Miracle-dz/UniFAR-FTUs huggingface dataset:
Value of the Qrs-T Angle in Predicting the Induction of Ventricular Tachyarrhythmias in Patients with Chagas Disease

Background The QRS-T angle correlates with prognosis in patients with heart failure and coronary artery disease, reflected by an increase in mortality proportional to an increase in the difference between the axes of the QRS complex and T wave in the frontal plane. The value of this correlation in patients with Chagas heart disease is currently unknown. Objective Determine the correlation of the QRS-T angle and the risk of induction of ventricular tachycardia / ventricular fibrillation (VT / VF) during electrophysiological study (EPS) in patients with Chagas disease. Methods Case-control study at a tertiary center. Patients without induction of VT / VF on EPS were used as controls. The QRS-T angle was categorized as normal (0-105º), borderline (105-135º) or abnormal (135-180º). Differences between groups for continuous variables were analyzed with the t test or Mann-Whitney test, and for categorical variables with Fisher's exact test. P values < 0.05 were considered significant. Results Of 116 patients undergoing EPS, 37.9% were excluded due to incomplete information / inactive records or due to the impossibility to correctly calculate the QRS-T angle (presence of left bundle branch block and atrial fibrillation). Of 72 patients included in the study, 31 induced VT / VF on EPS. Of these, the QRS-T angle was normal in 41.9%, borderline in 12.9% and abnormal in 45.2%. Among patients without induction of VT / VF on EPS, the QRS-T angle was normal in 63.4%, borderline in 14.6% and abnormal in 17.1% (p = 0.04). When compared with patients with normal QRS-T angle, those with abnormal angle had a fourfold higher risk of inducing ventricular tachycardia / ventricular fibrillation on EPS [odds ratio (OR) 4; confidence interval (CI) 1.298-12.325; p = 0.028]. After adjustment for other variables such as age, ejection fraction (EF) and QRS size, there was a trend for the abnormal QRS-T angle to identify patients with increased risk of inducing VT / VF during EPS (OR 3.95; CI 0.99-15.82; p = 0.052). The EF also emerged as a predictor of induction of VT / VF: for each point increase in EF, there was a 4% reduction in the rate of sustained ventricular arrhythmia on EPS. Conclusions Changes in the QRS-T angle and decreases in EF were associated with an increased risk of induction of VT / VF on EPS.
ICRC_HITSZ at RITE: Leveraging Multiple Classifiers Voting for Textual Entailment Recognition

The NTCIR-9 RITE challenge is a generic benchmark task that evaluates systems' ability to automatically detect tex- tual entailment, paraphrase and contradiction. This paper describes the ICRC HITSZ system for RITE. We partici- pate in the binary-class (BC), the multi-class (MC) and the RITE4QA subtask. More specifically, we build textual en- tailment recognition models for the MC subtask. The pre- dicted multiple class labels are then mapped into Yes/No la- bels for the BC and RITE4QA subtasks. Different linguistic level features are extracted by using hybrid NLP resources and tools. Based on the hierarchical relations between the labels of the MC subtask, three different classification strate- gies are designed. Multiple machine learning methods are employed for each strategy. On the assumption that classi- fiers built from different classification strategies are comple- mentary to each other, so are the different machine learning methods. The final classifier is built with a cascade vot- ing. Evaluation results show that the voting strategies are effective, with the highest performance ranked at the fourth place in terms of accuracy, and at the second place in terms of participant groups in both tasks.
Send Orders of Reprints at Reprints@benthamscience.org High Prevalence and Diminished Awareness of Overweight and Obesity in a Mediterranean Population. an Alarming Call for Action

Introduction: The epidemic proportions of overweight, obesity and diabetes in most European countries stress the need for the implementation of an effective action plan for the prevention of cardiovascular (CV) disease. This questionnaire study was designed to evaluate the viewpoint of the general population regarding the relative significance of CV risk factors in the cumulative risk of CV disease. Methods: All participants answered a questionnaire regarding the self-reported presence of CV disease risk factors and the perceived notion of having excess weight. They were also asked to list CV disease risk factors, ranking them in order of perceived relative significance. Participants were also subjected to total cholesterol measurement using a portable total cholesterol testing meter. Results: The survey population consisted of 32,736 individuals (49.1% males). According to participant self reporting, 32.9% were smokers, 24.7% had hypertension, 9.8% had diabetes, 74.8% reported having stress, 41.9% had insufficient physical activity and 43.3% had hyperlipidemia. The prevalence of overweight was 43.9% and the prevalence of obesity (BMI ≥30 kg/m 2) was 18.6%. Only 24.4% of participants reported that they had excess weight. The 45.2% of the questioned individuals considered that stress was the most important CV risk factor. Conclusions: Despite the high prevalence of overweight and obesity, the majority of participants were unaware of the contribution of these well-established risk factors to the occurrence of CV disease. Improving public awareness is important in order to control the epidemic proportions of these modifiable risk factors.
Bayesian Structure Learning for Stationary Time Series

While much work has explored probabilistic graphical models for independent data, less attention has been paid to time series. The goal in this setting is to determine conditional independence relations between entire time series, which for stationary series, are encoded by zeros in the inverse spectral density matrix. We take a Bayesian approach to structure learning, placing priors on (i) the graph structure and (ii) spectral matrices given the graph. We leverage a Whittle likelihood approximation and define a conjugate prior—the hyper complex inverse Wishart—on the complex-valued and graph-constrained spectral matrices. Due to conjugacy, we can analytically marginalize the spectral matrices and obtain a closed-form marginal likelihood of the time series given a graph. Importantly, our analytic marginal likelihood allows us to avoid inference of the complex spectral matrices themselves and places us back into the framework of standard (Bayesian) structure learning. In particular, combining this marginal likelihood with our graph prior leads to efficient inference of the time series graph itself, which we base on a stochastic search procedure, though any standard approach can be straightforwardly modified to our time series case. We demonstrate our methods on analyzing stock data and neuroimaging data of brain activity during various auditory tasks.
Accelerating average consensus by using the information of second-order neighbours with communication delays

This article investigates the problem of accelerating average consensus in undirected and connected networks. The protocol using the information of second-order neighbours with communication delays is proposed and the delay effects on stability and the convergence speed are analysed, respectively, under an assumption about the network topologies. It is proved that, for appropriate communication delays, networks reach average consensus faster under the proposed protocol than the standard protocol using only the information of first-order neighbours. Finally, a simulation example is presented to illustrate the proposed results.
A Novel Visualization Approach for Efficient Network Scans Detection

Network scans visualization provides very effective means for to detection large scale network scans. Many visualization methods have been developed to monitor network traffic, but all the techniques or tools still heavily rely on human detection. They seldom consider the importance of network event characteristics to the network data visualization, and cannot detect slow scans, hidden scans etc. In this paper a visual interactive network scans detection system called ScanViewer is designed to represent traffic activities that reside in network flows and their patterns. The ScanViewer combines the characteristics of network scan with novel visual structures, and utilizes a set of different visual concepts to map the collected datagram to the graphs that emphasize their patterns. Additionally, a new tool named localport is designed for to capture large-scale ports information. The experiments show that ScanViewer can not only detect network scans, port scans, distributed port scans, but also can detect the hidden scans etc.
Disruption of Fas Receptor Signaling by Nitric Oxide in Eosinophils

It has been suggested that Fas ligand–Fas receptor interactions are involved in the regulation of eosinophil apoptosis and that dysfunctions in this system could contribute to the accumulation of these cells in allergic and asthmatic diseases. Here, we demonstrate that nitric oxide (NO) specifically prevents Fas receptor–mediated apoptosis in freshly isolated human eosinophils. In contrast, rapid acceleration of eosinophil apoptosis by activation of the Fas receptor occurs in the presence of eosinophil hematopoietins. Analysis of the intracellular mechanisms revealed that NO disrupts Fas receptor–mediated signaling events at the level of, or proximal to, Jun kinase (JNK), but distal to sphingomyelinase (SMase) activation and ceramide generation. In addition, activation of SMase occurs downstream of an interleukin 1 converting enzyme–like (ICE-like) protease(s) that is not blocked by NO. However, NO prevents activation of a protease that targets lamin B1. These findings suggest a role for an additional NO-sensitive apoptotic signaling pathway that amplifies the proteolytic cascade initialized by activation of the Fas receptor. Therefore, NO concentrations within allergic inflammatory sites may be important in determining whether an eosinophil survives or undergoes apoptosis upon Fas ligand stimulation.
A New Test for the Success of Inflation Targeting

We propose a new test, derived from a set of variance decompositions of a structural VAR, for the success of inflation targeting. In contrast to standard sacrifice ratios this test considers changes in the structure of real and nominal shocks; second moment effects. We find strong support for IT with 7 of the 9 countries in our sample having negative “sacrifices” and many countries with “benefits”. However, we also find very different performances across IT countries. We find that “IT success” depends on the size of the real shocks suffered but controlling for this there are differences in country performance.
Diabetes and all-cause and coronary heart disease mortality among US male physicians.

BACKGROUND While diabetes has long been associated with increased risk of coronary heart disease (CHD), the magnitude of risk of diabetes-related CHD is uncertain. OBJECTIVE To evaluate the impact of diabetes and prior CHD on all-cause and CHD mortality. METHODS In a prospective cohort study of 91 285 US male physicians aged 40 to 84 years, participants were divided into 4 groups: (1) a reference group of 82 247 men free of both diabetes and CHD (previous myocardial infarction and/or angina) at baseline, (2) 2317 men with a history of diabetes but not CHD, (3) 5906 men with a history of CHD but not diabetes, and (4) 815 men with a history of both diabetes and CHD. Rates of all-cause and CHD mortality were compared in these groups. RESULTS Over 5 years (49 7952 person-years of follow-up), 3627 deaths from all causes were documented, including 1242 deaths from CHD. Compared with men with no diabetes or CHD, the age-adjusted relative risk of death from any cause was 2.3 (95% confidence interval [CI], 2.0-2.6) among men with diabetes and without CHD, 2.2 (95% CI, 2.0-2.4) among men with CHD and without diabetes, and 4.7 (95% CI, 4.0-5.4) among men with both diabetes and CHD. The relative risk of CHD death was 3.3 (95% CI, 2.6-4.1) among men with diabetes and without CHD, 5.6 (95% CI, 4.9-6.3) among men with CHD and without diabetes, and 12.0 (95% CI, 9.9-14.6) among men with both diabetes and CHD. Multivariate adjustment for body mass index, smoking status, alcohol intake, and physical activity as well as stratification by these variables did not materially alter these associations. CONCLUSIONS These prospective data indicate that diabetes is associated with a substantial increase in all-cause and CHD mortality. For all-cause mortality, the magnitude of excess risk conferred by diabetes is similar to that conferred by a history of CHD; for mortality from CHD, a history of CHD is a more potent predictor of death. The presence of both diabetes and CHD, however, identifies a particularly high-risk group.
Writing Lives: The Development of Nurse Education by Using the Biographies of Older People

The development of gerontological nurse education in North Wales is an ongoing project and is mainly centred on partnership between the School of Nursing, University of Wales, Bangor and the three local NHS Trusts that cover a substantial, and largely rural, geographical area. The Health care and ageing module coursework focuses on a biographical approach to understanding ageing and chronicity, underpinned by an emic viewpoint. Following preparation by the primary lecturer (SW), this requires the production of a 3,500 word assignment based on taking the life history of an older person from their work setting. The module assessment and engagement strategy utilises Johnson’s (1978) framework as the basis for completing an older person’s life history. The current Health care and ageing programme commenced in 1998 and so far five cohorts (n=46) have successfully completed the module. This chapter explores the theoretical rationale for developing and evaluating the ‘Health care and ageing: integrating perspectives’ module on the DPP framework, a short 15 day course that is accessed by nurse practitioners from a range of settings within the NHS and private sectors, including acute, and community sectors, mental health and continuing care.
Ieee Transactions on Systems, Man and Cybernetics -part C: Applications and Reviews 1 Navigation Technologies for Autonomous Underwater Vehicles

—With recent advances in battery capacity and the development of hydrogen fuel cells, autonomous underwater vehicles (AUVs) are being used to undertake longer missions that were previously performed by manned or tethered vehicles. As a result, more advanced navigation systems are needed to maintain an accurate position over a larger operational area. The accuracy of the navigation system is critical to the quality of the data collected during survey missions and the recovery of the AUV. Many different methods for navigation in different underwater environments have been proposed in the literature. In this paper, the state of the art in navigation technologies for AUVs is investigated for theoretical and operational systems. Their suitability for use in different environments is compared and current limitations of these methods are identified. In addition, new approaches to address these current problems and areas for future research are suggested. Finally, it is concluded that only geophysically referenced methods will enable AUVs to navigate accurately over large areas and that advances in underwater feature recognition are required before these methods can be implemented in operational AUVs.
Separation of 2-D nonsingular MIMO digital filters into a cascade of 1-D filters

A simple algebraic criterion is presented to check if a given 2-D MIMO (multi-input/multioutput) filter described by a nonsingular transfer matrix is in fact a cascade of two 1-D filters with a minimal number of inputs and outputs. The criterion offers the advantage that whenever a filter is found to be separable, it is almost trivial to construct the two 1-D filters. >
Symbolic Grey-Box Learning of Input-Output Relations

Learning of stateful models has been extensively used in verification. Some applications include inference of interface invariants, learning-guided concolic execution, compositional verification, and regular model checking. Learning shows a great promise for verification, but suffers from two fundamental limitati ons. First, learning stateful models over concrete alphabets does not scale in practice, as alphabets can be large or even infinite in size. S econd, learning techniques produce conjectures, which might be neither over- nor under-approximations, but rather some mix of the two. The new technique we propose — Sigma ∗ — overcomes these problems by combining black- and white-box analysis techniques: learning and abstraction. Such grey-box setting al lows inspection of the internal symbolic state of the program, allowing us to learn symbolic transducers with input and output alphabets ranging over finite sets of symbolic terms. The technique alt ernates between symbolic conjectures and sound over-approximations of the program. As such, the technique presents a novel twist to the more standard alternation among under- and over-approximations often used in verification. Sigma ∗ is parameterized by an abstraction function and a class of symbolic transducers. In this pa per, we develop Sigma ∗ parameterized by a variant of predicate abstraction, and k-lookback symbolic transducers — a new variant of symbolic transducers, for which we present learning and separation sequence computation algorithms. Verification of such transducers i s, for instance, important for security of web applications and might find its applications in other areas of verification. The main tec hnical result we present is that Sigma ∗ is complete relative to abstraction function.
Cell-cycle regulation of formin-mediated actin cable assembly

Significance Actin filaments are protein polymers that facilitate multiple biological functions, including cell migration, vesicle trafficking, and polarity establishment in eukaryotic cells throughout the cell cycle. Mechanisms of spatial and temporal regulation of actin assembly in vivo are incompletely understood. Formin proteins nucleate cables, which are bundles of unbranched actin filaments. We developed a cell-extract system to reconstitute actin cable assembly nucleated by formins in a physiological context. Using this unique reconstitution system, we identified an actin cable parts list. We also discovered that actin cable assembly is regulated in a cell-cycle–dependent manner both in vivo and in vitro. Assembly of appropriately oriented actin cables nucleated by formin proteins is necessary for many biological processes in diverse eukaryotes. However, compared with knowledge of how nucleation of dendritic actin filament arrays by the actin-related protein-2/3 complex is regulated, the in vivo regulatory mechanisms for actin cable formation are less clear. To gain insights into mechanisms for regulating actin cable assembly, we reconstituted the assembly process in vitro by introducing microspheres functionalized with the C terminus of the budding yeast formin Bni1 into extracts prepared from yeast cells at different cell-cycle stages. EM studies showed that unbranched actin filament bundles were reconstituted successfully in the yeast extracts. Only extracts enriched in the mitotic cyclin Clb2 were competent for actin cable assembly, and cyclin-dependent kinase 1 activity was indispensible. Cyclin-dependent kinase 1 activity also was found to regulate cable assembly in vivo. Here we present evidence that formin cell-cycle regulation is conserved in vertebrates. The use of the cable-reconstitution system to test roles for the key actin-binding proteins tropomyosin, capping protein, and cofilin provided important insights into assembly regulation. Furthermore, using mass spectrometry, we identified components of the actin cables formed in yeast extracts, providing the basis for comprehensive understanding of cable assembly and regulation.
Multilingual Stochastic N-gram Class Language Models 1. Training: Automatic Classification of Untagged Words

Stochastic language models are widely used in continuous speech recognition systems where a priori probabilites of word sequences are needed. These probabilities are usually given by n-gram word models, estimated on very large training texts. When n increases, it becomes harder to nd reliable statistics, even with huge texts. Grouping words is a way to overcome this problem. We have developed an automatic language independant classiication procedure, which is able to optimize the clas-siication of tens of millions of untagged words in less than a few hours on a Unix workstation. With this language independent approach, three corpora each containing about 30 million words of newspaper texts, in French, German and English, have been mapped into diierent numbers of classes. From these classiications, bi-gram and tri-gram class language models have been built. The perplexities of held-out test texts have been assessed, showing that tri-gram class models give lower values than those obtained with tri-gram word models, for the three languages. Starting with very large texts, n-gram word probabilities are directly evaluated from counts. Diierent smoothings algorithms are used to interpolate the probabilities of rare or unseen n-gram events: the best ones relate n-gram probabilities to lower order probabilities 1], 2], 3]. Nevertheless , these probabilities become less and less accurate as n increases. A method to get correct statistics is to generalize, gathering words into classes so that the probability of a given word depends on its class and on the classes of the preceding words 4]. 1.1. Classiication criterion If T is the training text, the classiication criterion is deduced from the probability that the word string T, the context of each word to the preceding one, this probability becomes: P (w) T = P (w1) N Y i=2 P (wijwi? 1) Gathering words into C classes, P (w) T averages to P (C) T , deened as: P (C) T = P (w1) N Y i=2 P wijC(wi)] P C(wi)jC(wi? 1)] where C (wi) is the class which contains the word wi. This expression assumes : only one class C (wi) for each word wi, but the class C (wi) can contain more than one word; a word distribution within each class, which depends on the word occurrences. It is clear that P (C) T is always lower than P (w) T , except when each vocabulary word corresponds to its own class and in this case, P (C) T …
A small-size, fast-settling, low-cost thermal regulator for chip surface measurements

This paper presents a thermal regulator developed to allow the electrical characterization of integrated circuits over a wide temperature range. The proposed solution is based on a Peltier cell, which is placed close to the bottom surface of the device under test (DUT) and provides the required cooling/heating power. The basic features of the proposed thermal regulator are low cost, small size, easy and accurate temperature adjustment, and fast settling time. No need exists for a thermal chamber, which allows the DUT to be characterized by means of chip surface measurements by using a microprobe station. Main measured performances include low power consumption (about 30 W in the worst case), a settling time of /spl sim/4 min, and a temperature range adequate for most applications (-40/spl deg/C to +120/spl deg/C, which can be further extended by using two Peltier cells connected in series).