Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetic Nephropathy Induced by Increased Ace Gene Dosage Is Associated with High Renal Levels of Angiotensin (1–7) and Bradykinin

Population studies have shown an association between diabetic nephropathy (DN) and insertion/deletion (I/D) polymorphism of the angiotensin-converting enzyme (ACE) gene (ACE in humans, Ace in mice). The aim was to evaluate the modulation of Ace copies number and diabetes mellitus (DM) on renal RAS and correlate it with indicators of kidney function. Increased number of copies of the Ace gene, associated with DM, induces renal dysfunction. The susceptibility to the development of DN in 3 copies of animals is associated with an imbalance in activity of RAS enzymes leading to increased synthesis of Ang II and Ang-(1–7). Increased concentration of renal Ang-(1–7) appears to potentiate the deleterious effects triggered by Ang II on kidney structure and function. Results also show increased bradykinin concentration in 3 copies diabetic group. Taken together, results indicate that the deleterious effects described in 3 copies diabetic group are, at least in part, due to a combination of factors not usually described in the literature. Thus, the data presented here show up innovative and contribute to understanding the complex mechanisms involved in the development of DN, in order to optimize the treatment of patients with this complication.
Anti-Aβ Autoantibodies in Amyloid Related Imaging Abnormalities (ARIA): Candidate Biomarker for Immunotherapy in Alzheimer’s Disease and Cerebral Amyloid Angiopathy

Amyloid-related imaging abnormalities (ARIA) represent the major severe side effect of amyloid-beta (Aβ) immunotherapy for Alzheimer’s disease (AD). Early biomarkers of ARIA represent an important challenge to ensure safe and beneficial effects of immunotherapies, given that different promising clinical trials in prodromal and subjects at risk for AD are underway. The recent demonstration that cerebrospinal fluid (CSF) anti-Aβ autoantibodies play a key role in the development of the ARIA-like events characterizing cerebral amyloid angiopathy-related inflammation generated great interest in the field of immunotherapy. Herein, we critically review the growing body of evidence supporting the monitoring of CSF anti-Aβ autoantibody as a promising candidate biomarker for ARIA in clinical trials.
Efficient Advert Assignment

We develop a framework for the analysis of large-scale ad auctions where adverts are assigned over a continuum of search types. For this pay-per-click market, we provide an efficient mechanism that maximizes social welfare. In particular, we show that the social welfare optimization can be solved in separate optimizations conducted on the time scales relevant to the search platform and advertisers. Here, on each search occurrence, the platform solves an assignment problem and, on a slower time scale, each advertiser submits a bid that matches its demand for click-throughs with supply. Importantly, knowledge of global parameters, such as the distribution of search terms, is not required when separating the problem in this way. Exploiting the information asymmetry between the platform and advertiser, we describe a simple mechanism that incentivizes truthful bidding, has a unique Nash equilibrium that is socially optimal, and thus implements our decomposition. Further, we consider models where advertisers ad.
Left–right reversed

Existing analyses of the Turkish party system suggest that it is unique in several respects. Upper-class voters tend to support the Republican People’s Party (CHP), the centre-left social democratic party, while poorer voters support the right. Unlike party systems in Western democracies, expert surveys find that a religious–secular divide, and not a socio-economic divide, best explains the general left–right dimension. Qualitative literature stresses Turkey’s uniqueness due to the long history of the CHP, its close ties to the bureaucracy and military, and the role of the military in politics. Lastly, existing quantitative measures of policy positions disagree about the placement of major parties. We estimate the principal dimension of Turkish party competition using electoral manifestos as data by applying the Wordfish scaling algorithm. We find that ideology in Turkish politics is reversed, with the nominally centre-left CHP employing more populist rhetoric typically associated with right-wing parties in the West, and vice versa.
A Naïve Bayes Approach for Word Sense Disambiguation

The word sense disambiguation (WSD) is the task ofautomatically selecting the correct sense given a context and it helps in solving many ambiguity problems inherently existing in all natural languages.Statistical Natural Language Processing (NLP),which is based on probabilistic, stochastic and statistical methods, has been used to solve many NLP problems.The Naive Bayes algorithm which is one of the supervised learning techniques has worked well in many classification problems. In the present work, WSD task to disambiguate the senses of different words from the standard corpora available in the "1998 SENSEVAL Word Sense Disambiguation (WSD) shared task" is performed by applying Naive Bayes machine learning technique. It is observed that senses of ambiguous word having lesser number of part-of-speeches are disambiguated more correctly. Other key observation is that with lesser number of senses to be disambiguated, the chances of words being disambiguated with correct senses are more.
Rejoinder: Bayesian Checking of the Second Levels of Hierarchical Models

able insights and for commenting on important aspects of model checking that we did not touch in our pa per. Our goal was modest (but crucial): to select an appropriate distribution with which to judge the com patibility of the data with a hypothesized (hierarchical) model, when the test statistic is not ancillary and an improper prior is used for the hyperpammeters. Since it is important to emphasize that this is by no means the only aspect of model checking, the discussants' com plementary contributions and comments are all most welcome. The specific technical contributions of Evans and Johnson are also appreciated, since their develop ments in this area were not mentioned in our review.
Industrial experience with building a web portal product line using a lightweight, reactive approach

Imprecise, frequently changing requirements and short time-to-market create challenges for application of conventional software methods in Web Portal engineering. To address these challenges, ST Electronics (Info-Software Systems) Pte. Ltd. applied a lightweight, reactive approach to support a Web Portal product line. Unique characteristics of the approach were fast, low-cost migration from a single conventional Web Portal towards a reusable "generic Web Portal" solution, effective handling of large number of functional variants and their dependencies, the ability to rapidly develop new Web Portals from the generic one, and to independently evolve multiple Web Portals without ever losing a connection between them and the "generic Web Portal". The initial Web Portal was built using state-of-the-art conventional methods. The Web Portal was not flexible enough to reap the benefits of new business opportunities that required the company to rapidly develop and further maintain many similar Web Portals. To overcome the limitations of the conventional solution, a reuse technique called XVCL was applied incrementally. Over a period of three weeks, the conventional solution was converted into a Web architecture capable of handling nine Web Portals from a base of code smaller than the original Web Portal. In the paper, we describe the process that led to building the above Web Portal product line. We explain the difficulties in building an effective generic Web solution using conventional techniques. We analyze our reuse-based solution in qualitative and quantitative ways.
Subjectively Related Association Term Discovery towards Personalized Web Information Retrieval

In this paper, we propose a new semi-supervised clustering methodology to extract topically coherent contents from given Web pages, according to a user's topic interests. It is an effort to resolve low information retrieval performance, caused by one fact that even a single Web page often contains multi-topic related contents. Our evaluation results showed some advantages of our semi-supervised clustering methodology: it reduces the needs of term classification knowledge between the given Web pages and a user's topic interests. It also gets better clustering performances than those which can be achieved with the well-known supervised feature-term selection method chi2 statistics.
Exploring the Dark Side: Relationships between the Dark Triad Traits and Cluster B Personality Disorder Features

The present study explored relationships between the Dark Triad (Machiavellianism, narcissism, and psychopathy) and cluster B personality disorder (antisocial, borderline, histrionic, and narcissistic) features. A sample of 661 participants were recruited from a Midwestern university and Amazon Mechanical Turk. A series of t-tests were conducted to examine sex differences in the Dark Triad and cluster B personality disorders. Sex differences were found on all traits and disorders, with men significantly higher than women on Machiavellianism, psychopathy, and antisocial personality disorder, and women higher than men on borderline, histrionic, and narcissistic personality disorders. Zero-order correlations and linear regression analyses were conducted to examine the contribution of each Dark Triad trait to each personality disorder. Significant associations were found for all personality disorders, with at least one Dark Triad trait making a significant contribution to each disorder. The relevance of the present study to the workplace and politics was discussed.
Improving Projections of Panoramic Images with Möbius Transformations

Wide-angle images gained a huge popularity in the last years due to the development of computational photography and technological advances. They present the information of a scene in a much more natural way but, on the other hand, they introduce unreal artifacts such as bent lines. These artifacts become more and more unnatural as long as the field of view increases. In this work, we present a technique aimed to improve the visual quality of the panoramas. The main ingredients of our approach are, on one hand, considering the viewing sphere as a Riemann sphere, what makes it natural to apply Möbius (complex) transformations to the input image, and, on the other hand, a projection scheme which changes in function of the field of view used. Moreover, we present a method to quantify the distortion in projections of a sphere and argue, based on this, about the quality of the images produced by our approach. We also introduce an implementation of our approach, showing that the transformations can be done in real-time, which makes our method very appealing for existing interactive panorama applications.
Dispatch distributed generation and load forecasting by GSO algorithm and natural network optimized by genetic

This paper mainly discuss a problem that how to optimize the ways on electric system dispatching. The GSO algorithm and natural network optimized by genetic are used to solve the problem. The distributed generations and the loads can be seen as producer, requester and random dispersion members which is in the nature. Due to the electric system is a dynamic system, the load forecasting must be done by the natural network. This algorithm is optimized by genetic algorithm to solve the problem that need lots of data. Through previous algorithm can solve the electric system dispatching problem.
An information-theoretic approach to automatic query expansion

Techniques for automatic query expansion from top retrieved documents have shown promise for improving retrieval effectiveness on large collections; however, they often rely on an empirical ground, and there is a shortage of cross-system comparisons. Using ideas from Information Theory, we present a computationally simple and theoretically justified method for assigning scores to candidate expansion terms. Such scores are used to select and weight expansion terms within Rocchio's framework for query reweigthing. We compare ranking with information-theoretic query expansion versus ranking with other query expansion techniques, showing that the former achieves better retrieval effectiveness on several performance measures. We also discuss the effect on retrieval effectiveness of the main parameters involved in automatic query expansion, such as data sparseness, query difficulty, number of selected documents, and number of selected terms, pointing out interesting relationships.
More on WZ action of non-BPS branes

We calculate the disk level S-matrix element of one Ramond-Ramond, two gauge field and one tachyon vertex operators in the world volume of non-BPS branes. We then find the momentum expansion of this amplitude and show that the infinite tachyon/massless poles and the contact terms of this amplitude can be reproduced by the tachyon DBI and the WZ actions, and by their higher derivative corrections.
Gender differences in and risk factors for depression in adolescence: A 4-year longitudinal study

The current study used longitudinal data (N 1/4 1322; 648 males, 674 females) from adolescents ages 12 to 19 years (in 1994) to investigate gender differences in and risk factors for depressive symptoms and major depressive episodes (MDEs). The sample had participated in three waves of Canada’s National Population Health Survey (1994, 1996, and 1998). Results showed that although there was not a statistically significant increase in depressive symptoms in early adolescence, there was a robust gender difference in the levels of depressive symptoms and the prevalence of MDE, with girls more affected than boys. Over time, decreases in social support and increases in smoking were both linked to increases in depressive symptoms. Moreover, youth who smoked and who were free from major depression in 1994 were 1.4 times more likely to report a MDE in 1996 or 1998. To be effective, prevention and treatment programmes for depression may also need to address risks such as poor social support and smoking, as these risk factors may serve to maintain depressive symptoms over time.
Defining “Community” in Community Health Evaluation

This article examines the theoretical basis of the community as it is evoked in health evaluation. In particular, we examine how hospitals carrying out Community Health Needs Assessments (CHNAs) define communities as well as the implications for these definitions for how to study and engage community problems. We present qualitative findings from a sample of Appalachian nonprofit hospitals, who we asked to describe their approach to defining the community in their most recent Internal Revenue Service–mandated CHNA. Drawing upon a theoretical debate in the history of evaluation research, the authors argue that the contemporary community cannot be circumscribed merely by geographic boundaries, nor can it be identified easily with a bounded group of clearly demarcated individuals. Instead, following the tenets of community-based health research, the authors argue for a richer, more dynamic conceptualization of the community in evaluation research in which definitions arise from community bodies themselves.
Unsupervised Feature Selection with Controlled Redundancy (UFeSCoR)

Features selected by a supervised/ unsupervised technique often include redundant or correlated features. While use of correlated features may result in an increase in the design and decision making cost, removing redundancy completely can make the system vulnerable to measurement errors. Most feature selection schemes do not account for redundancy at all, while a few supervised methods try to discard correlated features. We propose a novel unsupervised feature selection scheme (UFeSCoR), which not only discards irrelevant features, but also selects features with controlled redundancy. Here, the number of selected features can also be directed. Our algorithm optimizes an objective function, which tries to select a specified number of features, with a controlled level of redundancy, such that the topology of the original data set can be maintained in the reduced dimension. Here, we have used Sammon's error as a measure of preservation of topology. We demonstrate the effectiveness of the algorithm in terms of choosing relevant features, controlling redundancy, and selecting a given number of features using several data sets. We make a comparative study with five unsupervised feature selection methods. Our results reveal that the proposed method can select useful features with controlled redundancy.
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A Penalty-Sensitive Branch Predictor

Branch predictor design is typically focused only on minimizing the misprediction rate (MR), while ignores misprediction penalty.Because the misprediction penalty varies widely from branch to branch, performance might get improved by using a predictor that makes a greater effort to predict high-penalty branches, at the expense of the other, even if the total number of mispredictions doesn't change. A penalty-sensitive predictor was developed based on this idea. It includes a penalty predictor to predict whether a branch is high or low penalty. Then, a twoclass TAGE predictor is developed to favor high-penalty branches at the expense of low-penalty branches. Experiment shows although the overall performance improvement is limited, the penalty-sensitive mechanism successfully decreases the MR of the highpenalty branches while increasing the MR of the lowpenalty branches by a small amount.
A Load Balancing Model in Public Cloud Using ANFIS and GSO

Recent days, cloud computing is an improving area in research and industry, which includes virtualization, distributed computing, internet, and web services. A cloud contains elements such as data centers, clients, distributed servers, internet which includes fault tolerance, high accessibility, efficiency, scalability, flexibility, reduced overhead for users, less cost of ownership, on demand services and etc. Cloud computing services are becoming omnipresent and serve as the primary source of calculating power for different applications like activity and personal computing applications. It has many benefits all along with some fundamental problems to be resolved in order to improve reliability of cloud environment. Also that, these problems is associated with load management, tolerance and different security issues in cloud environment. In this paper introduces a better load balancing model for the public cloud based on the cloud partitioning concept with a switch mechanism to select different strategies for different situations. Adaptive neuro-fuzzy inference system (ANFIS) based load balancing algorithm and Glowworm swarm optimization (GSO) based load balancing algorithm are proposed to the load balancing strategy to improve the efficiency in the public cloud environment. Experimental result shows that the proposed method gives better results when compared with other traditional methods.
Relationship between Self-Esteem and Impostor Syndrome among Undergraduate Medical Students in a Nigerian University

Most medical educators, patients, caregivers, health team members and stakeholders would prefer a medical practitioner and /or a future doctor that has high self-esteem and low impostor scores. This cross-sectional descriptive study among the medical students of Imo State University, Nigeria, was under taken to assess the relationship between self-esteem and impostor phenomenon. Two hundred students participated in this study. Rosenberg Self-Esteem scale and Clance Impostor Phenomenon scale were the research instruments used to measure self-esteem and impostor syndrome respectively. The results showed that self-esteem correlated significantly and positively with age (r =.274, p = 0.001) and marital status (r = .174, p = 0.05) and negatively with impostor syndrome (r = -.649, p = 0.001). Impostor syndrome correlated negatively and significantly with age (r = -.301, p= 0.001), marital status (r= -.203, p= 0.001) and self-esteem(r= -.649, p = 0.001). Majority of the students (63.5%) had high self-esteem and low impostor (54.5%) scores. Married students had high self-esteem and low impostor scores compared to their single peers (p<0.05). Prevalence of marriage among the medical students was low (12%). Statistically significant difference was noted in the age of the males and females (p<0.05). No statistically significant gender differences in the scores for self-esteem and impostor syndrome. In conclusion, the students had high levels of self-esteem and low levels of impostor phenomenon, self-esteem correlated positively and significantly with age and marital status and negatively with impostor syndrome. Marriage rate was low and married students had high levels of self-esteem and low impostor score compared to their single peers. No significant gender differences in self-esteem and impostor syndrome.
Acsys Trec-7 Experiments (draft for Notebook)

Experiments relating to TREC-7 Ad Hoc, HP and VLC tasks are described and results reported. Minor reenements last year's Ad Hoc methods do not appear to have resulted in worthwile improvements in performance. However, larger beneets were gained from automatic feedback than last year and concept scoring was very beneecial in the Manual Ad Hoc category. The HP track was used to validate the usibility of the combination of PADRE and the Quokka GUI. In the VLC track, the 100 gigabyte collection was indexed in under eight hours and moderately eeective queries were processed in less than two seconds.
The effect of noise whitening on methods for determining the intrinsic dimension of a hyperspectral image

Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process, and under- or over-estimation of this number may lead to incorrect unmixing for unsupervised methods. It is known that most real images contain noise that is not i.i. d. across bands, and so methods that assume i.i. d. noise are often avoided. However, this problem may be alleviated by implementing a noise whitening procedure as a pre-processing step. In this paper we will investigate one particular noise whitening approach, as well as a noise removal approach, and consider how the application of these methods may improve several methods for determining the intrinsic dimension of an image, including Malinowski's Empirical Indicator Function [1], Random Matrix Theory [2], and Harsanyi-Farrand-Chang [3].