bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brief Report: Drop in CD4+ Counts Below 200 Cells/μL After Reaching (or Starting From) Values Higher than 350 Cells/μL in HIV-Infected Patients With Virological Suppression
Authors: Gianotti Nicola , Marchetti Giulia , Antinori Andrea , Saracino Annalisa , Gori Andrea , Rizzardini Giuliano , Lichtner Miriam , Bandera Alessandra , Mussini Cristina , Girardi Enrico , d'Arminio Monforte Antonella , Cozzi-Lepri Alessandro ,   on behalf of the ICONA Foundation Study Group
Published: August 2017

Background:
The aim of the study was to quantify the risk of a drop in CD4+ counts below 200 cells/μL after reaching values >350 cells/μL on antiretroviral therapy (ART) (or after starting ART with CD4+ count >350 cells/μL) in the absence of virological failure.

Setting:
Ambulatory care services, Italy.

Methods:
Prospective cohort study of patients enrolled in the ICONA Foundation Study cohort who started ART with >350 CD4+/μL or with ≤350 CD4+/μL and reached values >350 cells/μL after virological suppression (VS, defined by 2 consecutive viral loads ≤50 copies/mL). The date of CD4 count >350 was the baseline for the analysis and those with ≥1 viral load and CD4+ count after baseline were included. The primary end point was the cumulative risk (estimated using the Kaplan–Meier method) of a CD4+ drop below 200 cells/μL over follow-up, which was censored at the date of virological failure (confirmed HIV-RNA >50 copies/mL), death, or last visit.

Results:
Six thousand six hundred sixty-three patients were included. A confirmed CD4+ drop below 200 cells/μL was never observed over a median follow-up of 45 (Q1: 21, Q3: 89) months, as long as VS was maintained. Upper limits of the 97.5% confidence interval of rates of confirmed CD4+ drop below 200 cells/μL were 0.28 and 0.38/1000 person-years of follow-up for patients with ≤350 and >350 CD4+ cells/μL at starting ART.

Conclusions:
In patients who started ART in Italy with >350 CD4+ cells/μL or reached >350 CD4+ cells/μL after VS, the risk of a CD4+ drop below 200 cells/μL in those maintaining VS was negligible.
Alternatively Activated Macrophages Drive Browning of White Adipose Tissue in Burns
Authors: Abdullahi Abdikarim , Auger Christopher , Stanojcic Mile , Patsouris David , Parousis Alexandra , Epelman Slava , Jeschke Marc G. 
Published: August 2017

Objective:
The aim of this study was to uncover the mediators and mechanistic events that facilitate the browning of white adipose tissue (WAT) in response to burns.

Background:
In hypermetabolic patients (eg, burns, cancer), the browning of WAT has presented substantial clinical challenges related to cachexia, atherosclerosis, and poor clinical outcomes. Browning of the adipose tissue has recently been found to induce and sustain hypermetabolism. Although browning appears central in trauma-, burn-, or cancer-induced hypermetabolic catabolism, the mediators are essentially unknown.

Methods:
WAT and blood samples were collected from patients admitted to the Ross Tilley Burn Centre at Sunnybrook Hospital. Wild type, CCR2 KO, and interleukin (IL)-6 KO male mice were purchased from Jax laboratories and subjected to a 30% total body surface area burn injury. WAT and serum collected were analyzed for browning markers, macrophages, and metabolic state via histology, gene expression, and mitochondrial respiration.

Results:
In the present study, we show that burn-induced browning is associated with an increased macrophage infiltration, with a greater type 2 macrophage profile in the fat of burn patients. Similar to our clinical findings in burn patients, both an increase in macrophage recruitment and a type 2 macrophage profile were also observed in post burn mice. Genetic loss of the chemokine CCR2 responsible for macrophage migration to the adipose impairs burn-induced browning. Mechanistically, we show that macrophages recruited to burn-stressed subcutaneous WAT (sWAT) undergo alternative activation to induce tyrosine hydroxylase expression and catecholamine production mediated by IL-6, factors required for browning of sWAT.

Conclusion:
Together, our findings uncover macrophages as the key instigators and missing link in trauma-induced browning.
Fatal Cerebral Edema, Seizures, and Hyponatremia After Trazodone Overdose
Authors: Avila Jose David 
Published: August 2017

AbstractTrazodone is a serotonin antagonist and reuptake inhibitor that is widely used for the treatment of depression and insomnia. Fatal overdose is rare and usually occurs when combined with other drugs or alcohol. Only a few lethal cases of pure trazodone overdose have been reported, all attributed to cardiotoxicity. We reported a 37-year-old woman who presented after ingesting 6.45 g of trazodone in a suicidal attempt. She was hyponatremic because of the syndrome of inappropriate antidiuretic hormone secretion and, shortly after, had a seizure and developed fatal cerebral edema. Others have described seizures and hyponatremia after pure trazodone overdose, but this is the first report of cerebral edema and death from a neurological complication. Careful monitoring and correction of sodium levels may be necessary in these patients.
Reversible Splenial Lesion Syndrome After Intravenous Immunoglobulin Treatment for Guillain-Barre Syndrome
Authors: Uygur Kucukseymen Elif , Yuksel Burcu , Genc Fatma , Ozaydin Goksu Eylem , Yildiz Sevim , Bicer Gomceli Yasemin 
Published: August 2017

Abstract
Reversible corpus callosum splenial (CCS) lesions have been described in patients with varied etiologies. The most common causes of previously reported reversible focal lesions of the CCS are viral encephalitis, antiepileptic drug toxicity/withdrawal, and metabolic disorders. Intravenous immunoglobulin (IVIG) therapy is used for different immune-mediated diseases. It is generally safe, and serious adverse reactions are uncommon. We presented a rare case of disturbed consciousness with reversible CCS lesions after IVIG therapy for Guillain-Barre syndrome in an adult woman. In this case, we believe that IVIG therapy caused reversible CCS lesions with encephalopathy and probably result of cytotoxic edema and/or cerebral arterial vasospasm.
School Refusal Successfully Treated With Divalproex
Authors: Ye Lu , Shin Jeongwon , Lippmann Steven 
Published: August 2017

Abstract
Divalproex was successfully used in a 7-year-old boy suffering from treatment-refractory school refusal complicated with a mood disturbance and anxiety. Divalproex had demonstrated a rapid effect in reducing school anxiety and improving school attendance after multiple failures of other agents. Divalproex may be an effective agent for treating school refusal in children with emotional and/or behavior disturbances.
Horizontal gene cluster transfer increased hallucinogenic mushroom diversity
Authors: Reynolds Hannah T. , Vijayakumar Vinod , Gluck-Thaler Emile , Korotkin Hailee Brynn , Matheny Patrick Brandon , Slot Jason Christopher 
Published: August 2017

AbstractSecondary metabolites are heterogeneous natural products that often mediate interactions between species. The tryptophan-derived secondary metabolite, psilocin, is a serotonin receptor agonist that induces altered states of consciousness. A phylogenetically disjunct group of mushroom-forming fungi in the Agaricales produce the psilocin prodrug, psilocybin. Spotty phylogenetic distributions of fungal compounds are sometimes explained by horizontal transfer of metabolic gene clusters among unrelated fungi with overlapping niches. We report the discovery of a psilocybin gene cluster in three hallucinogenic mushroom genomes, and evidence for its horizontal transfer between fungal lineages. Patterns of gene distribution and transmission suggest that psilocybin provides a fitness advantage in the dung and late wood-decay niches, which may be reservoirs of fungal indole-based metabolites that alter behavior of mycophagous and wood-eating invertebrates. These hallucinogenic mushroom genomes will serve as models in neurochemical ecology, advancing the prospecting and synthetic biology of novel neuropharmaceuticals.
Evaluating a topic model approach for parsing microbiome data structure
Authors: Woloszynek Stephen , Zhao Zhengqiao , Simpson Gideon , O’Connor Michael P. , Mell Joshua Chang , Rosen Gail L. 
Published: August 2017

ABSTRACTThe increasing availability of microbiome survey data has led to the use of complex machine learning and statistical approaches to measure taxonomic diversity and extract relationships between taxa and their host or environment. However, many approaches inadequately account for the difficulties inherent to microbiome data. These difficulties include (1) insufficient sequencing depth resulting in sparse count data, (2) a large feature space relative to sample space, resulting in data prone to overfitting, (3) library size imbalance, requiring normalization strategies that lead to compositional artifacts, and (4) zero-inflation. Recent work has used probabilistic topics models to more appropriately model microbiome data, but a thorough inspection of just how well topic models capture underlying microbiome signal is lacking. Also, no work has determined whether library size or variance normalization improves model fitting. Here, we assessed a topic model approach on 16S rRNA gene survey data. Through simulation, we show, for small sample sizes, library-size or variance normalization is unnecessary prior to fitting the topic model. In addition, by exploiting topic-to-topic correlations, the topic model successfully captured dynamic time-series behavior of simulated taxonomic subcommunities. Lastly, when the topic model was applied to the David et al. time-series dataset, three distinct gut configurations emerged. However, unlike the David et al. approach, we characterized the events in terms of topics, which captured taxonomic co-occurrence, and posterior uncertainty, which facilitated the interpretation of how the taxonomic configurations evolved over time.
The Complex Ecosystem in Non Small Cell Lung Cancer Invasion
Authors: Haney Seth , Konen Jessica , Marcus Adam I. , Bazhenov Maxim 
Published: August 2017

AbstractMany tumors are characterized by genetic instability, producing an assortment of genetic variants of tumor cells called subclones. These tumors and their surrounding environments form complex multi-cellular ecosystems, where subclones compete for resources and cooperate to perform multiple tasks, including cancer invasion. Our recent empirical studies revealed existence of such distinct phenotypes of cancer cells, leaders and followers, in lung cancer. These two cellular subclones exchange a complex array of extracellular signals demonstrating a symbiotic relationship at the cellular level. Here, we develop a computational model of the microenvironment of the lung cancer ecosystem to explore how the interactions between subclones can advance or inhibit invasion. We found that, due to the complexity of the ecosystem, invasion may have very different dynamics characterized by the different levels of aggressiveness. By altering the signaling environment, we could alter the ecological relationship between the cell types and the overall ecosystem development. Competition between leader and follower cell populations (defined by the limited amount of resources), positive feedback within the leader cell population (controlled by the focal adhesion kinase and fibronectin signaling), and impact of the follower cells to the leaders (represented by yet undetermined proliferation signal) all had major effects on the outcome of the collective dynamics. Specifically, our analysis revealed a class of tumors (defined by the strengths of fibronectin signaling and competition) that are particularly sensitive to manipulations of the signaling environment. This class can undergo irreversible changes to the tumor ecosystem that outlast these manipulations of feedbacks and have a profound impact on invasive potential. Our study predicts a complex division of labor between cancer cell subclones and suggests new treatment strategies targeting signaling within the tumor ecosystem.Author SummaryCancer is an elusive disease due to the wide variety of cancer types and adaptability to treatment. How is this adaptability accomplished? Loss of genetic stability, a hallmark of cancer, leads to the emergence of many different types of cancer cells within a tumor. This creates a complex ecosystem where cancer cell types can cooperate, compete, and exploit each other. We have previously used an image-guided technology to isolatedistinct cancer subclones and identify how they interact. Here, we have employed mathematical modeling to understand how the dynamic feedbacks between different cancer cell types can impact the success of invasion in lung cancer. We found that successful invasion required for feedbacks to support the less viable but more invasive cell types. These predictions may have implications for novel clinical treatment options and emphasize the need to visualize and probe cancer as a tumor ecosystem.
Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator
Authors: McMillan Beth , Gavaghan David J. , Mirams Gary R. 
Published: August 2017

AbstractDrug-induced Torsades de Pointes (TdP) arrhythmia is of major interest in predictive toxicology. Drugs which cause TdP block the hERG cardiac potassium channel. However, not all drugs that block hERG cause TdP. As such, further understanding of the mechanistic route to TdP is needed. Early afterde-polarisations (EADs) are a cell-level phenomenon in which the membrane of a cardiac cell depolarises a second time before repolarisation, and EADs are seen in hearts during TdP. Therefore, we propose a method of predicting TdP using induced EADs combined with multiple ion channel block in simulations using biophysically-based mathematical models of human ventricular cell electrophysiology. EADs were induced in cardiac action potential models using interventions based on diseases that are known to cause EADs, including: increasing the conduction of the L-type calcium channel, decreasing the conduction of the hERG channel, and shifting the inactivation curve of the fast sodium channel. The threshold of intervention that was required to cause an EAD was used to classify drugs into clinical risk categories. The metric that used L-type calcium induced EADs was the most accurate of the EAD metrics at classifying drugs into the correct risk categories, and increased in accuracy when combined with action potential duration measurements. The EAD metrics were all more accurate than hERG block alone, but not as predictive as simpler measures such as simulated action potential duration. This may be because different routes to EADs represent risk well for different patient subgroups, something that is difficult to assess at present.
The limitations of correlation-based inference in complex virus-microbe communities
Authors: Coenen Ashley R. , Weitz Joshua S. 
Published: August 2017

1AbstractMicrobes are present in high abundances in the environment and in human-associated microbiomes, often exceeding one million per milliliter. Viruses of microbes are present in even higher abundances and are important in shaping microbial populations, communities, and ecosystems. Given the relative specificity of viral infection, it is essential to identify the functional linkages between viruses and their microbial hosts, particularly given dynamic changes in virus and host abundances. Multiple approaches have been proposed to infer infection networks from time-series ofin situcommunities, among which correlation-based approaches have emerged as thede factostandard. In this work, we evaluate the accuracy of correlation-based inference methods using anin silicoapproach. In doing so, we compare predicted networks to actual networks to assess the self-consistency of correlation-based inference. At odds with assumptions underlying its widespread use, we find that correlation is a poor predictor of interactions in the context of viral infection and lysis of microbial hosts. The failure to predict interactions holds for methods which leverage product-moment, time-lagged, and relative-abundance based correlations. In closing, we discuss alternative inference methods, particularly model-based methods, as a means to infer interactions in complex microbial communities with viruses.2ImportanceInferring interactions from population time-series is an active and ongoing area of research. It is relevant across many biological systems – in particular in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference – using correlations to predict interactions – is widespread. However, it is well known that “correlation does not imply causation”. Despite this, many studies apply correlation-based inference methods to experimental time-series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions withinin silicavirus-microbe communities, raising questions on their relevance when appliedin situ.
The genomic determinants of adaptive evolution in a fungal pathogen
Authors: Grandaubert Jonathan , Dutheil Julien Y. , Stukenbrock Eva H. 
Published: August 2017

AbstractAntagonistic host-pathogen co-evolution is a determining factor in the outcome of infection and shapes genetic diversity at the population level of both partners. While the molecular function of an increasing number of genes involved in pathogenicity is being uncovered, little is known about the molecular bases and genomic impact of hst-pathogen coevolution and rapid adaptation. Here, we apply a population genomic approach to infer genome-wide patterns of selection among thirteen isolates of the fungal pathogenZymoseptoria tritici. Using whole genome alignments, we characterize intragenic polymorphism, and we apply different test statistics based on the distribution of non-synonymous and synonymous polymorphisms (pN/pS) and substitutions (dN/dS) to (1) characterise the selection regime acting on each gene, (2) estimate rates of adaptation and (3) identify targets of selection. We correlate our estimates with different genome variables to identify the main determinants of past and ongoing adaptive evolution, as well as purifying and balancing selection. We report a negative relationship between pN/pS and fine-scale recombination rate and a strong positive correlation between the rate of adaptive non-synonymous substitutions (ωa) and recombination rate. This result suggests a pervasive role of Hill-Robertson interference even in a species with an exceptionally high recombination rate (60 cM/Mb). Moreover, we report that the genome-wide fraction of adaptive non-synonymous substitutions (α) is ~ 44%, however in genes encoding determinants of pathogenicity we find a mean value of alpha ~ 68% demonstrating a considerably faster rate of adaptive evolution in this class of genes. We identify 787 candidate genes under balancing selection with an enrichment of genes involved in secondary metabolism and host infection, but not predicted effectors. This suggests that different classes of pathogenicity-related genes evolve according to distinct selection regimes. Overall our study shows that sexual recombination is a main driver of genome evolution in this pathogen.
Longitudinal evaluation of efficiency in a petrochemical company
Authors: Gilsa Charles Von , Lacerda Daniel Pacheco , Camargo Luis Felipe Riehs , Souza Iberê Guarani , Cassel Ricardo Augusto 
Published: August 2017

Purpose
The purpose of this paper is to longitudinally assess the technical efficiency and productivity, considering investment projects and technological change, in a second-generation petrochemical company.

Design/methodology/approach
The study uses data envelopment analysis (DEA) together with the Malmquist index to measure efficiency during the analysis periods. The working method consists of four main phases, namely development of the conceptual model, construction of the mathematical model, application of model to the case, and analysis of the results. The study utilizes a quantitative approach with descriptive goals seeking to evaluate the impacts of technical changes on the operational efficiency and productivity of the production process.

Findings
The use of DEA associated with the Malmquist index proved to be viable for analyzing a single company and identifying efficiency improvements, as well as the impacts of the learning process and the implementation of improvement projects. However, the results of the improvement projects and learning process were not representative and had no statistical significance on the actual change in efficiency of the company during the periods analyzed. For the case in question, the learning process and continuous improvement were not observed during all study periods.

Practical implications
The proposition that the improvement projects and investments implemented increased the efficiency of the company was rejected. Hence, with this work, it was possible to determine that the company unnecessarily invested resources in projects to increase efficiency. Furthermore, the company could have explored more internal resources before making significant investments in increased efficiency.

Originality/value
As for the value of this research in the theoretical and academic scope, this paper advances knowledge on the application of DEA because it proposes to establish an internal reference benchmarking for comparison. The literature contains few studies that analyze organizations using continuous processes, such as petrochemical processes, in longitudinal studies as a function of time, especially with the use of DEA.
Critical factors and performance indicators: accreditation of testing- and calibration-laboratories
Authors: Karthiyayini Nagarajan , Rajendran Chandrasekharan 
Published: August 2017

Purpose
The purpose of this paper is to determine the critical factors (CRFs) of IS/ISO/IEC 17025:2005 laboratory accreditation and indicators of the performance (IOPs) in the testing/calibration laboratories. The impact of accreditation on the performance is analyzed using the level of presence of critical factors that can be used to benchmark the best practices in the accredited testing and calibration laboratories.

Design/methodology/approach
A questionnaire is designed on the basis of an extensive literature review on laboratory accreditation and performance. Based on the responses from pilot study, the developed instrument is tested for its unidimensionality, reliability and validity. Multiple regression analysis is carried out to analyze the impact of critical factors on the IOPs.

Findings
The findings reveal that the six distinct critical dimensions of laboratory accreditation (which are the independent variables) have a significant impact on the performance of the accredited testing/calibration laboratories. The performance is measured by the dependent variables which are the IOPs. The customer satisfaction, employee satisfaction, laboratory performance, image of the laboratory, the number of increased customers and loyalty of the existing customers are highly influenced by the accreditation which in turn improves the performance to gain the competitive advantage.

Research limitations/implications
Contribution to research is in the area of laboratory accreditation and the performance measurement by the development of a comprehensive instrument to analyze the Laboratory Quality Management System. The results are dependent on the number of respondents who are the quality/technical managers.

Practical implications
This instrument would enable the accredited laboratories to analyze their performance through the feedback from the quality/technical managers to gain credibility and to continually improve and benchmark the quality management system in alignment with the quality policies.

Originality/value
This paper proposes an instrument to measure and benchmark the performance of the accredited testing and calibration laboratories.