bluuebunny/crossref_metadata_2025_split huggingface dataset:
Patient Preferences to Undergo Low-Value CT Coronary Angiography in the Emergency Department
Authors: Winkels Jessica L. , Smith Chelsea Morrow , Iyengar Rahul , Meka Arjun P. , Porath Jonathan D. , Meurer William J. 
Published: October 2019

AbstractBackgroundLow-value diagnostic testing adds billions of dollars to the cost of health care in the US annually. Addressing patient preference for these tests is one possible strategy to limit overuse. In previous work, we showed that patient preference for testing can be influenced by test benefit, risk, and financial measures. Our objective was to examine the effect of these variables in another clinical scenario involving chest pain.MethodsIn this cross-sectional survey of patients at the University of Michigan Emergency Department (ED), participants were given a hypothetical scenario involving an ED visit for chest pain, along with information regarding potential benefit (detecting a life-threatening condition; 0.1 or 1%) and risk (developing cancer; 0.1 or 1%) of CTCA, as well as an incentive of $0 or $100 to forego testing. Values for risk, benefit, and financial incentive varied across participants. Our primary outcome was patient preference to undergo testing. We also obtained demographic and numeracy information. Then, we used logistic regression to estimate odds ratios, adjusting for multiple potential confounders. Our sample size was designed to find at least 300 events (test acceptance) to allow for up to 30 covariates in fully adjusted models. We had 85-90% power to detect a 10% absolute difference in testing rate across groups, assuming a 95% significance level.Results913 patients were surveyed. A $100 financial incentive (adjusted OR [AOR] 0.57; 95% Confidence Interval [CI] 0.42-0.78) and increased test risk (AOR 0.61; 95% CI 0.44-0.84) both significantly decreased test acceptance in fully adjusted models, whereas increased test benefit significantly increased test acceptance (AOR 2.45; 95% CI 1.79-3.36).ConclusionsOffering a financial incentive deterred patients from accepting testing despite varying levels of risk and benefit. In the context of previous work, we provide preliminary evidence supporting that financial interventions may impact patient preference more than test risk and benefit.
Computational Modeling of Ovarian Cancer Reveals Optimal Strategies for Therapy and Screening
Authors: Gu Shengqing , Lheureux Stephanie , Sayad Azin , Cybulska Paulina , Hogen Liat Ben-David , Vyarvelska Iryna , Tu Dongsheng , Parulekar Wendy , Nankivell Matthew , Kehoe Sean , Chi Dennis , Levine Douglas A. , Bernardini Marcus Q. , Rosen Barry , Oza Amit , Neel Benjamin G. 
Published: October 2019

AbstractHigh-grade serous tubo-ovarian carcinoma (HGSC) is a major cause of cancer-related death. Whether treatment order—primary debulking surgery followed by adjuvant chemotherapy (PDS) or neo-adjuvant chemotherapy with interval surgery (NACT)—affects outcome is controversial. We developed a mathematical framework that holds for hierarchical or stochastic models of tumor initiation and reproduces HGSC clinical course. After estimating parameter values, we infer that most patients harbor chemo-resistant HGSC cells at diagnosis, and that if complete debulking (<1 mm residual tumor) can be achieved, PDS is superior to NACT due to better depletion of resistant cells. We further predict that earlier diagnosis of primary HGSC, followed by complete debulking, could improve survival, but its benefit in relapsed patients is likely to be limited. Our predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HGSC management.Significance StatementThe optimal order and timing of surgery and chemotherapy, and the potential benefits of earlier diagnosis of HGSC, remain controversial. We developed a mathematical framework of tumor dynamics to address such issues, populated the model with primary clinical data and reliably recapitulated clinical observations. Our model prospectively predicts that: (1) PDS is superior to NACT when complete debulking is feasible; (2) timely adjuvant chemotherapy is critical for the outcome of PDS with <1mm, but not >1mm, residual tumors; (3) earlier detection of relapse is unlikely to be beneficial with current therapies; (4) earlier detection of primary HGSC, followed by complete debulking, could have substantial benefit. Our model provides insights into the evolutionary dynamics of HGSC, argues for new clinical trials to optimize HGSC therapy, and is potentially applicable to other tumor types.
Yersinia canariaesp. nov., isolated from a human yersiniosis case
Authors: Nguyen Scott V. , Greig David R. , Hurley Daniel , Cao Yu , McCabe Evonne , Mitchell Molly , Jenkins Claire , Fanning Séamus 
Published: October 2019

ABSTRACTA Gram-negative rod from theYersiniagenus was isolated from a clinical case of yersiniosis in the United Kingdom. Long read sequencing data from an Oxford Nanopore Technology (ONT) MinION in conjunction with Illumina HiSeq reads were used to generate a finished quality genome of this strain. Overall Genome Related Index (OGRI) of the strain was used to determine that it was a novel species withinYersinia, despite biochemical similarities toYersinia enterocolitica. The 16S ribosomal RNA gene accessions areMN434982-MN434987and the accession number for the complete and closed chromosome isCP043727.The type strain is CFS3336T(=NCTC 14382T/ =LMGAccession under process).
Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns
Authors: Osmala Maria , Lähdesmäki Harri 
Published: October 2019

AbstractBackgroundThe binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq). The resulting chromatin feature data has been successfully adopted for genome-wide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type and do not utilise the pattern of the ChIP-seq coverage profiles efficiently.ResultsIn this work, we propose a PRobabilistic Enhancer PRedictIoN Tool (PREPRINT) that assumes characteristic coverage patterns of chromatin features at enhancers and employs a statistical model to account for their variability. PREPRINT defines probabilistic distance measures to quantify the similarity of the genomic query regions and the characteristic coverage patterns. The probabilistic scores of the enhancer and non-enhancer samples are utilised to train a kernel-based classifier. The performance of the method is demonstrated on ENCODE data for two cell lines. The predicted enhancers are computationally validated based on the transcriptional regulatory protein binding sites and compared to the predictions obtained by state-of-the-art methods.ConclusionPREPRINT performs favorably to the state-of-the-art methods, especially when requiring the methods to predict a larger set of enhancers. PREPRINT generalises successfully to data from cell type not utilised for training, and often the PREPRINT performs better than the previous methods. The PREPRINT enhancers are less sensitive to the choice of prediction threshold. PREPRINT identifies biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.Availabilityhttps://github.com/MariaOsmala/preprintContactmaria.osmala@aalto.fi
Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping
Authors: Artzet Simon , Chen Tsu-Wei , Chopard Jérôme , Brichet Nicolas , Mielewczik Michael , Cohen-Boulakia Sarah , Cabrera-Bosquet Llorenç , Tardieu François , Fournier Christian , Pradal Christophe 
Published: October 2019

AbstractIn the era of high-throughput visual plant phenotyping, it is crucial to design fully automated and flexible workflows able to derive quantitative traits from plant images. Over the last years, several software supports the extraction of architectural features of shoot systems. Yet currently no end-to-end systems are able to extract both 3D shoot topology and geometry of plants automatically from images on large datasets and a large range of species. In particular, these software essentially deal with dicotyledons, whose architecture is comparatively easier to analyze than monocotyledons. To tackle these challenges, we designed the Phenomenal software featured with: (i) a completely automatic workflow system including data import, reconstruction of 3D plant architecture for a range of species and quantitative measurements on the reconstructed plants; (ii) an open source library for the development and comparison of new algorithms to perform 3D shoot reconstruction and (iii) an integration framework to couple workflow outputs with existing models towards model-assisted phenotyping. Phenomenal analyzes a large variety of data sets and species from images of high-throughput phenotyping platform experiments to published data obtained in different conditions and provided in a different format. Phenomenal has been validated both on manual measurements and synthetic data simulated by 3D models. It has been also tested on other published datasets to reproduce a published semi-automatic reconstruction workflow in an automatic way. Phenomenal is available as an open-source software on a public repository.
How do we segment text? Two-stage chunking operation in reading
Authors: Yang Jinbiao , Cai Qing , Tian Xing 
Published: October 2019

AbstractChunking in language comprehension is a process that segments continuous linguistic input into smaller chunks that are in reader’s mental lexicon. Effective chunking during reading facilitates disambiguation and enhances efficiency for comprehension. However, the mechanisms of chunking remain elusive, especially in reading given that information arrives simultaneously yet the written systems may not have explicit cues for labeling boundaries such as Chinese. What are the mechanisms of chunking operation that mediates the reading of the text that normally contains hierarchical information? We investigated this question by manipulating the lexical status of the chunks at distinct levels of grain-size in four-character Chinese strings, including the two-character local chunk and four-character global chunk. Participants were asked to make lexical decision on these strings in a behavioral experiment, followed by a passive reading task when their electroencephalography (EEG) were recorded. The behavioral results showed that the lexical decision time of lexicalized two-character local chunks was influenced by the lexical status of four-character global chunk, but not vice versa, which indicated that the processing of global chunks possessed priority over the local chunks. The EEG results revealed that familiar lexical chunks were detected simultaneously at both levels and further processed in a different temporal order -- the onset of lexical access for the global chunks was earlier than that of local chunks. These consistent behavioral and EEG results suggest that chunking in reading occurs at multiple levels via a two-stage operation -- simultaneous detection and global-first recognition.Significance StatementThe learners of a new language often read word by word. But why can proficient readers read multiple words at a time? The current study investigates how we efficiently segment a complicate text into smaller pieces and how we process these pieces. Participants read Chinese strings with different structures while their key-press responses and brain EEG signals were recorded. We found that texts were quickly (about 100 ms from their occurrences) segmented to varied sizes of pieces, and larger pieces were then processed earlier than small pieces. Our results suggest that readers can use existing knowledge to efficiently segment and process written information.
Functional analysis of phospholipase Dδ family in tobacco pollen tubes
Authors: Pejchar Přemysl , Sekereš Juraj , Novotný Ondřej , Žárský Viktor , Potocký Martin 
Published: October 2019

SummaryPhosphatidic acid (PA), important signalling and metabolic phospholipid, is predominantly localized in the subapical plasma membrane (PM) of growing pollen tubes. PA can be produced from structural phospholipids by phospholipase D (PLD) but the isoforms responsible for production of plasma membrane PA were not identified yet and their functional roles remain unknown. Following genome-wide bioinformatic analysis of PLD family in tobacco, we focused on the pollen-overrepresented PLDδ class. Combining live-cell imaging, gene overexpression or knock-down, lipid-binding and structural bioinformatics, we characterized 5 NtPLDδ isoforms. Distinct PLDδ isoforms preferentially localize to the cytoplasm or subapical PM. Using fluorescence recovery after photobleaching, domain deletion and swapping analyses we show that membrane-bound PLDδs are tightly bound to PM, primarily via the central catalytic domain. Knock-down, overexpression andin vivoPA level analyses revealed isofom PLDδ3 as the most important member of the PLDδ subfamily active in pollen tubes. PA promotes binding of PLDδ3 to the PM, thus creating a positive feedback loop, where PA accumulation leads to the formation of massive PM invaginations. Tightly controlled production of PA generated by PLDδ3 at the PM is important for maintaining the balance between various membrane trafficking processes, that are crucial for plant cell tip growth.
Mitochondrial Dysfunction Induces Epigenetic Dysregulation by H3K27 Hyperacetylation to Perturb Active Enhancers in Parkinson’s Disease Models
Authors: Huang Minhong , Lou Dan , Charli Adhithiya , Kong Dehui , Jin Huajun , Anantharam Vellareddy , Kanthasamy Arthi , Wang Zhibin , Kanthasamy Anumantha G. 
Published: October 2019

AbstractGenetic mutations explain only 10-15% of cases of Parkinson’s disease (PD), while an overriding environmental component has been implicated in the etiopathogenesis of PD. But regardless of where the underlying triggers for the onset of familial and sporadic PD fall on the gene-environment axis, mitochondrial dysfunction emerges as a common mediator of dopaminergic neuronal degeneration. Herein, we employ a multidisciplinary approach to convincingly demonstrate that neurotoxicant exposure- and genetic mutation-driven mitochondrial dysfunction share a common mechanism of epigenetic dysregulation. Under both scenarios, lysine 27 acetylation of likely variant H3.2 (H3.2K27ac) increased in dopaminergic neuronal models of PD, thereby opening that region to active enhancer activity via H3K27 hyperacetylation. These vulnerable epigenomic loci represent potential transcription factor motifs for PD pathogenesis. We further confirmed the mitochondrial dysfunction induced H3K27ac during neurodegeneration inex vivomodels of PD. Our results reveal an exciting axis of ‘exposure/mutation-mitochondrial dysfunction-metabolism-H3K27ac-transcriptome’ for PD pathogenesis. Collectively, the novel mechanistic insights presented here interlinks mitochondrial dysfunction to epigenetic transcriptional regulation in dopaminergic degeneration as well as offer potential new epigenetic intervention strategies for PD.
Semiparametric Partial Common Principal Component Analysis for Covariance Matrices
Authors: Wang Bingkai , Luo Xi , Zhao Yi , Caffo Brian 
Published: October 2019

SummaryWe consider the problem of jointly modeling multiple covariance matrices by partial common principal component analysis (PCPCA), which assumes a proportion of eigenvectors to be shared across covariance matrices and the rest to be individual-specific. This paper proposes consistent estimators of the shared eigenvectors in PCPCA as the number of matrices or the number of samples to estimate each matrix goes to infinity. We prove such asymptotic results without making any assumptions on the ranks of eigenvalues that are associated with the shared eigenvectors. When the number of samples goes to infinity, our results do not require the data to be Gaussian distributed. Furthermore, this paper introduces a sequential testing procedure to identify the number of shared eigenvectors in PCPCA. In simulation studies, our method shows higher accuracy in estimating the shared eigenvectors than competing methods. Applied to a motor-task functional magnetic resonance imaging data set, our estimator identifies meaningful brain networks that are consistent with current scientific understandings of motor networks during a motor paradigm.
The unfolded protein response regulates pathogenic development ofUstilago maydisby Rok1-dependent inhibition of mating-type signaling
Authors: Schmitz Lara , Schwier Melina Ayaka , Heimel Kai 
Published: October 2019

AbstractFungal pathogens require the unfolded protein response (UPR) to maintain protein homeostasis of the endoplasmic reticulum (ER) during pathogenic development. In the corn smut fungusUstilago maydis, pathogenic development is controlled by theaandbmating-type loci. The UPR is specifically activated after plant penetration and required for efficient secretion of effectors and suppression of the plant defense response. The interaction between the UPR regulator Cib1 and the central developmental regulator Clp1 modulates the pathogenic program and triggers fungal colonization of the host plant. By contrast, when activated before plant penetration, the UPR interferes with fungal virulence by reducing expression ofbEandbW, the central regulators of pathogenic development encoded by thebmating-type locus. Here we show that this inhibitory effect results from UPR-mediated suppression of the pheromone response pathway upstream of theb-regulatory network. UPR activity prompts dephosphorylation of the pheromone-responsive MAPK Kpp2, reducing activity of the pheromone response factor Prf1 that regulates expression ofbEandbW. Deletion of the dual specificity phosphataserok1fully suppressed UPR-dependent inhibition of Kpp2 phosphorylation, formation of infectious filaments and fungal virulence. Rok1 determines activity of mating-type signaling pathways and thus the degree of fungal virulence. We propose that UPR-dependent regulation of Rok1 aligns ER physiology with fungal aggressiveness and effector gene expression during biotrophic growth ofU. maydisin the host plant.ImportanceThe unfolded protein response (UPR) is crucial for ER homeostasis and disease development in fungal pathogens. In the plant pathogenic fungusUstilago maydis, the UPR supports fungal proliferationin plantaand effector secretion for plant defense suppression. In this study, we uncovered that UPR activity, which is normally restricted to the biotrophic stagein planta, inhibits mating and the formation of infectious filaments by Rok1-dependent dephosphorylation of the pheromone responsive MAPK Kpp2. This observation is relevant for understanding how the fungal virulence program is regulated by cellular physiology. UPR-mediated control of mating-type signaling pathways predicts that effector gene expression and the virulence potential are controlled by ER stress levels.
Call for participation: Collaborative benchmarking of functional-structural root architecture models. The case of root water uptake
Authors: Schnepf Andrea , Black Christopher K. , Couvreur Valentin , Delory Benjamin M. , Doussan Claude , Koch Axelle , Koch Timo , Javaux Mathieu , Landl Magdalena , Leitner Daniel , Lobet Guillaume , Mai Trung Hieu , Meunier Félicien , Petrich Lukas , Postma Johannes A. , Priesack Eckart , Schmidt Volker , Vanderborght Jan , Vereecken Harry , Weber Matthias 
Published: October 2019

AbstractThree-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute towards improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development.
DeCoDe: degenerate codon design for complete protein-coding DNA libraries
Authors: Shimko Tyler C. , Fordyce Polly M. , Orenstein Yaron 
Published: October 2019

AbstractMotivationHigh-throughput protein screening is a critical technique for dissecting and designing protein function. Libraries for these assays can be created through a number of means, including targeted or random mutagenesis of a template protein sequence or direct DNA synthesis. However, mutagenic library construction methods often yield vastly more non-functional than functional variants and, despite advances in large-scale DNA synthesis, individual synthesis of each desired DNA template is often prohibitively ex-pensive. Consequently, many protein screening libraries rely on the use of degenerate codons (DCs), mixtures of DNA bases incorporated at specific positions during DNA synthesis, to generate highly diverse protein variant pools from only a few low-cost synthesis reactions. However, selecting DCs for sets of sequences that covary at multiple positions dramatically increases the difficulty of designing a DC library and leads to the creation of many undesired variants that can quickly outstrip screening capacity.ResultsWe introduce a novel algorithm for total DC library optimization, DeCoDe, based on integer linear programming. DeCoDe significantly outperforms state-of-the-art DC optimization algorithms and scales well to more than a hundred proteins sharing complex patterns of covariation (e.g.the lab-derived avGFP lineage). Moreover, DeCoDe is, to our knowledge, the first DC design algorithm with the capability to encode mixed-length protein libraries. We anticipate DeCoDe to be broadly useful for a variety of library generation problems, ranging from protein engineering attempts that leverage mutual information to the reconstruction of ancestral protein states.Availabilitygithub.com/OrensteinLab/DeCoDeContactyaronore@bgu.ac.il