Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of Annexin I in Rheumatoid Synovial Cells by Glucocorticoids and Interleukin-1

The glucocorticoid (GC)-induced antiinflammatory molecule annexin I is expressed in leukocytes and has antiinflammatory effects in animal models of arthritis, but the expression of annexin I in rheumatoid arthritis (RA) fibroblast-like synoviocytes (FLS) is unknown. We report the constitutive and dexamethasone (DEX)-inducible expression of annexin I in RA FLS. DEX increased FLS annexin I protein translocation and mRNA expression. Interleukin (IL)-1β also induced annexin I translocation and mRNA but also increased intracellular protein. DEX and IL-1 had additive effects on annexin I mRNA, but DEX inhibited the inducing effect of IL-1β on cell surface annexin I. These results indicate that glucocorticoids and IL-1β upregulate the synthesis and translocation of annexin I in RA FLS, but interdependent signalling pathways are involved.
Comprehensive Comparison of Hardware Performance of Fourteen Round 2 SHA-3 Candidates with 512-bit Outputs Using Field Programmable Gate Arrays

In this paper, we extend our evaluation of the hardware performance of 14 Round 2 SHA-3 candidates, accepted for the presentation at CHES 2010, to the case of high security variants, with 512 bit outputs. A straightforward method for predicting the performance of 512-bit variants, based on the results for 256-bit versions of investigated hash functions is presented, and confirmed experimentally. The VHDL codes for 512-bit variants of all 14 SHA-3 Round 2 candidates and the old standard SHA-2 have been developed and thoroughly verified. These codes have been then used to evaluate the relative performance of all aforementioned algorithms using seven modern families of Field Programmable Gate Arrays (FPGAs) from two major vendors, Xilinx and Altera. The results point to very significant differences among all evaluated algorithms in terms of both throughput and area. Only two candidates, Keccak and CubeHash, outperform SHA-512 in terms of the primary optimization target used in this study, throughput to area ratio.
Sentiment Classification by Semantic Orientation Using SentiWordNet Lexicon from Onli ne Customer Reviews

Sentiment analysis and classification is an area of text classification that began around 2001 and has recently been receiving a lot of attention from researchers. Sentiments about a given subject, be it a commercial product or any other topic of interest can contain a wealth of information not only of the generic opinion toward the subject but also individual opinions of its several features or sub-topics. Sentiment analysis involves analyzing textual datasets which contain opinions (e.g., online review, social media, blogs, discussion groups) with the objective of classifying the opinions as positive, negative, or neutral. Opinion plays essential part in our informationgathering behaviour before taking a decision. In this work we describe a simple technique to perform sentiment classification based on an unsupervised linguistic approach. Our pattern-based method applies a classification rule according to which each review is classified as positive or negative depending on its overall sentiment score, calculated with the aid of SentiWordNet. The results indicate SentiWordNet could be used as an important resource for sentiment classification tasks. Additional considerations are made on possible further improvements to the method.
Privacy Preserving Record Linkage via grams Projections

Record linkage has been extensively used in various data mining applications involving sharing data. While the amount of available data is growing, the concern of disclosing sensitive information poses the problem of utility vs privacy. In this paper, we study the problem of private record linkage via secure data transformations. In contrast to the existing techniques in this area, we propose a novel approach that provides strong privacy guarantees under the formal framework of differential privacy. We develop an embedding strategy based on frequent variable length grams mined in a private way from the original data. We also introduce personalized threshold for matching individual records in the embedded space which achieves better linkage accuracy than the existing global threshold approach. Compared with the state-of-the-art secure matching schema, our approach provides formal, provable privacy guarantees and achieves better scalability while providing comparable utility.
CD39/Ectonucleoside Triphosphate Diphosphohydrolase 1 Provides Myocardial Protection During Cardiac Ischemia/Reperfusion Injury

Background— Extracellular adenosine, generated from extracellular nucleotides via ectonucleotidases, binds to specific receptors and provides cardioprotection from ischemia and reperfusion. In the present study, we studied ecto-enzymatic ATP/ADP-phosphohydrolysis by select members of the ectonucleoside triphosphate diphosphohydrolase (E-NTPDase) family during myocardial ischemia. Methods and Results— As a first step, we used a murine model of myocardial ischemia and in situ preconditioning and performed pharmacological studies with polyoxometalate 1, a potent E-NTPDase inhibitor (Na6[H2W12O40]). Polyoxometalate 1 treatment increased infarct sizes and abolished beneficial effects of preconditioning. To define relative contributions of distinct E-NTPDases, we investigated transcriptional responses of E-NTPDases 1 to 3 and 8 to preconditioning. We noted robust and selective induction of E-NTPDase 1 (CD39) transcript and protein. Histological analysis of preconditioned myocardium localized CD39 induction to endothelia and myocytes. Cd39−/− mice exhibited larger infarct sizes with ischemia (cd39+/+ 43.0±3.3% versus cd39−/− 52%±1.8; P<0.05), and cardioprotection was abrogated by preconditioning (cd39+/+ 13.3%±1.5 versus cd39−/− 50.5%±2.8; P<0.01). Heightened levels of injury after myocardial ischemia and negligible preconditioning benefits in cd39−/− mice were corrected by infusion of the metabolic product (AMP) or apyrase. Moreover, apyrase treatment of wild-type mice resulted in 43±4.2% infarct size reduction (P<0.01). Conclusions— Taken together, these studies reveal E-NTPDase 1 in cardioprotection and suggest apyrase in the treatment of myocardial ischemia.
Hepatitis C viremia and the risk of chronic kidney disease in HIV-infected individuals.

BACKGROUND The role of active hepatitis C virus (HCV) replication in chronic kidney disease (CKD) risk has not been clarified. METHODS We compared CKD incidence in a large cohort of HIV-infected subjects who were HCV seronegative, HCV viremic (detectable HCV RNA), or HCV aviremic (HCV seropositive, undetectable HCV RNA). Stages 3 and 5 CKD were defined according to standard criteria. Progressive CKD was defined as a sustained 25% glomerular filtration rate (GFR) decrease from baseline to a GFR < 60 mL/min/1.73 m2. We used Cox models to calculate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS A total of 52 602 HCV seronegative, 9508 HCV viremic, and 913 HCV aviremic subjects were included. Compared with HCV seronegative subjects, HCV viremic subjects were at increased risk for stage 3 CKD (adjusted HR 1.36 [95% CI, 1.26, 1.46]), stage 5 CKD (1.95 [1.64, 2.31]), and progressive CKD (1.31 [1.19, 1.44]), while HCV aviremic subjects were also at increased risk for stage 3 CKD (1.19 [0.98, 1.45]), stage 5 CKD (1.69 [1.07, 2.65]), and progressive CKD (1.31 [1.02, 1.68]). CONCLUSIONS Compared with HIV-infected subjects who were HCV seronegative, both HCV viremic and HCV aviremic individuals were at increased risk for moderate and advanced CKD.
A novel approach to design classifiers using genetic programming

We propose a new approach for designing classifiers for a c-class (c/spl ges/2) problem using genetic programming (GP). The proposed approach takes an integrated view of all classes when the GP evolves. A multitree representation of chromosomes is used. In this context, we propose a modified crossover operation and a new mutation operation that reduces the destructive nature of conventional genetic operations. We use a new concept of unfitness of a tree to select trees for genetic operations. This gives more opportunity to unfit trees to become fit. A new concept of OR-ing chromosomes in the terminal population is introduced, which enables us to get a classifier with better performance. Finally, a weight-based scheme and some heuristic rules characterizing typical ambiguous situations are used for conflict resolution. The classifier is capable of saying "don't know" when faced with unfamiliar examples. The effectiveness of our scheme is demonstrated on several real data sets.
Border Feature Detection and Adaptation for Classification of Multispectral and Hyperspectral Remote Sensing Images

Effective partitioning of feature space for high classification accuracy with due attention to rare class members is often a difficult task. In this paper, the border feature detection and adaptation (BFDA) algorithm is proposed for this purpose. The BFDA consist of two parts. In the first part of the algorithm, some specially selected training samples are assigned as initial reference vectors called border features. In the second part of the algorithm, the border features are adapted by moving them towards the decision boundaries. At the end of the adaptation process, the border features are finalized. The method next uses the minimum distance to border feature rule for classification. In supervised learning, the training process should be unbiased to reach more accurate results in testing. In the BFDA, decision region borders are related to the initialization of the border features and the input ordering of the training samples. Consensus strategy can be applied with cross validation to reduce these dependencies. The performance of the BFDA and Consensual BFDA (C-BFDA) were studied in comparison to other classification algorithms including neural network with back-propagation learning (NNBP), support vector machines (SVMs), and some statistical classification techniques.
Profound Amnesia After Damage to the Medial Temporal Lobe: A Neuroanatomical and Neuropsychological Profile of Patient E. P.

E. P. became profoundly amnesic in 1992 after viral encephalitis, which damaged his medial temporal lobe bilaterally. Because of the rarity of such patients, we have performed a detailed neuroanatomical analysis of E. P.'s lesion using magnetic resonance imaging, and we have assessed his cognitive abilities with a wide range of neuropsychological tests. Finally, we have compared and contrasted the findings for E. P. with the noted amnesic patient H.M, whose surgical lesion is strikingly similar to E. P.'s lesion.
Generic face alignment using an improved Active Shape Model

Although conventional Active Shape Model (ASM) and Active Appearance Model (AAM) based approaches have achieved some success, however, evidence suggests that the performance of a person-specific face alignment which aligns the variation in appearance of a single person across pose, illumination, and expression is substantially better than the performance of generic face alignment which aligns the variation in appearance of many faces, including unseen faces not in the training set. This paper proposes a discriminative framework for generic face alignment. This technique is presented under the framework of conventional Active Shape Model (ASM) but has three improvements. First, random forest classifiers are trained to recognize local appearance around each landmark. This discriminative learning provides more robustness weight for the optimization fitting procedure. Second, to impose constrains, shape vectors are restricted to the vector space spanned by the training database. Third, data augment scheme is used for the benefit of a large training set. Experimental results show that this approach can achieve good performance on generic face alignment.
The ReSearch Engine : Helping People Return to Information in Dynamic Information Environments

I investigate how people return to information in a dynamic information environment. For example, people often use search results not just to find new information, but also to return to information found via previous searches. However, search results change as search engines update their indices to reflect the current state of the Web. These changes can benefit users by providing new information, but they hinder returning to previously viewed information. I propose that the benefits can be gained without the drawbacks by giving people awareness and control over noticeable changes. In this proposal, I present work I have already done to support this thesis and outline the further research in the area I plan complete as part of my dissertation. Thus far, I have conducted two observational studies, one a modified diary study of how people search and the other an analysis of instances found on the Web where people expressed difficulty returning to information that had moved. These studies highlight the importance of preserving the path originally taken to the information target. Consequently, to support returning to information in a dynamic environment, I will augment a standard search engine to create a “Re-Search Engine”. The Re-Search Engine will preserve previously taken paths by remembering results to repeated queries. Based on a study presented here, it appears that people do not notice many changes, and this suggests it will be possible to preserve remembered search results while still presenting new ones. I will use implicit measures of attention to determine which aspects of the results page should and should not change. Nothing endures but change. Heraclitus (540 BCE 480 BCE)
Relativistic models of galaxies

A special form of the isotropic metric in cylindrical coordinates is used to construct what may be interpreted as the general relativistic versions of some well-known potential-density pairs used in Newtonian gravity to model three-dimensional distributions of matter in galaxies. The components of the energy-momentum tensor are calculated for the first two Miyamoto-Nagai potentials and a particular potential due to Satoh. The three potentials yield distributions of matter in which all tensions are pressures and all energy conditions are satisfied for certain ranges of the free parameters. A few non-planar geodesic orbits are computed for one of the potentials and compared with the Newtonian case. Rotation is also incorporated in the models and the effects of the source rotation on the rotation profile are calculated as first-order corrections by using an approximate form of the Kerr metric in isotropic coordinates.
Non-Parametric Sequential Estimation of a Regression Function Based on Dependent Observations

Abstract This article presents a sequential estimation procedure for an unknown regression function. Observed regressors and noises of the model are supposed to be dependent and form sequences of dependent numbers. Two types of estimators are considered. Both estimators are constructed on the basis of Nadaraya-Watson kernel estimators. First, sequential estimators with given bias and mean square error are defined. According to the sequential approach the duration of observations is a special stopping time. Then on the basis of these estimators of a regression function, truncated sequential estimators on a time interval of a fixed length are constructed. At the same time, the variance of these estimators is controlled by a (non-asymptotic) bound. In addition to nonasymptotic properties, the limiting behavior of presented estimators is investigated. It is shown, in particular, that by the appropriate chosen bandwidths both estimators have optimal (as compared to the case of independent data) rates of convergence of Nadaraya-Watson kernel estimators.
Security Analysis for an Order Preserving Encryption Scheme

The development of third-party hosting, IT outsourcing, service clouds, etc. raises important security concerns. It would be safer to encrypt critical data hosted by a third-parity, but meanwhile, the database should be able to process queries on encrypted data. Many research works have been developed to support search query processing on encrypted data, including the order preserving encryption (OPE) schemes. Security analysis plays an important role on secure algorithm design. It can help understand the level of security assurance of the algorithm. Currently, security analysis for OPE schemes is limited. In [8], a cryptographic-based OPE scheme, SEm,n, has been proposed. It defines the ideal model and the real model and construct the OPE in the real model to satisfy the idea model security. However, the security of the idea model itself has not been analyzed. In this paper, we first analyze the information leaks in SEm,n, and then use information theory to analyze the security of SEm,n. More specifically, we derive an upper bound on the probability for an adversary to recover the plaintext encrypted by SEm,n.
Acoustic echo cancellation based on cesptral analysis

In a teleconference system, the acoustic coupling between loudspeakers and microphones causes that, after talking, the user receives back his own voice. The use of adaptive filters based on the Wiener theory has been established during the last decades in acoustic echo cancellation. This paper proposes a new approach based on cepstral analysis to identify and cancel the echo path. The results show that the proposed method is able to achieve better estimates of the echo path than those obtained by some of the most used adaptive algorithms.
Mental Health in Equestrian Sport

Equestrian media is showing an increasing interest in the impact of mental health on performance and general wellbeing of equestrian athletes. This study explores the awareness of mental health difficulties and psychological wellbeing within equestrian sport from the perspectives of equestrian athletes, instructors/coaches and parents. The exploratory nature of the research offered opportunity to use a dual approach including e-surveys and semi-structured interviews. Analysis of the qualitative data identified five key themes (Emotional WellbeinginBalance; Emotional WellbeingImbalance; WellbeingImbalance—Impact on Equestrian Sportspeople; Impact of Equestrian Sport on Wellbeing; Regaining Balance) and 22 sub-themes. The findings determine a compelling need for education, promotion of sharing experiences, facilitation of specialist (clinical and sport) professional training and intervention as well as a review of regulations from equestrian Governing Bodies.
DeustoTech Internet at TASS 2015: Sentiment Analysis and Polarity Classification in Spanish Tweets

This article describes our system presented at the workshop for sentiment analysis TASS 2015. Our system approaches the task 1 of the workshop, which consists on performing an automatic sentiment analysis to determine the global polarity of a set of tweets in Spanish. To do this, our system is based on a model supervised Linear Support Vector Machines combined with some polarity lexicons. The inuence of the dierent linguistic features and the dierent sizes of n-grams in improving algorithm performance. Also the results obtained, the various tests that have been conducted, and a discussion of the results are presented.
A competitive minimax approach to robust estimation of random parameters

We consider the problem of estimating, in the presence of model uncertainties, a random vector x that is observed through a linear transformation H and corrupted by additive noise. We first assume that both the covariance matrix of x and the transformation H are not completely specified and develop the linear estimator that minimizes the worst-case mean-squared error (MSE) across all possible covariance matrices and transformations H in the region of uncertainty. Although the minimax approach has enjoyed widespread use in the design of robust methods, we show that its performance is often unsatisfactory. To improve the performance over the minimax MSE estimator, we develop a competitive minimax approach for the case where H is known but the covariance of x is subject to uncertainties and seek the linear estimator that minimizes the worst-case regret, namely, the worst-case difference between the MSE attainable using a linear estimator, ignorant of the signal covariance, and the optimal MSE attained using a linear estimator that knows the signal covariance. The linear minimax regret estimator is shown to be equal to a minimum MSE (MMSE) estimator corresponding to a certain choice of signal covariance that depends explicitly on the uncertainty region. We demonstrate, through examples, that the minimax regret approach can improve the performance over both the minimax MSE approach and a "plug in" approach, in which the estimator is chosen to be equal to the MMSE estimator with an estimated covariance matrix replacing the true unknown covariance. We then show that although the optimal minimax regret estimator in the case in which the signal and noise are jointly Gaussian is nonlinear, we often do not lose much by restricting attention to linear estimators.
A Novel Approach for Document Ranking in Digital Libraries using Extractive Summarization

Digital libraries have become an important tool for searching the scientific literature. The growth of digital libraries is exponential based upon the fact that researchers are utilizing it more and more by the days. To make the search better, the content of digital libraries should be ranked properly. The rank of research paper depends upon many factors like citations to paper; content, authors and publications of the paper etc. Based upon these parameters, different ranking algorithms have been proposed till now. In this paper, a new algorithm C3 ranking is proposed, which considers two important factors i.e. citations to the paper and the relevancy of the content with the query.
Narrative “End States” and the Dynamics of Participation in Civic Crowdfunding

The growing phenomenon of “paying to participate” is an important yet understudied aspect of crowdfunding, especially in the context of civic crowdfunding, where meaningful participation is linked to the production of public goods. In this study, we propose “end states” as a theoretical and methodological vehicle to examine how narratives of civic crowdfunding projects stimulate crowd participation and how this participation shapes the broader institutional and organizational context. We analyze narrative end states in two civic crowdfunding projects taking place in the Netherlands. The results of our analysis show three types of narrative end states: plotted, revised, and future. Initial end states were plotted to fulfill a specific need of the project initiators, however, these proved unsuccessful in mobilizing the crowd. Through an intense revision of end states, project initiators learned how to connect the crowd-community to the project, ultimately inspiring new end states for greater crowd-community participation in the future. We discuss the implications of our findings for research on narrative approaches to crowdfunding and the theory and practice of civic crowdfunding.
An Adaptive Quasi Monte Carlo Alternative to Metropolis

We present a manually-adaptive extension of Quasi Monte Carlo (QMC) integration for approximating marginal densities, moments, and quantiles when the joint density is known up to a normalization constant. Randomization and a batch-wise approach involving (0; s)-sequences are the cornerstones of the technique. By incorporating a variety of graphical diagnostics the method allows the user to adap-tively allocate points for joint density function evaluations. Through intelligent allocation of resources to diierent regions of the marginal space, the method can quickly produce reliable marginal density approximations in moderate dimensions. We demonstrate by examples that adaptive QMC can be a viable alternative to the Metropolis algorithm.