nt RNAs
Authors: Ly Elina , Powell Abigail E. , Goodrich James A. , Kugel Jennifer F. 
Published: December 2019

AbstractRNA polymerase II (Pol II) and its general transcription factors assemble on the promoters of mRNA genes to form large macromolecular complexes that initiate transcription in a regulated manner. During early transcription these complexes undergo dynamic rearrangement and disassembly as Pol II moves away from the start site of transcription and transitions into elongation. One step in disassembly is the release of the general transcription factor TFIIB, although the mechanism of release and its relationship to the activity of transcribing Pol II is not understood. We developed a single molecule fluorescence transcription system to investigate TFIIB release in vitro. Leveraging our ability to distinguish active from inactive complexes, we found that nearly all transcriptionally active complexes release TFIIB during early transcription. Release is not dependent on the contacts TFIIB makes with its recognition element in promoter DNA. We identified two different points in early transcription at which release is triggered, reflecting heterogeneity across the population of actively transcribing complexes. TFIIB releases after both trigger points with similar kinetics, suggesting the rate of release is independent of the molecular transformations that prompt release. Together our data support the model that TFIIB release is important to maintain the transcriptional activity of Pol II as initiating complexes transition into elongation complexes.
Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter
Authors: Richie-Halford Adam , Yeatman Jason , Simon Noah , Rokem Ariel 
Published: December 2019

AbstractThe white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) data to quantify tissue properties (e.g. fractional anisotropy (FA), mean diffusivity (MD), etc.), along the trajectories of these connections [1]. Statistical inference from tractometry usually either (a) averages these quantities along the length of each bundle in each individual, or (b) performs analysis point-by-point along each bundle, with group comparisons or regression models computed separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. In the present work, we developed a method based on the sparse group lasso (SGL) [2] that takes into account tissue properties measured along all of the bundles, and selects informative features by enforcing sparsity, not only at the level of individual bundles, but also across the entire set of bundles and all of the measured tissue properties. The sparsity penalties for each of these constraints is identified using a nested cross-validation scheme that guards against over-fitting and simultaneously identifies the correct level of sparsity. We demonstrate the accuracy of the method in two settings: i) In a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls [3]. Furthermore, SGL automatically identifies FA in the corticospinal tract as important for this classification – correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, dMRI is used to accurately predict “brain age” [4, 5]. In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change with development and contribute to the prediction of age. Thus, SGL makes it possible to leverage the multivariate relationship between diffusion properties measured along multiple bundles to make accurate predictions of subject characteristics while simultaneously discovering the most relevant features of the white matter for the characteristic of interest.
From village to globe: A dynamic real-time map of African fields through PlantVillage
Authors: Kehs Annalyse , McCloskey Peter , Chelal John , Morr Derek , Amakove Stellah , Plimo Bismark , Mayieka John , Ntango Gladys , Nyongesa Kelvin , Pamba Lawrence , Jeptoo Melodine , Mugo James , Tsuma Mercyline , Onyango Winnie , Hughes David 
Published: December 2019

AbstractA major bottleneck to the application of machine learning tools to satellite data of African farms is the lack of high-quality ground truth data. Here we describe a high throughput method using youth in Kenya that results in a cost-effective method for high-quality data in near real-time. This data is presented to the global community, as a public good, on the day it is collected and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.
Tuft cells restrain pancreatic tumorigenesis through paracrine eicosanoid signaling
Authors: DelGiorno Kathleen E. , Chung Chi-Yeh , Mauer H. Carlo , Novak Sammy Weiser , Giraddi Rajshekhar R. , Wang Dezhen , Naeem Razia F. , Fang Linjing , Andrade Leonardo R. , Lytle Nikki K. , Ali Wahida H. , Tsui Crystal , Gubbala Vikas B. , Ridinger-Saison Maya , Ohmoto Makoto , O’Connor Carolyn , Erikson Galina A. , Shokhirev Maxim Nikolaievich , Urade Yoshihiro , Matsumoto Ichiro , Vavinskaya Vera , Singh Pankaj K. , Manor Uri , Olive Kenneth P. , Wahl Geoffrey M. 
Published: December 2019

AbstractDespite numerous advances in our understanding of pancreatic ductal adenocarcinoma (PDA) genetics and biology, this disease is expected to become the second leading cause of cancer-related U.S. deaths within the next few years. Incomplete understanding of how it arises precludes development of early detection and interception strategies to improve therapeutic outcomes. Acinar to ductal metaplasia involving genesis of tuft cells is one early step in PDA formation, but their functional significance has remained obscure due to their rarity and a lack of methods and relevant animal models for their molecular and functional analysis. Here, we show that deletion of tuft cell master regulator Pou2f3 eliminates pancreatic tuft cells and increases fibrosis, alters immune cell activation, and accelerates disease progression. We demonstrate that tuft cell expression of the prostaglandin D2synthase Hpgds restrains pancreatic disease progression in early stages by inhibiting stromal activation. Analyses of human data sets are consistent with mouse studies. We propose that tuft cells and, by inference, the associated metaplastic lesions, play a protective role early in pancreatic tumorigenesis.SignificanceWe find that tuft cell formation in response to oncogenicKrasis protective and restrains tumorigenesis through local production of anti-inflammatory substances, including paracrine prostaglandin D2signaling to the stroma. Our findings establish tuft cells as a metaplasia-induced tumor suppressive cell type.
Endocytosis frustration potentiates compression-induced receptor signaling
Authors: Baschieri Francesco , Le Devedec Dahiana , Elkhatib Nadia , Montagnac Guillaume 
Published: December 2019

AbstractCells experience mechanical stresses in different physiological and pathological settings. Clathrin-coated structures (CCSs) are sensitive to such perturbations in a way that often results in a mechanical impairment of their capacity to bud, ultimately impairing endocytosis. Compressive stress is a particular mechanical perturbation that leads to increased membrane tension and promotes proliferative signals. Here, we report that compression leads to CCSs frustration and that CCSs are required to potentiate receptor-mediated signaling in these conditions. We first confirmed that pressure stalls CCSs dynamics and showed that it also slows down the dynamic exchange of CCSs building blocks. As previously reported, compression-induced paracrine activation of the epidermal growth factor receptor (EGFR) was the primary cause of ERK activation in these conditions. We observed that the EGFR was efficiently recruited at CCSs upon compression and that CCSs were required for full ERK activation. In addition, we demonstrated that compression-induced frustrated CCSs could also serve as signaling platforms for the hepatocyte growth factor receptor (HGFR), provided HGF was present in the medium. We thus propose that, besides the particular case of EGFR paracrine activation, CCS frustration resulting from mechanical perturbations can potentiate signaling through different receptors with potential important consequences on cell adaptation to its environment.
Quantifying and modelling non-local information processing of associative brain regions
Authors: Balkenhol Johannes , Prada Juan , Ehrenreich Hannelore , Grohmann Johannes , Kistowski Jóakim v. , Wojcik Sonja M. , Blum Robert , Kounev Samuel , Dandekar Thomas 
Published: December 2019

Short abstractBrain world representation emerges not by philosophy but from integrating simple followed by more complex actions (due to drives, instincts) with sensory feedback and inputs such as rewards. Our simulation provides this world representation holistically by identical information encoded as holographic wave patterns for all associative cortex regions. Observed circular activation in cell culture experiments provides building blocks from which such an integrative circuit can evolve just by excitation and inhibition transfer to neighbouring neurons. Large-scale grid-computing of the simulation brought no new emergent phenomena but rather linear gains and losses regarding performance. The circuit integrates perceptions and actions. The resulting simulation compares well with data from electrophysiology, visual perception tasks, and oscillations in cortical areas. Non-local, wave-like information processing in the cortex agrees well with EEG observations such as cortical alpha, beta, and gamma oscillations. Non-local information processing has powerful emergent properties, including key features of conscious information processing.
Phenotypic delay in the evolution of bacterial antibiotic resistance: mechanistic models and their implications
Authors: Carballo-Pacheco Martín , Nicholson Michael D. , Lilja Elin E. , Allen Rosalind J. , Waclaw Bartlomiej 
Published: December 2019

AbstractPhenotypic delay – the time delay between genetic mutation and expression of the corresponding phenotype – is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data.Author SummaryUnderstanding precisely how some bacteria survive exposure to antibiotics is a major research focus. Specific mutations in the bacterial genome are known to provide protection. However, it remains unclear how much time passes between a bacterium acquiring the genetic change and being able to tolerate antibiotics - termed the phenotypic delay - and what controls this delay. Here, using computer simulations and mathematical arguments we discuss three biologically plausible mechanisms of phenotypic delay. We investigate how each mechanism would affect the outcome of laboratory experiments often used to study the evolution of antibiotic resistance, and we highlight how the delay might be detected in such experiments. We also show that the existence of the delay could explain an observed discrepancy in the measurement of mutation rates, and demonstrate that one of our models provides a superior fit to experimental data. Our work exposes how molecular details at the intracellular level can have a direct effect on evolution at the population level.
High-polyphenol extracts fromSorghum bicolorattenuate replication ofLegionella pneumophilawithin RAW 264.7 macrophages
Authors: Gilchrist Aubrey M. , Smolensky Dmitriy , Cox Sarah , Perumal Ramasamy , Noronha Leela E. , Shames Stephanie R. 
Published: December 2019

AbstractPolyphenols derived from a variety of plants have demonstrated antimicrobial activity against diverse microbial pathogens.Legionella pneumophilais an intracellular bacterial pathogen that opportunistically causes a severe inflammatory pneumonia in humans, called Legionnaires’ Disease, via replication within macrophages. Previous studies demonstrated that tea polyphenols attenuateL. pneumophilaintracellular replication within mouse macrophages via amplification of tumor necrosis factor (TNF) production.Sorghum bicoloris a sustainable and resilient cereal crop that thrives in arid environments and is well suited to continued production in warming climates. Polyphenols derived from sorghum have anticancer and antioxidant properties, but their antimicrobial activity has not been evaluated. Here, we investigated the impact of sorghum polyphenols onL. pneumophilaintracellular replication within RAW 264.7 mouse macrophages. We discovered that sorghum high-polyphenol extract (HPE) treatment attenuatesL. pneumophilaintracellular replication in a dose-dependent manner. Sorghum HPE did not impair bacterial replicationin vitroor impact macrophage viability. Moreover, in contrast to tea polyphenols, HPE treatment impaired TNF secretion from infected macrophages. Thus, polyphenols derived from sorghum enhance macrophage restriction ofL. pneumophilaby a novel mechanism. This work provides a foundation for the use of sorghum as an antimicrobial agent.
CerebrA: Accurate registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template
Authors: Manera Ana L. , Dadar Mahsa , Fonov Vladimir , Collins D. Louis 
Published: December 2019

AbstractAccurate anatomical atlases are recognized as important tools in brain-imaging research. They are widely used to estimate disease-specific changes and therefore, are of great relevance in extracting regional information on volumetric variations in clinical cohorts in comparison to healthy populations. The use of high spatial resolution magnetic resonance imaging and the improvement in data preprocessing methods have enabled the study of structural volume changes on a wide range of disorders, particularly in neurodegenerative diseases where different brain morphometry analyses are being broadly used in an effort to improve diagnostic biomarkers.In the present dataset, we introduce the Cerebrum Atlas (CerebrA) along with the MNI-ICBM2009c average template. MNI-ICBM2009c is the most recent version of the MNI-ICBM152 brain average, providing a higher level of anatomical details. Cerebra is based on an accurate non-linear registration of cortical and subcortical labelling from Mindboggle 101 to the symmetric MNI-ICBM2009c atlas, followed by manual editing.
Energy-based Graph Convolutional Networks for Scoring Protein Docking Models
Authors: Cao Yue , Shen Yang 
Published: December 2019

AbstractStructural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative to predict such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent proteins and encounter complexes as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that pool interacting nodes’ features through edge features so that generalized interaction energies can be learned directly from graph data. The resulting energy-based graph convolutional networks (EGCN) with multi-head attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN has significantly improved ranking for a CAPRI test set involving homology docking; and is comparable for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.
Quantifying and Modelling Transfer Learning in Mice Between Consecutive Training Stages of a Change Detection Task
Authors: de Abril Ildefons Magrans , Garrett Marina , Ollerenshaw Douglas R. , Groblewski Peter A. , Olsen Shawn , Mihalas Stefan 
Published: December 2019

ABSTRACTAnimals are known to be able to rapidly transfer knowledge between tasks with similar structure. We trained a set of mice on a visual change detection task with multiple stages, starting with direct transitions between gratings, adding an intervening gray screen, and subsequently moving to multiple sets of natural images. We observe that, when progressing to new stages, the performance increases very fast. However, when transitioning to a task of higher complexity, the peak performance decreases. This setup facilitates for the first time an experimental platform to study the transfer learning phenomena in mice using visual stimuli. Based on these results and additional neuroscience insight, we propose a cognitive model to explain the quick adaptation observed in mice. It extends a deep Q learning agent with a multi-tiered architecture and the possibility of performing a representation remapping at every level of the hierarchy to prevent the downstream propagation of representation anomalies. This architecture provides the substrate of an adaptation algorithm based on ideas of optimal transport of probability distributions. It matches well key behavioral aspects observed in mice and the experimental constraint that the mice are initially trained using a single task variant before it transitions to the new training phase. The modelling process helped us to gain biological insights: first, the optimal transport mechanism of the representation remapping indicated that a possible reason for the reduced performance when mice move to a new more complex task could be due to limited representation resources which were optimized for the previous task. Second, the multi-tiered architecture was first an engineering constraint and later a biologic insight confirmed by the literature. A final insight came from the computations required to perform the representation remapping. These computations are interesting because they could help to confirm this transfer learning theory by looking for similar neural correlates during the adaptation process.