bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Brain Stimulation: Imaging on a group level
Authors: Treu Svenja , Strange Bryan , Oxenford Simon , Kühn Andrea , Li Ningfei , Horn Andreas 
Published: January 2020

AbstractDeep Brain Stimulation (DBS) is an established treatment option for movement disorders and is investigated to treat a growing number of other brain disorders. It has been shown that DBS effects are highly dependent on exact electrode placement, which is especially important when probing novel indications or stereotactic targets. Thus, considering precise electrode placement is crucial when investigating efficacy of DBS targets. To measure clinical improvement as a function of electrode placement, neuroscientific methodology and specialized software tools are needed. Such tools should have the goal to make electrode placement comparable across patients and DBS centers, and include statistical analysis options to validate and define optimal targets. Moreover, to allow for comparability across different research sites, these need to be performed within an algorithmically and anatomically standardized and openly available group space. With the publication of Lead-DBS software in 2014, an open-source tool was introduced that allowed for precise electrode reconstructions based on pre- and postoperative neuroimaging data. Here, we introduce Lead Group, implemented within the Lead-DBS environment and specifically designed to meet aforementioned demands. In the present article, we showcase the various processing streams of Lead Group in a retrospective cohort of 51 patients suffering from Parkinson’s disease, who were implanted with DBS electrodes to the subthalamic nucleus (STN). Specifically, we demonstrate various ways to visualize placement of all electrodes in the group and map clinical improvement values to subcortical space. We do so by using active coordinates and volumes of tissue activated, showing converging evidence of an optimal DBS target in the dorsolateral STN. Second, we relate DBS outcome to the impact of each electrode on local structures by measuring overlap of stimulation volumes with the STN. Finally, we explore the software functions for connectomic mapping, which may be used to relate DBS outcomes to connectivity estimates with remote brain areas. We isolate a specific fiber bundle – which structurally resembles the hyperdirect pathway – that is associated with good clinical outcome in the cohort. The manuscript is accompanied by a walkthrough tutorial through which users are able to reproduce all main results presented in the present manuscript. All data and code needed to reproduce results are openly available.HighlightsWe present a novel toolbox to carry out DBS imaging analyses on a group-levelGroup electrodes are visualized in 2D and 3D and related to clinical regressorsA favorable target and connectivity profiles for the treatment of PD are validated
Complementary effects of adaptation and gain control on sound encoding in primary auditory cortex
Authors: Pennington Jacob , David Stephen 
Published: January 2020

AbstractAn important step toward understanding how the brain represents complex natural sounds is to develop accurate models of auditory coding by single neurons. A common model for auditory coding is the linear-nonlinear spectro-temporal receptive field (LN model). The LN model accounts for many features of auditory tuning, but it cannot account for long-lasting effects of sensory context on sound-evoked activity. Two mechanisms that may support these contextual effects are short-term plasticity (STP) and contrast-dependent gain control (GC), each of which has inspired an expanded version of the LN model. Both of these models improve performance over the LN model, but they have never been compared directly. Thus, it is unclear whether they account for distinct processes or describe the same phenomenon in different ways. To address this question, we recorded activity of neurons in primary auditory cortex of awake ferrets during presentation of natural sounds. We then fit models incorporating one nonlinear mechanism (GC or STP) or both (GC+STP) using this single dataset, and measured the correlation between the models’ predictions and the recorded neural activity. Both the STP and GC models performed significantly better than the LN model, but the GC+STP model performed better than either individual model. We also quantified the similarity between STP and GC model predictions and found only modest equivalence between them. Similar results were observed for a smaller dataset collected in clean and noisy acoustic contexts. These results suggest that the STP and GC models describe distinct, complementary processes in the auditory system.Significance StatementComputational models are used widely to study neural sensory coding. However, models developed in separate studies are often difficult to compare because of differences in stimuli and experimental preparation. This study develops an approach for making systematic comparisons between models that measures the net benefit of incorporating additional nonlinear elements into models of auditory encoding. This approach was then used to compare two different hypotheses for how sensory context, that is, slow changes in the statistics of the acoustic environment, influences activity in auditory cortex. Both models accounted for complementary aspects of the neural response, indicating that a hybrid model incorporating elements of both models provides the most complete characterization of auditory processing.
Integrating Proteomes for Lung Tissue and Lavage Reveals Pathways that Link Responses in Allergen-Challenged Mice
Authors: Mahood Thomas H , Pascoe Christopher D , Karakach Tobias K , Jha Aruni , Basu Sujata , Ezzati Peyman , Spicer Victor , Mookherjee Neeloffer , Halayko Andrew J 
Published: January 2020

AbstractTo capture interplay between biological pathways we analyzed the proteome from matched lung tissue and bronchoalveolar lavage fluid (BALF) of individual allergen-naïve and house dust mite (HDM)-challenged BALB/c mice, a model of allergic asthma. Unbiased label-free LC-MS/MS analysis quantified 2,675 proteins from tissue and BALF of allergen-naïve and HDM-exposed mice. In comparing the four datasets we found significantly greater diversity in proteins between lung tissue and BALF than in the changes induced by HDM challenge. The biological pathways enriched after allergen exposure were compartment-dependent. Lung tissue featured innate immune responses and oxidative stress, while BALF most strongly revealed changes in metabolism. We combined lung tissue and BALF proteomes, which principally highlighted oxidation reduction (redox) pathways, a finding influenced chiefly by the lung tissue dataset. Integrating lung and BALF proteomes also uncovered new proteins and biological pathways that may mediate lung tissue and BALF interactions after allergen challenge, for example, B Cell Receptor signaling. We demonstrate that enhanced insight is fostered when different biological compartments from the lung are investigated in parallel. Integration of proteomes from lung tissue and BALF compartments reveals new information about protein networks in the response to environmental challenge and interaction between intracellular and extracellular process.
Investigating the Genetic Architecture of Non-Cognitive Skills Using GWAS-by-Subtraction
Authors: Demange Perline A. , Malanchini Margherita , Mallard Travis T. , Biroli Pietro , Cox Simon R. , Grotzinger Andrew D. , Tucker-Drob Elliot M. , Abdellaoui Abdel , Arseneault Louise , Caspi Avshalom , Corcoran David , Domingue Benjamin , Mitchell Colter , van Bergen Elsje , Boomsma Dorret I. , Harris Kathleen M. , Ip Hill F. , Moffitt Terrie E. , Poulton Richie , Prinz Joseph , Sugden Karen , Wertz Jasmin , Williams Benjamin , de Zeeuw Eveline L. , Belsky Daniel W. , Harden K. Paige , Nivard Michel G. 
Published: January 2020

AbstractEducational attainment (EA) is influenced by cognitive abilities and by other characteristics and traits. However little is known about the genetic architecture of these “non-cognitive” contributions to EA. Here, we use Genomic Structural Equation Modelling and results of prior genome-wide association studies (GWASs) of EA (N = 1,131,881) and cognitive test performance (N = 257,841) to estimate SNP associations with variation in EA that is independent of cognitive ability. We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in EA. Phenotypic and biological annotation revealed that (1) both cognitive and non-cognitive contributions to EA were genetically correlated with socioeconomic success and longevity; and (2) non-cognitive contributions to EA were related to personality, decision making, risk-behavior, and increased risk for psychiatric disorders; (3) non-cognitive and cognitive contributions to EA were enriched in the same tissues and cell types, but (4) showed different associations with gray-matter neuroimaging phenotypes.
Longitudinal cancer evolution from single cells
Authors: Ramazzotti Daniele , Angaroni Fabrizio , Maspero Davide , Ascolani Gianluca , Castiglioni Isabella , Piazza Rocco , Antoniotti Marco , Graudenzi Alex 
Published: January 2020

ABSTRACTThe rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution in single tumors and to investigate intra-tumor heterogeneity. This is particularly relevant when assessing the efficacy of therapies over time on the clonal composition of a tumor and in the identification of resistant subclones.We here introduce LACE (Longitudinal Analysis of Cancer Evolution), the first algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. Our approach solves a Boolean matrix factorization problem with phylogenetic constraints, by maximizing a weighted likelihood function computed on multiple time points, and we show with simulations that it outperforms state-of-the-art methods for both bulk and single-cell sequencing data.Remarkably, as the results are robust with respect to high levels of data-specific errors, LACE can be employed to process single-cell mutational profiles as generated by calling variants from the increasingly available scRNA-seq data, thus obviating the need of relying on rarer and more expensive genome sequencing experiments. This also allows to investigate the relation between genomic clonal evolution and phenotype at the single-cell level.To illustrate the capabilities of LACE, we show its application to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/Kmutant melanomas, in which we characterize the impact of concurrent BRAF/MEK-inhibition on clonal evolution, also by showing that distinct genetic clones reveal different sensitivity to the therapy. Furthermore, the analysis of a longitudinal dataset of breast cancer PDXs from targeted scDNA-sequencing experiments delivers a high-resolution characterization of intra-tumor heterogeneity, also allowing the detection of a late de novo subclone.
Improved bacterial recombineering by parallelized protein discovery
Authors: Wannier Timothy M. , Nyerges Akos , Kuchwara Helene M. , Czikkely Márton , Balogh Dávid , Filsinger Gabriel T. , Borders Nathaniel C. , Gregg Christopher J. , Lajoie Marc J. , Rios Xavier , Pál Csaba , Church George M. 
Published: January 2020

AbstractExploiting bacteriophage-derived homologous recombination processes has enabled precise, multiplex editing of microbial genomes and the construction of billions of customized genetic variants in a single day. The techniques that enable this, Multiplex Automated Genome Engineering (MAGE) and directed evolution with random genomic mutations (DIvERGE), are however currently limited to a handful of microorganisms for which single-stranded DNA-annealing proteins (SSAPs) that promote efficient recombineering have been identified. Thus, to enable genome-scale engineering in new hosts, highly efficient SSAPs must first be found. Here we introduce a high-throughput method for SSAP discovery that we call Serial Enrichment for Efficient Recombineering (SEER). By performing SEER inE. colito screen hundreds of putative SSAPs, we identify highly active variants PapRecT and CspRecT. CspRecT increases the efficiency of single-locus editing to as high as 50% and improves multiplex editing by 5 to 10-fold inE. coli, while PapRecT enables efficient recombineering inPseudomonas aeruginosa, a concerning human pathogen. CspRecT and PapRecT are also active in other, clinically and biotechnologically relevant enterobacteria. We envision that the deployment of SEER in new species will pave the way toward pooled interrogation of genotype-to-phenotype relationships in previously intractable bacteria.
A global map of root biomass across the world’s forests
Authors: Huang Yuanyuan , Ciais Phillipe , Santoro Maurizio , Makowski David , Chave Jerome , Schepaschenko Dmitry , Abramoff Rose , Goll Daniel S. , Yang Hui , Chen Ye , Wei Wei , Piao Shilong 
Published: January 2020

Abstract (150 words limits)Root plays a key role in plant growth and functioning. Here we combine 10307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially-explicit global high-resolution (~ 1km) root biomass dataset, including fine and coarse roots. In total, 142 ± 32 Pg of live dry matter biomass is stored below-ground, that is a global average root:shoot biomass ratio of 0.25 ± 0.10. Our estimations of total root biomass in tropical, temperate and boreal forests are 44-226% smaller than earlier studies1–3. The smaller estimation is attributable to the updated forest area, spatially explicit above-ground biomass density used to predict the patterns of root biomass, new root measurements and upscaling methodology. We show specifically that the root shoot allometry is one underlying driver that leads to methodological overestimation of root biomass in previous estimations.
Carbon limitation leads to thermodynamic regulation of aerobic metabolism
Authors: Garayburu-Caruso Vanessa A. , Stegen James C. , Song Hyun-Seob , Renteria Lupita , Wells Jaqueline , Garcia Whitney , Resch Charles T. , Goldman Amy , Chu Rosalie , Toyoda Jason , Graham Emily B. 
Published: January 2020

AbstractOrganic matter (OM) metabolism in freshwater ecosystems is a critical source of uncertainty in global biogeochemical cycles, yet aquatic OM cycling remains poorly understood. Here, we present the first work to explicitly test OM thermodynamics as a key regulator of aerobic respiration, challenging long-held beliefs that organic carbon and oxygen concentrations are the primary determinants of respiration rates. We pair controlled microcosm experiments with ultrahigh-resolution OM characterization to demonstrate a clear relationship between OM thermodynamic favorability and aerobic respiration under carbon limitation. We also demonstrate a shift in the regulation of aerobic respiration from OM thermodynamics to nitrogen content when carbon is in excess, highlighting a central role for OM thermodynamics in aquatic biogeochemical cycling particularly in carbon-limited ecosystems. Our work therefore illuminates a structural gap in aquatic biogeochemical models and presents a new paradigm in which OM thermodynamics and nitrogen content interactively govern aerobic respiration.
Linked-read sequencing enables haplotype-resolved resequencing at population scale
Authors: Lutgen Dave , Ritter Raphael , Olsen Remi-André , Schielzeth Holger , Gruselius Joel , Ewels Phil , García Jesús T. , Shirihai Hadoram , Schweizer Manuel , Suh Alexander , Burri Reto 
Published: January 2020

AbstractThe feasibility to sequence entire genomes of virtually any organism provides unprecedented insights into the evolutionary history of populations and species. Nevertheless, many population genomic inferences – including the quantification and dating of admixture, introgression and demographic events, and the inference of selective sweeps – are still limited by the lack of high-quality haplotype information. In this respect, the newest generation of sequencing technology now promises significant progress. To establish the feasibility of haplotype-resolved genome resequencing at population scale, we investigated properties of linked-read sequencing data of songbirds of the genusOenantheacross a range of sequencing depths. Our results based on the comparison of downsampled (25x, 20x, 15x, 10x, 7x, and 5x) with high-coverage data (46-68x) of seven bird genomes suggest that phasing contiguities and accuracies adequate for most population genomic analyses can be reached already with moderate sequencing effort. At 15x coverage, phased haplotypes span about 90% of the genome assembly, with 50 and 90 percent of the phased sequence located in phase blocks longer than 1.25-4.6 Mb (N50) and 0.27-0.72 Mb (N90), respectively. Phasing accuracy reaches beyond 99% starting from 15x coverage. Higher coverages yielded higher contiguities (up to about 7 Mb/1Mb (N50/N90) at 25x coverage), but only marginally improved phasing accuracy. Finally, phasing contiguity improved with input DNA molecule length; thus, higher-quality DNA may help keeping sequencing costs at bay. In conclusion, even for organisms with gigabase-sized genomes like birds, linked-read sequencing at moderate depth opens an affordable avenue towards haplotype-resolved genome resequencing data at population scale.
The structure and global distribution of the endoplasmic reticulum network is actively regulated by lysosomes
Authors: Lu Meng , van Tartwijk Francesca W. , Lin Julie Qiaojin , Nijenhuis Wilco , Parutto Pierre , Fantham Marcus , Christensen Charles N. , Avezov Edward , Holt Christine E. , Tunnacliffe Alan , Holcman David , Kapitein Lukas C. , Schierle Gabriele Kaminski , Kaminski Clemens F. 
Published: January 2020

AbstractThe endoplasmic reticulum (ER) comprises morphologically and functionally distinct domains, sheets and interconnected tubules. These domains undergo dynamic reshaping, in response to changes in the cellular environment. However, the mechanisms behind this rapid remodeling within minutes are largely unknown. Here, we report that ER remodeling is actively driven by lysosomes, following lysosome repositioning in response to changes in nutritional status. The anchorage of lysosomes to ER growth tips is critical for ER tubule elongation and connection. We validate this causal link via the chemo- and optogenetically driven re-positioning of lysosomes, which leads to both a redistribution of the ER tubules and its global morphology. Lysosomes sense metabolic change in the cell and regulate ER tubule distribution accordingly. Dysfunction in this mechanism during axonal extension may lead to axonal growth defects. Our results demonstrate a critical role of lysosome-regulated ER dynamics and reshaping in nutrient responses and neuronal development.
A Connectivity-based Psychometric Prediction Framework for Brain-behavior Relationship Studies
Authors: Wu Jianxiao , Eickhoff Simon B. , Hoffstaedter Felix , Patil Kaustubh R. , Schwender Holger , Yeo B.T. Thomas , Genon Sarah 
Published: January 2020

AbstractThe recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions’ connectivity profiles. We first illustrate two main applications: 1) single brain region’s predictive power for a range of psychometric variables, and 2) single psychometric variable’s predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.
RNA-dependent structures of the RNA-binding loop in the flavivirus NS3 helicase
Authors: Davidson Russell B. , Hendrix Josie , Geiss Brian J. , McCullagh Martin 
Published: January 2020

AbstractThe flavivirus NS3 protein is a helicase that has pivotal functions during the viral genome replication process, where it unwinds double-stranded RNA and translocates along the nucleic acid polymer in a nucleoside triphosphate hydrolysis-dependent mechanism. An increased interest in this enzyme as a potential target for development of antiviral therapeutics was sparked by the 2015 Zika virus epidemic in the Americas. Crystallographic and computational studies of the flavivirus NS3 helicase have identified the RNA-binding loop as an interesting structural element, which may function as an origin for the RNA-enhanced NTPase activity observed for this family of helicases. Microsecond-long unbiased molecular dynamics as well as extensive replica exchange umbrella sampling simulations of the Zika NS3 helicase have been performed to investigate the RNA-dependence of this loop’s structural conformations. Specifically, the effect of the bound single-stranded RNA (ssRNA) oligomer on the putative “open” and “closed” conformations of this loop are studied. In the Apo substrate state, the two structures are nearly isoergonic (ΔGO→C = −0.22 kcal mol−1), explaining the structural ambiguity observed in Apo NS3h crystal structures. The bound ssRNA is seen to stabilize the “open” conformation (ΔGO→C = 1.97 kcal mol−1) through direct protein-RNA interactions at the top of the loop. Interestingly, a small ssRNA oligomer bound over 13 Å away from the loop is seen to affect the free energy surface to favor the “open” structure while minimizing barriers between the two states. The mechanism of the transition between “open” and “closed” states is characterized as are residues of importance for the RNA-binding loop structures. From these results, the loop is hypothesized to be a viable region in the protein for targeted small-molecule inhibition and mutagenesis studies, where stabilization of the “closed” RNA-binding loop will negatively impact RNA-binding and the RNA-enhanced NTPase activity.