bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bridging evolutionary game theory and metabolic models for predicting microbial metabolic interactions
Authors: Cai Jingyi , Tan Tianwei , Joshua Chan Siu Hung 
Published: May 2019

ABSTRACTMicrobial metabolic interactions impact ecosystems, human health and biotechnological processes profoundly. However, their determination remains elusive, invoking an urgent need for predictive models that seamlessly integrate metabolic details with ecological and evolutionary principles which shape the interactions within microbial communities. Inspired by the evolutionary game theory, we formulated a bi-level optimization framework termed NECom for the prediction of Nash equilibria of microbial community metabolic models with significantly enhanced accuracy. NECom is free of a long hidden ‘forced altruism’ setup in previous static algorithm while allowing for ‘sensing and responding’ between microbial members that is missing in dynamic methods. We successfully predicted several classical games in the context of metabolic interactions that were falsely or incompletely predicted by existing methods, including prisoner’s dilemma, snowdrift game and mutualism. The results provided insights into why mutualism is favorable despite seemingly costly cross-feeding metabolites, and demonstrated the potential to predict heterogeneous phenotypes among the same species. NECom was then applied to a reported algae-yeast co-culture system that shares typical cross-feeding features of lichen, a model system of mutualism. More than 1200 growth conditions were simulated, of which 488 conditions correspond to 3221 experimental data points. Without fitting any ad-hoc parameters, an overall 63.5% and 81.7% reduction in root-mean-square error in predicted growth rates for the two species respectively was achieved when compared with the standard flux balance analysis. The simulation results further show that growth-limiting crossfeeding metabolites can be pinpointed by shadow price analysis to explain the predicted frequency-dependent growth pattern, offering insights into how stabilizing microbial interactions control microbial populations.
Tracking progress towards malaria elimination in China: estimates of reproduction numbers and their spatiotemporal variation
Authors: Routledge Isobel , Lai Shengjie , Battle Katherine E , Ghani Azra C , Gomez-Rodriguez Manuel , Gustafson Kyle B , Mishra Swapnil , Proctor Joshua L , Tatem Andrew J , Li Zhongjie , Bhatt Samir 
Published: May 2019

AbstractChina reported zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we jointly estimate the case reproduction number, Rc, and the number of unobserved sources of infection. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean Rc of 0.005 projected for the year 2019, locally-acquired cases are possible due to high levels of importation.
Informed and uninformed empirical therapy policies
Authors: Houy Nicolas , Flaig Julien 
Published: May 2019

AbstractWe argue that a proper distinction must be made between informed and uninformed decision making when setting empirical therapy policies, as this allows to estimate the value of gathering more information and to set research priorities. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in a health care facility, and the emergence and spread of resistance to two drugs. We focus on information and uncertainty regarding the parameters of this model. We consider a family of adaptive policies. In the uninformed setting, the best adaptive policy allows to reduce the average cumulative infected patient-days over two years by 39.3% (95% CI: 30.3% – 48.1%) compared to the combination therapy. Choosing empirical therapy policies while knowing the exact parameter values allows to further decrease the cumulative infected patient-days on average by 3.9% (95% CI: 2.1% – 5.8%). In our setting, the benefit of perfect information might be offset by increased drug consumption.
A toolbox of nanobodies developed and validated for diverse neuroscience research applications
Authors: Dong Jie-Xian , Lee Yongam , Kirmiz Michael , Palacio Stephanie , Dumitras Camelia , Moreno Claudia M. , Sando Richard , Santana L. Fernando , Südhof Thomas C. , Gong Belvin , Murray Karl D. , Trimmer James S. 
Published: May 2019

SUMMARYNanobodies (nAbs) are small, minimal antibodies that have distinct attributes that make them uniquely suited for certain biomedical research, diagnostic and therapeutic applications. Prominent uses include as intracellular antibodies or intrabodies to bind and deliver cargo to specific proteins and/or subcellular sites within cells, and as nanoscale immunolabels for enhanced tissue penetration and improved spatial imaging resolution. Here, we report the generation and validation of nAbs against a set of proteins prominently expressed at specific subcellular sites in brain neurons. We describe a novel hierarchical validation pipeline to systematically evaluate nAbs isolated by phage display for effective and specific use as intrabodies and immunolabels in mammalian cells including brain neurons. These nAbs form part of a robust toolbox for targeting proteins with distinct and highly spatially-restricted subcellular localization in mammalian brain neurons, allowing for visualization and/or modulation of structure and function at those sites.
Patch Testing in Lao medical students
Authors: Wootton Catriona I. , Soukavong Mick , Kidoikhammouan Sonexai , Samountry Bounthome , English John S.C. , Mayfong Mayxay 
Published: May 2019

AbstractBackgroundDermatological services in Laos, South East Asia are limited to the capital and patch testing is currently not available, so no data exists regarding the common cutaneous allergens in this population.ObjectivesThe aim of this study was to document common allergens in medical students in Laos. Patients/Materials/MethodsOne hundred and fifty medical students were patch tested using TRUE Test® panels 1 to 3 (35 allergens). Readings were taken at Days 2 and 4.ResultsThirty-eight students (25.3%) had a positive reaction to at least one allergen, accounting for 52 reactions in total. The proportion of the students with positive patch test reading was significantly higher in the female [33/96 (34%)] than in the male [5/54 (9%)], p<0.001. The most common allergens were: nickel (10%), gold (6.6%), thiomersal (6.6%), cobalt dichloride (2%) and p-tert-Butylphenol formaldehyde resin (2%). Balsam of Peru (0.66%), black rubber mix (0.66%), Cl+Me-Isothiazolinone (0.66%), fragrance mix 1 (0.66%), quinolone mix (0.66%), methyldibromo glutaronitrile (0.66%), mercapto mix (0.66%), epoxy resin (0.66%), paraben mix (0.66%), thiuram (0.66%) and wool alcohols (0.66%) accounted for all of the other positive reactions.ConclusionThis study represents the first documented patch test results in Lao medical students and in the adult Lao population. The results of this study will inform any future research into contact allergy in Laos and give an insight into the background level of contact sensitivity in this population.
TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity
Authors: Theaud Guillaume , Houde Jean-Christophe , Boré Arnaud , Rheault François , Morency Felix , Descoteaux Maxime 
Published: May 2019

AbstractA diffusion MRI (dMRI) tractography processing pipeline should be: i) reproducible in immediate test-test, ii) reproducible in time, iii) efficient and iv) easy to use. Two runs of the same processing pipeline with the same input data should give the same output today, tomorrow and in 2 years. However, processing dMRI data requires a large number of steps (20+ steps) that, at this time, may not be reproducible between runs or over time. If parameters such as the number of threads or the random number generator are not carefully set in the brain extraction, registration and fiber tracking steps, the end tractography results obtained can be far from reproducible and limit brain connectivity studies. Moreover, processing can take several hours to days of computation for a large database, even more so if the steps are running sequentially.To handle these issues, we presentTractoFlow, a fully automated pipeline that processes datasets from the raw diffusion weighted images (DWI) to tractography. It also outputs classical diffusion tensor imaging measures (fractional anisotropy (FA) and diffu-sivities) and several HARDI measures (Number of Fiber Orientation (NuFO), Apparent Fiber Density (AFD)). The pipeline requires a DWI and T1-weighted image as NIfTI files and b-values/b-vectors in FSL format. An optional reversed phase encoded b=0 image can also be used. This pipeline is based on two technologies:NextflowandSingularity, as well as recommended pre-processing and processing steps from the dMRI community. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98% to 100% depending on parameter choices. For example, 105 subjects from the Human Connectome Project (HCP) were fully ran in twenty-five (25) hours to produce, for each subject, a whole-brain tractogram with 4 million streamlines. The contribution of this paper is to introduce the importance of a robust pipeline in terms of runtime and reproducibility over time. In the era of open data and open science, efficiency and reproducibility is critical in neuroimaging projects. Our TractoFlow pipeline is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.
Inferring the ecological drivers of arboviral outbreaks
Authors: Tennant Warren , McKinley Trevelyan , Recker Mario 
Published: May 2019

AbstractThe emergence and wide-spread circulation of mosquito-transmitted viral diseases, such as dengue, Zika and Chikungunya, is a global public health concern. In the absence of effective vaccines, current control measures are mostly targeted against the mosquito vector and have so far only shown limited success. The reliance on mosquitoes for transmission also imposes strong ecological constraints that can introduce significant spatial and temporal variations in disease incidence. However, the way that epidemiological and ecological factors interact and determine population-level disease dynamics is only partially understood. Here we fit a spatially-explicit individual based model defined within a Bayesian framework to Zika incidence data from Feira de Santana, allowing us to more precisely quantify the relationships between socio-ecological factors and arboviral outbreaks. Our results further demonstrated that the virus was likely introduced into multiple spatially segregated locations at the start of the outbreak, highlighting the benefits that spatio-temporal incidence data would bring in making modelling approaches more realistic for public health planning.
Systematic comparative analysis of single cell RNA-sequencing methods
Authors: Ding Jiarui , Adiconis Xian , Simmons Sean K. , Kowalczyk Monika S. , Hession Cynthia C. , Marjanovic Nemanja D. , Hughes Travis K. , Wadsworth Marc H. , Burks Tyler , Nguyen Lan T. , Kwon John Y. H. , Barak Boaz , Ge William , Kedaigle Amanda J. , Carroll Shaina , Li Shuqiang , Hacohen Nir , Rozenblatt-Rosen Orit , Shalek Alex K. , Villani Alexandra-Chloé , Regev Aviv , Levin Joshua Z. 
Published: May 2019

ABSTRACTA multitude of single-cell RNA sequencing methods have been developed in recent years, with dramatic advances in scale and power, and enabling major discoveries and large scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single cell and/or single nucleus profiling from three types of samples – cell lines, peripheral blood mononuclear cells and brain tissue – generating 36 libraries in six separate experiments in a single center. To analyze these datasets, we developed and applied scumi, a flexible computational pipeline that can be used for any scRNA-seq method. We evaluated the methods for both basic performance and for their ability to recover known biological information in the samples. Our study will help guide experiments with the methods in this study as well as serve as a benchmark for future studies and for computational algorithm development.
Mutational mimics of allosteric effectors: a genome editing design to validate allosteric drug targets
Authors: Tang Qingling , Villar Maria T. , Artigues Antonio , Thyfault John P. , Apte Udayan , Zhu Hao , Peterson Kenneth R. , Fenton Aron W. 
Published: May 2019

AbstractDevelopment of drugs that allosterically regulate enzyme functions to treat disease is a costly venture. Screening mutations that mimic allosteric effectorsin vitrowill identify therapeutic regulatory targets enhancing the likelihood of developing a disease treatment at a reasonable cost. We demonstrate the potential of this approach utilizing human liver pyruvate kinase (hLPYK) as a model. Inhibition of hLPYK was the first desired outcome of our screen. We identified individual point mutations that: 1) mimicked allosteric inhibition by alanine, 2) mimicked inhibition by protein phosphorylation, and 3) prevented binding of fructose-1,6-bisphosphate (Fru-1,6-BP). Our second desired screening outcome was activation of hLPYK. We identified individual point mutations that: 1) prevented hLPYK from binding alanine, the allosteric inhibitor, 2) prevented inhibitory protein phosphorylation, or 3) mimicked allosteric activation by Fru-1,6-BP. Combining the three activating point mutations produced a constitutively activated enzyme that was unresponsive to regulators. Expression of a mutant hLPYK transgene containing these three mutations in a mouse model was not lethal. Thus, mutational mimics of allosteric effectors will be useful to confirm whether allosteric activation of hLPYK will control glycolytic flux in the diabetic liver to reduce hepatic glucose production and, in turn, reduce or prevent hyperglycemia.
Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm
Authors: Paul Robert , Cho Kyu , Mellins Claude , Malee Kathleen , Robbins Reuben , Kerr Stephen , Sophonphan Jiratchaya , Jahanshad Neda , Aurpibul Linda , Thongpibul Kulvadee , Kosalaraksa Pope , Kanjanavanit Suparat , Ngampiyaskul Chaiwat , Luesomboon Wicharn , Wongsawat Jurai , Vonthanak Saphonn , Chettra Kea , Suwanlerk Tulathip , Valcour Victor , Balla Lila , Garcia-Egan Paola M. , Preston Rebecca , Ananworanich Jintanat , Puthanakit Thanyawee ,   on behalf of the PREDICT Study Group
Published: May 2019

ABSTRACTBackgroundA subset of children with perinatal HIV (pHIV) experience long-term neurocognitive symptoms despite treatment with antiretroviral therapy. However, predictors of neurocognitive outcomes remain elusive, particularly for children with pHIV residing in low-to-middle income countries. The present study utilized a novel data analytic approach to identify clinically-relevant predictors of neurocognitive development in children with pHIV.MethodsAnalyses were conducted on a large repository of longitudinal data from 285 children with pHIV in Thailand (n=170) and Cambodia (n=115). Participants were designated as neurocognitively resilient (i.e., positive slope; n=143) or at risk (i.e., negative slope; n=142) according to annual performances on the Beery-Buktenica Developmental Test of Visual-Motor Integration over an average of 5.4 years. Gradient-boosted multivariate regression (GBM) with 5-fold cross validation was utilized to identify the optimal combination of demographic, HIV disease, blood markers, and emotional health indices that predicted classification into the two neurocognitive subgroups. Model performance was assessed using Receiver Operator Curves and sensitivity/specificity.ResultsThe analytic approach distinguished neurocognitive subgroups with high accuracy (93%; sensitivity and specificity each > 90%). Dynamic change indices and interactions between mental health and biological indices emerged as key predictors.ConclusionMachine learning-based regression defined a unique explanatory model of neurocognitive outcomes among children with pHIV. The predictive algorithm included a combination of HIV, physical health, and mental health indices extracted from readily available clinical measures. Studies are needed to explore the clinical relevance of the data-driven explanatory model, including potential to inform targeted interventions aimed at modifiable risk factors.
Cell cycle regulators control mesoderm specification in human pluripotent stem cells
Authors: Yiangou Loukia , Grandy Rodrigo A. , Sinha Sanjay , Vallier Ludovic 
Published: May 2019

ABSTRACTMesoderm is one of the three germ layers produced during gastrulation from which muscle, bones, kidneys and the cardiovascular system originate. Understanding the mechanisms controlling mesoderm specification could be essential for a diversity of applications, including the development of regenerative medicine therapies against diseases affecting these tissues. Here, we use human pluripotent stem cells (hPSCs) to investigate the role of cell cycle in mesoderm formation. For that, proteins controlling G1 and G2/M cell cycle phases were inhibited during differentiation of hPSCs into lateral plate, cardiac and presomitic mesoderm using small molecules or by conditional knock down. These loss of function experiments revealed that CDKs and pRb phosphorylation are necessary for efficient mesoderm formation in a context-dependent manner. Further investigations showed that inhibition of the G2/M regulator CDK1 decreases BMP signaling activity specifically during lateral plate mesoderm formation while reducing FGF/ERK1/2 activity in all mesoderm subtypes. Taken together, our findings reveal that cell cycle regulators direct mesoderm formation by controlling the activity of key developmental pathways.
DEAD-box protein family member DDX28 is a negative regulator of HIF-2α and eIF4E2-directed hypoxic translation
Authors: Evagelou Sonia L. , Bebenek Olivia , Specker Erin J. , Uniacke James 
Published: May 2019

ABSTRACTHypoxia occurs when there is a deficiency in oxygen delivery to tissues and is connected to physiological and pathophysiological processes such as embryonic development, wound healing, heart disease and cancer. The master regulators of oxygen homeostasis in mammalian cells are the heterodimeric hypoxia-inducible transcription factors HIF-1 and HIF-2. The oxygen-labile HIF-2α subunit has not only been implicated in transcription, but also as a regulator of eIF4E2-directed hypoxic translation. Here, we have identified the DEAD-box protein family member DDX28 as a novel interactor and negative regulator of HIF-2α that suppresses its ability to activate eIF4E2-directed translation. We demonstrate that stable silencing of DDX28 via shRNA in hypoxic human U87MG glioblastoma cells caused an increase, relative to control, to: HIF-2α protein levels, the ability of eIF4E2 to bind the m7GTP cap structure, and the translation of select eIF4E2 target mRNAs. DDX28 depletion elevated both nuclear and cytoplasmic HIF-2α, but HIF-2α transcriptional activity did not increase possibly due to its already high nuclear abundance in hypoxic control cells. Depletion of DDX28 conferred a proliferative advantage to hypoxic, but not normoxic cells, which is likely a consequence of the translational upregulation of a subset of hypoxia-response mRNAs. DDX28 protein levels are reduced in several cancers, including glioma, relative to normal tissue. Therefore, we uncover a regulatory mechanism for this potential tumor suppressor in the repression of HIF-2α- and eIF4E2-mediated translation activation of oncogenic mRNAs.
The role of tectonics and hydrothermalism in early human evolution at Olduvai Gorge
Authors: Sistiaga Ainara , Husain Fatima , Uribelarrea David , Martín-Perea David , Domínguez-Rodrigo Manuel , Ferland Troy , Freeman Katherine H. , Diez-Martín Fernando , Baquedano Enrique , Mabulla Audax , Summons Roger E. 
Published: May 2019

AbstractHominin encephalization has been at the centre of debates concerning human evolution with a consensus on a greater role for improved dietary quality. To sustain the energetic demands of larger brains, cooking was likely essential for increasing the digestibility and energy gain of meat and readily available, yet toxic starches. Here, we present the oldest geochemical evidence for a landscape influenced by tectonic activity and hydrothermal features that potentially shaped early hominin behaviour at Olduvai Gorge. Although use of fire at this time is controversial, hot springs may have provided an alternative way to thermally process dietary resources available in the 1.7 Myo Olduvai wetland. Our data supports the presence of an aquatic-dominated landscape with hydrothermal features that offered hominins new opportunities to hunt and cook readily available tubers and herbivore prey at the emergence of the Acheulean technology. Future studies should further examine whether hydrothermalism similarly influenced other critical aspects of human evolution.
Cholinergic modulation of dentate gyrus processing through dynamic reconfiguration of inhibitory circuits
Authors: Ogando Mora B. , Pedroncini Olivia , Federman Noel , Romano Sebastián A. , Brum Luciano A. , Lanuza Guillermo M. , Refojo Damian , Marin-Burgin Antonia 
Published: May 2019

SUMMARYThe dentate gyrus (DG) of the hippocampus plays a key role in memory formation and it is known to be modulated by septal projections. By performing electrophysiology and optogenetics we evaluated the role of cholinergic modulation in the processing of afferent inputs in the DG. We showed that mature granule cells (GCs), but not adult-born immature neurons, have increased responses to afferent perforant path stimuli upon cholinergic modulation. This is due to a highly precise reconfiguration of inhibitory circuits, differentially affecting Parvalbumin and Somatostatin interneurons, resulting in a nicotinic-dependent perisomatic disinhibition of GCs. This circuit reorganization provides a mechanism by which mature GCs could escape the strong inhibition they receive, creating a window of opportunity for plasticity. Indeed, coincident activation of perforant path inputs with optogenetic release of acetylcholine produced a long-term potentiated response in GCs, essential for memory formation.