bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cross-Sectional Evaluation of the Economic Burden on Patients Seeking Surgical Treatment for Chronic Nerve Injuries
Authors: Felder John M. , Ducic Ivica 
Published: June 2021

Introduction
Little emphasis has been paid toward characterizing the socioeconomic burdens experienced by patients seeking treatment for chronic nerve injuries. The aim of this study was to characterize the direct and indirect costs faced by patients with chronic nerve injuries and their attendant health care utilization.

Materials and Methods
A cross-sectional survey was distributed to all patients (N = 767) from a single nerve surgeon's practice treated for chronic nerve injuries in the ambulatory setting between 2014 and 2020. Data collected included demographics, etiology, comorbidities, duration and severity of symptoms, history of prior medical and interventional treatments, work or school time lost due to injury or treatment, money spent on treatment, and health care utilization.

Results
Of the 767 patients, 209 (27.2%) completed the survey. Average age was 48.8 (SD = 19.1) years, 68.9% female and 31.1% male. Patients with chronic nerve injury reported significant direct costs, indirect costs, and health care utilization associated with their nerve injury symptoms. Direct costs consisted of out-of-pocket spending (68.4% had spent >$1000 per year), physician specialists visits (71.3% had seen at least 4 specialists), and prior interventional procedures intended to address symptoms. Indirect costs included lost time from work or school (24.6% had missed more than 12 months). Health care utilization, represented by annual emergency room visits and hospitalizations related to nerve symptoms, was increased relative to the general population. Detailed statistics are presented in the manuscript.

Conclusions
Chronic nerve injuries may be associated with a notable socioeconomic burden to the patient, including missed work or school, frequent physician visits and procedures, hospital visits, and out-of-pocket costs. Interdisciplinary algorithms recognizing a role for surgical evaluation in patients with chronic neuropathic pain due to underlying nerve injuries would facilitate future research into whether timely surgical intervention may reduce this economic burden.
Determinants of heart rate in Svalbard reindeer reveal mechanisms of seasonal energy management
Authors: Trondrud L. Monica , Pigeon Gabriel , Albon Steve , Arnold Walter , Evans Alina L. , Irvine R. Justin , Król Elżbieta , Ropstad Erik , Stien Audun , Veiberg Vebjørn , Speakman John R. , Loe Leif Egil 
Published: June 2021

Seasonal energetic challenges may constrain an animal's ability to respond to changing individual and environmental conditions. Here, we investigated variation in heart rate, a well-established proxy for metabolic rate, in Svalbard reindeer (Rangifer tarandus platyrhynchus), a species with strong seasonal changes in foraging and metabolic activity. In 19 adult females, we recorded heart rate, subcutaneous temperature and activity using biologgers. Mean heart rate more than doubled from winter to summer. Typical drivers of energy expenditure, such as reproduction and activity, explained a relatively limited amount of variation (2–6% in winter and 16–24% in summer) compared to seasonality, which explained 75% of annual variation in heart rate. The relationship between heart rate and subcutaneous temperature depended on individual state via body mass, age and reproductive status, and the results suggested that peripheral heterothermy is an important pathway of energy management in both winter and summer. While the seasonal plasticity in energetics makes Svalbard reindeer well-adapted to their highly seasonal environment, intraseasonal constraints on modulation of their heart rate may limit their ability to respond to severe environmental change. This study emphasizes the importance of encompassing individual state and seasonal context when studying energetics in free-living animals.This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.
Out of the laboratory, into the field: perspectives on social, ethical and regulatory challenges in UK wildlife research
Authors: Palmer Alexandra , Greenhough Beth 
Published: June 2021

Drawing on insights from qualitative social science research, this paper aims to prompt reflection on social, ethical and regulatory challenges faced by scientists undertaking invasive animal research in the field and propose ways of addressing these challenges to promote good care for animals and environments. In particular, we explore challenges relating to the management of (i) relationships with publics and stakeholders, who may be present at field sites or crucial to research success; (ii) ethical considerations not present in the laboratory, such as the impacts of research on populations and ecosystems; (iii) working under an array of regulations, which may operate in accordance with competing ethical principles or objectives; and (iv) relationships with regulators (especially vets), which may involve disagreements over ethics and expertise, especially because regulators may be more accustomed to overseeing research in the laboratory than the field. We argue that flexibility—at a personal and policy level—and respect for others' expertise emerged as two key ways of negotiating ethical challenges, fostering positive working relationships and promoting good care for individual animals and broader ecosystems. While our analysis focuses on the UK, we propose that many of these lessons are broadly applicable to international contexts.This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.
A brief introduction to the analysis of time-series data from biologging studies
Authors: Harrison Xavier A. 
Published: June 2021

Recent advances in tagging and biologging technology have yielded unprecedented insights into wild animal physiology. However, time-series data from such wild tracking studies present numerous analytical challenges owing to their unique nature, often exhibiting strong autocorrelation within and among samples, low samples sizes and complicated random effect structures. Gleaning robust quantitative estimates from these physiological data, and, therefore, accurate insights into the life histories of the animals they pertain to, requires careful and thoughtful application of existing statistical tools. Using a combination of both simulated and real datasets, I highlight the key pitfalls associated with analysing physiological data from wild monitoring studies, and investigate issues of optimal study design, statistical power, and model precision and accuracy. I also recommend best practice approaches for dealing with their inherent limitations. This work will provide a concise, accessible roadmap for researchers looking to maximize the yield of information from complex and hard-won biologging datasets.This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.
Animal tag technology keeps coming of age: an engineering perspective
Authors: Holton Mark D. , Wilson Rory P. , Teilmann Jonas , Siebert Ursula 
Published: June 2021

Animal-borne tags (biologgers) have now become extremely sophisticated, recording data from multiple sensors at high frequencies for long periods and, as such, have become a powerful tool for behavioural ecologists and physiologists studying wild animals. But the design and implementation of these tags is not trivial because engineers have to maximize performance and ability to function under onerous conditions while minimizing tag mass and volume (footprint) to maximize the wellbeing of the animal carriers. We present some of the major issues faced by tag engineers and show how tag designers must accept compromises while maintaining systems that can answer the questions being posed. We also argue that basic understanding of engineering issues in tag design by biologists will help feedback to engineers to better tag construction but also reduce the likelihood that tag-deploying biologists will misunderstand their own results. Finally, we suggest that proper consideration of conventional technology together with new approaches will lead to further step changes in our understanding of wild-animal biology using smart tags.
This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.
No effect of tDCS on fatigue and depression in chronic stroke patients: an exploratory randomized sham-controlled trial combining tDCS with computerized cognitive training
Authors: Ulrichsen Kristine M. , Kolskår Knut K. , Richard Geneviève , Pedersen Mads L. , Alnæs Dag , Dørum Erlend S. , Sanders Anne-Marthe , Tornås Sveinung , Maglanoc Luigi A. , Engvig Andreas , Ihle-Hansen Hege , Nordvik Jan E. , Westlye Lars T. 
Published: June 2021

AbstractFatigue and emotional distress rank high among self-reported unmet needs in stroke survivors. Currently, few treatment options exist for post stroke fatigue, a condition frequently associated with depression. Non-invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) have shown promise in alleviating fatigue and depression in other patient groups, but the acceptability and effects for chronic phase stroke survivors are not established. Here, we used a randomized sham-controlled design to evaluate the added effect of tDCS combined with computerized cognitive training to alleviate symptoms of fatigue and depression. 74 patients were enrolled at baseline (mean time since stroke = 26 months) and 54 patients completed the intervention. Self-report measures of fatigue and depression were collected at five consecutive timepoints, spanning a period of two months. While fatigue and depression severity were reduced during the course of the intervention, Bayesian analyses provided evidence for no added effect of tDCS. Less severe symptoms of fatigue and depression were associated with higher improvement rate in select tasks, and study withdrawal was higher in patients with more severe fatigue and younger age. Time-resolved analyses of individual symptoms by a network-approach suggested overall higher centrality of fatigue symptoms (except item 1 and 2) than depression symptoms. In conclusion, the results support the notion of fatigue as a significant stroke sequela with possible implications for treatment adherence and response, but reveal no effect of tDCS on fatigue or depression.
Quantifying representativeness in randomized clinical trials using machine learning fairness metrics
Authors: Qi Miao , Cahan Owen , Foreman Morgan A. , Gruen Daniel M. , Das Amar K. , Bennett Kristin P. 
Published: June 2021

AbstractObjectiveWe formulate population representativeness of randomized clinical trials (RCTs) as a machine learning (ML) fairness problem, derive new representation metrics, and deploy them in visualization tools which help users identify subpopulations that are underrepresented in RCT cohorts with respect to national, community-based or health system target populations.Materials and MethodsWe represent RCT cohort enrollment as random binary classification fairness problems, and then show how ML fairness metrics based on enrollment fraction can be efficiently calculated using easily computed rates of subpopulations in RCT cohorts and target populations. We propose standardized versions of these metrics and deploy them in an interactive tool to analyze three RCTs with respect to type-2 diabetes and hypertension target populations in the National Health and Nutrition Examination Survey (NHANES).ResultsWe demonstrate how the proposed metrics and associated statistics enable users to rapidly examine representativeness of all subpopulations in the RCT defined by a set of categorical traits (e.g., sex, race, ethnicity, smoker status, and blood pressure) with respect to target populations.DiscussionThe normalized metrics provide an intuitive standardized scale for evaluating representation across subgroups, which may have vastly different enrollment fractions and rates in RCT study cohorts. The metrics are beneficial complements to other approaches (e.g., enrollment fractions and GIST) used to identify generalizability and health equity of RCTs.ConclusionBy quantifying the gaps between RCT and target populations, the proposed methods can support generalizability evaluation of existing RCT cohorts, enrollment target decisions for new RCTs, and monitoring of RCT recruitment, ultimately contributing to more equitable public health outcomes.
Macro-Level Drivers of SARS-CoV-2 Transmission: A Data-Driven Analysis of Factors Contributing to Epidemic Growth During the First Wave of outbreaks in the United States
Authors: Watts Matthew J 
Published: June 2021

AbstractBackgroundMany questions remain unanswered about how SARS-CoV-2 transmission is influenced by aspects of the economy, environment, and health. A better understanding of how these factors interact can help us to design early health prevention and control strategies, and develop better predictive models for public health risk management of SARS-CoV-2. This study examines the associations between COVID-19 epidemic growth and macro-level determinants of transmission such as climate, socio-economic factors, demographic factors, and population health, during the first wave of outbreaks in the United States.MethodsA spatial-temporal data-set was created by collating information from a variety of data sources including the Johns Hopkins University’s Centre for Systems Science and Engineering, the United States Census Bureau, the USDA Economic Research Service, the United States EPA, the National Climatic Data Center, the CDC and the Oxford COVID-19 Government Response Tracker (OxCGRT). A unique data-driven study design was implemented that allows us to assess the relationship between COVID-19 case and death epidemic doubling times and explanatory variables using a Generalized Additive Model (GAM).ResultsThe main factors associated with case doubling times are higher population density, home overcrowding, manufacturing, and recreation industries. Poverty was also an important predictor of faster epidemic growth perhaps because of factors associated with in-work poverty-related conditions, although poverty is also a predictor of poor population health which is likely driving case and death reporting. Air pollution and diabetes were other important drivers of case reporting. Warmer temperatures are associated with slower epidemic growth, which is most likely explained by human behaviors associated with warmer locations i.e ventilating homes and workplaces. and socializing outdoors. The main factors associated with death doubling times were population density, poverty older age, diabetes, and air pollution. Temperature was also slightly significant slowing death doubling times.ConclusionsSuch findings help underpin current understanding of the disease epidemiology and also supports current policy and advice recommending ventilation of homes, work-spaces, and schools, along with social distancing and mask-wearing. The results also suggest that states which adopted more stringent containment measures early on did have some success suppressing the virus. We can presume that if this was replicated at a federal level, much better outcomes would have been observed across the United States.
An extract of hops (Humulus lupulus L.) modulates gut peptide hormone secretion and reduces energy intake in healthy weight men: a randomised, cross-over clinical trial
Authors: Walker Edward G , Lo Kim R , Pahl Malcolm C , Shin Hyun Sang , Lang Claudia , Wohlers Mark W , Poppitt Sally D , Sutton Kevin H , Ingram John R 
Published: June 2021

ABSTRACTBackgroundGastrointestinal enteroendocrine cells express a range of chemosensory receptors involved in detecting the chemical composition of food during digestion. These receptors, including bitter taste receptors (T2Rs), may play an important role in regulating gut function and appetite.ObjectiveTo establish the ability of Amarasate®, a bitter supercritical CO2 extract of hops (Humulus lupulus L.) to modify acute energy intake, appetite and hormonal responses and establish a site of action.DesignNineteen healthy-weight (BMI = 23.5 ± 0.3 kg/m2) male volunteers completed a randomised three-treatment, double blind, cross-over study with a 1 week washout between treatments. Overnight-fasted participants were cannulated and provided with a standardised 2 MJ breakfast meal at 0900h. Treatments comprised a vehicle control (Placebo) or 500 mg of hops extract administered in either delayed release capsules (Duodenal) at 1100 h or quick release capsules (Gastric) at 1130 h. Ad libitum energy intake was recorded at an outcome meal (1200 h) and afternoon snack (1400 h), with blood samples taken and subjective ratings of appetite, gastrointestinal discomfort, vitality, meal palatability and mood assessed throughout the day.ResultsCompared with placebo, both gastric and duodenal treatments significantly reduced (p < 0.05) total ad libitum energy intake by 911 ± 308 kJ and 944 ± 309 kJ, respectively. Both gastric and duodenal treatments significantly increased (p < 0.05) pre-meal ghrelin and post-prandial CCK, GLP-1 and PYY responses while reducing postprandial insulin, GIP and PP secretion with no significant impact on glycemia. In addition, gastric and duodenal treatments produced small but significant (p < 0.05) changes in vitality and gastrointestinal discomfort (e.g. nausea, bloating, abdominal discomfort) with mild-moderate adverse GI symptoms reported in the gastric treatment only. However, no significant treatment effects were observed for any subjective measures of appetite or meal palatability.ConclusionBoth gastric and duodenal delivery of Amarasate® modulate the release of hormones involved in appetite and glycaemic regulation, providing a potential “bitter brake” on energy intake in healthy-weight men.
Sleep as a random walk - A superstatistical analysis of EEG data across sleep stages
Authors: Metzner C. , Schilling A. , Traxdorf M. , Schulze H. , Krauss P. 
Published: June 2021

AbstractIn clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a superstatistical analysis by computing ‘hyper-parameters’, such as the standard deviation, kurtosis and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than subdivided process that controls human sleep.
Temporal derivative computation in the dorsal raphe network revealed by an experimentally-driven augmented integrate-and-fire modeling framework
Authors: Harkin Emerson F. , Lynn Michael B. , Payeur Alexandre , Boucher Jean-François , Caya-Bissonnette Léa , Cyr Dominic , Stewart Chloe , Longtin André , Naud Richard , Béïque Jean-Claude 
Published: June 2021

AbstractBy means of an expansive innervation, the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically-identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire (GIF) models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior.
Prediction of antibiotic resistant strains of bacteria from their beta-lactamases protein
Authors: Maryam Lubna , Dhall Anjali , Patiyal Sumeet , Usmani Salman Sadullah , Sharma Neelam , Raghava Gajendra Pal Singh 
Published: June 2021

AbstractNumber of beta-lactamase variants have ability to deactivate ceftazidime antibiotic, which is the most commonly used antibiotic for treating infection by Gram-negative bacteria. In this study an attempt has been made to develop a method that can predict ceftazidime resistant strains of bacteria from amino acid sequence of beta-lactamases. We obtained beta-lactamases proteins from the β-lactamase database, corresponding to 87 ceftazidime-sensitive and 112 ceftazidime-resistant bacterial strains. All models developed in this study were trained, tested, and evaluated on a dataset of 199 beta-lactamases proteins. We generate 9149 features for beta-lactamases using Pfeature and select relevant features using different algorithms in scikit-learn package. A wide range of machine learning techniques (like KNN, DT, RF, GNB, LR, SVC, XGB) has been used to develop prediction models. Our random forest-based model achieved maximum performance with AUROC of 0.80 on training dataset and 0.79 on the validation dataset. The study also revealed that ceftazidime-resistant beta-lactamases have amino acids with non-polar side chains in abundance. In contrast, ceftazidime-sensitive beta-lactamases have amino acids with polar side chains and charged entities in abundance. Finally, we developed a webserver “ABCRpred”, for the scientific community working in the era of antibiotic resistance to predict the antibiotic resistance/susceptibility of beta-lactamase protein sequences. The server is freely available at (http://webs.iiitd.edu.in/raghava/abcrpred/).Key PointsCeftazidime is commonly used to treat infection caused by Gram-negative bacteria.Beta-lactamase is responsible for lysing ceftazidime, make it resistant to bacteria.Comparison of resistant and sensitive variants of beta-lactamase.Classification of sensitive and resistant strain of bacteria based on beta-lactamase.Prediction models have been developed using different machine learning techniques.