Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient analytics on ordered datasets using MapReduce

Efficiently analyzing data on a large scale can be vital for data owners to gain useful business intelligence. One of the most common datasets used to gain business intelligence is event log files. Oftentimes, records in event log files that are time sorted, need to be grouped by user ID or transaction ID in order to mine user behaviors, such as click through rate, while preserving the time order. This kind of analytical workload is here referred to as RElative Order-pReserving based Grouping (Re-Org). Using MapReduce/Hadoop, a popular big data analysis tool, in an as-is manner for executing Re-Org tasks on ordered datasets is not efficient due to its internal sort-merge mechanism. We propose a framework that adopts an efficient group-order-merge mechanism to provide faster execution of Re-Org tasks and implement it by extending Hadoop. Experimental results show a 2.2x speedup over executing Re-Org tasks in plain vanilla Hadoop.
Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Summary Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.
New functions for a vertebrate Rho guanine nucleotide exchange factor in ciliated epithelia

Human ARHGEF11, a PDZ-domain-containing Rho guanine nucleotide exchange factor (RhoGEF), has been studied primarily in tissue culture, where it exhibits transforming ability, associates with and modulates the actin cytoskeleton, regulates neurite outgrowth, and mediates activation of Rho in response to stimulation by activated Gα12/13 or Plexin B1. The fruit fly homolog, RhoGEF2, interacts with heterotrimeric G protein subunits to activate Rho, associates with microtubules, and is required during gastrulation for cell shape changes that mediate epithelial folding. Here, we report functional characterization of a zebrafish homolog of ARHGEF11 that is expressed ubiquitously at blastula and gastrula stages and is enriched in neural tissues and the pronephros during later embryogenesis. Similar to its human homolog, zebrafish Arhgef11 stimulated actin stress fiber formation in cultured cells, whereas overexpression in the embryo of either the zebrafish or human protein impaired gastrulation movements. Loss-of-function experiments utilizing a chromosomal deletion that encompasses the arhgef11 locus, and antisense morpholino oligonucleotides designed to block either translation or splicing, produced embryos with ventrally-curved axes and a number of other phenotypes associated with ciliated epithelia. Arhgef11-deficient embryos often exhibited altered expression of laterality markers, enlarged brain ventricles, kidney cysts, and an excess number of otoliths in the otic vesicles. Although cilia formed and were motile in these embryos, polarized distribution of F-actin and Na+/K+-ATPase in the pronephric ducts was disturbed. Our studies in zebrafish embryos have identified new, essential roles for this RhoGEF in ciliated epithelia during vertebrate development.
Neuromuscular Factors Contributing to Reductions in Muscle Force After Repeated, High-Intensity Muscular Efforts

Multiple neuromuscular processes contribute to the loss of force production following repeated, high-intensity muscular efforts; however, the relative contribution of each process is unclear. In Experiment 1, 16 resistance trained men performed six sets of unilateral isometric plantar flexor contractions of the right leg (3 s contraction/2 s rest; 85% maximal voluntary contraction torque; 90-s inter-set rest) until failure with and without caffeine ingestion (3 mg kg-1) on two separate days. Corticospinal excitability and cortical silent period (cSP) were assessed before and immediately, 10 and 20 min after the exercise. In Experiment 2, electrically evoked tetanic force and persistent inward current (PIC)-mediated facilitation of the motor neuron pool (estimated using neuromuscular electrical stimulation with tendon vibration) were assessed before and after the same exercise intervention in 17 resistance trained men. Results showed decreases in peak plantar flexion torque (Experiment 1: -12.2%, Experiment 2: -16.9%), electrically evoked torque (20 Hz -15.3%, 80 Hz -15.3%, variable-frequency train -17.9%), and cSP (-3.8%; i.e., reduced inhibition) post-exercise which did not recover by 20 min. Electromyographic activity (EMG; -6%), corticospinal excitability (-9%), and PIC facilitation (-24.8%) were also reduced post-exercise but recovered by 10 min. Caffeine ingestion increased torque and EMG but did not notably affect corticospinal excitability, PIC amplification, or electrically evoked torque. The data indicate that a decrease in muscle function largely underpins the loss of force after repeated, high-intensity muscular efforts, but that the loss is exacerbated immediately after the exercise by simultaneous decreases in corticospinal excitability and PIC amplitudes at the motor neurons.
A Bayesian heteroscedastic GLM with application to fMRI data with motion spikes

We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a highly efficient Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable selection among the regressors to model both the mean (i.e., the design matrix) and variance. This makes it possible to include a broad range of explanatory variables in both the mean and variance (e.g., time trends, activation stimuli, head motion parameters and their temporal derivatives), and to compute the posterior probability of inclusion from the MCMC output. Variable selection is also applied to the lags in the autoregressive noise process, making it possible to infer the lag order from the data simultaneously with all other model parameters. We use both simulated data and real fMRI data from OpenfMRI to illustrate the importance of proper modeling of heteroscedasticity in fMRI data analysis. Our results show that the GLMH tends to detect more brain activity, compared to its homoscedastic counterpart, by allowing the variance to change over time depending on the degree of head motion.
The vascular endothelium: A regulator of arterial tone and interface for the immune system

Abstract As the primary interface between the blood and various tissues of the body, the vascular endothelium exhibits a diverse range of roles and activities, all of which contribute to the overall health and function of the cardiovascular system. In this focused review, we discuss several key aspects of endothelial function, how this may be compromised and subsequent consequences. Specifically, we examine the dynamic regulation of arterial contractility and distribution of blood flow through the generation of chemical and electrical signaling events that impinge upon vascular smooth muscle. The endothelium can generate a diverse range of vasoactive compounds and signals, most of which act locally to adjust blood flow in a dynamic fashion to match tissue metabolism. Disruption of these vascular signaling processes (e.g. reduced nitric oxide bioavailability) is typically referred to as endothelial dysfunction, which is a recognized risk factor for cardiovascular disease in patients and occurs early in the development and progression of hypertension, atherosclerosis and tissue ischemia. Endothelial dysfunction is also associated with type-2 Diabetes and aging and increased mechanistic knowledge of the cellular changes contributing to these effects may provide important clues for interventional strategies. The endothelium also serves as the initial site of interaction for immune cells entering tissues in response to damage and acts to facilitate the actions of both the innate and acquired immune systems to interact with the vascular wall. In addition to representing the main cell type responsible for the formation of new blood vessels (i.e. angiogenesis) within the vasculature, the endothelium is also emerging as a source of extracellular vesicle or microparticles for the transport of signaling molecules and other cellular materials to nearby, or remote, sites in the body. The characteristics of released microparticles appear to change with the functional status of the endothelium; thus, these microparticles may represent novel biomarkers of endothelial health and more serious cardiovascular disease.
Probabilistic provisioning and scheduling in uncertain Cloud environments

Resource provisioning and task scheduling in Cloud environments are quite challenging because of the fluctuating workload patterns and of the unpredictable behaviors and unstable performance of the infrastructure. It is therefore important to properly master the uncertainties associated with Cloud workloads and infrastructure. In this paper, we propose a probabilistic approach for resource provisioning and task scheduling that allows users to estimate in advance, i.e., offline, the resources to be provisioned, thus reducing the risk and the impact of overprovisioning or underprovisioning. In particular, we formulate an optimization problem whose objective is to identify scheduling plans that minimize the overall monetary cost for leasing Cloud resources subject to some workload constraints. This cost-aware model ensures that the execution time of an application does not exceed with a given probability a specified deadline, even in presence of uncertainties. To evaluate the behavior and sensitivity to uncertainties of the proposed approach, we simulate a simple batch workload consisting of MapReduce jobs. The experimental results show that, despite the provisioning and scheduling approaches that do not take into account the uncertainties in their decision process, our probabilistic approach nicely adapts to workload and Cloud uncertainties.
Breaking down the complement system: a review and update on novel therapies

Purpose of review The complement system represents one of the more primitive forms of innate immunity. It has increasingly been found to contribute to pathologies in the native and transplanted kidney. We provide a concise review of the physiology of the complement cascade, and discuss current and upcoming complement-based therapies. Recent findings Current agents in clinical use either bind to complement components directly or prevent complement from binding to antibodies affixed to the endothelial surface. These include C1 esterase inhibitors, anti-C5 mAbs, anti-CD20 mAbs, and proteasome inhibitors. Treatment continues to show efficacy in the atypical hemolytic uremic syndrome and antibody-mediated rejection. Promising agents not currently available include CCX168, TP10, AMY-101, factor D inhibitors, coversin, and compstatin. Several new trials are targeting complement inhibition to treat antineutrophilic cystoplasmic antibody (ANCA)-associated vasculitis, C3 glomerulopathy, thrombotic microangiopathy, and IgA nephropathy. New agents for the treatment of the atypical hemolytic uremic syndrome are also in development. Summary Complement-based therapies are being considered for targeted therapy in the atypical hemolytic uremic syndrome and antibody-mediated rejection, C3 glomerulopathy, and ANCA-associated vasculitis. A few agents are currently in use as orphan drugs. A number of other drugs are in clinical trials and, overall, are showing promising preliminary results.
Modelling the sound insulation of corrugated roof structures : an extended transfer matrix approach

Over the course of this work, the transfer matrix method (TMM) was implemented and studied for its potential in predicting the sound insulation characteristics of corrugated dual leaf roof structures. A model of periodically connected plates, using Fourier expansions, was derived independently and extended in this work to include the stiffness of connectors and orthotropic plates, and intermediate beams in the framework of the structure. The same principles were applied in deriving the transmission loss through periodically stiffened plates, as a proxy for corrugated panels. At the same time, corrugated plates were modelled as equivalent orthotropic panels, a process which is compatible with the TMM without particular modifications. Infill materials were modelled using simple fluid representations and more complex poroelastic behaviour. The point-connected plate models were combined with the TMM to enable multiple layers of infill materials between the two plates, by using an effective fluid approach. The stiffness of typical roof connectors was obtained with numerical simulations, and used in predicting transmission loss. Corrections were introduced to simulate diffuse laboratory conditions and enable meaningful comparisons to available data. The limitations of a simple orthotropic plate model compared to laboratory measurements and the ribbed plate model were identified. A number of measurements of dual leaf partitions and full roof systems were compared to the TMM and point-connected plate models, finding typically good agreement at low to mid-frequency, and a strong dependence of high frequency transmission loss on connector stiffness.
State-of-Health Estimation of Lithium-Ion Batteries Based on Partial Charging Voltage Profiles

Different state-of-health (SOH) estimation methods for Lithium-ion batteries have been proposed in the literature. Even though these methods are showing good accuracy, they are developed for optimal laboratory conditions and do not consider various limitations, which are encountered in real-life applications. Therefore, in this paper, we proposed an alternative method for SOH estimation of NMC-based Li-ion batteries considering a reduced voltage interval for which the capacity fade is determined. For validation of the proposed SOH method, six different calendar aging cases have been considered.
Assessment of vectorial total variation penalties on realistic dual-energy CT data

Vectorial extensions of total variation have recently been developed for regularizing the reconstruction and denoising of multi-channel images, such as those arising in spectral computed tomography. Early studies have focused mainly on simulated, piecewise-constant images whose structure may favor total-variation penalties. In the current manuscript, we apply vectorial total variation to real dual-energy CT data of a whole turkey in order to determine if the same benefits can be observed in more complex images with anatomically realistic textures. We consider the total nuclear variation (TVN) as well as another vectorial total variation based on the Frobenius norm (TVF) and standard channel-by-channel total variation (TVS). We performed a series of 3D TV denoising experiments comparing the three TV variants across a wide range of smoothness parameter settings, optimizing each regularizer according to a very-high-dose ‘ground truth’ image. Consistent with the simulation studies, we find that both vectorial TV variants achieve a lower error than the channel-by-channel TV and are better able to suppress noise while preserving actual image features. In this real data study, the advantages are subtler than in the previous simulation study, although the TVN penalty is found to have clear advantages over either TVS or TVF when comparing material images formed from linear combinations of the denoised energy images.
Prediction of chorioamnionitis in cases of intraamniotic infection by ureaplasma urealyticum in women with very preterm premature rupture of membranes or preterm labour

Abstract Objectives: First, to determinate the frequency of chorioamnionitis and funisitis in cases of intramniotic detection of Ureaplasma urealyticum. Second, to assess the predictive capability of some biological markers in the amniotic fluid of these women to predict histological inflammation. Subjects and methods: We prospectively studied 20 cases of women with premature rupture of membranes or preterm labour (PROM) or preterm labour and intraamniotic detection of Ureaplasma urealyticum. Gestational age at admission was 26.74 ± 2.53 weeks. Amniotic fluid concentrations of IL18, IL 2, IL4, IL6, IL10, IL12, TNF-alpha, IFN-g, and MMP-8 were measured by the Multiplex method. Amniotic fluid glucose and leukocyte count were also measured by standard methods. Placental detailed histological studies were performed. Student’s t-test, forward stepwise conditional binary logistic regression analysis and ROC curves were used. Results: Histological chorioamnionitis was present in 45% of cases (9/20) and funisitis just in 15% (3/20). Interleukins 6, 8, 12, MMP-8, and leukocyte count were significantly elevated in cases of histological inflammation, defined as choriamnionitis or chorioamniotis + funisitis (p = .007, .03, .01, .03, .03, respectively) while glucose was decreased (p = .04). Binary logistic regression for the prediction of inflammation showed a high predictive value (R2 = .66, p = .002) including in the equation only the IL6 value. Conclusions: A significant percentage of cases with intraamniotic detection of Ureaplasma urealyticum shows no pathological signs of histological inflammation. Concentration of Interleukin 6 in amniotic fluid can be useful for the diagnosis of subclinical chorioamnionitis in these cases.
Changing the environment to improve population health: a framework for considering exposure in natural experimental studies

There is renewed optimism regarding the use of natural experimental studies to generate evidence as to the effectiveness of population health interventions. Natural experimental studies capitalise on environmental and policy events that alter exposure to certain social, economic or environmental factors that influence health. Natural experimental studies can be useful for examining the impact of changes to ‘upstream’ determinants, which may not be amenable to controlled experiments. However, while natural experiments provide opportunities to generate evidence, they often present certain conceptual and methodological obstacles. Population health interventions that alter the physical or social environment are usually administered broadly across populations and communities. The breadth of these interventions means that variation in exposure, uptake and impact may be complex. Yet many evaluations of natural experiments focus narrowly on identifying suitable ‘exposed’ and ‘unexposed’ populations for comparison. In this paper, we discuss conceptual and analytical issues relating to defining and measuring exposure to interventions in this context, including how recent advances in technology may enable researchers to better understand the nature of population exposure to changes in the built environment. We argue that when it is unclear whether populations are exposed to an intervention, it may be advantageous to supplement traditional impact assessments with observational approaches that investigate differing levels of exposure. We suggest that an improved understanding of changes in exposure will assist the investigation of the impact of complex natural experiments in population health.
Treatment response and non‐response in CBT and Network Support for alcohol disorders: targeted mechanisms and common factors

AIMS To determine whether mechanisms of drinking behavior change that are targeted by specific treatments mediate the effects of Packaged Cognitive Behavior Therapy (PCBT) and Network Support (NS) on abstinence rates throughout 27 months. DESIGN Secondary analysis of data from two Network Support Project randomized clinical trials, in which participants were assigned to either a case management control treatment (control) or NS treatment in trial 1, or to PCBT or NS treatment in trial 2. SETTING An out-patient substance abuse treatment site at a university medical center in the United States. PARTICIPANTS A total of 249 men and 154 women (n = 403) with alcohol use disorder. MEASUREMENTS The primary outcome was membership in a treatment responder class determined by growth mixture modeling (GMM) of monthly proportion days abstinent (PDA) to 27 months. Key predictors of responder class membership included network change variables, and changes in coping scores and self-efficacy. FINDINGS GMM analyses indicated that a three-class solution provided the best fit to the data: a treatment responder class comprising almost 55% of patients, a late relapsing class that showed post-treatment gains followed by a return to baseline drinking (12.7% of patients) and a non-responder class (32% of patients). Analyses indicated that treatment effects on responder class membership were moderated by baseline drinking (P < 0.001). PCBT effects were mediated by changes in coping scores (P < 0.05). NS treatment effects were also mediated by coping change, as well as by increases in self-efficacy and in number of abstinent friends (P < 0.05). CONCLUSIONS Long-term success in Packaged Cognitive Behavior Therapy and Network Support treatments for alcohol use disorder appears to be mediated by both general mechanisms (developing coping skills and self-efficacy) and treatment-targeted mechanisms (developing network strategies that emphasize social support and avoiding friends who drink).
Sentence similarity measures for essay coherence

This paper describes the use of different methods for semantic similarity calculation for predicting a specific type of textual coherence. We show that Random Indexing can be used to locate documents in a semantic space as well as terms, but not by straightforwardly summing term vectors. Using a mathematical translation of the semantic space, we are able to use Random Indexing to assess textual coherence as well as LSA, but with considerably lower computational overhead.