bluuebunny/crossref_metadata_2025_split huggingface dataset:
STAX Appeal?
Authors: Yehouenou Lauriane , Barnett Barry J , Harri Ardian , Coble Keith H 
Published: December 2017

AbstractThe Stacked Income Protection Plan (STAX) county‐level insurance product is analyzed for cotton producers in Texas. In contrast to studies based on representative farms, this analysis uses actual farm‐level yield data, which allows one to observe the heterogeneity of STAX effectiveness across farms in a county. The findings indicate that, for most farms, STAX is not a very effective alternative to farm‐level crop insurance. However, contrary to observed behavior, the findings suggest that many cotton producers in Texas would benefit from using STAX as a complement to their farm‐level crop insurance.
myTAI: evolutionary transcriptomics with R
Authors: Drost Hajk-Georg , Gabel Alexander , Liu Jialin , Quint Marcel , Grosse Ivo 
Published: December 2017

AbstractMotivationNext Generation Sequencing (NGS) technologies generate a large amount of high quality transcriptome datasets enabling the investigation of molecular processes on a genomic and metagenomic scale. These transcriptomics studies aim to quantify and compare the molecular phenotypes of the biological processes at hand. Despite the vast increase of available transcriptome datasets, little is known about the evolutionary conservation of those characterized transcriptomes.ResultsThe myTAI package implements exploratory analysis functions to infer transcriptome conservation patterns in any transcriptome dataset. Comprehensive documentation of myTAI functions and tutorial vignettes provide step-by-step instructions on how to use the package in an exploratory and computationally reproducible manner.Availability and implementationThe open source myTAI package is available at https://github.com/HajkD/myTAI and https://cran.r-project.org/web/packages/myTAI/index.html.Supplementary informationSupplementary data are available at Bioinformatics online.
Neural Mobilization Attenuates Mechanical Allodynia and Decreases Proinflammatory Cytokine Concentrations in Rats With Painful Diabetic Neuropathy
Authors: Zhu Guan-Cheng , Tsai Kun-Ling , Chen Yu-Wen , Hung Ching-Hsia 
Published: December 2017

AbstractBackgroundPainful diabetic neuropathy (PDN) is a common complication in patients with diabetes. It is related to ischemic nerve damage and the increase in the levels of proinflammatory mediators, such as tumor necrosis factor α (TNF-α) and interleukin 1β (IL-1β). Neural mobilization may have the potential to alleviate PDN, but it has not yet been tested. Also, the physiological mechanism of neural mobilization is unclear.ObjectiveThe objective of this study was to investigate treatment effect and physiological mechanism of neural mobilization.DesignThis was an experimental study using rats with streptozocin (or streptozotocin)-induced type 1 diabetes.MethodsThree groups were used in the study, the control group (vehicle), the diabetes group (PDN group), and the neural mobilization treatment group (PDN-NM group) (n = 6). Rats in the vehicle group were healthy rats. Rats in the PDN and PDN-NM groups were rats with diabetes. Rats in the PDN-NM group received treatment in the right sciatic nerve, whereas rats in the PDN group did not. Mechanical pain sensitivity and the levels of IL-1β and TNF-α in the sciatic nerve branches and trunk, the L4 to L6 dorsal horn ganglion, and the spinal cord dorsal horn were measured.ResultsTechanical allodynia was alleviated after treatment, but the effect was limited to the treatment side. The concentrations of proinflammatory cytokines were decreased in the nerves that received treatment compared with those on the other side, indicating that neural mobilization may reduce mechanical sensitivity by decreasing the concentrations of local sensitizing agents.LimitationsA limitation of this study was that no direct measurement of nerve blood flow was done.ConclusionsThe results of this study showed that neural mobilization effectively alleviated mechanical allodynia in rats with PDN. The side that received treatment had lower concentrations of TNF-α and IL-1β in the sciatic nerve branches and sciatic nerve trunk; this result may have been related to the alleviation of mechanical allodynia.
Mindfulness and Chronic Headache/Migraine
Authors: Komandur Biyanka , Martin Paul R. , Bandarian-Balooch Siavash 
Published: December 2017

Objectives:
To replicate a study by Schutze and colleagues on a headache sample, rather than a heterogenous chronic pain sample, investigating whether level of mindfulness predicts key components in the Fear-Avoidance Model of chronic pain (pain intensity, negative affect, pain catastrophizing, pain-related fear, pain hypervigilance, and functional disability); to investigate the relationships between level of mindfulness and headache/migraine pain intensity, frequency, and duration.

Materials and Methods:
Participants were 217 individuals who self-reported chronic headache/migraine (51 male, 166 female), aged between 18 and 65 years. Participants completed an online survey measuring demographics, mindfulness, the key components of the Fear-Avoidance Model, and headache pain intensity, duration, and frequency.

Results:
Mindfulness had significant negative correlations (P<0.05) with all variables except headache pain intensity and headache frequency. Mindfulness significantly predicted negative affect, pain catastrophizing, fear of pain, pain hypervigilance, and headache duration (P<0.05). Mindfulness remained a significant predictor of negative affect and pain hypervigilance after controlling for other key components and background characteristics (P<0.05). Mindfulness did not moderate the relationship between pain intensity and pain catastrophizing (P=0.204).

Discussion:
Findings suggest that mindfulness may be integrated into the Fear-Avoidance Model of chronic pain for individuals with chronic headache/migraine. Directions for future research are discussed.
Defining Multiple Chronic Conditions for Quality Measurement
Authors: Drye Elizabeth E. , Altaf Faseeha K. , Lipska Kasia J. , Spatz Erica S. , Montague Julia A. , Bao Haikun , Parzynski Craig S. , Ross Joseph S. , Bernheim Susannah M. , Krumholz Harlan M. , Lin Zhenqiu 
Published: December 2017

Background/Objective:
Patients with multiple chronic conditions (MCCs) are a critical but undefined group for quality measurement. We present a generally applicable systematic approach to defining an MCC cohort of Medicare fee-for-service beneficiaries that we developed for a national quality measure, risk-standardized rates of unplanned admissions for Accountable Care Organizations.

Research Design:
To define the MCC cohort we: (1) identified potential chronic conditions; (2) set criteria for cohort conditions based on MCC framework and measure concept; (3) applied the criteria informed by empirical analysis, experts, and the public; (4) described “broader” and “narrower” cohorts; and (5) selected final cohort with stakeholder input.

Subjects:
Subjects were patients with chronic conditions. Participants included 21.8 million Medicare fee-for-service beneficiaries in 2012 aged 65 years and above with ≥1 of 27 Medicare Chronic Condition Warehouse condition(s).

Results:
In total, 10 chronic conditions were identified based on our criteria; 8 of these 10 were associated with notably increased admission risk when co-occurring. A broader cohort (2+ of the 8 conditions) included 4.9 million beneficiaries (23% of total cohort) with an admission rate of 70 per 100 person-years. It captured 53% of total admissions. The narrower cohort (3+ conditions) had 2.2 million beneficiaries (10%) with 100 admissions per 100 person-years and captured 32% of admissions. Most stakeholders viewed the broader cohort as best aligned with the measure concept.

Conclusions:
By systematically narrowing chronic conditions to those most relevant to the outcome and incorporating stakeholder input, we defined an MCC admission measure cohort supported by stakeholders. This approach can be used as a model for other MCC outcome measures.
Resistance in chronic myeloid leukemia: definitions and novel therapeutic agents
Authors: Talati Chetasi , Pinilla-Ibarz Javier 
Published: December 2017

Purpose of reviewChronic myeloid leukemia (CML) is hallmarked by the presence of fusion protein kinase derived from a reciprocal translocation between chromosome 9 and 22, breakpoint cluster region (BCR)–Abelson leukemia virus (ABL) 1, causing aberrant regulation of the downstream pathways leading to unchecked CML leukemia stem cells (LSCs) proliferation. Since the discovery of tyrosine kinase inhibitors (TKI), CML, once a fatal disease, has become a chronic illness if managed appropriately. Changing treatment landscape has unsurfaced the challenge of TKI resistance that is clinically difficult to overcome.Recent findingsIn this review, we discuss the concept of TKI resistance and pathways leading to the resistance which allows for a survival advantage to CML LSCs. Aside from BCR–ABL-dependent mechanisms of resistance which involves aberrant expression in the regulatory pumps involving efflux and influx of the TKI affecting drug bioavailability, activation of alternate survival pathways may be accountable for primary or secondary resistance. Activation of these pathways, intrinsically and extrinsically to LSCs, may be mediated through various upstream and downstream signaling as well as conditions affecting the microenvironment. Several therapeutic approaches that combine TKI with an additional agent that inhibits the activation of an alternate pathway have been studied as part of clinical trials which we will discuss here.SummaryWe categorize the resistance into BCR–ABL-dependent and BCR–ABL-independent subgroups to further describe the complex molecular pathways which can potentially serve as a therapeutic target. We further discuss novel combination strategies currently in early or advanced phase clinical trials aimed to overcome the TKI resistance. We further highlight the need for further research despite the tremendous strides already made in the management of CML.
Evoked Death-Related Thoughts in the Aftermath of Terror Attack
Authors: Ring Lia , Lavenda Osnat , Hamama-Raz Yaira , Ben-Ezra Menachem , Pitcho-Prelorentzos Shani , David Udi Y. , Zaken Adi , Mahat-Shamir Michal 
Published: December 2017

Abstract

ICD-11 has provided a revised definition for adjustment disorder (AjD). The current study examined whether mortality salience effect, a possible consequence of a terror attack, may serve as a significant predictor associated with each of the AjD subscales. Using an online survey, 379 adult participants were recruited and filled out self-reported questionnaires dealing with adjustment disorder symptoms as well as mortality salience effect. Findings revealed that mortality salience effect was a significant predictor of all AjD subscales. The importance of mortality salience effect for AjD is discussed in light of terror management theory.
Validation of a new American Association for the Surgery of Trauma (AAST) anatomic severity grading system for acute cholecystitis
Authors: Vera Kenneth , Pei Kevin Y. , Schuster Kevin M. , Davis Kimberly A. 
Published: December 2017

BACKGROUND
The American Association for the Surgery of Trauma (AAST) established anatomic grading to facilitate risk stratification and risk-adjusted outcomes in emergency general surgery. Cholecystitis severity was graded based on clinical, imaging, operative, and pathologic criteria. We aimed to validate the AAST anatomic grading system for acute cholecystitis.

METHODS
This is a retrospective cohort study including consecutive patients admitted with acute cholecystitis at an urban, tertiary medical center between 2013 and 2016. Grade 1 is acute cholecystitis, Grade 2 is gangrenous or emphysematous cholecystitis, Grade 3 is localized perforation, and Grades 4 and 5 have regional and systemic peritonitis, respectively. Concordance between the AAST grade and outcome including mortality, length of stay (LOS), ICU use, readmission, and complications were assessed using logistic regression.

RESULTS
A total of 315 patients were included. There was very good inter-rater (two independent raters) reliability for anatomic grading, κ = 1.00, p < 0.005. The majority of patients were Grade 1 or Grade 2 (94%). Incidence of complications, LOS, ICU use, and any adverse event increased with increasing anatomic grade. When compared to Grade 1 disease, patients with Grade 2 were more likely to undergo cholecystectomy (OR 4.07 [1.93–8.56]). Grade 3 patients were at higher risk of adverse events (OR 3.83 [1.34–10.94]), longer LOS (OR 1.73 [1.03–2.92]), and ICU use (OR 8.07 [2.43–26.80]).

CONCLUSIONS
AAST severity scores were independently associated with clinical outcomes in patients with acute cholecystitis. Despite low-grade disease, complications were common, and therefore a refinement of the scoring system may be necessary for more granular prediction.

LEVEL OF EVIDENCE
Epidemiologic/prognostic, level III
Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
Authors: Young Alexandra L , Marinescu Razvan V , Oxtoby Neil P , Bocchetta Martina , Yong Keir , Firth Nicholas , Cash David M , Thomas David L , Dick Katrina M , Cardoso Jorge , Swieten John van , Borroni Barbara , Galimberti Daniela , Masellis Mario , Tartaglia Maria Carmela , Rowe James B , Graff Caroline , Tagliavini Fabrizio , Frisoni Giovanni , Laforce Robert , Finger Elizabeth , Mendonça Alexandre , Sorbi Sandro , Warren Jason D , Crutch Sebastian , Fox Nick C , Ourselin Sebastien , Schott Jonathan M , Rohrer Jonathan D , Alexander Daniel C 
Published: December 2017

SummaryThe heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we present a new machine learning technique – Subtype and Stage Inference (SuStaIn) – able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available crosssectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal new subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes, and characterises within-group heterogeneity for the first time. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely revealing their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p=7.18×10--4) or temporal stage (p=3.96×10−5). SuStaIn thus offers new promise for enabling disease subtype discovery and precision medicine.
Analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy
Authors: Kinney-Lang Eli , Yoong Michael , Hunter Matthew , Tallur Krishnaraya Kamath , Shetty Jay , McLellan Ailsa , Chin Richard FM , Escudero Javier 
Published: December 2017

AbstractObjective: Cognitive impairment (CI) is common in children with epilepsy and can have devastating effects on their quality of life and that of their family. Early identification of CI is a priority to improve outcomes, but the current gold standard of detection with psychometric assessment is resource intensive and not always available. This paper proposes a novel technique of network analysis using routine clinical electroencephalography (EEG) to help identify CI in children with early-onset epilepsy (CWEOE) (0-5 y.o.).Methods: We analyzed functional networks from routinely acquired EEGs of 51 newly diagnosed CWEOE from a prospective population-based study. Combinations of connectivity metrics (e.g. phase-slope index (PSI)) with sub-network analysis (e.g. cluster-span threshold (CST)) identified significant correlations between network properties and cognition scores via rank correlation analysis with Kendall’s τ. Predictive properties were investigated using a 5-fold cross-validated K-Nearest Neighbor classification model with normal cognition, mild/moderate CI and severe CI classes.Results: Phase-dependent connectivity metrics had higher sensitivity to cognition scores, with sub-networks identifying significant functional network changes over a broad range of spectral frequencies. Approximately 70.5% of all children were appropriately classified as normal cognition, mild/moderate CI or severe CI using CST network features. CST classification predicted CI classes 55% better than chance, and reduced misclassification penalties by half.Conclusions: CI in CWEOE can be detected with sensitivity at 85% (with respect to identifying either mild/moderate or severe CI) and specificity of 84%, by EEG network analysis.Significance: This study outlines a data-driven methodology for identifying candidate biomarkers of CI in CWEOE from network features. Following additional replication, the proposed method and its use of routinely acquired EEG forms an attractive proposition for supporting clinical assessment of CI.
Comprehensive analysis ofVerticillium nonalfalfae in silicosecretome uncovers putative effector proteins expressed during hop invasion
Authors: Marton Kristina , Flajšman Marko , Radišek Sebastjan , Košmelj Katarina , Jakše Jernej , Javornik Branka , Berne Sabina 
Published: December 2017

AbstractBackgroundThe vascular plant pathogenVerticillium nonalfalfaecauses Verticillium wilt in several important crops. VnaSSP4.2 was recently discovered as aV. nonalfalfaevirulence effector protein in the xylem sap of infected hop. Here, we expanded our search for candidate secreted effector proteins (CSEPs) in theV. nonalfalfaepredicted secretome using a bioinformatic pipeline built onV. nonalfalfaegenome data, RNA-Seq and proteomic studies of the interaction with hop.ResultsThe secretome, rich in carbohydrate active enzymes, proteases, redox proteins and proteins involved in secondary metabolism, cellular processing and signaling, includes 263 CSEPs. Several homologs of known fungal effectors (LysM, NLPs, Hce2, Cerato-platanins, Cyanovirin-N lectins, hydrophobins and CFEM domain containing proteins) and avirulence determinants in the PHI database (Avr-Pita1 and MgSM1) were found. The majority of CSEPs were non-annotated and were narrowed down to 44 top priority candidates based on their likelihood of being effectors. These were examined by spatio-temporal gene expression profiling of infected hop. Among the highestin plantaexpressed CSEPs, five deletion mutants were tested in pathogenicity assays. A deletion mutant ofVnaUn.279, a lethal pathotype specific gene with sequence similarity to SAM-dependent methyltransferase (LaeA), had lower infectivity and showed highly reduced virulence, but no changes in morphology, fungal growth or conidiation were observed.ConclusionsSeveral putative secreted effector proteins that probably contribute toV. nonalfalfaecolonization of hop were identified in this study. Among them,LaeAgene homolog was found to act as a potential novel virulence effector ofV. nonalfalfae. The combined results will serve for future characterization ofV. nonalfalfaeeffectors, which will advance our understanding of Verticillium wilt disease.
Modeling acute myeloid leukemia in a continuum of differentiation states
Authors: Cho H. , Ayers K. , DePills L. , Kuo Y-H. , Park J. , Radunskaya A. , Rockne R. 
Published: December 2017

ABSTRACTHere we present a mathematical model of movement in an abstract space representing states of cellular differentiation. We motivate this work with recent examples that demonstrate a continuum of cellular differentiation using single cell RNA sequencing data to characterize cellular states in a high-dimensional space, which is then mapped into ℝ2or ℝ3with dimension reduction techniques. We represent trajectories in the differentiation space as a graph, and model directed and random movement on the graph with partial differential equations. We hypothesize that flow in this space can be used to model normal differentiation processes as well as predict the evolution of abnormal differentiation processes such as those observed during pathogenesis of acute myeloid leukemia (AML).
Engineering resistance against Tomato yellow leaf curl virus via the CRISPR/Cas9 system in tomato
Authors: Tashkandi Manal , Ali Zahir , Aljedaani Fatimah , Shami Ashwag , Mahfouz Magdy M. 
Published: December 2017

AbstractCRISPR/Cas systems confer molecular immunity against phages and conjugative plasmids in prokaryotes. Recently, CRISPR/Cas9 systems have been used to confer interference against eukaryotic viruses. Here, we engineered Nicotiana benthamiana and tomato (Solanum lycopersicum) plants with the CRISPR/Cas9 system to confer immunity against the Tomato yellow leaf curl virus (TYLCV). Targeting the TYLCV genome with Cas9-single guide RNA at the sequences encoding the coat protein (CP) or replicase (Rep) resulted in efficient virus interference, as evidenced by low accumulation of the TYLCV DNA genome in the transgenic plants. The CRISPR/Cas9-based immunity remained active across multiple generations in the N. benthamiana and tomato plants. Together, our results confirmed the efficiency of the CRISPR/Cas9 system for stable engineering of TYLCV resistance in N. benthamiana and tomato, and opens the possibilities of engineering virus resistance against single and multiple infectious viruses in other crops.
The co-regulatory interaction of transpiration and water potential, and implications for stomatal conductance models
Authors: Beyer Robert , Pretzsch Hans , Cournède Paul-Henry 
Published: December 2017

AbstractLeaf water potential decreases with increasing transpiration rate according to an analogue of Ohm’s law, while transpiration rate decreases with decreasing leaf water potential in the framework of stomatal control. This interaction is not accommodated in present-day models of stomatal conductance. We formally derive the equilibrium between these two counteracting processes for steady-state water conditions. We show that the mechanism considered causes an attenuation of the immediate effect of atmospheric variables on transpiration, which can improve existing models of stomatal conductance that presume noninterdependent variables. Parameters from European beech (Fagus sylvaticaL.) are used to illustrate the results.
Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components
Authors: Wang Min , Kornblau Steven M. , Coombes Kevin R. 
Published: December 2017

AbstractPrincipal component analysis (PCA) is one of the most common techniques in the analysis of biological data sets, but applying PCA raises two challenges. First, one must determine the number of significant principal components (PCs). Second, because each PC is a linear combination of genes, it rarely has a biological interpretation. Existing methods to determine the number of PCs are either subjective or computationally extensive. We review several methods and describe a new R package, PCDimension, that implements additional methods, the most important being an algorithm that extends and automates a graphical Bayesian method. Using simulations, we compared the methods. Our newly automated procedure performs best when considering both accuracy and speed. We applied the method to a proteomics data set from acute myeloid leukemia patients. Proteins in the apoptosis pathway could be explained using six PCs. By clustering the proteins in PC space, we were able to replace the PCs by six “biological components”, three of which could be immediately interpreted from the current literature. We expect this approach combining PCA with clustering to be widely applicable.