Miracle-dz/UniFAR-FTUs huggingface dataset:
Serum Insulin-Like Growth Factor 1 and the Risk of Ischemic Stroke: The Framingham Study

Background and Purpose— Low insulin-like growth factor 1 (IGF-1) has been associated with increased risk of atherosclerosis and atrial fibrillation in cross-sectional studies. Yet, prospective data linking IGF-1 levels to the development of ischemic stroke remain inconclusive. We examined prospectively the association between serum IGF-1 levels and incident ischemic stroke. Methods— We measured serum IGF-1 levels in 757 elderly individuals (mean age 79±5, 62% women), free of prevalent stroke, from the Framingham original cohort participants at the 22nd examination cycle (1990–1994) and were followed up for the development of ischemic stroke. Cox models were used to relate IGF-1 levels to the risk for incident ischemic stroke, adjusted for potential confounders. Results— During a mean follow-up of 10.2 years, 99 individuals developed ischemic stroke. After adjustment for age, sex, and potential confounders, higher IGF-1 levels were associated with a lower risk of incident ischemic stroke, with subjects in the lowest quintile of IGF-1 levels having a 2.3-fold higher risk of incident ischemic stroke (95% confidence interval, 1.09–5.06; P=0.03) as compared with those in the top quintile. We observed an effect modification by diabetes mellitus and waist–hip ratio for the association between IGF-1 and ischemic stroke (P<0.1). In subgroup analyses, the effects were restricted to subjects with diabetics and those in top waist–hip ratio quartile, in whom each standard deviation increase in IGF-1 was associated with a 61% (hazard ratio, 0.39; 95% confidence interval, 0.20–0.78; P=0.007) and 41% (hazard ratio, 0.59; 95% confidence interval, 0.37–0.95; P=0.031) lower risk of incident ischemic stroke, respectively. Conclusions— IGF-1 levels were inversely associated with ischemic stroke, especially among persons with insulin resistance.
Novel Reactive Distillation–Pervaporation Coupled Process for Ethyl Acetate Production with Water Removal from Reboiler and Acetic Acid Recycle

Ethyl acetate (EtAc) is an essential raw material widely used in the chemical industry. In this paper, a new reactive distillation (RD)–pervaporation (PV) coupled process for ethyl acetate (EtAc) production is proposed, in which the PV membrane is located in the bottom stream in order to selectively remove the water from the reboiler and recycle the acetic acid (HAc) into the feed. The main operating conditions of the RD process are discussed by experimental and simulation methods. The RD–PV coupled process was established by coupling the PVA/ceramic composite membrane with the RD reboiler. The effects of PV operating temperature and HAc/ethanol molar ratio on the RD performance are thoroughly discussed. It was found that the PVA/ceramic composite membrane exhibited good PV performance and stability with a total flux of 600 g·m–2·h–1 and a separation factor of 14 in 90 wt % HAc/water solution at 70 °C. Owing to the water removal and HAc recycle from the reboiler via PV, both the ethanol conversion and EtA.
FO model checking of geometric graphs

Over the past two decades the main focus of research into first-order (FO) model checking algorithms has been on sparse relational structures - culminating in the FPT algorithm by Grohe, Kreutzer and Siebertz for FO model checking of nowhere dense classes of graphs. On contrary to that, except the case of locally bounded clique-width only little is currently known about FO model checking of dense classes of graphs or other structures. We study the FO model checking problem for dense graph classes definable by geometric means (intersection and visibility graphs). We obtain new nontrivial FPT results, e.g., for restricted subclasses of circular-arc, circle, box, disk, and polygon-visibility graphs. These results use the FPT algorithm by Gajarsk\'y et al. for FO model checking of posets of bounded width. We also complement the tractability results by related hardness reductions.
Diversity of Grammars and Their Diverging Evolutionary and Processing Paths: Evidence From Functional MRI Study of Serbian

We address the puzzle of “unity in diversity” in human languages by advocating the (minimal) common denominator for the diverse expressions of transitivity across human languages, consistent with the view that early in language evolution there was a modest beginning for syntax and that this beginning provided the foundation for the further elaboration of syntactic complexity. This study reports the results of a functional MRI experiment investigating differential patterns of brain activation during processing of sentences with minimal versus fuller syntactic structures. These structural layers have been postulated to represent different stages in the evolution of syntax, potentially engaging different brain networks. We focused on the Serbian “middles,” analyzed as lacking the transitivity (vP) layer, contrasted with matched transitives, containing the transitivity layer. Our main hypothesis was that transitives will produce more activation in the syntactic (Broca's–Basal Ganglia) brain network, in comparison to more rudimentary middles. The participants (n = 14) were healthy adults (Mean age = 33.36; SD = 12.23), native speakers of Serbo-Croatian. The task consisted of reading a series of sentences (middles and transitives; n = 64) presented in blocks of 8, while being engaged in a detection of repetition task. We found that the processing of transitives, compared to middles, was associated with an increase in activation in the basal ganglia bilaterally. Although we did not find an effect in Broca's area, transitives, compared to middles, evoked greater activation in the precentral gyrus (BA 6), proposed to be part of the “Broca's complex. ” Our results add to the previous findings that Broca's area is not the sole center for syntactic processing, but rather is part of a larger circuit that involves subcortical structures. We discuss our results in the context of the recent findings concerning the gene-brain-language pathway involving mutations in FOXP2 that likely contributed to the enhancement of the frontal-striatal brain network, facilitating human capacity for complex syntax.
Chronic natural killer lymphoproliferative disorders: characteristics of an international cohort of 70 patients.

BACKGROUND The 2008 World Health Organization (WHO) classification distinguishes three entities among the large granular lymphocytic leukemia (LGL leukemia): T-cell LGL leukemia (T-LGL leukemia), aggressive natural killer (NK) cell leukemia, and chronic NK lymphoproliferative disorders (LPD), the later considered as a provisional entity. Only a few and small cohorts of chronic NK LPD have been published. PATIENTS AND METHODS We report here clinicobiological features collected retrospectively from 70 cases of chronic NK LPD, and compared with those of T-LGL leukemia. RESULTS There were no statistical differences between chronic NK LPD and T-LGL leukemia concerning median age [61 years (range 23-82 years)], organomegaly (26%), associated autoimmune diseases (24%), and associated hematological malignancies (11%). Patients with chronic NK LPD were significantly less symptomatic (49% versus 18%, P < 0.001) and the association with rheumatoid arthritis was more rarely observed (7% versus 17%, P = 0.03). The neutropenia (<0.5 × 10(9)/l) was less severe in chronic NK LPD (33% versus 61%, P < 0.001) without difference in the rate of recurrent infections. STAT3 mutation was detected in 12% of the cohort, which is lower than the frequency observed in T-LGL leukemia. Thirty-seven percent of the patients required specific therapy. Good results were obtained with cyclophosphamide. Overall and complete response rates were, respectively, 69% and 56%. Overall survival was 94% at 5 years. CONCLUSION This study suggests very high similarities between chronic NK LPD and T-LGL leukemias. Since chronic NK LPD is still a provisional entity, our findings should be helpful when considering further revisions of the WHO classification.
MEIS homeodomain proteins facilitate PARP1/ARTD1-mediated eviction of histone H1

Pre–B-cell leukemia homeobox (PBX) and myeloid ecotropic viral integration site (MEIS) proteins control cell fate decisions in many physiological and pathophysiological contexts, but how these proteins function mechanistically remains poorly defined. Focusing on the first hours of neuronal differentiation of adult subventricular zone–derived stem/progenitor cells, we describe a sequence of events by which PBX-MEIS facilitates chromatin accessibility of transcriptionally inactive genes: In undifferentiated cells, PBX1 is bound to the H1-compacted promoter/proximal enhancer of the neuron-specific gene doublecortin (Dcx). Once differentiation is induced, MEIS associates with chromatin-bound PBX1, recruits PARP1/ARTD1, and initiates PARP1-mediated eviction of H1 from the chromatin fiber. These results for the first time link MEIS proteins to PARP-regulated chromatin dynamics and provide a mechanistic basis to explain the profound cellular changes elicited by these proteins.
A Multilingual Datasets Repository of the Hadith Content

Knowledge extraction from unstructured data is a challenging research problem in research domain of Natural Language Processing (NLP). It requires complex NLP tasks like entity extraction and Information Extraction (IE), but one of the most challenging tasks is to extract all the required entities of data in the form of structured format so that data analysis can be applied. Our focus is to explain how the data is extracted in the form of datasets or conventional database so that further text and data analysis can be carried out. This paper presents a framework for Hadith data extraction from the Hadith authentic sources. Hadith is the collection of sayings of Holy Prophet Muhammad, who is the last holy prophet according to Islamic teachings. This paper discusses the preparation of the dataset repository and highlights issues in the relevant research domain. The research problem and their solutions of data extraction, pre-processing and data analysis are elaborated. The results have been evaluated using the standard performance evaluation measures. The dataset is available in multiple languages, multiple formats and is available free of cost for research purposes.
Hostility as a Predictor of Survival in Patients With Coronary Artery Disease

Objective: This article presents a reanalysis of an earlier study that reported a nonsignificant relation between the 50-item Cook-Medley Hostility Scale (CMHS) and survival in a sample of coronary patients. Since publication of those results, there have been significant developments in the measurement of hostility that suggest that an abbreviated scale may be a better predictor of health outcomes. This study examined the ability of the total CMHS and an abbreviated form of the CMHS (ACM) to predict survival in a sample of patients with documented coronary artery disease (CAD) with increased statistical power. Methods: Nine hundred thirty-six patients (83% were male; mean age = 51.48) with CAD who were followed for an average of 14.9 years. The ACM consisted of the combination of the cynicism, hostile attribution, hostile affect, and aggressive responding subscales that were identified in an earlier study (Barefoot et al. [1989]) by a rational analysis of the item content. The relation between hostility and survival was examined with Cox proportional hazard models (hazard ratios [HRs] based on a two standard deviation difference). Results: Controlling for disease severity, the ACM was a significant predictor for both CHD mortality (HR = 1.33, p < .009) and total mortality (HR = 1.28, p < .02). The total CMHS was only a marginally significant predictor of either outcome (p values < 0.06). Conclusion: The results of this study suggest that hostility is associated with poorer survival in CAD patients, and it may be possible to refine measures of hostility in order to improve prediction of health outcomes. CMHS = Cook-Medley Hostility Scale; CHD = coronary heart disease; CAD = coronary artery disease; MI = myocardial infarction; ACM = abbreviated Cook-Medley Hostility Scale; SD = standard deviation; HR = hazard ratio; CI = confidence interval.
Escitalopram and paroxetine in the treatment of generalised anxiety disorder

Background It is uncertain whether higher doses of selective serotonin reuptake inhibitors have greater efficacy in generalised anxiety disorder. Aims To assess the efficacy of different doses of escitalopram in generalised anxiety disorder. Method Randomised, double-blind, placebo-controlled, fixed-dose, parallel-group, 12-week study, with 681 patients: placebo (n=139); escitalopram, 5 mg/day, (n=134); 10 mg/day (n=136); 20 mg/day (n=133); paroxetine, 20 mg/day (n=139). Results Mean change in the primary efficacy measure was greater with escitalopram 10 and 20 mg than with placebo; 10 mg was more efficacious than paroxetine. Paroxetine was superior to placebo on some secondary measures, at sometime points. Compared with placebo, more patients withdrew because of adverse events with escitalopram 20 mg and paroxetine. Conclusions Escitalopram was efficacious in generalised anxiety disorder, 20 was not significantly superior to 10 mg/day Escitalopram 10 mg was more efficacious than paroxetine.
Hardware Implementation of an Image Interpolation Method with Controllable Sharpness

The technique of image scaling plays a critical role in the digital image processing applications for the coordination between different display devices. In this paper, an improved algorithm is proposed by using a controllable sharpness coefficient to obtain better resulting images under different scaling magnifications. In addition, a power saving module including a condition judgment mechanism is applied to reduce unnecessary power consumption as the scaling ratio increases. Experimental results show that the proposed architecture is still feasible for very large-scale integration implementation and effectively enhances the quality of image scaling. By using TSMC 0.13 μm cell library, the synthesis results show that the circuit can achieve 300 MHz with 12.1k gate counts and cell area is 293×293 μm2. The total power consumption is 6.75mW.
Tissue-specific exosome biomarkers for noninvasively monitoring immunologic rejection of transplanted tissue

In transplantation, there is a critical need for noninvasive biomarker platforms for monitoring immunologic rejection. We hypothesized that transplanted tissues release donor-specific exosomes into recipient circulation and that the quantitation and profiling of donor intra-exosomal cargoes may constitute a biomarker platform for monitoring rejection. Here, we have tested this hypothesis in a human-into-mouse xenogeneic islet transplant model and validated the concept in clinical settings of islet and renal transplantation. In the xenogeneic model, we quantified islet transplant exosomes in recipient blood over long-term follow-up using anti-HLA antibody, which was detectable only in xenoislet recipients of human islets. Transplant islet exosomes were purified using anti-HLA antibody–conjugated beads, and their cargoes contained the islet endocrine hormone markers insulin, glucagon, and somatostatin. Rejection led to a marked decrease in transplant islet exosome signal along with distinct changes in exosomal microRNA and proteomic profiles prior to appearance of hyperglycemia. In the clinical settings of islet and renal transplantation, donor exosomes with respective tissue specificity for islet &bgr; cells and renal epithelial cells were reliably characterized in recipient plasma over follow-up periods of up to 5 years. Collectively, these findings demonstrate the biomarker potential of transplant exosome characterization for providing a noninvasive window into the conditional state of transplant tissue.
Dry eye-induced conjunctival epithelial squamous metaplasia is modulated by interferon-gamma.

PURPOSE To investigate the role of interferon (IFN)-gamma in the pathogenesis of conjunctival squamous metaplasia in dry eye. METHODS Experimental dry eye was created by subjecting C57BL/6 and IFN-gamma-knockout mice to desiccating environmental stress for 5 or 10 days. T-cell antigens and IFN-gamma were detected by immunohistochemistry. Goblet cells were counted in periodic acid Schiff (PAS)-stained sections. Expression of small, proline-rich protein (SPRR)-2 was evaluated by confocal microscopy. Tear IFN-gamma was measured by immunobead assay. RESULTS Dry eye promoted migration of CD4+ T cells and IFN-gamma+ cells into goblet cell zones of the conjunctiva and increased the concentration of IFN-gamma in tears. This migration was accompanied by progressive goblet cell loss and an increase in SPRR-2 expression in the conjunctival epithelium. A significant inverse correlation was observed between the density of infiltrating CD4+ T cells and goblet cells. Dry eye had no effect on conjunctival goblet cell density in IFN-gamma-knockout mice; however, exogenous administration of IFN-gamma significantly decreased goblet cell density after 5 days. CONCLUSIONS Conjunctival epithelial response to experimental dryness is related to the degree of CD4+ T-cell infiltration and the level of IFN-gamma production. These findings suggest that IFN-gamma plays a pivotal role in promoting conjunctival squamous metaplasia in dry eye, and they provide insight into the immune pathogenesis of keratoconjunctivitis sicca.
Statistics of Visual Responses to Image Object Stimuli from Primate AIT Neurons to DNN Neurons

Under the goal-driven paradigm, Yamins et al. (2014; Yamins & DiCarlo, 2016) have shown that by optimizing only the final eight-way categorization performance of a four-layer hierarchical network, not only can its top output layer quantitatively predict IT neuron responses but its penultimate layer can also automatically predict V4 neuron responses. Currently, deep neural networks (DNNs) in the field of computer vision have reached image object categorization performance comparable to that of human beings on ImageNet, a data set that contains 1.3 million training images of 1000 categories. We explore whether the DNN neurons (units in DNNs) possess image object representational statistics similar to monkey IT neurons, particularly when the network becomes deeper and the number of image categories becomes larger, using VGG19, a typical and widely used deep network of 19 layers in the computer vision field. Following Lehky, Kiani, Esteky, and Tanaka (2011, 2014), where the response statistics of 674 IT neurons to 806 image stimuli are analyzed using three measures (kurtosis, Pareto tail index, and intrinsic dimensionality), we investigate the three issues in this letter using the same three measures: (1) the similarities and differences of the neural response statistics between VGG19 and primate IT cortex, (2) the variation trends of the response statistics of VGG19 neurons at different layers from low to high, and (3) the variation trends of the response statistics of VGG19 neurons when the numbers of stimuli and neurons increase. We find that the response statistics on both single-neuron selectivity and population sparseness of VGG19 neurons are fundamentally different from those of IT neurons in most cases; by increasing the number of neurons in different layers and the number of stimuli, the response statistics of neurons at different layers from low to high do not substantially change; and the estimated intrinsic dimensionality values at the low convolutional layers of VGG19 are considerably larger than the value of approximately 100 reported for IT neurons in Lehky et al. (2014), whereas those at the high fully connected layers are close to or lower than 100. To the best of our knowledge, this work is the first attempt to analyze the response statistics of DNN neurons with respect to primate IT neurons in image object representation.
Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal Analysis

This paper presents a comprehensive study on the analysis of neuromuscular signal activities to recognize 11 facial expressions for muscle computer interfacing applications. A robust denoising protocol comprised of Wavelet transform and Kalman filtering is proposed to enhance the electromyogram (EMG) signal-to-noise ratio and improve classification performance. The effectiveness of eight different time-domain facial EMG features on system performance is examined and compared in order to identify the most discriminative one. Fourteen pattern recognition-based algorithms are employed to classify the extracted features. These classifiers are evaluated in terms of classification accuracy and processing time. Finally, the best methods that obtain almost identical system performance are compared through the Normalized Mutual Information (NMI) criterion and a repeated measure analysis of variance (ANOVA) for a statistical significant test.To clarify the impact of signal denoising, all considered EMG features and classifiers are assessed with and without this stage. Results show that: (1) the proposed denosing step significantly improves the system performance; (2) root mean square is the most discriminative facial EMG feature; (3) discriminant analysis when the parameters are estimated by the Maximum Likelihood algorithm achieves the highest classification accuracy and NMI; however, ANOVA reveals no significant difference among the best methods with almost similar performance.
Follow-up Analysis of Serum TNF-Related Apoptosis-Inducing Ligand Protein and mRNA Expression in Peripheral Blood Mononuclear Cells from Patients with Ischemic Stroke

Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL), which is TNF receptor superfamily member, contributes to several diseases pathogenesis. The aim of this research was to investigate the relevance of serum TRAIL protein levels and mRNA expression in peripheral blood mononuclear cells (PBMC) of patients with stroke through 6 months follow-up. We enrolled patients with first-ever acute ischemic stroke (n = 95) and healthy controls (n = 95) in this study. Follow-up blood samples were collected from patients at day 7, 28, and 180 after the onset. The stroke severity was evaluated by National Institutes of Health Stroke Scale score. TRAIL protein levels were quantified by using ELISA kits and TRAIL mRNA expression by quantitative real-time PCR. Our study showed that stroke patients have statistically significant lower levels of serum TRAIL protein (p < 0.0001) and elevated TRAIL mRNA expression (p < 0.0001) in PBMC at the disease onset. Our follow-up study revealed that TRAIL protein levels were increased while mRNA expression levels were downregulated in later periods. Overall, our findings suggest that serum TRAIL levels and mRNA expression in PBMC could reliably serve as a predictor of stroke outcome. Additionally, our study supports that TRAIL plays a role in pathogenesis and progression of ischemic stroke.
Knowledge Sharing : Understanding the Effects of Job Design

The knowledge management literature emphasizes intrinsic motivation in promoting employees’ knowledge sharing due to its consistently positive and lasting effect. Yet, how intrinsic motivation to share knowledge can be nurtured remains elusive and it is often left to random development. This study examines how job design, which determines the conditions in which employees develop and function, influences their intrinsic motivation to share knowledge. A model that specifies the effect of different job design characteristics and clarifies the underlying mechanism through which job design affects intrinsic motivation is developed. Data collected in a survey of 255 employees supported the model. Implications of the findings for research and practice are discussed.