Miracle-dz/UniFAR-FTUs huggingface dataset:
Venous thromboembolism does not share strong familial susceptibility with coronary heart disease: a nationwide family study in Sweden.

AIMS This nationwide study aimed to determine whether venous thromboembolism (VTE) shares familial susceptibility with coronary heart disease (CHD). METHODS AND RESULTS Data from the Swedish Multigeneration Register for subjects aged 0-76 years old for the period 1964-2008 were linked to the Hospital Discharge Register and Cause of Death Register. Familial risks of VTE and CHD were examined in two ways: risk of CHD in offspring whose parents had been diagnosed with VTE, and risk of VTE in offspring whose parents had been diagnosed with CHD. The analyses were repeated separately for siblings and spouses. In total, 174 768 offspring had CHD and 56 302 VTE. No association between VTE and CHD was observed among siblings. Among offspring, a lower risk of CHD was observed in subjects whose parents had suffered from VTE [standardized incidence ratio (SIR) 0.94 (95% confidence interval (CI) 0.92-0.95)], while offspring of parents with CHD had an increased risk of VTE [SIR 1.03 (95% CI 1.01-1.04)]. In spouses of VTE patients, an increased risk of CHD was observed [SIR 1.02 (95% CI 1.01-1.03)]. Conversely, risk of VTE was increased among spouses of CHD patients [SIR 1.03 (95% CI 1.02-1.03)]. Subanalyses of cases of myocardial infarction and pulmonary embolism/deep venous thrombosis showed similar results. CONCLUSION The familial background of CHD is different from that of VTE. The present study suggests that it is unlikely that shared disease-causing mutations exist to a large extent in the population.
Between Frustration and Elation: Sense of Control Regulates the lntrinsic Motivation for Motor Learning

Frustration has been generally viewed in a negative light and its potential role in learning neglected. We propose a new approach to intrinsically motivated learning where frustration is a key factor that allows to dynamically balance exploration and exploitation. Moreover, based on the result obtained from our experiment with older infants, we propose that a temporary decrease in learning from negative feedback can also be beneficial in fine-tuning a newly learned behavior. We suggest that this temporal indifference to the outcome of an action may be related to the sense of control, and results from the state of elation, that is the experience of overcoming a very difficult task after prolonged frustration. Our preliminary simulation results serve as a proof-of-concept for our approach.
MicroRNA-195-5p is a potential diagnostic and therapeutic target for breast cancer

MicroRNAs (miRNAs) are a class of highly conserved, small endogenous single-strand non-coding RNAs. They are aberrantly expressed in the circulation and tissue of patients with cancer. Therefore, it has been suggested that they may act as key regulators of carcinogenesis. The aim of the present study was to examine the expression level of miR-195-5p in human breast cancer and its potential role in carcinogenesis. The expression level of miR-195-5p was measured in 40 breast cancer specimens and adjacent normal breast tissues by quantitative polymerase chain reaction (qPCR). Next, to explore the potential function of miR-195-5p, we used MDA-MB-231 human breast cancer cells and carried out MTT, colony formation, Transwell chamber migration and cell cycle assays. The dual-luciferase reporter assay was also performed to determine putative targets of miR-195-5p, which were validated using qPCR and western blot assays. We found that miR-195-5p expression was significantly decreased in the 40 breast cancer specimens when compared with that in the adjacent normal breast tissues (P<0.05). Overexpression of miR-195-5p inhibited cell proliferation, reduced cell colony formation, suppressed cell migration and caused an accumulation of cells in the G1 phase of the cell cycle. In the 3′-untranslated region (3′-UTR) of cyclin E1 (CCNE1), we found two putative target sites which may bind miR-195-5p, suggesting that CCNE1 is a direct target of miR-195-5p. Furthermore, through qPCR and western blot assays we showed that overexpression of miR-195-5p reduced CCNE1 mRNA and protein levels, respectively. Our study suggests that miR-195-5p may act as a tumor suppressor in breast cancer. Therefore, targeting of this miRNA may provide a novel strategy for the diagnosis and treatment of patients with this lethal disease.
Combined Source-Channel Coding of Images under Power and Bandwidth Constraints

This paper proposes a framework for combined source-channel coding for a power and bandwidth constrained noisy channel. The framework is applied to progressive image transmission using constant envelope-ary phase shift key (-PSK) signaling over an additive white Gaussian noise channel. First, the framework is developed for uncoded-PSK signaling (with). Then, it is extended to include coded-PSK modulation using trellis coded modulation (TCM). An adaptive TCM system is also presented. Simulation results show that, depending on the constellation size, coded-PSK signaling performs 3.1 to 5.2 dB better than uncoded-PSK signaling. Finally, the performance of our combined source-channel coding scheme is investigated from the channel capacity point of view. Our framework is further extended to include powerful channel codes like turbo and low-density parity-check (LDPC) codes. With these powerful codes, our proposed scheme performs about one dB away from the capacity-achieving SNR value of the QPSK channel.
Oxidation and nitration in dopaminergic areas of the prefrontal cortex from patients with bipolar disorder and schizophrenia.

BACKGROUND Increased oxidative stress is strongly implicated in bipolar disorder (BD), where protein oxidation, lipid peroxidation and oxidative damage to DNA have been consistently reported. High levels of dopamine (DA) in mania are also well-recognized in patients with BD, and DA produces reactive oxygen species and electron-deficient quinones that can oxidize proteins when it is metabolized. METHODS Using immunohistochemistry and acceptor photobleaching Förster resonance energy transfer (FRET), we examined oxidation and nitration of areas immunoreactive for the DA transporter (DAT) and tyrosine hydroxylase (TH) in the postmortem prefrontal cortex from patients with BD, schizophrenia and major depression as well as nonpsychiatric controls. RESULTS We found increased oxidation of DAT-immunoreactive regions in patients with BD (F3,48 = 6.76, p = 0.001; Dunnett post hoc test p = 0.001) and decreased nitration of TH-immunoreactive regions in both patients with BD (F3,45 = 3.10, p = 0.036; Dunnett post hoc test p = 0.011) and schizophrenia (p = 0.027). On the other hand, we found increased global levels of oxidation in patients with BD (F3,44 = 6.74, p = 0.001; Dunnett post hoc test p = 0.001) and schizophrenia (p = 0.020), although nitration levels did not differ between the groups (F3,46 = 1.75; p = 0.17). LIMITATIONS Limitations of this study include the use of postmortem brain sections, which may have been affected by factors such as postmortem interval and antemortem agonal states, although demographic factors and postmortem interval were accounted for in our statistical analysis. CONCLUSION These findings suggest alterations in levels of protein oxidation and nitration in DA-rich regions of the prefrontal cortex in patients with BD and schizophrenia, but more markedly in those with BD.
The efficacy of psychotherapy in reducing post-psychotic trauma

There is now growing evidence to suggest that the experience of psychosis may be so traumatic for some that it can lead to Post Traumatic Stress Disorder (PTSD)-type symptoms or post-psychotic trauma symptoms (PPTS). There is, however, less knowledge about what psychological interventions may be helpful in reducing these symptoms. Evidence from the literature, to date, suggests that of the seven studies that have addressed this issue only four were randomized controlled trials (RCTs). However, all these studies included less than 100 patients with the vast majority reporting positive results. Overall, it seems that although cognitive-behavioural treatment (CBT)-based psychological interventions appear to be efficacious in the treatment of PPTS, the studies are too small to draw any firm conclusions and should be subjected to larger good-quality RCTs. Further research will also need to establish the role of mediating variables such as shame and depression in the treatment of PPTS.
Automatic Deformation Detection for Aircraft Engine Disk Inspection

Computer vision algorithms are seeing increased use in industrial inspection applications. Here, we present an “Aid to Visual” system that can detect post deformations of less than 0.005 inches in jet engine high pressure turbine disks. We create a gold-standard reference post from the posts of sample turbine disks and then use registration, edge detection, and curve-similarity algorithms to identify unacceptable post deformations. We address the challenges associated with adapting academic algorithms for use in functioning inspection systems. We present novel solutions to deal with practical issues such as accuracy, speed, robustness, and ease of use. We also present a novel, highly-efficient sub-pixel contour matching algorithm and demonstrate the effectiveness of using sub-pixel distance calculation. We demonstrate overall error rates less than 1% on over 2400 images of posts. We have integrated our algorithms into the commercial LabVIEW software running on the Aid To Visual workstation. Our algorithms will enable plant-factory inspectors to identify minute post deformations that were previously difficult to detect.
Small signal analysis and closed loop control design of L-L type active-clamped ZVS current-fed isolated DC-DC converter

This paper presents the small signal model and closed loop control design using average current control for an L-L type active-clamped zero-voltage switching (ZVS) current-fed isolated DC-DC converter. State-space averaging technique is used to derive the small signal model. The controller is designed for fixed-frequency duty cycle modulation and works for wide input voltage and load variation. A complete design procedure is presented. Frequency response curves are obtained at different input voltage and load conditions to verify the stability and design, and to evaluate the performance of the controller. Simulation results for step changes in input voltage and load are given to check the transient response of the converter.
Information Recovery in a Dynamic Statistical Markov Model

Although economic processes and systems are in general simple in nature, the underlying dynamics are complicated and seldom understood. Recognizing this, in this paper we use a nonstationary-conditional Markov process model of observed aggregate data to learn about and recover causal influence information associated with the underlying dynamic micro-behavior. Estimating equations are used as a link to the data and to model the dynamic conditional Markov process. To recover the unknown transition probabilities, we use an information theoretic approach to model the data and derive a new class of conditional Markov models. A quadratic loss function is used as a basis for selecting the optimal member from the family of possible likelihood-entropy functional(s). The asymptotic properties of the resulting estimators are demonstrated, and a range of potential applications is discussed.
A Novel Algorithm for Dynamic Clustering: Properties and Performance

In this paper, we present a dynamic clustering algorithm that efficiently deals with data streams and achieves several important properties which are not generally found together in the same algorithm. The dynamic clustering algorithm operates online in two different time-scale stages, a fast distance-based stage that generates micro-clusters and a density-based stage that groups the micro-clusters according to their density and generates the final clusters. The algorithm achieves novelty detection and concept drift thanks to a forgetting function that allows micro-clusters and final clusters to appear, drift, merge, split or disappear. This algorithm has been designed to be able to detect complex patterns even in multi-density distributions and making no assumption of cluster convexity. The performance of the dynamic clustering algorithm is assessed theoretically through complexity analysis and empirically through a set of experiments.
’ s repository of research publications and other research outputs RAVE spectroscopy of luminous blue variables in the Large Magellanic Cloud

Context. The RAVE spectroscopic survey for galactic structure and evolution obtains 8400–8800 Å spectra at 7500 resolving power at the UK Schmidt Telescope using the 6dF multi-fiber positioner. More than 300 000 9 ≤ IC ≤ 12 and |b| ≥ 25◦ southern stars have been observed to date. Aims. This paper presents the first intrinsic examination of stellar spectra from the RAVE survey, aimed at evaluating their diagnostic potential for peculiar stars and at contributing to the general understanding of luminous blue variables (LBVs). Methods. We used the multi-epoch spectra for all seven LBVs observed, between 2005 and 2008, in the Large Magellanic Cloud (LMC) by the RAVE survey. Results. We demonstrate that RAVE spectra possess significant diagnostic potential when applied to peculiar stars and, in particular, LBVs. The behaviour of the radial velocities for both emission and absorption lines, and the spectral changes between outburst and quiescence states are described and found to agree with evidence gathered at more conventional wavelengths. The wind outflow signatures and their variability are investigated, with multi-components detected in S Doradus. Photoionisation modelling of the rich emission line spectrum of R 127 shows evidence of a massive detached ionised shell that was ejected during the 1982–2000 outburst. Surface inhomogeneities in the nuclear-processed material, brought to the surface by heavy mass loss, could have been observed in S Doradus, even if alternative explanations are possible. We also detect the transition from quiescence to outburst state in R 71. Finally, our spectrum of R 84 offers one of the clearest views of its cool companion.
Non-rigid point set registration: A bidirectional approach

In this paper we present a novel point set registration algorithm based on the robust Gaussian Mixture Model(GMM). We take advantage of a robust estimation to weigh the noise component in GMM. Moreover, a bidirectional EM process is introduced to model outliers in both point sets in contrast to traditional methods. The performance of the method is demonstrated and validated in carefully designed synthetic data and point sets extracted from medical images. Results show that the proposed method can improve the robustness and accuracy as compared to the traditional registration techniques.
Articulatory Synthesis Based on Real-Time Magnetic Resonance Imaging Data

This paper presents a methodology for articulatory synthesis of running speech in American English driven by real-time magnetic resonance imaging (rtMRI) mid-sagittal vocal-tract data. At the core of the methodology is a time-domain simulation of the propagation of sound in the vocal tract developed previously by Maeda. The first step of the methodology is the automatic derivation of air-tissue boundaries from the rtMRI data. These articulatory outlines are then modified in a systematic way in order to introduce additional precision in the formation of consonantal vocal-tract constrictions. Other elements of the methodology include a previously reported set of empirical rules for setting the time-varying characteristics of the glottis and the velopharyngeal port, and a revised sagittal-to-area conversion. Results are promising towards the development of a full-fledged text-to-speech synthesis system leveraging directly observed vocal-tract dynamics.
Notice of RetractionDetermination of component proportion based on BP neural networks

Component proportion sometimes is necessary in order to evaluate the state of research objects quantitatively or to do some specific studies. Back propagation neural network (BPNN) is one of mostly used neural networks because of its ability to achieve a high precision simulation for data problem or target function which is hardly to be established by conventional mathematical theory. In this paper, the conventional neural network model, which can effectively separate the influences of weights and bias on output, is established. Combined with the proposed method, component proportion can be obtained taking advantage of structure of this model. Through simulations of concrete examples, the results show that the method is feasibility and validity in dealing with both linear problem and some nonlinear problems.
In Silico Design and Implemetnation of a Polyketide Synthesis System for Production of Virtual Libraries of Macrolides

The RIO family [1] of atypical protein kinases are present in organism varying from archea to humans. RIO1 and RIO2 are required for proper ribosome processing in yeast, and for proper cell cycle progression. Deletion of either RIO1 or RIO2 is lethal. Although the biological substrates of RIO kinases are still not known, this family is an important member of the regulation network in a cell. Hence, we decided to search for potential candidates as inhibitors, using a modelling approach. Crystallographic structures of RIO1 [2] are the basis for this approach. They are, however, not sufficient. The starting point for modeling is, apart from modeling the missing loops, determination of the protonation states of the more than one hundred ionizable groups of the protein. Poisson-Boltzmann equations were solved (MEAD program package), using the OPLS force-field parameters in the GROMACS program. To determine the probabilities of protonation of different ionizable groups of the protein at different pH values, the DOPS package [3, 4] was used. Analysis of protonation states within different potential binding sites will be presented.
Finding Musically Meaningful Words by Sparse CCA

A musically meaningful vocabulary is one of the keystones in building a computer audition system that can model the semantics of audio conten t. If a word in the vocabulary is not clearly represented by the underlying aco ustic representation, the word can be considered noisy and should be removed from the vocabulary. This paper proposes an approach to construct a vocabulary of predictive semantic concepts based on sparse canonical component analysis (sparse CCA). The goal is to find words that are highly correlated with the under lying audio feature representation with the expectation that these words can me modeled more accurately. Experimental results illustrate that, by identify ing these musically meaningful words, we can improve the performance of a previously proposed computer audition system for music annotation and retrieval.
The New Area-Wide Model of the Euro Area: A Micro-Founded Open-Economy Model for Forecasting and Policy Analysis

In this paper, we outline a version of the New Area-Wide Model (NAWM) of the euro area designed for use in the (Broad) Macroeconomic Projection Exercises regularly undertaken by ECB/Eurosystem staff. We present estimation results for the NAWM that are obtained by employing Bayesian inference methods and document the properties of the estimated model by reporting its impulse-response functions and forecast-error-variance decompositions, by inspecting the model-based sample moments, and by examining the model’s forecasting performance relative to a number of benchmarks, including a Bayesian VAR. We finally consider several applications to illustrate the potential contributions the NAWM can make to forecasting and policy analysis. JEL Classification: C11, C32, E32, E37
Detection strategy for kidnapped robot problem in Monte Carlo Localization based on similarity measure of environment

This paper investigates new alternative approaches to detect the kidnapped robot problem event in Monte Carlo Localization. The underlying idea is based on the local similarity measures of the environment seen by the robot at two consecutive time instances. Six different similarity measures are investigated and tested against particles weight-based detectors to see how good each detector's ability to distinguish normal condition from kidnapping event. These simulations show that similarity based-detectors promises better general accuracy across all kidnapping points compared to particles weight-based detectors and the accuracy is not affected by the result of re-localization
New methods for text categorization

Text categorization - the assignment of texts to one or more predefined categories based on their content-represents an important component of different information organization and management tasks. Significance of text categorization leads many researchers to find more and more effective methods for this task. It has recently been shown that artificial immune system (AIS) can be successfully used in many machine learning tasks so it can be also used for text categorization task. The aim of this paper is to check applicability of AIS algorithms (Immunos-1, Immunos-2 and Immunos-2A) on text classification task. The results are compared with some classical document classification methods: naive Bayesian classifier and K-NN classifier.
An LP with Integrality Gap 1+epsilon for Multidimensional Knapsack

In this note we study packing or covering integer programs with at most k constraints, which are also known as k-dimensional knapsack problems. For any integer k > 0 and real epsilon > 0, we observe there is a polynomial-sized LP for the k-dimensional knapsack problem with integrality gap at most 1+epsilon. The variables may be unbounded or have arbitrary upper bounds. In the packing case, we can also remove the dependence of the LP on the cost-function, yielding a polyhedral approximation of the integer hull. This generalizes a recent result of Bienstock on the classical knapsack problem.
Value-based management : context and application

Over the past ten years hundreds of leading companies worldwide have adopted the principles of value-based management (VBM), an approach to corporate strategy and business organisation in which the primary objective is always shareholder wealth maximisation. As VBM has become more widespread the idea of shareholder value has become integral to business. Consultancies have prospered by selling prescriptions for developing a value-oriented firm and managers thought to be excellent in generating shareholder value are held in high esteem and command huge salaries. This book draws on empirical evidence to demonstrate the success of VBM ideas as well as highlighting many of the hidden questions, doubts and difficulties. It examines the validity of some of the underlying assumptions of VBM and tackles many of the key technical issues. This book provides a timely assessment of the theory and practice of VBM as it grows to maturity.