Miracle-dz/UniFAR-FTUs huggingface dataset:
Relations Between Subclinical Disease Markers and Type 2 Diabetes, Metabolic Syndrome, and Incident Cardiovascular Disease: The Jackson Heart Study

OBJECTIVE The presence of subclinical disease measures has been directly associated with the development of cardiovascular disease (CVD) in whites. African Americans (AAs) in the U.S. are at higher risk of CVD compared with non-Hispanic whites; however, data on the prevalence of subclinical disease measures in AAs and their association to CVD remain unclear and may explain the higher CVD risk in this group. RESEARCH DESIGN AND METHODS We evaluated 4,416 participants attending the first examination of the Jackson Heart Study (mean age 54 years; 64% women) with available subclinical disease measures. RESULTS There were 1,155 participants (26%) with subclinical disease, defined as the presence of one or more of the following: peripheral arterial disease, left ventricular hypertrophy, microalbuminuria, high coronary artery calcium (CAC) score, and low left ventricular ejection fraction. In cross-sectional analyses using multivariable-adjusted logistic regression, participants with metabolic syndrome (MetS) or diabetes (DM) had higher odds of subclinical disease compared with those without MetS and DM (odds ratios 1.55 [95% CI 1.30–1.85] and 2.86 [95% CI 2.32–3.53], respectively). Furthermore, the presence of a high CAC score and left ventricular hypertrophy were directly associated with the incidence of CVD (265 events) in multivariable-adjusted Cox proportional hazards regression models (P < 0.05). In prospective analyses, having MetS or DM significantly increased the hazard of incident CVD, independent of the presence of subclinical disease (P < 0.001). CONCLUSIONS In our community-based sample of AAs, we observed a moderately high prevalence of subclinical disease, which in turn translated into a greater risk of CVD, especially in people with MetS and DM.
Threshold and identity-based key management and authentication for wireless ad hoc networks

As various applications of wireless ad hoc network have been proposed, security has become one of the big research challenges and is receiving increasing attention. In this paper, we propose a distributed key management and authentication approach by deploying the recently developed concepts of identity-based cryptography and threshold secret sharing. Without any assumption of prefixed trust relationship between nodes, the ad hoc network works in a self-organizing way to provide the key generation and key management service, which effectively solves the problem of single point of failure in the traditional public key infrastructure (PKI)-supported system. The identity-based cryptography mechanism is applied here not only to provide end-to-end authenticity and confidentiality, but also to save network bandwidth and computational power of wireless nodes.
Customer Card Classification Based on C 5 . 0 & CART Algorithms * Prof

Data mining is the useful tool to discovering the knowledge from large data. For this we require proper classification methods & algorithms. Classification is most common method used for finding the mine rule from the large database. Decision tree method generally used for the Classification, because it is the simple hierarchical structure for the user understanding & decision making. Different data mining algorithms available for classification but decision tree mining is simple way. The objective of this paper is to provide the way for Decision Making process of Customer for recommended the membership card. Here I am using C5.0 & CART algorithms on customer database for classification. Both algorithms first applied on training dataset & created the decision tree, some rules are derived from decision tree. Same rules then applied on evaluation data set. Comparing the results of both algorithms recommended the card to the new customer those having similar characteristics. KeywordsData mining, classification algorithm, decision tree, Regression tree, membership card
The role of JAM-B in cancer and cancer metastasis (Review)

The junctional adhesion molecule B (JAM-B) is a multifunctional transmembrane protein, which belongs to the immunoglobulin superfamily (IgSF). JAM-B is localized to cell-cell contacts and enriched at cell junctions in epithelial and endothelial cells, as well as on the surface of erythrocytes, leukocytes, and platelets. Recent research in this field has shown that JAM-B plays an important role in numerous cellular processes, such as tight junction assembly, spermatogenesis, regulation of paracellular permeability, leukocytic transmigration, angiogenesis, tumor metastasis and cell proliferation. This study provides a new research direction for the diagnosis and treatment of relevant diseases. In this review, we briefly focus on what is currently known about the structure, function, and mechanism of JAM-B, with particular emphasis on cancer.
A dynamic web services selection based on decomposition of global QoS constraints

The objective of web services selection is to maximize an application-specific utility function under the end-to-end QoS constraints. Existing methods can be divided into two types: local selection method and global selection method. The local selection method is simple and efficient, but can not meet the end-to-end QoS constraints. The global selection method can satisfy the global QoS constraints, but at the price of higher computational time, and it is not suitable for the dynamic environment. To address this issue, this paper proposes an algorithm to combine global QoS constraints with local selection, which first splits the global QoS constraints into local constraints using heuristic method, and then uses local selection to find the optimal solution under the local constraints. The experimental results show that our approach improves the performance by reducing the computation time greatly while achieving close-to-optimal results.
Gender Inequality at Work.

Introduction - Jerry A Jacobs PART ONE: GENDER AND COMPENSATION Sex Composition and Gendered Earnings Inequality - Donald Tomaskovic-Devey A Comparison of Job and Occupational Models Gendered Instructions - Ronnie J Steinberg Cultural Lag and Gender Bias in the Hay System of Job Evaluation Further Evidence on Compensating Differentials and the Gender Gap in Wages - Jerry A Jacobs and Ronnie J Steinberg PART TWO: WOMEN AND AUTHORITY Jobs, Authority, and Earnings among Managers - Barbara F Reskin and Catherine E Ross The Continuing Significance of Sex Women's Entry into Management - Jerry A Jacobs Trends in Earnings, Authority, and Values among Salaried Managers Gender, Power, and Postindustrialism - Monica Boyd, Mary Ann Mulvihill, and John Myles PART THREE: CAREER PROCESSES AND TRENDS Cumulative versus Continuous Disadvantage in an Unstructured Labor Market - William T Bielby and Denise D Bielby Gender Differences in the Careers of Television Writers Occupational Sex Segregation and Women's Early Career Job Shifts - Rachel A Rosenfeld and Kenneth I Spenner Trends in Occupational and Industrial Sex Segregation in 56 Countries, 1960-1980 - Jerry A Jacobs and Suet T Lim PART FOUR: PERSPECTIVES ON OCCUPATIONAL RESEGREGATION Shifting Gender Boundaries - Patricia A Roos and Katharine W Jones Women's Inroads into Academic Sociology Male Flight from Computer Work - Rosemary Wright and Jerry A Jacobs A New Look at Occupational Resegregation and Ghettoization Gender and the Formation of a Women's Profession - Jo Anne Preston The Case of Public School Teaching Assessing Gender at Work - Pamela Stone Evidence and Issues
The potential approaches to achieve channel reciprocity in FDD system with frequency correction algorithms

Channel reciprocity is an inherit feature of time division duplex (TDD) system, which is widely used to get uplink (UL)/downlink (DL) channel knowledge from DL/UL channel measurements without additional feedback. While in frequency division duplex (FDD) system, the transmitter usually obtains the DL channel state information (CSI) through a variety of feedback strategies in order to enhance the system performance. However, the impacts of feedback overhead and delay on Multiple-Input-Multiple-Output (MIMO) performance can't be ignored. The paper focuses on the potential approaches to realize FDD channel reciprocity so as to reduce the feedback channel overhead. Compared with TDD system, UL and DL are allocated by different frequencies in FDD system thus the frequency correction is needed to achieve channel reciprocity. The paper presents the performance analysis and comparison of typical frequency correction algorithms, especially the algorithms based on the long-term statistical channel characteristics, which mainly include the channel covariance matrix, the channel space-time correlation, and the direction of arrival (DoA). The potential application scenario for each approach is presented as conclusion.
Running Head : NEURAL ACTIVITY DURING INFERENCES Neural Activity During Inference Generation

In this event-related functional magnetic resonance imaging (fMRI) study, participants listened to and comprehended short stories implying or explicitly stating inference events. The aim of this study was to examine the neural mechanisms that underlie inference generation, a process essential to successful comprehension. We observed distinct patterns of increased fMRI signal for implied over explicit events at two critical points during the stories: 1) within the right superior temporal gyrus when a verb in the text implied the inference; and 2) within the left superior temporal gyrus at the coherence break, or when participants need to generate an inference to understand the story. To find the most compelling evidence of neural activity during inference generation, we examined fMRI signal at these two critical points separately for people with high working memory capacity (i.e., those individuals who are most likely to draw inferences during text comprehension). Interestingly, high working memory individuals showed greater fMRI signal for implied than explicit events in the left inferior frontal gyrus at the coherence break compared to low working memory individuals. This present study provides evidence that areas within the superior temporal gyrus and inferior frontal gyrus are heavily recruited when individuals generate inferences, even during ongoing comprehension that demands many cognitive processes. In addition, the data suggest that the right hemisphere superior temporal gyrus is particularly involved during early inferential processing, whereas the left hemisphere superior temporal gyrus is particularly involved during later inferential processing in story comprehension.
EVALUATION OF CLASSIFICATION ALGORITHMS FOR DISEASE DIAGNOSIS

Data classification is the categorization of data for its most effective and efficient use. Data can be classified according to any criteria, not only relative importance or frequency of use. Classification plays a major role in disease diagnosis. The paper contains brief discussion of various classification methods that includes Case Based Reasoning, decision trees, K-nearest neighbour classifier, naive bayes classifier and neural network. The paper also discusses some applications of classification model. The performance of the classification methods are observed where the CBR classification model results in 90.7% of specificity, 92.3% of sensitivity and 95.5% of prediction accuracy
Improvement on rules matching algorithm of snort based on dynamic adjustment

With the increasing network security accidents, intrusion detection systems (IDS) have been an indispensable part of information system. As a popular light network intrusion detection system, Snort has been a focus in research field. In this paper, dynamic adjustment algorithm is applied to the improvement of rule matching based on the analysis of original mechanism of Snort. Additionally, further optimization is discussed against the problem of simple dynamic adjustment, and improved two step dynamic rule adjustment algorithm is provided. Experiment results show that this method increases the speed of rules matching and improve the detection efficiency of Snort.
Improving the Delivery of Radionuclides for Imaging and Therapy of Cancer Using Pretargeting Methods

The article reviews the background and current status of pretargeting for cancer imaging and therapy with radionuclides. Pretargeting procedures were introduced ∼20 years ago as an alternative to directly radiolabeled antibodies. Because they were multistep processes, they were met with resistance but have since progressed to simple and improved procedures that could become the next generation of imaging and therapy with radionuclides. The separation of the radiolabeled compound from the antibody-targeting agent affords pretargeting procedures considerable flexibility in the radiolabeling process, providing opportunities for molecular imaging using γ- or positron-emitting radionuclides and a variety of β- and α-emitting radionuclides of therapeutic applications. Pretargeting methods improve tumor/nontumor ratios, exceeding that achieved with directly radiolabeled Fab′ fragments, particularly within just a few hours of the radionuclide injection. In addition, tumor uptake exceeds that of a Fab′ fragment by as much as 10-fold, giving pretargeting a greatly enhanced sensitivity for imaging. Advances in molecular biology have led to the development of novel binding proteins that have further improved radionuclide delivery in these systems. Studies in a variety of hematologic and solid tumor models have shown advantages of pretargeting compared with directly radiolabeled IgG for therapy, and there are several clinical studies under way that are also showing promising results. Thus, the next generation of targeting agents will likely employ pretargeting approaches to optimize radionuclide delivery for a wide range of applications.
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation

Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other fields of application. These models however lack transparency due to their complex nonlinear structure and to the complex data distributions to which they typically apply. As a result, it is difficult to fully characterize what makes these models reach a particular decision for a given input. This lack of transparency can be a drawback, especially in the context of sensitive applications such as medical analysis or security. In this short paper, we summarize a recent technique introduced by Bach et al. [1] that explains predictions by decomposing the classification decision of DNN models in terms of input variables.
The Neglected Tropical Diseases of India and South Asia: Review of Their Prevalence, Distribution, and Control or Elimination

The neglected tropical diseases (NTDs) are the most common infections of the world's poorest people living in Africa, Asia, and the Americas [1]. Occurring predominantly among people who live on less than US$2 per day or below the World Bank poverty figure of US$1.25 per day, the NTDs represent a group of chronic parasitic and related bacterial and viral infections that actually promote poverty because of their impact on child development, pregnancy outcome, and worker productivity [2]. The NTDs differ significantly in their prevalence and disease burden according to their geographic and regional presence. Such features for the NTDs in sub-Saharan Africa [3], China and East Asia [4], and the Americas [5]–[7], respectively, were reviewed previously. Here, we summarize current knowledge on the prevalence, distribution, and disease burden of the NTDs in India and South Asia, focusing on aspects particular to the region. The review of the literature was conducted using the online database PubMed from 2003 to 2010 with the Medical Subject Headings, the specific diseases listed in the World Health Organization's (WHO) first report on NTDs [8], and the geographic regions and countries of South Asia. Reference lists of identified articles and reviews were also hand searched as were WHO databases (http://www.who.int/), including the WHO's Weekly Epidemiological Record. Recently, a comprehensive review on the continuing challenge of infectious diseases in India was published [9]. However, this review focuses exclusively on NTDs, many of which, especially the helminthiases, were not emphasized previously [9].
Oral Ingestion of Transgenic RIDL Ae. aegypti Larvae Has No Negative Effect on Two Predator Toxorhynchites Species

Dengue is the most important mosquito-borne viral disease. No specific treatment or vaccine is currently available; traditional vector control methods can rarely achieve adequate control. Recently, the RIDL (Release of Insect carrying Dominant Lethality) approach has been developed, based on the sterile insect technique, in which genetically engineered ‘sterile’ homozygous RIDL male insects are released to mate wild females; the offspring inherit a copy of the RIDL construct and die. A RIDL strain of the dengue mosquito, Aedes aegypti, OX513A, expresses a fluorescent marker gene for identification (DsRed2) and a protein (tTAV) that causes the offspring to die. We examined whether these proteins could adversely affect predators that may feed on the insect. Aedes aegypti is a peri-domestic mosquito that typically breeds in small, rain-water-filled containers and has no specific predators. Toxorhynchites larvae feed on small aquatic organisms and are easily reared in the laboratory where they can be fed exclusively on mosquito larvae. To evaluate the effect of a predator feeding on a diet of RIDL insects, OX513A Ae. aegypti larvae were fed to two different species of Toxorhynchites (Tx. splendens and Tx. amboinensis) and effects on life table parameters of all life stages were compared to being fed on wild type larvae. No significant negative effect was observed on any life table parameter studied; this outcome and the benign nature of the expressed proteins (tTAV and DsRed2) indicate that Ae. aegypti OX513A RIDL strain is unlikely to have any adverse effects on predators in the environment.
Experiments in Telugu NER: A Conditional Random Field Approach

Named Entity Recognition(NER) is the task of identifying and classifying tokens in a text document into predefined set of classes. In this paper we show our experiments with various feature combinations for Telugu NER. We also observed that the prefix and suffix information helps a lot in finding the class of the token. We also show the effect of the training data on the performance of the system. The best performing model gave an Fb=1 measure of 44.91. The language independent features gave an Fb=1 measure of 44.89 which is close to Fb=1 measure obtained even by including the language dependent features.
The interplay of microRNA and neuronal activity in health and disease

MicroRNAs (miRNAs) are small 19–23 nucleotide regulatory RNAs that function by modulating mRNA translation and/or turnover in a sequence-specific fashion. In the nervous system, miRNAs regulate the production of numerous proteins involved in synaptic transmission. In turn, neuronal activity can regulate the production and turnover of miRNA through a variety of mechanisms. In this way, miRNAs and neuronal activity are in a reciprocal homeostatic relationship that balances neuronal function. The miRNA function is critical in pathological states related to overexcitation such as epilepsy and stroke, suggesting miRNA’s potential as a therapeutic target. We review the current literature relating the interplay of miRNA and neuronal activity and provide future directions for defining miRNA’s role in disease.
A probabilistic method for species sensitivity distributions taking into account the inherent uncertainty and variability of effects to estimate environmental risk

This article presents a method of probabilistically computing species sensitivity distributions (SSD) that is well‐suited to cope with distinct data scarcity and variability. First, a probability distribution that reflects the uncertainty and variability of sensitivity is modeled for each species considered. These single species sensitivity distributions are then combined to create an SSD for a particular ecosystem. A probabilistic estimation of the risk is carried out by combining the probability of critical environmental concentrations with the probability of organisms being impacted negatively by these concentrations. To evaluate the performance of the method, we developed SSD and risk calculations for the aquatic environment exposed to triclosan. The case studies showed that the probabilistic results reflect the empirical information well, and the method provides a valuable alternative or supplement to more traditional methods for calculating SSDs based on averaging raw data and/or on using theoretical distributional forms. A comparison and evaluation with single SSD values (5th‐percentile [HC5]) revealed the robustness of the proposed method. Integr Environ Assess Manag 2013; 9: 79–86. © 2012 SETAC
Optimal link categorization for minimal retrieval effort

Various studies have shown that categorizing search results can help users to retrieve their target pages faster. The categorizations save the users the time needed to consider links from irrelevant categories. However, what is often missed out is that the category selections also introduce extra effort for users who are looking for one of the highest ranked results. In general, the expected gain of categorization depends on the relative probability that the user is looking for each of the search results. In this work we present a method to balance the costs of presenting categories against the expected savings of the categorization. In an experiment we demonstrate that this method can reduce retrieval time substantially compared to flat result lists and hierarchies created through traditional hierarchical clustering.