Miracle-dz/UniFAR-FTUs huggingface dataset:
Activation of the cGMP/Protein Kinase G Pathway by Nitric Oxide Can Decrease TRPV1 Activity in Cultured Rat Dorsal Root Ganglion Neurons

Recent studies have demonstrated that nitric oxide (NO) activates transient receptor potential vanilloid subtype 1 (TRPV1) via S-nitrosylation of the channel protein. NO also modulates various cellular functions via activation of the soluble guanylyl cyclase (sGC)/protein kinase G (PKG) pathway and the direct modification of proteins. Thus, in the present study, we investigated whether NO could indirectly modulate the activity of TRPV1 via a cGMP/PKG-dependent pathway in cultured rat dorsal root ganglion (DRG) neurons. NO donors, sodium nitroprusside (SNP) and S-nitro-N-acetylpenicillamine (SNAP), decreased capsaicin-evoked currents (Icap). NO scavengers, hemoglobin and 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (CPTIO), prevented the inhibitory effect of SNP on Icap. Membrane-permeable cGMP analogs, 8-bromoguanosine 3', 5'-cyclic monophosphate (8bromo-cGMP) and 8-(4chlorophenylthio)-guanosine 3',5'-cyclic monophosphate (8-pCPT-cGMP), and the guanylyl cyclase stimulator YC-1 mimicked the effect of SNP on Icap. The PKG inhibitor KT5823 prevented the inhibition of Icap by SNP. These results suggest that NO can downregulate the function of TRPV1 through activation of the cGMP/PKG pathway in peripheral sensory neurons.
Multivariable PID controller synthesis of discrete linear systems based on LMIs

We investigate a new approach for multivariable PID controller synthesis for time-discrete linear systems. The design of multivariable PID controller is challenging because it is necessary to consider coupling effects. As yet, there is no simple general method to solve this problem. This paper should contribute to find a solution. For the presented method a extended state space representation of the time-discrete closed-loop is necessary. The Schur complement is used to reformulate the Lyapunov stability theorem. This yields a non-linear matrix inequality. The non-linear component in this Schur complement is linearized by the Neumann series. Thus, we have a linear matrix inequality (LMI). This LMI is used to build an iterative algorithm, which determines the PID control parameters. We evaluate this algorithm on two examples to illustrate the results.
Sensitization by Dietary Docosahexaenoic Acid of Rat Mammary Carcinoma to Anthracycline: A Role for Tumor Vascularization

Purpose: To investigate whether dietary docosahexaenoic acid (DHA), a peroxidizable polyunsaturated ω-3 fatty acids, sensitizes rat mammary tumors to anthracyclines and whether its action interferes with tumor vascularization, a critical determinant of tumor growth. Experimental Design: Female Sprague-Dawley rats were initiated by N-methylnitrosourea to develop mammary tumors and then assigned to a control group (n = 18), receiving a supplementation of palm oil, or to a DHA group (n = 54), supplemented with a microalgae-produced oil (DHASCO, 1.5 g/d). The DHA group was equally subdivided into three subgroups with addition of different amounts of α-tocopherol. Epirubicin was injected weekly during 6 weeks after the largest tumor reached 1.5 cm2, and subsequent changes in the tumor surface were evaluated. Tumor vascularization was assessed by power Doppler sonography before and during chemotherapy. Results: DHA and α-tocopherol were readily absorbed and incorporated into rat tissues. Epirubicin induced a 45% mammary tumor regression in the DHA-supplemented group, whereas no tumor regression was observed in the control group. In the DHA group, before chemotherapy was initiated, tumor vascular density was 43% lower than in the control group and remained lower during chemotherapy. Enhancement of epirubicin efficacy by DHA was abolished in a dose-dependent manner by α-tocopherol, and the same trend was observed for DHA-induced reduction in tumor vascular density. Conclusions: Dietary DHA supplementation led to a reduction in tumor vascularization before the enhancement of any response to anthracyclines, suggesting that DHA chemosensitizes mammary tumors through an inhibition of the host vascular response to the tumor.
A distributed data-balanced dictionary based on the B-link tree

Many concurrent dictionary data structures have been proposed, but usually in the context of shared memory multiprocessors. The paper presents an algorithm for a concurrent distributed B-tree that can be implemented on message passing parallel computers. This distributed B-tree (the dB-tree) replicates the interior nodes in order to improve parallelism and reduce message passing. It is shown how the dB-tree algorithm can be used to build an efficient, highly parallel, data-balanced distributed dictionary, the dE-tree. <<ETX>>
Parallelization in Extracting Fresh Information from Online Social Network

Online Social Network (OSN) is one of the most hottest services in the past years. It preserves the life of users and provides great potential for journalists, sociologists and business analysts. Crawling data from social network is a basic step for social network information analysis and processing. As the net- work becomes huge and information on the network updates faster than web pages, crawling is more dicult because of the limitations of band-width, po- liteness etiquette and computation power. To extract fresh information from social network eciently and eectively, this paper presents a novel crawling method and discusses parallelization architecture of social network. To dis- cover the feature of social network, we gather data from real social network, analyze them and build a model to describe the discipline of users' behavior. With the modeled behavior, we propose methods to predict users' behavior. According to the prediction, we schedule our crawler more reasonably and ex- tract more fresh information with parallelization technologies. Experimental results demonstrate that our strategies could obtain information from OSN eciently and eectively.
Dynamic load balancing in GPU-based systems for a MPI program

The dynamic load-balancing framework Charm++/AMPI, developed at the University of Illinois, is based on processor virtualization to allow thread migration across processors. This framework has been successfully applied to many scientific applications in the past, such as BRAMS, NAMD, ChaNGa, and others. Most of these applications use only CPUs, that is, they do not use accelerators. However, the use of GPUs to improve computational performance is quickly getting massively disseminated in the high-performance computing community. This paper aims to investigate how the same Charm++/AMPI framework can be extended to balance load in a synthetic application inspired by the BRAMS numerical forecast model, running on GPUs instead of CPUs. Many major questions involving the use of GPUs with AMPI where handled in this work, including: how to measure the GPU's load, how to use and share GPUs among user-level threads, and what results are obtained when applying the required over-decomposition technique to a GPU-accelerated program.
Systematic Software-Based Self-Test for Pipelined Processors

Software-based self-test (SBST) has recently emerged as an effective methodology for the manufacturing test of processors and other components in systems-on-chip (SoCs). By moving test related functions from external resources to the SoC's interior, in the form of test programs that the on-chip processor executes, SBST significantly reduces the need for high-cost, big-iron testers, and enables high-quality at-speed testing and performance binning. Thus far, SBST approaches have focused almost exclusively on the functional (programmer visible) components of the processor. In this paper, we analyze the challenges involved in testing an important component of modern processors, namely, the pipelining logic, and propose a systematic SBST methodology to address them. We first demonstrate that SBST programs that only target the functional components of the processor are not sufficient to test the pipeline logic, resulting in a significant loss of overall processor fault coverage. We further identify the testability hotspots in the pipeline logic using two fully pipelined reduced instruction set computer (RISC) processor benchmarks. Finally, we develop a systematic SBST methodology that enhances existing SBST programs so that they comprehensively test the pipeline logic. The proposed methodology is complementary to previous SBST techniques that target functional components (their results can form the input to our methodology, and thus we can reuse the test development effort behind preexisting SBST programs). We automate our methodology and incorporate it in an integrated software environment (developed using Java, XML, and archC) for the automatic generation of SBST routines for microprocessors. We apply the methodology to the two complex benchmark RISC processors with respect to two fault models: stuck-at fault model and transition delay fault model. Simulation results show that our methodology provides significant improvements for the two fault models, both for the entire processor (12% fault coverage improvement on average) and for the pipeline logic itself (19% fault coverage improvement on average), compared to a conventional SBST approach.
Construction of Skew-Normal Random Variables : Are They Linear Combinations of Normal and Half-Normal ?

Skew-normal distributions extend the normal distributions through a shape parameter α; they reduce to the standard normal random variable Z for α = 0 and to |Z| or the half-normal when α → ∞. In spite of the skewness they (dis)inherit some properties of normal random variables: Square of a skew-normal random variable has a chi-square distribution with one degree of freedom, but the sum of two independent skew-normal random variables is not generally skew-normal. We review and explain this lack of closure and other properties of skew-normal random variables via their representations as a special linear combination of independent normal and half-normal random variables. Analogues of such representations are used to define multivariate skew-normal distributions with a closure property similar to that of multivariate normal distributions.
Reading Comprehension in Aging: The Role of Working Memory and Metacomprehension

Abstract This study examines age-related differences in reading comprehension analyzing the role of working memory and metacomprehension components in a sample of young (18–30 years), young-old (65–74 years), and old-old (75–85 years) participants. Text comprehension abilities were measured by a standardized test, including two texts: a narrative and an expository text. The elderly's reading comprehension performance, when compared to the norm, emerged to be adequate. More specifically, the young-old showed an equivalent level of comprehension as the young adults for the narrative text. However, a clear age-related decline was found in the case of the expository text. Hierarchical regression analyses showed that working memory capacity, as well as different metacomprehension components but not age, are the key aspects in explaining the different patterns of changes in the comprehension of narrative and expository texts.
Release of GLP-1 and PYY in response to the activation of G protein-coupled bile acid receptor TGR5 is mediated by Epac/PLC-ε pathway and modulated by endogenous H2S

Activation of plasma membrane TGR5 receptors in enteroendocrine cells by bile acids is known to regulate gastrointestinal secretion and motility and glucose homeostasis. The endocrine functions of the gut are modulated by microenvironment of the distal gut predominantly by sulfur-reducing bacteria of the microbiota that produce H2S. However, the mechanisms involved in the release of peptide hormones, GLP-1 and PYY in response to TGR5 activation by bile acids and the effect of H2S on bile acid-induced release of GLP-1 and PYY are unclear. In the present study, we have identified the signaling pathways activated by the bile acid receptor TGR5 to mediate GLP-1 and PYY release and the mechanism of inhibition of their release by H2S in enteroendocrine cells. The TGR5 ligand oleanolic acid (OA) stimulated Gαs and cAMP formation, and caused GLP-1 and PYY release. OA-induced cAMP formation and peptide release were blocked by TGR5 siRNA. OA also caused an increase in PI hydrolysis and intracellular Ca2+. Increase in PI hydrolysis was abolished in cells transfected with PLC-ε siRNA. 8-pCPT-2′-O-Me-cAMP, a selective activator of Epac, stimulated PI hydrolysis, and GLP-1 and PYY release. L-Cysteine, which activates endogenous H2S producing enzymes cystathionine-γ-lyase and cystathionine-β-synthase, and NaHS and GYY4137, which generate H2S, inhibited PI hydrolysis and GLP-1 and PYY release in response to OA or 8-pCPT-2′-O-Me-cAMP. Propargylglycine, an inhibitor of CSE, reversed the effect of L-cysteine on PI hydrolysis and GLP-1 and PYY release. We conclude: (i) activation of Gαs-coupled TGR5 receptors causes stimulation of PI hydrolysis, and release of GLP-1 and PYY via a PKA-independent, cAMP-dependent mechanism involving Epac/PLC-ε/Ca2+ pathway, and (ii) H2S has potent inhibitory effects on GLP-1 and PYY release in response to TGR5 activation, and the mechanism involves inhibition of PLC-ε/Ca2+ pathway.
Benchmarking XML-Schema Matching Algorithms for Improving Automated Tuning

Several matching algorithms were recently developed in order to automate or semi-automate the process of correspondences discovery between XML schemas. These algorithms use a wide range of approaches and matching techniques covering linguistic similarity, structural similarity, constraints, etc. The final matching combines arithmetically different results stemmed from these techniques. The aggregation of the results uses often many parameters and weights to be adjusted manually. Generally, this task is achieved by human experts and requires a perfect understanding of the matching algorithm. In order to reduce the human intervention and improve matching quality, we suggest automating the tuning of the various structural parameters used within XML-Schema matching algorithms. In this work, we offer a benchmark, for three tools, that seeks mathematical relations between parameters values and schema topology. In consequent, we propose an algorithm for the tuning of these parameters for studied tools.
Detection of Red lesions in diabetic retinopathy affected fundus images

Computer based automated system is one of the important diagnostic tools in medical field. Diabetic Retinopathy is an eye disorder in which red lesions due to blood leakages can be spotted on retinal surface. This disease is commonly observed in long term diabetic patients. Ignorance to this disease can result into permanent blindness. Early stage signs of diabetic retinopathy are called as Red lesions viz. microaneurysms and hemorrhages. This paper presents a unique methodology for automatic detection of red lesions in fundus images. Proposed methodology employs modified approach to matched filtering for extraction of retinal vasculature and detection of candidate lesions. Features of all candidate lesions are extracted and are used to train Support Vector Machine classifier. In turn support Vector Machine classifies input image object into lesion or non-lesion category. The method is tested on 89 fundus images from DIARETDB1 database. The proposed algorithm gives performance as sensitivity 96.42%, specificity 100% and accuracy 96.62%.
A Unified Graph Model for Sentence-Based Opinion Retrieval

There is a growing research interest in opinion retrieval as on-line users' opinions are becoming more and more popular in business, social networks, etc. Practically speaking, the goal of opinion retrieval is to retrieve documents, which entail opinions or comments, relevant to a target subject specified by the user's query. A fundamental challenge in opinion retrieval is information representation. Existing research focuses on document-based approaches and documents are represented by bag-of-word. However, due to loss of contextual information, this representation fails to capture the associative information between an opinion and its corresponding target. It cannot distinguish different degrees of a sentiment word when associated with different targets. This in turn seriously affects opinion retrieval performance. In this paper, we propose a sentence-based approach based on a new information representation, namely topic-sentiment word pair, to capture intra-sentence contextual information between an opinion and its target. Additionally, we consider inter-sentence information to capture the relationships among the opinions on the same topic. Finally, the two types of information are combined in a unified graph-based model, which can effectively rank the documents. Compared with existing approaches, experimental results on the COAE08 dataset showed that our graph-based model achieved significant improvement.
Labeled Morphological Segmentation with Semi-Markov Models

We present labeled morphological segmentation—an alternative view of morphological processing that unifies several tasks. We introduce a new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmentation system that, contrary to previous work, explicitly models morphotactics. We show improved performance on three tasks for all six languages: (i) morphological segmentation, (ii) stemming and (iii) morphological tag classification. For morphological segmentation our method shows absolute improvements of 2-6 points F1 over a strong baseline.
Value of clinical factors in selecting postmenopausal women with rheumatoid arthritis for bone densitometry

OBJECTIVE Criteria to decide which patients with rheumatoid arthritis (RA) should be examined by dual energy x ray absorptiometry (DXA) are currently not available. The rheumatologists from Amsterdam have proposed preliminary criteria based on clinical risk factors (age, disease activity, and functional status). These criteria are preliminary and not widely accepted but might be helpful in practice. The value of the proposal in a group of Spanish postmenopausal women with RA is analysed. METHODS DXA (lumbar spine and femoral neck) was performed in 128 patients recruited from a clinical setting, and the proposed criteria were applied. T and Z scores were established for a Spanish reference population. RESULTS The mean (SD) age of the patients was 61.3 (10.7) and mean duration of the postmenopausal period 14.5 (10.1) years. Mean duration of RA was 13.7 (7.7) years. Mean C reactive protein was 22 (21) mg/l; mean erythrocyte sedimentation rate 26 (18) mm/1st h; and mean Health Assessment Questionnaire score 1.25 (0.79). Ninety (70%) patients fulfilled the proposed criteria. Their sensitivity for the diagnosis of osteoporosis (T score ⩽−2.5 SD) was 86% and their specificity, 43%. Positive predictive value was 54% and negative predictive value, 79%. CONCLUSIONS The proposed criteria seem a good screening method for the selection of those patients with RA whose bone mineral density should be assessed as the sensitivity and negative predictive value are acceptable.
The Effect of Solution-Focused Brief Counseling on Reducing Test Anxiety

La ansiedad ante la evaluación es un fenómeno frecuente que se observa en los grados educativos K-12. Esto puede tornarse más complejo y estresante en países que utilizan los exámenes nacionales para el ingreso a la universidad. Turquía es uno de esos países. Debido a que la educación secundaria es gratuita para todos los ciudadanos, los estudiantes se enfrentan a una cultura de evaluación muy competitiva. Consecuentemente, el propósito de este estudio consistió en evaluar la efectividad de una intervención conforme al Modelo de Consejería Breve Enfocada en la Solución, realizada con estudiantes Senior de secundaria, en la reducción de la ansiedad ante la evaluación. El estudio se realizó en un colegio de bachillerato Anatolí en Bolu/Turquía en el año académico 2015-2016. Participaron dieciséis estudiantes, 8 en el grupo experimental y 8 en el grupo control. La edad de los participantes variaba entre 16 y 18 años; la edad promedio en el grupo experimental fue 17, y 16.87 en el grupo control. Seis de los participantes eran hombres y 10 eran mujeres. Debido a que los datos no se distribuyeron normalmente, y el número de participantes era menor a 30, en el análisis se utilizaron tests no paramétricos: Mann Whitney, y prueba de rangos con signo de Wilcoxon. Los resultados sugieren que cuatro sesiones de intervención conforme al modelo de Consejería Breve Enfocada en la Solución son efectivas para mitigar la ansiedad ante la evaluación. Estos hallazgos son consistentes con la literatura disponible.Palabras clave: modelo de Consejería Breve Enfocada en la Solución, ansiedad ante la evaluación, adolescentes, estudiante de secundaria, examen de ingreso a la universidad.
Which Answer is Best?: Predicting Accepted Answers in MOOC Forums

Massive Open Online Courses (MOOCs) have grown in reach and importance over the last few years, enabling a vast userbase to enroll in online courses. Besides watching videos, user participate in discussion forums to further their understanding of the course material. As in other community-based question-answering communities, in many MOOC forums a user posting a question can mark the answer they are most satisfied with. In this paper, we present a machine learning model that predicts this accepted answer to a forum question using historical forum data.
A Ball Vector Machine based on improved enclosing ball iterative solution strategies

Aimed at the problem of Ball Vector Machine's long training time for large scale data, an improved enclosing ball vector machine (IEBVM) based on new iterative solution strategy was proposed. When solving enclosing ball (EB) problem, IEBVM caches the dot product of training points and ball center for the distance solution next time, making the solution independent of support vectors weights. The training points which are unable to become the furthest point are ruled out. In addition, the support vectors weights can be updated once in a certain number of iterations to reduce the calculation amount. Moreover, the number of search times in the support vectors set is increased. Compared with BVM and LIBSVM in large scale datasets, IEBVM significantly reduces the training time and the number of support vectors, simultaneously keeping high testing accuracies.
A TRANSPARANT MULTI-LEVEL ROUTING SCHEME FOR AD HOC NETWORKS

This paper presents a transparent multi-level routing sche me, named MORHE, that improves the scalability of the OLSR protocol by exploi ting the heterogeneous nature of nodes in the network. In our work we try to take an approach that focuses on scenarios where ad hoc technology can be applied, but where we also find nodes in the network with varying capacity. The MORHE pro toc l makes use of nodes which have a large capacity (e.g. more energy, la er transmission range) to build something that could best be described as an a d hoc infrastructure. Nodes are grouped in clusters that need to be interconn e ted by specific nodes. This implies that a node no longer needs to know the ent ire network topology as is the case of the OLSR protocol, but only needs to maintain routes to the nodes inside its own cluster and to the other clusters. Using this approach the signalling overhead which is one of the main reasons why OLSR is not scalable is greatly reduced. We also introduce a simple mob ility management scheme to allow nodes to roam the different ad hoc clusters.