bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical and video-polysomnographic analysis of rapid eye movement sleep behavior disorder and other sleep disturbances in dementia with Lewy bodies
Authors: Fernández-Arcos Ana , Morenas-Rodríguez Estrella , Santamaria Joan , Sánchez-Valle Raquel , Lladó Albert , Gaig Carles , Lleó Alberto , Iranzo Alex 
Published: April 2019

Abstract

Objective
The main objective of this study was to study rapid eye movement (REM) sleep behavior disorder (RBD) and other sleep disorders in dementia with Lewy bodies (DLB).

Methods
Consecutive patients with DLB and mild dementia severity were recruited irrespective of sleep complaints. Patients underwent clinical interview, assessment of sleep scales, and video-polysomnography (V-PSG). RBD was diagnosed with V-PSG based on electromyographic and audiovisual analysis.

Results
Thirty-five patients (65.7% men; mean age 77.7 ± 6.1 years) were evaluated. Poor sleep quality (54.3%), hypersomnia (37.1%), snoring (60%), and abnormal nocturnal behaviors (77.1%) were reported. Sleep–wake architecture abnormalities occurred in 75% patients and consisted of occipital slowing on awake electroencephalography (EEG; 34.4%), the absence of sleep spindles and K complexes (12.9%), slow frequency sleep spindles (12.9%), delta activity in REM sleep (19.2%), and REM sleep without atonia (44%). Three patients showed hallucinatory-like behaviors and 10 patients showed abnormal behaviors during arousals mimicking RBD. RBD was diagnosed in 50% of those patients in whom sufficient REM sleep was attained. Of these, 72.7% were not aware of displaying dream-enacting behaviors and in 63.7% RBD preceded the onset of cognitive impairment. For RBD diagnosis, the sensitivity of Mayo Sleep Questionnaire was 50%, specificity was 66.7%, positive predictive value was 83.3%, and negative predictive value was 28%. False-positive RBD cases according to clinical history had hallucinatory-like behaviors, severe obstructive sleep apnea, and prominent periodic limb movements in sleep. Occipital EEG frequency while awake and rate of electromyographic activity in REM sleep were negatively correlated, suggesting a common subcortical origin.

Conclusion
In DLB, RBD and sleep–wake disorders are common, heterogeneous, and complex, challenging their identification without performing V-PSG.
The assessment of supplementation requirements of grazing ruminants using nutrition models
Authors: Tedeschi Luis O , Molle Giovanni , Menendez Hector M , Cannas Antonello , Fonseca Mozart A 
Published: April 2019

AbstractThis paper was aimed to summarize known concepts needed to comprehend the intricate interface between the ruminant animal and the pasture when predicting animal performance, acknowledge current efforts in the mathematical modeling domain of grazing ruminants, and highlight current thinking and technologies that can guide the development of advanced mathematical modeling tools for grazing ruminants. The scientific knowledge of factors that affect intake of ruminants is broad and rich, and decision-support tools (DST) for modeling energy expenditure and feed intake of grazing animals abound in the literature but the adequate predictability of forage intake is still lacking, remaining a major challenge that has been deceiving at times. Despite the mathematical advancements in translating experimental research of grazing ruminants into DST, numerous shortages have been identified in current models designed to predict intake of forages by grazing ruminants. Many of which are mechanistic models that rely heavily on preceding mathematical constructions that were developed to predict energy and nutrient requirements and feed intake of confined animals. The data collection of grazing (forage selection, grazing behavior, pasture growth/regrowth, pasture quality) and animal (nutrient digestion and absorption, volatile fatty acids production and profile, energy requirement) components remains a critical bottleneck for adequate modeling of forage intake by ruminants. An unresolved question that has impeded DST is how to assess the quantity and quality, ideally simultaneously, of pasture forages given that ruminant animals can be selective. The inadequate assessment of quantity and quality has been a hindrance in assessing energy expenditure of grazing animals for physical activities such as walking, grazing, and forage selection of grazing animals. The advancement of sensors might provide some insights that will likely enhance our understanding and assist in determining key variables that control forage intake and animal activity. Sensors might provide additional insights to improve the quantification of individual animal variation as the sensor data are collected on each subject over time. As a group of scientists, however, despite many obstacles in animal and forage science research, we have thrived, and progress has been made. The scientific community may need to change the angle of which the problem has been attacked, and focus more on holistic approaches.
The Alzheimer’s Disease Metabolome: Effects of Sex andAPOEε4 genotype
Authors: Arnold Matthias , Nho Kwangsik , Kueider-Paisley Alexandra , Massaro Tyler , Brauner Barbara , MahmoudianDehkordi Siamak , Louie Gregory , Arthur Moseley M. , Will Thompson J. , John Williams Lisa St , Tenenbaum Jessica D. , Blach Colette , Chang Rui , Brinton Roberta D. , Baillie Rebecca , Han Xianlin , Trojanowski John Q. , Shaw Leslie M. , Weiner Michael W. , Trushina Eugenia , Toledo Jon B. , Krumsiek Jan , Murali Doraiswamy P. , Saykin Andrew J. , Kaddurah-Daouk Rima , Kastenmüller Gabi ,   for the Alzheimer’s Disease Neuroimaging Initiative,   the Alzheimer’s Disease Metabolomics Consortium
Published: April 2019

AbstractRecent studies have provided evidence that late-onset Alzheimer’s disease (AD) can in part be considered a metabolic disease. Besides age, female sex andAPOEε4 genotype represent strong risk factors for AD. They also both give rise to large metabolic differences, suggesting that metabolic aspects of AD pathogenesis may differ between males and females and betweenAPOEε4 carriers and non-carriers. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 140 metabolites measured in serum samples of 1,517 AD neuroimaging initiative subjects, with AD biomarkers for Aβ and tau pathology and neurodegeneration. We observed substantial sex differences in effects of 15 metabolites on AD biomarkers with partially overlapping differences forAPOEε4 status groups. These metabolites highlighted several group-specific alterations not observed in unstratified analyses using sex andAPOEε4 as covariates. Combined stratification by both variables uncovered further subgroup-specific metabolic effects limited to the group with presumably the highest AD risk:APOEε4+ females. Pathways linked to the observed metabolic alterations suggest that females experience more expressed impairment of mitochondrial energy production in AD than males. These findings indicate that dissecting metabolic heterogeneity in AD pathogenesis may enable grading of the biomedical relevance of specific pathways for specific subgroups. Extending our approach beyond simple one- or two-fold stratification may thus guide the way to personalized medicine.Significance statementResearch provides substantial evidence that late-onset Alzheimer’s disease (AD) is a metabolic disease. Besides age, female sex and APOEε4 genotype represent strong risk factors for AD, and at the same time give rise to large metabolic differences. Our systematic investigation of sex and APOE ε4 genotype differences in the link between metabolism and measures of pre-symptomatic AD using stratified analysis revealed several group-specific metabolic alterations that were not observed without sex and genotype stratification of the same cohort. Pathways linked to the observed metabolic alterations suggest females are more affected by impairment of mitochondrial energy production in AD than males, highlighting the importance of tailored treatment approaches towards a precision medicine approach.
Contraction of the ROS scavenging enzyme glutathioneS-transferase gene family in cetaceans
Authors: Tian Ran , Seim Inge , Ren Wenhua , Xu Shixia , Yang Guang 
Published: April 2019

ABSTRACTCetaceans are a group of marine mammals whose ancestors were adaptated for life on land. Life in an aquatic environment poses many challenges for air-breathing mammals. Diving marine mammals have adapted to rapid reoxygenation and reactive oxygen species (ROS)-mediated reperfusion injury. Here, we considered the evolution of the glutathione transferase (GST) gene family which has important roles in the detoxification of endogenously-derived ROS and environmental pollutants. We characterized the cytosolic GST gene family in 21 mammalian species; cetaceans, sirenians, pinnipeds, and their terrestrial relatives. All seven GST classes were identified, showing that GSTs are ubiquitous in mammals. Some GST genes are the product of lineage-specific duplications and losses, in line with a birth-and-death evolutionary model. We detected sites with signatures of positive selection that possibly influence GST structure and function, suggesting that adaptive evolution of GST genes is important for defending mammals from various types of noxious environmental compounds. We also found evidence for loss of alpha and mu GST subclass genes in cetacean lineages. Notably, cetaceans have retained a homolog of at least one of the genesGSTA1,GSTA4, andGSTM1; GSTs that are present in both the cytosol and mitochondria. The observed variation in number and selection pressure on GST genes suggest that the gene family structure is dynamic within cetaceans. Taken together, our results indicate that the cytosolic GST family in cetaceans reflects unique evolutionary dynamics related to oxygen-poor aquatic environments.
A Single-cell Transcriptomic Atlas of the Developing Chicken Limb
Authors: Feregrino Christian , Sacher Fabio , Parnas Oren , Tschopp Patrick 
Published: April 2019

AbstractBackgroundThrough precise implementation of distinct cell type specification programs, differentially regulated in both space and time, complex patterns emerge during organogenesis. Thanks to its easy experimental accessibility, the developing chicken limb has long served as a paradigm to study vertebrate pattern formation. Through decades’ worth of research, we now have a firm grasp on the molecular mechanisms driving limb formation at the tissue-level. However, to elucidate the dynamic interplay between transcriptional cell type specification programs and pattern formation at its relevant cellular scale, we lack appropriately resolved molecular data at the genome-wide level. Here, making use of droplet-based single-cell RNA-sequencing, we catalogue the developmental emergence of distinct tissue types and their transcriptome dynamics in the distal chicken limb, the so-called autopod, at cellular resolution.ResultsUsing single-cell RNA-sequencing technology, we sequenced a total of 17,628 cells coming from three key developmental stages of chicken autopod patterning. Overall, we identified 23 cell populations with distinct transcriptional profiles. Amongst them were small, albeit essential populations like the apical ectodermal ridge, demonstrating the ability to detect even rare cell types. Moreover, we uncovered the existence of molecularly distinct sub-populations within previously defined compartments of the developing limb, some of which have important signaling functions during autopod pattern formation. Finally, we inferred gene co-expression modules that coincide with distinct tissue types across developmental time, and used them to track patterning-relevant cell populations of the forming digits.ConclusionsWe provide a comprehensive functional genomics resource to study the molecular effectors of chicken limb patterning at cellular resolution. Our single-cell transcriptomic atlas captures all major cell populations of the developing autopod, and highlights the transcriptional complexity in many of its components. Finally, integrating our data-set with other single-cell transcriptomics resources will enable researchers to assess molecular similarities in orthologous cell types across the major tetrapod clades, and provide an extensive candidate gene list to functionally test cell-type-specific drivers of limb morphological diversification.
Proposal for a Global Adherence Scale for Acute Conditions (GASAC): a prospective cohort study in two Emergency Departments
Authors: Sustersic Mélanie , Gauchet Aurélie , Duvert Amélie , Gonnet Laure , Foote Alison , Vermorel Céline , Allenet Benoit , Bosson Jean-Luc 
Published: April 2019

AbstractBackgroundAdherence in the context of patients with acute conditions is a major public health issue. It is neglected by the research community and no clinically validated generic scale exists to measure it.ObjectiveTo construct and validate a Global Adherence Scale usable in the context of Acute Conditions (GASAC) that takes into account adherence both to advice and to all types of prescriptions that the doctor may give. To measure adherence and to study its determinants.Materials and methodWe based the construction of the GASAC questionnaire on a theoretical model and a literature search. Then, between 2013 and 2014, we validated it in a prospective observational study in two hospital emergency departments. Patients were contacted by phone about one week after their consultation to answer several questionnaires, including GASAC and the Girerd self-administered questionnaire about medication adherence as a control.ResultsGASAC consists of four adherence subscales: drug prescriptions; blood test/ radiography prescriptions; lifestyle advice and follow-up instructions. An analysis of the 154 sets of answers from patients showed that the GASAC drug subscale had satisfactory internal coherence (Cronbach’s alpha = 0.78) and was correlated with the Girerd score, as was GASAC as a whole (p<0.01)). The median score was 0.93 IQR [0.78-1] for a maximum value of 1 (n = 154). In multivariate analysis, infection was more conducive of good adherence (cut off at ≥ 0.8; n=115/154; 74.7% [67.0-81.3]) than trauma (OR 3.69; CI [1.60-8.52]). The Doctor-Patient Communication score (OR 1.06 by score point, CI [1.02-1.10]) also influenced adherence.ConclusionsGASAC is a generic score to measure all dimensions of adherence in emergency departments for clinical research and the evaluation of clinical practice. The level of adherence was high for acute conditions and could be further improved by good Doctor-Patient Communication.
Analysis of glue line and correlations between anatomical characteristics of Eucalyptus grandis × Eucalyptus urophylla glued-laminated timber
Authors: Oliveira Rafael G. E. , Gonçalves Fabricio G. , Segundinho Pedro G. A. , Oliveira José Tarcísio S. , Paes Juarez B. , Chaves Izabella Luzia S. , Brito Alice S. 
Published: April 2019

AbstractThe main goal of this study was to analyze glue line on eucalyptus wood. In order to do that, thickness of main and secondary glue lines were measured as well their interaction with apparent density of elements glued with resorcinol-formaldehyde (RF) and castor polyurethane (CP) adhesives. Anatomical wood characterization of Eucalyptus grandis × Eucalyptus urophylla was performed by correlating glue line thickness. According to normative instruction, specimens were produced for delamination tests. The experiment was conducted in a completely random 2 × 2 design factorial scheme (two classes of apparent density and two adhesives). Pearson correlation (t < 0.01) was performed among variables. It was found that there was adhesive penetration into wood pots and rays. Glue line thickness was higher in woods with density higher than 0.58 g cm−3 glued with RF adhesive. There was low correlation among density, vessel diameter, main and secondary glue lines (t < 0.01).
Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition
Authors: Thompson Jaron , Johansen Renee , Dunbar John , Munsky Brian 
Published: April 2019

AbstractMicrobial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the complexity of the communities. This challenge can be addressed, in part, by integrating the advances in DNA sequencing technology with computational approaches like machine learning. Although machine learning techniques have been applied to microbiome data, use of these techniques remains rare, and user-friendly platforms to implement such techniques are not widely available. We developed a tool that implements neural network and random forest models to perform regression and feature selection tasks on microbiome data. In this study, we applied the tool to analyze soil microbiome (16S rRNA gene profiles) and dissolved organic carbon (DOC) data from a 44-day plant litter decomposition experiment. The microbiome data includes 1709 total bacterial operational taxonomic units (OTU) from 300+ microcosms. Regression analysis of predicted and actual DOC for a held-out test set of 51 samples yield Pearson’s correlation coefficients of .636 and .676 for neural network and random forest approaches, respectively. Important taxa identified by the machine learning techniques are compared to results from a standard tool (indicator species analysis) widely used by microbial ecologists. Of 1709 bacterial taxa, indicator species analysis identified 285 taxa as significant determinants of DOC concentration. Of the top 285 ranked features determined by machine learning methods, a subset of 86 taxa are common to all feature selection techniques. Using this subset of features, prediction results for random permutations of the data set are at least equally accurate compared to predictions determined using the entire feature set. Our results suggest that integration of multiple methods can aid identification of a robust subset of taxa within complex communities that may drive specific functional outcomes of interest.
Predicting sediment and nutrient concentrations from high-frequency water-quality data
Authors: Leigh Catherine , Kandanaarachchi Sevvandi , McGree James M. , Hyndman Rob J. , Alsibai Omar , Mengersen Kerrie , Peterson Erin E. 
Published: April 2019

AbstractWater-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.
Rainfall financial risk assessment in the hospitality industry
Authors: Franzoni Simona , Pelizzari Cristian 
Published: April 2019

PurposeThe variability of weather at tourist destinations can significantly affect travel decisions by tourists and their comfort. In particular, rain affects the profitability of hospitality firms that can hardly contrast the phenomenon of heavy rain. Therefore, the assessment of rainfall financial risks, i.e. the negative economic effects caused by rain, becomes crucial to safeguarding the profitability of the hospitality industry. The purpose of this study is to assess such risks.Design/methodology/approachThe present work contributes to the literature on weather/climate change and tourism by advancing a model for the rainfall financial risk assessment of hospitality firms. The model is based on scenario correlation between business performances and rain and originates from the Enterprise Risk Management (ERM) presented by the Committee of Sponsoring Organizations of the Treadway Commission (COSO), where some tools to adequately face business risks are advanced.FindingsThe model is complemented by an empirical experiment based on the business performances of the hospitality industry of Lake Garda and the amount of rainfall in the same area during the decade 2005-2014. The empirical application detects scenario correlation between those variables over time. In particular, the findings open opportunities to purchase financial instruments (insurance contracts, derivative instruments, etc.) with greater awareness, with the purpose of mitigating the negative impacts of rain on business performances of hospitality firms.Originality/valueThe model improves scenario analysis by introducing scenario correlation, which is a tool for assessing the highly nonlinear links between business performances and rain in today’s complex world. This is the essential step that firms should perform if they want to successfully adopt strategic decisions about rainfall financial risk management.
Influence of unmanned aerial vehicle in medical product transport
Authors: G. Prasad , P. Abishek , R. Karthick 
Published: April 2019

PurposeThe purpose of this paper is to discuss the special applications of unmanned aerial vehicles (UAVs) for the transport of medical goods.Design/methodology/approachExperimental work has been carried out to predict the performance characteristics of UAVs.FindingsThe results have been obtained to predict the range and endurance of UAVs, which can be optimized based on the payload and source of power.Originality/valueReal-time applications. As the medical products are necessary in the real time life saving events.