Miracle-dz/UniFAR-FTUs huggingface dataset:
White tea (Camellia sinensis) extract reduces oxidative stress and triacylglycerols in obese mice

White tea is an unfermented tea made from young shoots of Camellia sinensis protected from sunlight to avoid polyphenol degradation. Although its levels of catechins are higher than those of green tea (derived from the same plant), there are no studies addressing the relationship between this tea and obesity associated with oxidative stress.The objective of this study was to evaluate the effect of white tea on obesity and its complications using a diet induced obesity model. Forty male C57BL/6 mice were fed a high-fat diet to induce obesity (Obese group) or the same diet supplemented with 0.5% white tea extract (Obese + WTE) for 8 weeks. Adipose tissue, serum lipid profile, and oxidative stress were studied. White tea supplementation was not able to reduce food intake, body weight, or visceral adiposity. Similarly, there were no changes in cholesterol rich lipoprotein profile between the groups. A reduction in blood triacylglycerols associated with increased cecal lipids was observed in the group fed the diet supplemented with white tea. White tea supplementation also reduced oxidative stress in liver and adipose tissue. In conclusion, white tea extract supplementation (0.5%) does not influence body weight or adiposity in obese mice. Its benefits are restricted to the reduction in oxidative stress associated with obesity and improvement of hypertriacylglycerolemia.
Multi-channel non-invasive fetal electrocardiography detection using wavelet decomposition

Non-invasive fetal electrocardiography (fECG) has attracted the medical community because of the importance of fetal monitoring. However, its implementation in clinical practice is challenging: the fetal signal has a low Signal- to-Noise-Ratio and several signal sources are present in the maternal abdominal electrocardiography (AECG). This paper presents a novel method to detect the fetal signal from a multi-channel maternal AECG. The method begins by applying filters and signal detrending the AECG signals. Afterwards, the maternal QRS complexes are identified and subtracted. The residual signals are used to detect the fetal QRS complex. Intervals of these signals are analyzed by using a wavelet decomposition. The resulting representation feds a previously trained Random Forest (RF) classifier that identifies signal intervals associated to fetal QRS complex. The method was evaluated on a public available dataset: the Physionet2013 challenge. A set of 50 maternal AECG records were used to train the RF classifier. The evaluation was carried out in signals intervals extracted from additional 25 maternal AECG. The proposed method yielded an 83:77% accuracy in the fetal QRS complex classification task.
H.264 video streaming with data-partitioning and Growth codes

This paper demonstrates that Growth codes, based on Raptor channel coding, allow incremental protection of H.264 video codec data-partitioned Network Adaption Layer units. When combined with increased protection of video reference frames, in an ADSL erasure channel up to 10 dB in video quality (PSNR) can be gained through this scheme compared to equal error protection with rateless codes. Equivalent gains occur in a wireless channel from combining data-partitioning with error protection. The bitrate overhead from data-partitioning is also shown to be less than from other H.264 error resilient tools.
Distribution of Inorganic Mercury in Liver and Kidney of Beluga and Bowhead Whales Through Autometallographic Development of Light Microscopic Tissue Sections

Inorganic mercury was localized through autometallography (AMG) in kidney and liver of free-ranging, subsistence-harveste d beluga (Delphinapterus leucas; n 20) and bowhead (Balaena mysticetus; n 5) whales. AMG granules were not evident in bowhead tissues, con rming nominal mercury (Hg) concentrations (range 0.011 to 0.038 ug/g ww for total Hg). In belugas, total Hg ranged from 0.30 to 17.11 and from 0.33 to 82.47 ug/g ww in liver and kidney, respectively. AMG granules were restricted to cortical tubular epithelial cytoplasm in belugas with lower tissue burdens; whales with higher tissue burdens had granules throughout the uriniferous tubular epithelium. In liver, AMG granular densities differed between lobular zones, concentrating in stellate macrophages and bile cannalicular domains of hepatocytes . AMG granules aggregated in periportal regions in belugas with lower hepatic Hg concentrations , yet among whales with higher Hg, AMG granule deposition extended to pericentral and midzonal regions of liver lobules. Mean areas occupied by AMG granules correlated well with hepatic Hg concentrations and age. In beluga livers, AMG staining density was not associated with lipofuscin quantity (an index of oxidative damage). Occasionally, AMG granules and lipofuscin were colocalized, but more often were not, implying that Hg was not a prominent factor in hepatic lipofuscin deposition in belugas.
Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

OF THE DISSERTATION PRINCIPAL COMPONENT ANALYSIS AND ASSESSMENT OF LANGUAGE NETWORK ACTIVATION PATTERNS IN PEDIATRIC EPILEPSY by Xiaozhen You Florida International University, 2010 Miami, Florida Professor Malek Adjouadi, Major Professor This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of
An Improved Space-Time Decoder Under MIMO System

Optimum space-time convolution code, which provides maximum diversity and coding gain, are produced for cases with PSK modulation and various numbers of states and antennas. In this paper, we modify the conventional Viterbi algorithm used in space-time encoder under the MIMO system, and give the recursive metric equation. The modified Viterbi algorithm can be used in the receiver when the MIMO channel information is known a prior. The new approach provides an efficient method that allows a search for optimum path. Compared with the conventional decoding algorithm, the improved algorithm can be used in the every receiver respectively. It is shown that the improved algorithm is also an optimal algorithm, however, its metric convergence speed is faster than the standard Viterbi algorithm.
Social Bookmarking Induced Active Page Ranking

Social bookmarking services have recently made it possible for us to register and share our own bookmarks on the web and are attracting attention. The services let us get structured data: (URL, Username, Timestamp, Tag Set). And these data represent user interest in web pages. The number of bookmarks is a barometer of web page value. Some web pages have many bookmarks, but most of those bookmarks may have been posted far in the past. Therefore, even if a web page has many bookmarks, their value is not guaranteed. If most of the bookmarks are very old, the page may be obsolete. In this paper, by focusing on the timestamp sequence of social bookmarkings on web pages, we model their activation levels representing current values. Further, we improve our previously proposed ranking method for web search by introducing the activation level concept. Finally, through experiments, we show effectiveness of the proposed ranking method.
BAYESIAN MULTIDIlVIENSIONAL SCALING AND CHOICE OF DIMENSION by Man-SukOh

Multidimensional scaling is widely used to handle data which consist of dissimilarity measures between pairs of objects or people. We deal with two major problems in metric multidimensional scaling configuration of objects and determination of the dimension of object configuration within a Bayesian framework. A Markov chain Monte Carlo algorithm is proposed for object configuration, along with a simple Bayesian criterion for choosing their effective dimension, called MDSIC. Simulation results are presented, as well as examples on real data. Our method provides better results than classical multidimensional scaling for object configuration, and MDSIC seems to work well for dimension choice in the examples considered.
Reward Promotes Self-Face Processing: An Event-Related Potential Study

The present study adopted a reward-priming paradigm to investigate whether and how monetary reward cues affected self-face processing. Event-related potentials were recorded during judgments of head orientation of target faces (self, friend, and stranger), with performance associated with a monetary reward. The results showed self-faces elicited larger N2 mean amplitudes than other-faces, and mean N2 amplitudes increased after monetary reward as compared with no reward cue. Moreover, an interaction effect between cue type and face type was observed for the P3 component, suggesting that both self-faces and friend-faces elicited larger P3 mean amplitudes than stranger-faces after no reward cue, with no significant difference between self-faces and friend-faces under this condition. However, self-faces elicited larger P3 mean amplitudes than friend-faces when monetary reward cues were provided. Interestingly, the enhancement of reward on friend-faces processing was observed at late positive potentials (LPP; 450–600 ms), suggesting that the LPP difference between friend-faces and stranger-faces was enhanced with monetary reward cues. Thus, we found that the enhancement effect of reward on self-relevant processing occurred at the later stages, but not at the early stage. These findings suggest that the activation of the reward expectations can enhance self-face processing, yielding a robust and sustained modulation over their overlapped brain areas where reward and self-relevant processing mechanisms may operate together.
Fuzzy Logic based Maximum Power Point Tracker in Photovoltaic Cell

This paper presents the analysis, modeling and control of a grid connected photovoltaic generation system. The model contains a detailed representation of the solar array, grid side multilevel neutral point clamped voltage source inverter. Fuzzy logic controller for the maximum power point tracking of a photovoltaic system under variable temperature and insulation conditions is discussed. The PQ control approach has been presented for the multilevel inverter. One of the most common control strategies structures applied to decentralized power generator is based on power direct control employing a controller for the dc link voltage and a controller to regulate the injected current to the utility network. The proposed models were implemented in Matlab/Simulink.
Timing yield estimation with clock network correlations by propagating discrete probability distributions

Timing yield, in conjunction with other types of yield, directly affects profit; under-estimation is as bad as over-estimation, because large amount of time is unnecessarily spent to increase small amount of timing yield. The correlation that stems from clock network, when ignored, turns out to be one of the reasons of under-estimation in clocked sequential circuit. Three sources of topological correlation are identified; the key problem is to determine the correlations we can ignore without sacrificing accuracy so that we keep run time within control, which is addressed in this paper. A prototype tool was implemented with gate delay modeled as discrete probability distribution; experiments with benchmark circuits show that, compared to Monte Carlo simulation, speedup is 75× with 0.53% difference of timing yield on average.
Endonuclease Specificity and Sequence Dependence of Type IIS Restriction Enzymes

Restriction enzymes that recognize specific sequences but cleave unknown sequence outside the recognition site are extensively utilized tools in molecular biology. Despite this, systematic functional categorization of cleavage performance has largely been lacking. We established a simple and automatable model system to assay cleavage distance variation (termed slippage) and the sequence dependence thereof. We coupled this to massively parallel sequencing in order to provide sensitive and accurate measurement. With this system 14 enzymes were assayed (AcuI, BbvI, BpmI, BpuEI, BseRI, BsgI, Eco57I, Eco57MI, EcoP15I, FauI, FokI, GsuI, MmeI and SmuI). We report significant variation of slippage ranging from 1–54%, variations in sequence context dependence, as well as variation between isoschizomers. We believe this largely overlooked property of enzymes with shifted cleavage would benefit from further large scale classification and engineering efforts seeking to improve performance. The gained insights of in-vitro performance may also aid the in-vivo understanding of these enzymes.
A Sentiment-Aware Approach to Community Formation in Social Media

Participating in a community exemplifies the aspect of sharing, networking and interacting in a social media system. There has been extensive work on characterising on-line communities by their contents and tags using topic modelling tools. However, the role of sentiment and mood has not been studied. Arguably, mood is an integral feature of a text, and becomes more significant in the context of social media: two communities might discuss precisely the same topics, yet within an entirely different atmosphere. Such sentiment-related distinctions are important for many kinds of analysis and applications, such as community recommendation. We present a novel approach to identification of latent hyper-groups in social communities based on users’ sentiment. The results show that a sentiment-based approach can yield useful insights into community formation and meta-communities, having potential applications in, for example, mental health—by targeting support or surveillance to communities with negative mood—or in marketing—by targeting customer communities having the same sentiment on similar topics.
A Semi-Automated Map Fitting Procedure

The electron density applications available within QUANTA96 represent novel and effective tools for speeding up the processes of C a tracing experimental maps and manual rebuilding during refinement. The various modules (X-AUTOFIT, X-BUILD, X-SOLVATE and X-LIGAND), have been developed over the past year in close collaboration with the large number of crystallographers working on projects in the Protein Group at York. Crucially, these new tools are easy to learn, and natural to use, and provide a 10 fold reduction in the time spent at the graphics terminal.
LIA@Replab 2014

In this paper, we present the participation of the Laboratoire Informatique d’Avignon (LIA) to RepLab 2014 edition [2]. RepLab is an evaluation campaign for Online Reputation Management Systems. LIA has produced an important number of experiments for every tasks of the campaign: Reputation Dimensions and both Author Categorization and Author Ranking sub-tasks from Author Profiling. Our approaches rely on a large variety of machine learning methods. We have chosen to mainly exploit tweet contents. In several of our experiments we have also added selected meta-data. A fewer number of our proposals have integrated external information by using provided background messages.
Digital Diversity in Software Development Companies: Is It for Real ?

This study introduces the concept of ‘digital diversity’ to refer to the increasing workforce heterogeneity that stems from the recent rise of digital natives in the workforce. While digital diversity is expected to promise innovation and efficiency, it may also give rise to organizational challenges. The nature of the promise and challenge of digital diversity as well as how it is perceived across diverse groups, however, remain unexplored. Through a multisite study of software development companies, this study provides a deeper understanding of digital diversity and its promise and challenge in the particular context of software development workforce. Furthermore, drawn upon empirical data and insights from generational and diversity research, a model distinctly suited to studying digital diversity in software development workforce, is developed. Contributions to theory and implications for software development companies are discussed and new avenues for future research are outlined.
Real-time PCR investigation of parasite ecology: in situ determination of oyster parasite Perkinsus marinus transmission dynamics in lower Chesapeake Bay

Perkinsus marinus is a severe pathogen of the oyster Crassostrea virginica on the East Coast of the United States. Transmission dynamics of this parasite were investigated in situ for 2 consecutive years (May through October) at 2 lower Chesapeake Bay sites. Compared to previous studies where seasonal infection patterns in oysters were measured, this study also provided parasite water column abundance data measured using real-time PCR. As previously observed, salinity and temperature modulated parasite transmission dynamics. Using regression analysis, parasite prevalence, oyster mortalities and parasite water column abundance were significantly positively related to salinity. Perkinsus marinus weighted prevalence in wild oysters and parasite water column abundance both were significantly related to temperature, but the responses lagged 1 month behind temperature. Parasite water column abundance was the highest during August (up to 1200 cells/l) and was significantly related to P. marinus weighted prevalence in wild oysters, and to wild oyster mortality suggesting that parasites are released in the environment via both moribund and live hosts (i.e. through feces). Incidence was not significantly related to parasite water column abundance, which seems to indicate the absence of a linear relationship or that infection acquisition is controlled by a more complex set of parameters.
Collaboration alignment in distributed interference management in uplink cellular systems

We consider a cellular wireless system including several interfering multi-user multi-antenna uplink channels, where the base station of each cell has to locally recover the messages of its corresponding users. We use a linear Wyner model, where each base station experiences interference only from the users in the two neighboring cells. Each base station is connected to the two nearby base stations through a backhaul link. The objective is to achieve the maximum degrees of freedom per cell, with minimum aggregated load in the backhaul. We propose a successive cooperative alignment scheme, in which each base station forms backhaul messages by combining the previous received backhaul messages with the received signals at its wireless terminal. By some alignment schemes in signaling over wireless links as well as developing proper messages over backhaul links, each base station can help the neighboring base stations to peel off the aggregated interference with minimum help. This is done without propagating interference throughout the network. In this conference paper, we focus on linear one shot schemes and prove the optimality of the proposed scheme, in a robust set-up for a system with two antennas per base station and two users per cell, where each user is equipped with two antennas.
Ensemble Coupled Hidden Markov Models for Joint Characterisation of Dynamic Signals

How does one model data with the aid of labels, when the labels themselves are noisy, unreliable and have their own dynamics? How does one measure interactions between variables that are so different in their nature that a direct comparison using, say cross-correlations, is meaningless? In this paper these problems are approached using Coupled Hidden Markov Models which are estimated in the Variational Bayesian framework. Signals can be diverse since each chain has its own observation model. Signals can have their own dynamics and may temporally lag or lead one another by allowing linking edges in the network topology to be estimated and chosen according to the most probable posterior model. Integrated feature extraction and modelling is accomplished by providing the Markov models models with linear observations models. We derive Coupled Hidden Markov Models estimators, apply and compare them with sampling based approaches found in the literature.
A Memory Reduction Monte Carlo Simulation for Pricing Multi-assets American Options

When pricing American options on multi-assets (d) by Monte Carlo methods, one usually stores the simulated asset prices at all time steps on all paths in order to determine when to exercise the options. If N time steps and M paths are used, then the storage requirement is . It is undoubtedly enormous for Monte Carlo method which needs to increase the number of simulations to improve the accuracy. In this paper, we propose a memory reduction simulation method to price multi-asset American options and use it in low-discrepancy sequences. For machines with limited memory, we can now use larger values of M and N to improve the accuracy in pricing the options.
Is Arabic Part of Speech Tagging Feasible Without Word Segmentation?

In this paper, we compare two novel methods for part of speech tagging of Arabic without the use of gold standard word segmentation but with the full POS tagset of the Penn Arabic Treebank. The first approach uses complex tags without any word segmentation, the second approach is segmention-based, using a machine learning segmenter. Surprisingly, word-based POS tagging yields the best results, with a word accuracy of 94.74%.
Utilizing High-quality Feature Extension Mode to Classify Chinese Short-text

T his paper presents a method of classifying Chinese short-texts that have weak concept signal, in which high-quality feature extension mode s are extracted and used effectively. In th e method, a feature extension mode is considered as a set of terms that have co-occurrence relationship in the training data, and three measures that decide whether it is high-quality , i.e., Confidence, category homoplasy and relevancy strength, are presented. Then, an algorithm, which extracts high-quality feature extension mode s from training data, is designed. Next, Chinese short-text classification algorithm u tilizing f eature e xtension m ode s is presented, in which a short-text is extended by adding new features or modifying the weights of initial features, according to the relationship between non-feature term and feature e xtension m ode . The experiment s show that (1) A high-quality feature extension mode is helpful to improve Chinese short-text classification; (2) the proposed method can obtain a higher classification performance comparing with the conventional text classification methods