bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ecological Changes in the Nannoplankton Community Across A Shelf Transect During the Onset of the Paleocene‐Eocene Thermal Maximum
Authors: León y León Isabel A. , Bralower Timothy J. , Self‐Trail Jean M. 
Published: September 2018

AbstractWarming and other environmental changes during the Paleocene‐Eocene thermal maximum (PETM) led to profound shifts in the composition and structure of nannoplankton assemblages. Here we analyze the nature of these changes in expanded records from the Cambridge‐Dorchester and Mattawoman Creek‐Billingsley Road cores in Maryland. These cores comprise part of a transect of five paleoshelf cores from Maryland and New Jersey. We integrate multivariate analysis of assemblage data with proxy data to revise understanding of the paleoecological affinities of key species. In particular, Discoaster and Fasciculithus are interpreted as thermophiles without adaptation to particular nutrient levels, while Hornibrookina is considered an opportunist adapted to highly variable nearshore environments. Together the cores show consistent margin‐wide changes across the onset of the PETM, including a pulse of preevent warming, possibly combined with lower salinity, high seasonality, or increased turbidity. The event itself was characterized by continued warming and eutrophication across the paleoshelf. The Maryland sites experienced higher environmental variability as a result of their proximity to large river systems.
High step‐up dc–dc converter with multi‐winding CL and switched capacitor
Authors: Ye Yuanmao , Peng Wei , Jiang Bijia 
Published: September 2018

To achieve a high‐voltage gain, a step‐up converter is developed by integrating coupled‐inductor (CL) and switched capacitor. Low‐voltage‐rated transistors are used to improve the efficiency as the voltage stress on the main switch of the proposed converter is reduced. Circuit configuration, operation principle, ideal voltage transfer ratio and switches’ stresses are analysed in detail. The converter's structure can be easily extended for ultra‐high‐voltage gain by increasing the number of windings of the CL. The feasibility of the proposed converter is experimentally demonstrated by a 140 W prototype converter.
A novel bifunctional aspartate kinase-homoserine dehydrogenase from the hyperthermophilic bacterium, Thermotoga maritima
Authors: Ohshida Tatsuya , Koba Kohei , Hayashi Junji , Yoneda Kazunari , Ohmori Taketo , Ohshima Toshihisa , Sakuraba Haruhiko 
Published: September 2018

ABSTRACT
The orientation of the three domains in the bifunctional aspartate kinase-homoserine dehydrogenase (AK-HseDH) homologue found in Thermotoga maritima totally differs from those observed in previously known AK-HseDHs; the domains line up in the order HseDH, AK, and regulatory domain. In the present study, the enzyme produced in Escherichia coli was characterized. The enzyme exhibited substantial activities of both AK and HseDH. l-Threonine inhibits AK activity in a cooperative manner, similar to that of Arabidopsis thaliana AK-HseDH. However, the concentration required to inhibit the activity was much lower (K0.5 = 37 μM) than that needed to inhibit the A. thaliana enzyme (K0.5 = 500 μM). In contrast to A. thaliana AK-HseDH, Hse oxidation of the T. maritima enzyme was almost impervious to inhibition by l-threonine. Amino acid sequence comparison indicates that the distinctive sequence of the regulatory domain in T. maritima AK-HseDH is likely responsible for the unique sensitivity to l-threonine.
Abbreviations: AK: aspartate kinase; HseDH: homoserine dehydrogenase; AK–HseDH: bifunctional aspartate kinase–homoserine dehydrogenase; AsaDH: aspartate–β–semialdehyde dehydrogenase; ACT: aspartate kinases (A), chorismate mutases (C), and prephenate dehydrogenases (TyrA, T).
Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data
Authors: Wang Haohan , Lengerich Benjamin J , Aragam Bryon , Xing Eric P 
Published: September 2018

AbstractMotivationAssociation studies to discover links between genetic markers and phenotypes are central to bioinformatics. Methods of regularized regression, such as variants of the Lasso, are popular for this task. Despite the good predictive performance of these methods in the average case, they suffer from unstable selections of correlated variables and inconsistent selections of linearly dependent variables. Unfortunately, as we demonstrate empirically, such problematic situations of correlated and linearly dependent variables often exist in genomic datasets and lead to under-performance of classical methods of variable selection.ResultsTo address these challenges, we propose the Precision Lasso. Precision Lasso is a Lasso variant that promotes sparse variable selection by regularization governed by the covariance and inverse covariance matrices of explanatory variables. We illustrate its capacity for stable and consistent variable selection in simulated data with highly correlated and linearly dependent variables. We then demonstrate the effectiveness of the Precision Lasso to select meaningful variables from transcriptomic profiles of breast cancer patients. Our results indicate that in settings with correlated and linearly dependent variables, the Precision Lasso outperforms popular methods of variable selection such as the Lasso, the Elastic Net and Minimax Concave Penalty (MCP) regression.Availability and implementationSoftware is available at https://github.com/HaohanWang/thePrecisionLasso.Supplementary informationSupplementary data are available at Bioinformatics online.
Bayesian integrative model for multi-omics data with missingness
Authors: Fang Zhou , Ma Tianzhou , Tang Gong , Zhu Li , Yan Qi , Wang Ting , Celedón Juan C , Chen Wei , Tseng George C 
Published: September 2018

AbstractMotivationIntegrative analysis of multi-omics data from different high-throughput experimental platforms provides valuable insight into regulatory mechanisms associated with complex diseases, and gains statistical power to detect markers that are otherwise overlooked by single-platform omics analysis. In practice, a significant portion of samples may not be measured completely due to insufficient tissues or restricted budget (e.g. gene expression profile are measured but not methylation). Current multi-omics integrative methods require complete data. A common practice is to ignore samples with any missing platform and perform complete case analysis, which leads to substantial loss of statistical power.MethodsIn this article, inspired by the popular Integrative Bayesian Analysis of Genomics data (iBAG), we propose a full Bayesian model that allows incorporation of samples with missing omics data.ResultsSimulation results show improvement of the new full Bayesian approach in terms of outcome prediction accuracy and feature selection performance when sample size is limited and proportion of missingness is large. When sample size is large or the proportion of missingness is low, incorporating samples with missingness may introduce extra inference uncertainty and generate worse prediction and feature selection performance. To determine whether and how to incorporate samples with missingness, we propose a self-learning cross-validation (CV) decision scheme. Simulations and a real application on child asthma dataset demonstrate superior performance of the CV decision scheme when various types of missing mechanisms are evaluated.Availability and implementationFreely available on the GitHub at https://github.com/CHPGenetics/FBMSupplementary informationSupplementary data are available at Bioinformatics online.
When a single lineage is not enough: Uncertainty-Aware Tracking for spatio-temporal live-cell image analysis
Authors: Theorell Axel , Seiffarth Johannes , Grünberger Alexander , Nöh Katharina 
Published: September 2018

AbstractMotivationMicrofluidic platforms for live-cell analysis are in dire need of automated image analysis pipelines. In this context, producing reliable tracks of single cells in colonies has proven to be notoriously difficult without manual assistance, especially when image sequences experience low frame rates.ResultsWith Uncertainty-Aware Tracking (UAT), we propose a novel probabilistic tracking paradigm for simultaneous tracking and estimation of tracking-induced errors in biological quantities derived from live-cell experiments. To boost tracking accuracy, UAT relies on a Bayesian approach which exploits temporal information on growth patterns to guide the formation of lineage hypotheses. A biological study is presented, in which UAT demonstrates its ability to track cells, with comparable to better accuracy than state-of-the-art trackers, while simultaneously estimating tracking-induced errors.Availability and implementationImage sequences and Java executables for reproducing the results are available at https://doi.org/10.5281/zenodo.1299526.Supplementary informationSupplementary data are available at Bioinformatics online.
Differential methylation values in differential methylation analysis
Authors: Xie Changchun , Leung Yuet-Kin , Chen Aimin , Long Ding-Xin , Hoyo Catherine , Ho Shuk-Mei 
Published: September 2018

Abstract

Motivation
Both β-value and M-value have been used as metrics to measure methylation levels. The M-value is more statistically valid for the differential analysis of methylation levels. However, the β-value is much more biologically interpretable and needs to be reported when M-value method is used for conducting differential methylation analysis. There is an urgent need to know how to interpret the degree of differential methylation from the M-value. In M-value linear regression model, differential methylation M-value ΔM can be easily obtained from the coefficient estimate, but it is not straightforward to get the differential methylation β-value, Δβ since it cannot be obtained from the coefficient alone.

Results
To fill the gap, we have built a bridge to connect the statistically sound M-value linear regression model and the biologically interpretable Δβ. In this article, three methods were proposed to calculate differential methylation values, Δβ from M-value linear regression model and compared with the Δβ directly obtained from β-value linear regression model. We showed that under the condition that M-value linear regression model is correct, the method M-model-coef is the best among the four methods. M-model-M-mean method works very well too. If the coefficients α0, α2,…αp are not given (as ‘MethLAB’ package), the M-model-M-mean method should be used. The Δβ directly obtained from β-value linear regression model can give very biased results, especially when M-values are not in (−2, 2) or β-values are not in (0.2, 0.8).

Availability and implementation
The dataset for example is available at the National Center for Biotechnology Information Gene Expression Omnibus repository, GSE104778.

Supplementary information
Supplementary data are available at Bioinformatics online.
Efficiency Versus Equality in Bargaining
Authors: Galeotti Fabio , Montero Maria , Poulsen Anders 
Published: September 2018

AbstractWe consider how the outcome of bargaining varies with changes in the trade-off between equality, efficiency, and total-earnings maximization. We observe that subjects avoid an equal-earning outcome if it is Pareto inefficient; a large proportion of bargaining pairs avoids an equal and Pareto efficient outcome in favor of one giving unequal and total-earnings maximizing payoffs, and this proportion increases when unequal outcomes imply larger earnings to one of the players, even though this also implies higher inequality; finally, we document a compromise effect that violates the independence of irrelevant alternatives condition.
Pro: Tolvaptan delays the progression of autosomal dominant polycystic kidney disease
Authors: Torres Vicente E 
Published: September 2018

ABSTRACT
No treatment until now has directly targeted the mechanisms responsible for the development and growth of cysts in autosomal dominant polycystic kidney disease (ADPKD). Strong rationale and preclinical studies using in vitro and in vivo models justified the launching of two large phase 3 clinical trials of tolvaptan in early and later stages of ADPKD. Their design was based on preliminary studies informing on the pharmacokinetics, pharmacodynamics, short-term safety and self-reported tolerability in patients with ADPKD. Tolvaptan slowed kidney growth in the early stage and estimated glomerular filtration rate decline in early and later stages of the disease. All participants had the opportunity to enroll in open-label extension trials to ascertain long-term safety and efficacy. In a single-center analysis of long-term outcomes, the effect of tolvaptan was sustained and cumulative over time supporting a disease-modifying effect of tolvaptan in ADPKD. In the countries where tolvaptan has been approved by regulatory agencies, patients with rapidly progressive ADPKD should be informed about the option of treatment including possible benefits and risks. If a decision to initiate treatment is made, prescribing physicians should educate the patients on the prevention of aquaresis-related adverse events and should be vigilant in the surveillance and management of the potential tolvaptan hepatotoxicity. Other vasopressin V2 receptor antagonists, possibly without potential hepatotoxicity, alternative strategies targeting vasopressin and combination with other drugs able to enhance the efficacy or reduce the aquaresis associated with tolvaptan, deserve further study.
Urinary soluble CD163 and monocyte chemoattractant protein-1 in the identification of subtle renal flare in anti-neutrophil cytoplasmic antibody-associated vasculitis
Authors: Moran Sarah M , Monach Paul A , Zgaga Lina , Cuthbertson David , Carette Simon , Khalidi Nader A , Koening Curry L , Langford Carol A , McAlear Carol A , Moreland Larry , Pagnoux Christian , Seo Philip , Specks Ulrich , Sreih Antoine , Wyse Jason , Ytterberg Steven R , Merkel Peter A , Little Mark A ,   for the Vasculitis Clinical Research Consortium
Published: September 2018

Abstract

Background
Prior work has shown that urinary soluble CD163 (usCD163) displays excellent biomarker characteristics for detection of active renal vasculitis using samples that included new diagnoses with highly active renal disease. This study focused on the use of usCD163 in the detection of the more clinically relevant state of mild renal flare and compared results of usCD163 testing directly to testing of urinary monocyte chemoattractant protein-1 (uMCP-1).

Methods
Patients with anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV, n = 88) were identified within a serially sampled, longitudinal and multicentre cohort. Creatinine-normalized usCD163 and uMCP-1 levels were measured by enzyme-linked immunosorbent assay and, both alone and in combination, were compared between times of active renal AAV and during remission and/or active non-renal AAV.

Results
Samples from 320 study visits included times of active renal vasculitis (n = 39), remission (n = 233) and active extrarenal vasculitis (n = 48). Median creatinine levels were 0.9 mg/dL [interquartile range (IQR) 0.8–1.2] in remission and 1.4 mg/dL (IQR 1.0–1.8) during renal flare. usCD163 levels were higher in patients with active renal vasculitis compared with patients in remission and those with active extrarenal vasculitis, with median values of 162 ng/mmol (IQR 79–337), 44 (17–104) and 38 (7–76), respectively (P &lt; 0.001). uMCP-1 levels were also higher in patients with active renal vasculitis compared with patients in remission and those with active extrarenal vasculitis, with median values of 10.6 pg/mmol (IQR 4.6–23.5), 4.1 (2.5–8.4) and 4.1 (1.9–6.8), respectively (P &lt; 0.001). The proposed diagnostic cut-points for usCD163 and uMCP-1 were 72.9 ng/mmol and 10.0 pg/mmol, respectively. usCD163 and uMCP-1 levels were marginally correlated (r2 = 0.11, P &lt; 0.001). Combining novel and existing biomarkers using recursive tree partitioning indicated that elevated usCD163 plus either elevated uMCP-1 or new/worse proteinuria improved the positive likelihood ratio (PLR) of active renal vasculitis to 19.2.

Conclusion
A combination of usCD163 and uMCP-1 measurements appears to be useful in identifying the diagnosis of subtle renal vasculitis flare.
Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis
Authors: Karhade Aditya V , Thio Quirina C B S , Ogink Paul T , Shah Akash A , Bono Christopher M , Oh Kevin S , Saylor Phil J , Schoenfeld Andrew J , Shin John H , Harris Mitchel B , Schwab Joseph H 
Published: September 2018

Abstract

BACKGROUND
Preoperative prognostication of short-term postoperative mortality in patients with spinal metastatic disease can improve shared decision making around end-of-life care.

OBJECTIVE
To (1) develop machine learning algorithms for prediction of short-term mortality and (2) deploy these models in an open access web application.

METHODS
The American College of Surgeons, National Surgical Quality Improvement Program was used to identify patients that underwent operative intervention for metastatic disease. Four machine learning algorithms were developed, and the algorithm with the best performance across discrimination, calibration, and overall performance was integrated into an open access web application.

RESULTS
The 30-d mortality for the 1790 patients undergoing surgery for spinal metastatic disease was 8.49%. Preoperative factors used for prognostication were albumin, functional status, white blood cell count, hematocrit, alkaline phosphatase, spinal location (cervical, thoracic, lumbosacral), and severity of comorbid systemic disease (American Society of Anesthesiologist Class). In this population, machine learning algorithms developed to predict 30-d mortality performed well on discrimination (c-statistic), calibration (assessed by calibration slope and intercept), Brier score, and decision analysis. An open access web application was developed for the best performing model and this web application can be found here: https://sorg-apps.shinyapps.io/spinemets/.

CONCLUSION
Machine learning algorithms are promising for prediction of postoperative outcomes in spinal oncology and these algorithms can be integrated into clinically useful decision tools. As the volume of data in oncology continues to grow, creation of learning systems and deployment of these systems as accessible tools may significantly enhance prognostication and management.
Childhood locus of control and self-esteem, education, psychological distress and physical exercise as predictors of adult obesity
Authors: Cheng Helen , Furnham Adrian 
Published: September 2018

Abstract

Objective
To investigate to what extent locus of control, self-esteem, psychological distress, physical exercise, as well as socio-demographic factors are associated with obesity in 42-year-old adults in a longitudinal birth cohort study.

Method
The sample consisted of 5645 participants born in Great Britain in 1970 and followed up at 10, 34 and 42 years with data on body mass index measured at 34 and 42 years.

Results
There was an increase of adult obesity from 15.5% at age 34 to 21.2% at 42 years. Locus of control and self-esteem measured at age 10 years, psychological distress and educational qualifications assessed at age 34, and current occupational levels and physical exercise were all significantly associated with adult obesity at age 42. The associations remained significant after controlling for birth weight and gestation, maternal and paternal BMI, childhood BMI, and intelligence.

Conclusion
Childhood locus of control and self-esteem, educational qualifications, psychological distress and physical exercise were all significantly and independently associated with adult obesity.
The Effect of Product Misperception on Economic Outcomes: Evidence from the Extended Warranty Market
Authors: Abito Jose Miguel , Salant Yuval 
Published: September 2018

Abstract
Panel and experimental data are used to analyse the economic outcomes in the extended warranty market. We establish that the strong demand and high profits in this market are driven by consumers distorting the failure probability of the insured product, rather than standard risk aversion or sellers’ market power. Providing information to consumers about failure probabilities significantly reduces their willingness to pay for warranties, indicating the important role of information, or lack of, in driving consumers’ purchase behaviour. Such information provision is shown to be more effective in enhancing consumer welfare than additional market competition.
In silicoprediction of high-resolution Hi-C interaction matrices
Authors: Zhang Shilu , Chasman Deborah , Knaack Sara , Roy Sushmita 
Published: September 2018

AbstractThe three-dimensional organization of the genome plays an important role in gene regulation by enabling distal sequence elements to control the expression level of genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to measure the contact count of pairs of genomic loci needed to study three-dimensional organization. Due to experimental costs high resolution Hi-C datasets are available only for a handful of cell lines. Computational prediction of Hi-C contact counts can offer a scalable and inexpensive approach to examine three-dimensional genome organization across many cellular contexts. Here we present HiC-Reg, a novel approach to predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. HiC-Reg exploits the signal from the region spanning two interacting regions and from across multiple cell lines to generalize to new contexts. Using existing feature importance measures and a new matrix factorization based approach, we found CTCF and chromatin marks, especially repressive and elongation marks, as important for predictive performance. Predicted counts from HiC-Reg identify topologically associated domains as well as significant interactions that are enriched for CTCF bi-directional motifs and agree well with interactions identified from complementary long-range interaction assays. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions as well as higher-order organizational units of the genome.