Miracle-dz/UniFAR-FTUs huggingface dataset:
Mycophenolate mofetil inhibits T‐cell proliferation in kidney transplant recipients without lowering intracellular dGTP and GTP

To study if mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil (MMF), indeed inhibits T‐cell proliferation in kidney transplant recipients by lowering intracellular deoxyguanosine triphosphate (dGTP) and guanosine triphosphate (GTP) levels. Blood was drawn from 11 kidney transplant recipients. Ex vivo T‐cell proliferation was measured by stimulation with phytohemagglutin (PHA) and anti‐CD3 monoclonal antibody (mAb). Plasma MPA levels and intracellular dGTP and GTP in peripheral blood mononuclear cells were measured. MMF induces a significant decrease in T‐lymphocyte proliferation at all time points (i.e. 24 h, 10 days and 8 weeks) after stimulation with both PHA (P = 0.001, 0.002 and 0.013 respectively) and anti‐CD3 mAb (P = 0.004, 0.004 and 0.005 respectively). There was no significant change in intracellular dGTP (P = 0.31, 0.16 and 0.35) or GTP levels (P = 0.99, 0.32 and 0.49) between baseline and day 1, day 10 or week 8. All MPA levels were above the minimal required concentration for the inhibition of lymphocyte proliferation. MMF inhibits T‐lymphocyte proliferation in kidney transplant recipients without lowering intracellular dGTP or GTP levels. This suggests another mechanism underlying its immunosuppressive capacity.
Data-Driven Approximations to NP-Hard Problems

There exist a number of problem classes for which obtaining the exact solution becomes exponentially expensive with increasing problem size. The quadratic assignment problem (QAP) or the travelling salesman problem (TSP) are just two examples of such NP-hard problems. In practice, approximate algorithms are employed to obtain a suboptimal solution, where one must face a trade-off between computational complexity and solution quality. In this paper, we propose to learn to solve these problem from approximate examples, using recurrent neural networks (RNNs). Surprisingly, such architectures are capable of producing highly accurate solutions at minimal computational cost. Moreover, we introduce a simple, yet effective technique for improving the initial (weak) training set by incorporating the objective cost into the training procedure. We demonstrate the functionality of our approach on three exemplar applications: marginal distributions of a joint matching space, feature point matching and the travelling salesman problem. We show encouraging results on synthetic and real data in all three cases.
The Metacognitive Awareness Listening Questionnaire: Development and Validation

This article describes the development and validation of a listening questionnaire designed to assess second language (L2) listeners' metacognitive awareness and perceived use of strategies while listening to oral texts. The process of instrument development and validation is described, along with a review of the relevant literature related to metacognition and learners' regulation of listening comprehension strategies. An exploratory factor analysis of the responses of a large sample (N= 966) of language learners and a subsequent confirmatory factor analysis with another large but different sample (N= 512) resulted in a 21-item instrument with robust psychometric properties. Five distinct factors emerged: problem-solving, planning and evaluation, mental translation, person knowledge, and directed attention. The reliability and factorial validity of the instrument are presented along with evidence for a statistically significant relationship between student response on the instrument and L2 listening comprehension success. The article concludes with a discussion of the potential uses of the Metacognitive Awareness Listening Questionnaire (MALQ).
The online dating romance scam: The psychological impact on victims – both financial and non-financial

This article examines the psychological impact of the online dating romance scam. Unlike other mass-marketing fraud victims, these victims experienced a ‘double hit’ of the scam: a financial loss and the loss of a relationship. For most, the loss of the relationship was more upsetting than their financial losses (many described the loss of the relationship as a ‘death’). Some described their experience as traumatic and all were affected negatively by the crime. Most victims had not found ways to cope given the lack of understanding from family and friends. Denial (e.g. not accepting the scam was real or not being able to separate the fake identity with the criminal) was identified as an ineffective means of coping, leaving the victim vulnerable to a second wave of the scam. Suggestions are made as to how to change policy with regards to law enforcement to deal with this crime.
Predicting Mathematics Achievement by Motivation and Self-Efficacy Across Gender and Achievement Levels

This study investigated the extent to which self-efficacy and motivation served as a predictor for mathematics achievement of fifth grade students in United Arab Emirates (UAE) across gender and achievement levels. Self-efficacy was measured by two scales, which differed in levels of specificity—Category Specific and Task Specific. Motivation was measured through four sub-constructs of motivation— Amotivation, External Regulation, Introjected Regulation, and Intrinsic Motivation. A total of 287 fifth grade students with an average age of 10.3 years were randomly selected to participate in this study. The multiple regression model showed that the six predictors were able to explain together high percentage (32%) of the variance of mathematics achievement. Also the results indicated that the best three predictors were Task Specific, External Regulation, and Intrinsic Regulation. When conducting the regression model across gender, the results showed that 30% of the variance in mathematics achievement was explained by the six predictors for the male group while only 21% of the variance was explained for the female group. The regression model was not invariant across achievement levels. While the model predicted approximately 20% of the variance of mathematics achievement for each of the low and high achieving students, the model was not statistically appropriate for the medium achievement students as it predicted only 5% of the variance of mathematics achievement. Additionally, the performance of the six predictors varied according to the achievement level.
Accreditation outcomes, quality of and access to university education in Nigeria

Purpose – In response to the challenges of enhancing quality, the agency of government which is responsible for coordinating university education in Nigeria, the National Universities Commission, evolved a system of academic programme accreditation in 1991 to ensure conformity with minimum standards and to promote quality. The purpose of this paper is to examine the outcomes of some of those accreditation exercises and how they have influenced the quality of and access to university education.Design/methodology/approach – The paper analyses secondary source data from accreditation reports of 1999, mop up exercises and accreditation re‐visitation of 2000‐2005 and that of November 2005.Findings – The mean percentage of programmes with full accreditation status was found to have increased from 12.6 in 1999 to 48.5 in 2005, that of programmes which got interim accreditation decreased from 72.66 in 1999 to 48.30 in 2005, while the one for programmes which were denied accreditation decreased from 17.9 in 1999 t.
An Adaptive Intrusion Detection Model based on Machine Learning Techniques

Intrusion detection continues to be an active research field. Even after 20 years of research, the intrusion detection community still faces several difficult problems. Detecting unknown patterns of attack without generating too many false alerts remains an unresolved problem. Although recently, several results have shown that there is a potential resolution to this problem. Anomaly detection is a key element of intrusion detection in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, and defects. This paper proposes a hybrid machine learning model based on combining the unsupervised and supervised classification techniques. Clustering approach based on combining the K-means , fuzzy C-means and GSA algorithms to obtain the normal patterns of a user’s activity, the technique is used as the first component for pre-classification to improve attack detection. Then, a hybrid classification approach of Support Vector Machine (SVM) and Gravitational Search Algorithm (GSA) algorithm will be used to enhance the detection accuracy.this research used the KDD CUP 1999 to get initial results, which were encouraging.
Physical and mental health status of American grandparents providing extensive child care to their grandchildren.

OBJECTIVE to compare the physical, mental, and functional health status of grandparents providing extensive care to grandchildren (30+ hours per week or 90+ nights per year) with that of custodial grandparents, noncaregivers, and two categories of less intensive care providers. METHODS Data on a subsample of 3260 respondents to the National Survey of Families and Households who reported being grandparents during the 1992 to 1994 interviews were analyzed. Chi-square tests, 1-way ANOVAs, and multiple regression analyses compared self-reported functional health limitations, depressive symptoms, and change in self-reported health status and depression for extensive caregivers (223), custodial grandparents (173), and 3 other types of grandparents providing less or no child care. RESULTS Extensive caregivers had levels of depressive symptoms comparable to those of custodial caregivers and significantly higher than those of noncaregivers and less intense care providers. One in 5 extensive caregivers had clinically relevant levels of depressive symptoms. Two out of every 5 extensive caregivers had at least 1 limitation in activities of daily living. CONCLUSIONS Providing extensive care for a grandchild was associated with elevated levels of depression. Physicians should be alert to family role changes and symptoms of depression in their older patients.
Teaching and student outcomes : a study on teachers' thoughts and actions from an interpersonal and a learning activities perspective

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Effectiveness of the KLD Social Ratings as a Measure of Workforce Diversity and Corporate Governance

This article examines how well the Kinder, Lydenberg, Domini Research & Analytics (KLD) ratings measure past corporate social performance and predict future corporate social performance in Diversity and Governance categories. The results show that the KLD ratings effectively measure (past) and predict (future) social performance in both categories. The results also suggest that the KLD ratings may identify differences in the quality of management and firm which can affect future social performance and is not entirely explained by past social performance. The findings of this study lend some support for empirical studies relying on the KLD ratings to operationalize corporate social performance. The findings suggest that users of the ratings need to have a clear understanding of what information they are seeking from the KLD ratings in order to maximize the utility of these ratings.
The Role of Affect in Agenda Building for Public Relations

Grounded in an agenda-building framework, this experiment manipulated two dimensions of affective attributes—valence and arousal—from a corporate blog message to examine their influence on corporate reputation and stakeholder engagement. Overall, the findings showed significant valence effects on reputation and engagement, while arousal effects were only present on corporate reputation. This study adds empirical evidence to the explication of agenda building by focusing on affective attributes and by testing the compelling-arguments hypothesis concerning the role of affective attributes’ (second level) impact on object salience (first level).
Towards Effective Bug Triage with Software Data Reduction Techniques

Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Ourwork provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance.
Dimensions and Predictors of Literary Response

To test the validity of the Literary Response Questionnaire (lrq, Miall & Kuiken, 1995b) and to assess its relationships with student, home-background, and school-related variables, the lrq was administered to Dutch students in grades 7, 8, and 9. The seven first-order factors of the lrq, as identified by Miall and Kuiken {1995b), were confirmed. One of the two second-order factors was not identified. However, a model with two different second-order factors fitted the data. Trance, the first factor, represents the degree to which a reader is absorbed in a story. Literary Interpretation, the second factor, represents the degree to which a reader likes or dislikes literary criticism and reflection on the meaning of what is read. Grade, gender, type of education, vocabulary size, reading behavior, and cultural level of the home environment were the best predictors of the literary response scores. Results are discussed in relation to response theories and research and to literary education.
Dendritic spikes induce single-burst long-term potentiation

The hippocampus is essential for episodic memory, which requires single-trial learning. Although long-term potentiation (LTP) of synaptic strength is a candidate mechanism for learning, it is typically induced by using repeated synaptic activation to produce precisely timed, high-frequency, or rhythmic firing. Here we show that hippocampal synapses potentiate robustly in response to strong activation by a single burst. The induction mechanism of this single-burst LTP requires activation of NMDA receptors, L-type voltage-gated calcium channels, and dendritic spikes. Thus, dendritic spikes are a critical trigger for a form of LTP that is consistent with the function of the hippocampus in episodic memory.
A Generic Approach for Systematic Analysis of Sports Videos

Various innovative and original works have been applied and proposed in the field of sports video analysis. However, individual works have focused on sophisticated methodologies with particular sport types and there has been a lack of scalable and holistic frameworks in this field. This article proposes a solution and presents a systematic and generic approach which is experimented on a relatively large-scale sports consortia. The system aims at the event detection scenario of an input video with an orderly sequential process. Initially, domain knowledge-independent local descriptors are extracted homogeneously from the input video sequence. Then the video representation is created by adopting a bag-of-visual-words (BoW) model. The video’s genre is first identified by applying the k-nearest neighbor (k-NN) classifiers on the initially obtained video representation, and various dissimilarity measures are assessed and evaluated analytically. Subsequently, an unsupervised probabilistic latent semantic analysis (PLSA)-based approach is employed at the same histogram-based video representation, characterizing each frame of video sequence into one of four view groups, namely closed-up-view, mid-view, long-view, and outer-field-view. Finally, a hidden conditional random field (HCRF) structured prediction model is utilized for interesting event detection. From experimental results, k-NN classifier using KL-divergence measurement demonstrates the best accuracy at 82.16% for genre categorization. Supervised SVM and unsupervised PLSA have average classification accuracies at 82.86% and 68.13%, respectively. The HCRF model achieves 92.31% accuracy using the unsupervised PLSA based label input, which is comparable with the supervised SVM based input at an accuracy of 93.08%. In general, such a systematic approach can be widely applied in processing massive videos generically.
Experience-based and situational interview questions: Studies of validity.

This research compared the validity of two different types of structured interview questions (i.e., experience-based and situational) under tightly controlled conditions. The experience-based interview questions required that 108 study participants relate how they had handled situations in the past requiring skills and abilities necessary for effective performance on the job. Situational questions, administered to another group of 108 study participants, provided interviewees with hypothetical job-relevant situations and asked them how they would respond if they were confronted with these problems. The experience-based interview questions yielded higher levels of validity than the situational questions. Additional analyses showed that the interview added incrementally to the prediction of performance beyond the variance accounted for by a cognitive ability test. There were small differences in subgroup performance (White, Black, Hispanic, male, and female) on the experience-based interview, though it was equally valid for all subgroups.
Blind deconvolution of 3D data in wide field fluorescence microscopy

In this paper we propose a blind deconvolution algorithm for wide field fluorescence microscopy. The 3D PSF is modeled after a parametrized pupil function. The PSF parameters are estimated jointly with the object in a maximum a posteriori framework. We illustrate the performances of our algorithm on experimental data and show significant resolution improvement notably along the depth. Quantitative measurements on images of calibration beads demonstrate the benefits of blind deconvolution both in terms of contrast and resolution compared to non-blind deconvolution using a theoretical PSF.
Analysis of morbidity and mortality rates in bovine in Himachal Pradesh

Aim: The aim of the present study was to determine the morbidity and mortality rates of Bovine population present in Himachal Pradesh, India. In addition, the effect of age, sex, environmental conditions, management practices and other factors on the morbidity and mortality rate was also analyzed using suitable statistical techniques. Materials and Methods: Himachal Pradesh has been divided into four agro-climatic zones. Taking each agroclimatic zone as stratum, five veterinary hospitals from zone 1, two veterinary hospitals each from zone 2 and zone 3 and three veterinary hospitals from zone 4, were selected randomly. Three villages were selected from the villages covered by each veterinary hospital by simple random sampling without replacement scheme. A total of 15 livestock owners were selected from each selected village and thus a total 540 livestock owners constitute the ultimate sample from thirty six villages and twelve veterinary hospitals for the study. The sampling scheme followed in the present study is Stratified three-stage random sampling. Age, sex, season and cause wise morbidity and mortality data were collected from all selected bovine owners in the state for period of one year (January 2011 to December 2011). Results: The Overall analysis of morbidity rate in bovine was 31.22%. Among the three age groups, adults' bovine showed highest morbidity rate 35.73%, followed by calves 26.98% and young stocks 23.17%. Among the three age groups, calves had highest morbidity rate 16.09% due to poor management problems followed by adults 12.15% cases of reproductive problems. The overall mortality rate in bovine was found 9.14%. The overall analysis of mortality in bovine with respect to age revealed significance (p<0.01) difference between age and sex. Amongst the three age groups, calves showed highest mortality 21.53% followed by young stocks 9.35% and adults 4.73%. Among the three age groups, calves had highest mortality (16.09%) due to poor management problems followed by 2.48% cases of digestive diseases. Conclusion: Overall bovine morbidity and mortality rate were 31.22% and 9.14%. Major causes of morbidity were reproductive diseases followed by specific diseases and mortality were poor management problems followed by digestive diseases and specific diseases.
Invisible displacement understanding in primates on a new task

Great apes but not monkeys solve the invisible displacement task under conditions controlling for associative strategies. However, even chimpanzees and young children have difficulty with double invisible displacements in which an object is hidden at two non-adjacent boxes in a linear array. We tested chimpanzees and 24-month-old children on a new adaptation of the task involving four hiding boxes presented in a diamond shaped array on a vertical plane. Both species performed above chance on double invisible displacements using this format, suggesting that previous poor performance was due to inhibition problems rather than fundamental limitations in representational capacity. In a pilot study, four siamangs and a spider monkey were tested on single and double displacement tasks. Performance was mixed but there was some evidence that invisible displacements might be within the capacity of siamangs, with one subject passing double invisible displacements in the vertical format. The spider monkey failed double displacements and showed a significant tendency to search at a box adjacent to the displacement device on the standard single invisible displacement task. However, when this task was administered in the vertical format the spider monkey performed above chance.