Miracle-dz/UniFAR-FTUs huggingface dataset:
Design, development, and evaluation of printed educational materials for evidence-based practice dissemination

Aim:Printed educational materials (PEMs) are one of the most common dissemination strategies for communicating information about evidence-based practices (EBPs) to healthcare professionals and organizations; however, evidence is conflicting regarding the conditions and circumstances in which PEMs are effective in achieving desired outcomes. The effectiveness of PEMs is largely dependent on the manner in which they are developed. This article reports on the findings from a comprehensive review of the literature regarding best practices for creating PEMs for health professionals and illustrates how these practices were used to design, develop, and evaluate an informational packet to disseminate information about motivational interviewing. Methods:The informational packet was disseminated to 92 community health organizations not currently implementing motivational interviewing. Evaluation surveys were completed by 212 healthcare directors and providers to examine quality and perceived helpfulness of the packets, intention to use information from the packet, and sharing of the packet with others. Associations between these and individual and organizational characteristics were also assessed. Results:Overall, the packet was perceived as appropriate and helpful in making a decision to implement motivational interviewing. For example, 84.9% of participants stated that the content was ‘about right’. Three-quarters (75.9%) of participants reported plans to use the information in the packet and almost half (46.7%) reported talking about the packet with others in the organizations. Higher levels of baseline interest in motivational interviewing adoption were significantly related to packet use and wanting to utilize additional resources presented in the packet. Positive attitudes toward EBPs were also significantly related to the desire to obtain resources in the packet. Perceptions of the packet did not differ by type of community health organization (i.e., community health center, community behavioral health organization) or whether the individual was a director or provider. Conclusion:Results indicated that PEMs can be a useful tool to disseminate EBP information to healthcare professionals particularly if they have a prior interest in the EBP and have general attitudes supportive of EBPs. Recommendations for the improvement of future PEMs are discussed.
Examining the short-term anxiolytic and antidepressant effect of Floatation-REST

Floatation-REST (Reduced Environmental Stimulation Therapy) reduces sensory input to the nervous system through the act of floating supine in a pool of water saturated with Epsom salt. The float experience is calibrated so that sensory signals from visual, auditory, olfactory, gustatory, thermal, tactile, vestibular, gravitational and proprioceptive channels are minimized, as is most movement and speech. This open-label study aimed to examine whether Floatation-REST would attenuate symptoms of anxiety, stress, and depression in a clinical sample. Fifty participants were recruited across a spectrum of anxiety and stress-related disorders (posttraumatic stress, generalized anxiety, panic, agoraphobia, and social anxiety), most (n = 46) with comorbid unipolar depression. Measures of self-reported affect were collected immediately before and after a 1-hour float session, with the primary outcome measure being the pre- to post-float change score on the Spielberger State Anxiety Inventory. Irrespective of diagnosis, Floatation-REST substantially reduced state anxiety (estimated Cohen’s d > 2). Moreover, participants reported significant reductions in stress, muscle tension, pain, depression and negative affect, accompanied by a significant improvement in mood characterized by increases in serenity, relaxation, happiness and overall well-being (p < .0001 for all variables). In reference to a group of 30 non-anxious participants, the effects were found to be more robust in the anxious sample and approaching non-anxious levels during the post-float period. Further analysis revealed that the most severely anxious participants reported the largest effects. Overall, the procedure was well-tolerated, with no major safety concerns stemming from this single session. The findings from this initial study need to be replicated in larger controlled trials, but suggest that Floatation-REST may be a promising technique for transiently reducing the suffering in those with anxiety and depression. Trial registration: ClinicalTrials.gov NCT03051074
c-JUN Dimerization Protein 2 (JDP2) Is a Transcriptional Repressor of Follicle-stimulating Hormone β (FSHβ) and Is Required for Preventing Premature Reproductive Senescence in Female Mice*

Follicle-stimulating hormone (FSH) regulates follicular growth and stimulates estrogen synthesis in the ovaries. FSH is a heterodimer consisting of an α subunit, also present in luteinizing hormone, and a unique β subunit, which is transcriptionally regulated by gonadotropin-releasing hormone 1 (GNRH). Because most FSH is constitutively secreted, tight transcriptional regulation is critical for maintaining FSH levels within a narrow physiological range. Previously, we reported that GNRH induces FSHβ (Fshb) transcription via induction of the AP-1 transcription factor, a heterodimer of c-FOS and c-JUN. Herein, we identify c-JUN-dimerization protein 2 (JDP2) as a novel repressor of GNRH-mediated Fshb induction. JDP2 exhibited high basal expression and bound the Fshb promoter at an AP-1-binding site in a complex with c-JUN. GNRH treatment induced c-FOS to replace JDP2 as a c-JUN binding partner, forming transcriptionally active AP-1. Subsequently, rapid c-FOS degradation enabled reformation of the JDP2 complex. In vivo studies revealed that JDP2 null male mice have normal reproductive function, as expected from a negative regulator of the FSH hormone. Female JDP2 null mice, however, exhibited early puberty, observed as early vaginal opening, larger litters, and early reproductive senescence. JDP2 null females had increased levels of circulating FSH and higher expression of the Fshb subunit in the pituitary, resulting in elevated serum estrogen and higher numbers of large ovarian follicles. Disruption of JDP2 function therefore appears to cause early cessation of reproductive function, a condition that has been associated with elevated FSH in women.
A framework for short-term activity-aware load forecasting

In this paper, we present a framework for implementing short-term load forecasting, in which statistical time series prediction methods and machine learning-based regression methods, can be configured to benchmark their performance against each other on given data of smart meters and other related exogenous variables. Besides the prediction methods, forecasting performance also depends on the quality of training data. This is addressed by two characteristics of our framework on data collection and preprocessing. The first one is to introduce a human activity variable as an additional load influencing factor which reflects anomalous load patterns by aperiodic human activity. The second characteristic is to wavelet transform training data during the preprocessing stage to better extract redundant information from meter data. To investigate the feasibility of the proposed framework, a preliminary case study for predicting daily power consumption of several individual smart meters, using real-world data, is presented. The results indicate that, in general, the aggregation level of meter data and activity data matters.
Floating scale surface reconstruction

Any sampled point acquired from a real-world geometric object or scene represents a finite surface area and not just a single surface point. Samples therefore have an inherent scale, very valuable information that has been crucial for high quality reconstructions. We introduce a new method for surface reconstruction from oriented, scale-enabled sample points which operates on large, redundant and potentially noisy point sets. The approach draws upon a simple yet efficient mathematical formulation to construct an implicit function as the sum of compactly supported basis functions. The implicit function has spatially continuous "floating" scale and can be readily evaluated without any preprocessing. The final surface is extracted as the zero-level set of the implicit function. One of the key properties of the approach is that it is virtually parameter-free even for complex, mixed-scale datasets. In addition, our method is easy to implement, scalable and does not require any global operations. We evaluate our method on a wide range of datasets for which it compares favorably to popular classic and current methods.
Subgrouping the users of a specialized app for eating disorders

ABSTRACT Although mobile technologies for eating disorders (EDs) are burgeoning, there is limited data about the clinical characteristics of individuals using specialized smartphone applications (apps) without accompanying traditional forms of treatment. This study evaluated whether the users of an ED app cluster in clinically meaningful groups. Participants were 1,280 app users (91.3% female; mean age 27) who reported not being in a weekly treatment for their ED. A hierarchical cluster analysis distinguished five groups of participants, all approximating DSM-5 ED categories. One cluster comprised of non-female, ethnically diverse users with Bulimia Nervosa features. Findings suggest that app users resemble known patient classifications.
Internal Promotion in Competitive Sports: Evidence from the English Premier League

The analysis of English Premier League clubs' reliance on internal versus external sources for new additions to the first-team is motivated by relevant labor economics literature. We consider two dimensions for analysis: (i) the extensive margin that drives the selection of youth players in the first team, and (ii) the intensive margin that looks at their career lifespan once selected. Two uniquely created data sets are utilized to establish robust results in support of the notion that more reputable youth programs provide greater first-team opportunities through internal hiring. Foreign sourced players become more prevalent in the league after the Bosman ruling, and their probability of selection is positively correlated with club stature. Survival analysis results validate prior results in terms of youth training reputation of certain clubs, and establishes a presence of heterogeneity at youth club level that signals differences in player career prospects generated by their youth training. Further, when the unobserved heterogeneity is modelled using discrete finite mixtures we get new insights into the role unobservables in the analysis. In particular, two types of players are identified in the data, one type that represents 33% is the one that drives the exits of the youth players. In addition, this model shows that the Bosman ruling positively impacts the career duration of youth players, as opposed to its negative effect on first-team selection.
Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks

Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET (Simultaneously Inference of GeNEs and Tissues) to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype-associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in finding both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the prevention, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.
A human microdose study of the antimalarial drug GSK3191607 in healthy volunteers

GSK3191607, a novel inhibitor of the Plasmodium falciparum ATP4 (PfATP4) pathway, is being considered for development in humans. However, a key problem encountered during the preclinical evaluation of the compound was its inconsistent pharmacokinetic (PK) profile across preclinical species (mouse, rat and dog), which prevented reliable prediction of PK parameters in humans and precluded a well‐founded assessment of the potential for clinical development of the compound. Therefore, an open‐label microdose (100 μg, six subjects) first time in humans study was conducted to assess the human PK of GSK3191607 following intravenous administration of [14C]‐GSK3191607.
On the suitability of Bluetooth 5 for the Internet of Things: Performance and scalability analysis

The challenges related to the large-scale deployment of the Internet of Things to enable a wide range of application use cases have received significant attention. A number of communication technologies have been and are still developed ranging from evolved existing technologies such as 4G, WiFi or WPAN technologies to the much hyped 5G networks. Against the background of the on-going discussion on the most suitable technologies the Bluetooth Special Interest Group has proposed the next generation Bluetooth 5 specification and claims to be ready for the Internet of Things as well: mainly by increasing the maximum communication range and adapting the corresponding data rates. In this context, this paper evaluates the suitability of Bluetooth 5 for realistic application activity levels and interference scenarios, based on analytical models and validated by frequency hopping algorithm simulations. The performance results demonstrate that Bluetooth 5 is a valid technology candidate for implementation of Internet of Things applications, provided that activity level, expected coverage areas and the interference situation within the ISM band are carefully considered in the deployment process.
Population Genetic Analysis of Theileria annulata from Six Geographical Regions in China, Determined on the Basis of Micro- and Mini-satellite Markers

Theileria annulata, a tick-borne apicomplexan protozoan, causes a lymphoproliferative disease of cattle with high prevalence in tropical and sub-tropical regions. Understanding the genetic diversity and structure of local populations will provide more fundamental knowledge for the population genetics and epidemics of protozoa. In this study, 78 samples of T. annulata collected from cattle/yaks representing 6 different geographic populations in China were genotyped using eight micro- and mini-satellite markers. High genetic variation within population, moderate genetic differentiation, and high level of diversity co-occurring with significant linkage disequilibrium were observed, which indicates there is gene flow between these populations in spite of the existence of reproductive and geographical barriers among populations. Furthermore, some degree of genetic differentiation was also found between samples from China and Oman. These findings provide a first glimpse of the genetic diversity of the T. annulata populations in China, and might contribute to the knowledge of distribution, dynamics, and epidemiology of T. annulata populations and optimize the management strategies for control.
Online multiple kernel regression

Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this paper presents a novel scheme of Online Multiple Kernel Regression (OMKR), which sequentially learns the kernel-based regressor in an online and scalable fashion, and dynamically explore a pool of multiple diverse kernels to avoid suffering from a single fixed poor kernel so as to remedy the drawback of manual/heuristic kernel selection. The OMKR problem is more challenging than regular kernel-based regression tasks since we have to on-the-fly determine both the optimal kernel-based regressor for each individual kernel and the best combination of the multiple kernel regressors. In this paper, we propose a family of OMKR algorithms for regression and discuss their application to time series prediction tasks. We also analyze the theoretical bounds of the proposed OMKR method and conduct extensive experiments to evaluate its empirical performance on both real-world regression and times series prediction tasks.
Aging Potentiates Lateral but Not Local Inhibition of Orientation Processing in Primary Visual Cortex

Aging-related declines in vision can decrease well-being of the elderly. Concerning early sensory changes as in the primary visual cortex, physiological and behavioral reports seem contradictory. Neurophysiological studies on orientation tuning properties suggested that neuronal changes might come from decreased cortical local inhibition. However, behavioral results either showed no clear deficits in orientation processing in older adults, or proposed stronger surround suppression. Through psychophysical experiments and computational modeling, we resolved these discrepancies by suggesting that lateral inhibition increased in older adults while neuronal orientation tuning widths, related to local inhibition, stayed globally intact across age. We confirmed this later result by re-analyzing published neurophysiological data, which showed no systematic tuning width changes, but instead displayed a higher neuronal noise with aging. These results suggest a stronger lateral inhibition and mixed effects on local inhibition during aging, revealing a more complex picture of age-related effects in the central visual system than people previously thought.
Quantifying the Uptake of user-written Commands over Time

A major factor in the uptake of new statistical methods is the availability of user-friendly software implementations. One attractive feature of Stata is that users can write their own commands and release them to other users via Statistical Software Components at Boston College. Authors of statistical programs do not always get adequate credit, because programs are rarely cited properly. There is no obvious measure of a program's impact, but researchers are under increasing pressure to demonstrate the impact of their work to funders. In addition to encouraging proper citation of software, the number of downloads of a user-written package can be regarded as a measure of impact over time. In this article, we explain how such information can be accessed for any month from July 2007 and summarized using the new ssccount command.
Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple‐ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple‐ensemble probabilistic assessment, the median of simulated yield change was −4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple‐ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.
Two Attributes of Number Meaning: Numerical Associations With Visual Space and Size Exist in Parallel

Many studies demonstrated interactions between number processing and either spatial codes (effects of spatial-numerical associations) or visual size-related codes (size-congruity effect). However, the interrelatedness of these two number couplings is still unclear. The present study examines the simultaneous occurrence of space- and size-numerical congruency effects and their interactions both within and across trials. In a magnitude judgment task physically small or large digits were presented left or right from screen center. The reaction times analysis revealed that space- and size-congruency effects coexisted in parallel and combined additively. Moreover, a selective sequential modulation of the two congruency effects was found. The size-congruency effect was reduced after size incongruent trials. The space-congruency effect, however, was only affected by the previous space congruency. The observed independence of spatial-numerical and within-magnitude associations is interpreted as evidence that the two couplings reflect different attributes of numerical meaning possibly related to ordinality and cardinality.