Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetes in Urban African-Americans: II. High prevalence of microalbuminuria and nephropathy in African-Americans with diabetes

OBJECTIVE African-Americans with diabetes have an increased risk of endstage renal disease, but underlying mechanisms are poorly understood. We undertook this study to evaluate prevalence and risk factors for renal disease in an African-American population with diabetes. RESEARCH DESIGN AND METHODS We measured urine albumin excretion in 578 consecutive patients presenting for the first time to the Grady Memorial Hospital Diabetes Unit in Atlanta, GA. The unit serves an urban population that is predominantly African-American; 85% of patients have non-insulin-dependent diabetes mellitus (NIDDM). Subjects provided 24-h and/or ∼ 3-h urine collections for measurement of albumin and creatinine. RESULTS Correlation of the albumin/creatinine ratio (μg/mg) with the 24-h albumin excretion rate was 0.89 (P < 0.001, n = 123). Although the median duration of diabetes was only 1 year, among all subjects, the estimated prevalence of microalbuminuria (30–300 mg albumin/24 h) was 25% and that of nephropathy (> 300 mg albumin/24 h) was 11%. Among African-Americans with NIDDM (n = 466), the estimated prevalence of microalbuminuria was 24% and that of nephropathy was 12% prevalence remained high (25 and 5%, respectively) among 219 patients with < 1 year known duration of diabetes. Metabolic control was not associated with disease. However, among all subjects with NIDDM, the odds ratio for nephropathy among subjects with disease duration > 5 years compared with those with disease duration < 1 year was 4.65 (95% confidence interval [CI] 2.24–9.79), and the odds ratio for nephropathy among subjects with hypertension compared with those without hypertension was 2.64 (CI 1.42–4.93). Odds ratios were comparable among African-Americans with NIDDM. Trends were similar but less significant for subjects with microalbuminuria. CONCLUSIONS Albuminuria can be identified reliably and conveniently by the albumin/creatinine ratio in brief urine collections. In our patients, clinically significant albuminuria occurred in 36% of persons at first presentation. Since increased risk was associated with hypertension and control of hypertension can slow progression of renal disease, screening for albuminuria and treatment of hypertension should be aggressive in urban populations of African-Americans with diabetes.
Asymptotic Biometric Analysis for Large Gallery Sizes

Motivated by the need to predict the future biometric performance of the U.S. Visitor and Immigrant Status Indicator Technology program as it increases the size of its watchlist database, we use extreme-value theory (which is an asymptotic theory for the maximum of a large number of independent and identically distributed random variables) to analyze biometric performance as the gallery size gets very large. Due to the lack of published data for open-set fingerprint systems (where some users of the system are not on the watchlist), we assess the accuracy of our approach using the rank-one identification probability for closed-set fingerprint systems (where all users are on the watchlist). Consistent with earlier empirical observations, we find that the relationship between the rank-one identification probability and gallery size is log-linear to first-order and has a quadratic correction term, at least under our specific distributional assumptions. We also find that the probabilistic biometric model provides a good fit to empirical fingerprint data only when the genuine and impostor similarity scores are allowed to depend on the quality of the fingerprint images, which leads to genuine and impostor scores that are mixtures of distributions. Finally, we use the extreme-value approach to derive the receiver operating characteristic curve for open-set systems.
Causes and Impact of Stress on Teachers’ Productivity as Expressed by Primary School Teachers in Nigeria

The study investigated the causes and impact of stress on Teachers’ productivity as expressed by primary school teachers in Nigeria. A total 300 primary school teachers were randomly selected to form participants for the study. Four research questions formulated were directed towards issues investigated. A validated Instrument titled “Causes and Impact of Stress on Teachers Productivity Questionnaire” was administered to the participants. The survey research type was employed. Data collected were subjected to percentage analysis of variance and t-test analysis. The result revealed that majority of primary school teachers were stressed on the job and this had negative impacts on their productivity. The study revealed that lack of job satisfaction, inadequate school facilities, were major causes of stress among primary school teachers. The study also revealed that stress had negative impacts on teachers’ productivity. The study recommended the need for government to improve teachers’ conditions of service; teachers special salary scale to be implemented by government; adequate facilities should be provided in schools; establishment of counselling unit in schools to cater for both teachers and students. If the predisposing factors for stress are left unattended to, resultant consequences can have substantial effects on teachers’ productivity.
A Decision Tree Algorithm based on Rough Set Theory after Dimensionality Reduction

Decision tree technology has proven to be a valuable way of capturing human decision making within a computer. As ID3 select those attribute as splitting attributes which have different values whether it classify dataset properly or not. There is another drawback of ID3 which repeat sub tree many times and select same attribute many times. These drawbacks can be removed by the proposed algorithm. By the proposed algorithm firstly large volume of dataset which contain redundant instance are reduced. These redundant instance doesn’t make any contribution to take decision hence can be deleted from the dataset. After reducing the volume of the dataset decision tree is constructed through rough set. The main concept of rough set theory is degree of dependency which is used in the proposed algorithm to select splitting attribute on the compressed data. Thus the proposed algorithm reduces the complexity of tree and in addition increases the accuracy. We have used some UCI machine learning repository. By the experimental result it is shown that proposed algorithm gives better accuracy and diminishes the complexity of tree. General Terms Data mining, Classification, Machine Learning
Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.
Functional disability before myocardial infarction in the elderly as a determinant of infarction severity and postinfarction mortality.

BACKGROUND Functional disability is a common condition among elderly patients. However, to our knowledge, its effect on outcome of myocardial infarction (MI) has not been assessed. Our objectives were to determine whether disability in the activities of daily living measured before MI is a predictor of MI severity and mortality. METHODS Disability in activities of daily living was measured prospectively in a cohort of 222 patients who were hospitalized with acute MI. Outcome measures were severity characteristics on admission to the hospital (higher Killip class, presence of new Q waves in the first electrocardiogram, and lower systolic blood pressure), and 6-month mortality. RESULTS Patients with disability before hospitalization were older and had more comorbidity. After adjusting for these factors and for delay in hospital arrival, disability was still significantly associated with clinical severity on admission to the hospital and with mortality (adjusted relative risk of death for patients with disability vs patients without disability, 2.01; 95% confidence interval, 1.23-3.28). Clinical severity and hospital treatment explained the higher mortality of patients with disability. When these factors were added to the previous model, the relative risk of mortality for patients with disability vs patients without disability was 1.24, and the 95% confidence interval was 0.73 to 2.12. CONCLUSIONS Functional disability in activities of daily living before MI is an important predictor of clinical severity and mortality in elderly patients with MI. This effect is only minimally explained by the older age and higher comorbidity of patients with disability. However, higher clinical severity and lower use of treatment interventions are major determinants of their higher mortality compared with patients without disability.
Death Does Matter—Cancer Risk in Patients With End-Stage Renal Disease

Abstract Patients with end-stage renal disease (ESRD) have a high mortality rate. We hypothesized that not accounting for death as a competing risk overestimates the event rate caused by ESRD. Thus, we examined the cancer risk for patients with ESRD (ESRDPos) after death as a competing risk event had been adjusted for. Patients with newly diagnosed ESRD (n = 64,299) between 1999 and 2007, together with age- and sex-matched controls without ESRD (ESRDNeg) (n = 128,592) were enrolled (1:2). In a Cox proportional hazards model that included death as a competing risk, ESRDPos patients in Taiwan had a lower overall incidence (subdistribution hazard ratio [sdHR] = 1.29) of cancer than did ESRDNeg patients in a Cox model that did not include death as a competing risk (HR = 1.70). After competing mortality had been adjusted for, ESRDPos patients ≥70 (sdHR = 0.82) and ESRDPos patients on long-term dialysis (> 5 follow-up years, sdHR = 0.62), had a lower risk for developing cancer than did ESRDNeg patients. This finding supported our hypothesis that standard survival analyses overestimate the event rate, especially when the mortality rate is high. It also showed that ESRDPos patients, when they grow older, were far less likely to develop cancer and far more likely to die because of underlying illnesses that might also affect the risk of death because of ESRD.
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.
Folate bioavailability: UK Food Standards Agency workshop report

The UK Food Standards Agency convened a group of expert scientists to review current research investigating folate bioavailability. The workshop aimed to overview current research and establish priorities for future research. Discrepancies were observed in the evidence base for folate bioavailability, especially with regard to the relative bioavailability of natural folates compared with folic acid. A substantial body of evidence shows folic acid to have superior bioavailability relative to food folates; however, the exact relative bioavailability still needs to be determined, and in particular with regard to mixed diets. The bioavailability of folate in a mixed diet is probably not a weighted average of that in the various foods consumed; thus the workshop considered that assessment of folate bioavailability of whole diets should be a high priority for future research.
Flexible Bayesian additive joint models with an application to type 1 diabetes research

The joint modeling of longitudinal and time‐to‐event data is an important tool of growing popularity to gain insights into the association between a biomarker and an event process. We develop a general framework of flexible additive joint models that allows the specification of a variety of effects, such as smooth nonlinear, time‐varying and random effects, in the longitudinal and survival parts of the models. Our extensions are motivated by the investigation of the relationship between fluctuating disease‐specific markers, in this case autoantibodies, and the progression to the autoimmune disease type 1 diabetes. Using Bayesian P‐splines, we are in particular able to capture highly nonlinear subject‐specific marker trajectories as well as a time‐varying association between the marker and event process allowing new insights into disease progression. The model is estimated within a Bayesian framework and implemented in the R‐package bamlss.
Stochastic analysis of moisture plume dynamics of a field injection experiment

A vadose zone field injection experiment was conducted in the summer of 2000 at the Hanford Site, Washington. The unique moisture content (θ) database is used to identify the lithology at the field site and to interpret, visualize, and quantify the spatiotemporal evolution of the three‐dimensional (3‐D) moisture plume created by the injection experiment. We conducted a hierarchical geostatistical analysis to examine the large‐scale geologic structure for the entire field site and then investigated small‐scale features within different layers. Afterward, variogram analysis was applied to the θ field measured for seven different days during the injection experiment. Temporal variations of sills and ranges are related to the observed moisture plume dynamics. A visualization of the 3‐D moisture plume evolution illustrates effects of media heterogeneity. Statistics of changes in moisture content as a function of distance reveal large variance near the wetting front; the coefficient of variation increases with decreasing mean. These findings support the gradient‐ and mean‐dependent variability in the moisture content distribution as reported by existing stochastic theories. Spatial moment analysis is also conducted to quantify the rate and direction of movement of the plume mass center and its spatial spreading. The ratio of horizontal to vertical spreading at varying moisture contents suggests moisture‐dependent anisotropy in effective unsaturated hydraulic conductivity, confirming existing stochastic theories. However, the principal directions of the spatial moments are found to vary as the moisture plume evolves through local heterogeneity, a feature that has not been recognized in the theories.
Patterns of Alcohol Consumption among Male Adults at a Slum in Kolkata, India

Globally, alcohol-abuse is a major cause of mortality and morbidity. Consumption of alcohol has increased in India in the recent decades. It is imperative to know the patterns of alcohol consumption among different types of consumers to launch a well-planned nationwide programme for the prevention and control of this devastating social pathology. This community-based, cross-sectional study was undertaken to identify the patterns of alcohol intake among different types of alcohol consumers and to assess the clinical signs of chronic harmful alcohol-use. A predesigned, pretested, semi-structured alcohol-use disorders identification test (AUDIT) questionnaire was used for interviewing males, aged >18 years, selected by random sampling from an updated household list of a randomly-selected sector of the service area of the Urban Health Centre in Chetla, Kolkata, West Bengal, India. Written informed consents were obtained from all the respondents. Relevant clinical examination for chronic harmful alcohol-use was done according to the AUDIT clinical screening procedures. The results revealed that 65.8% (150/228) were current consumers of alcohol; 14% were alcohol-dependents; 8% were hazardous or harmful consumers, and 78% were non-hazardous non-harmful consumers. The mean age of the respondents at the initiation of drinking alcohol was 20.8+5.9 years. Eighty-six percent of dependents (n=21) took both Indian-made foreign liquor and locally-made alcoholic beverages. The proportions of alcohol consumers who drank alone among alcohol-dependents, hazardous or harmful consumers, and non-hazardous non-harmful consumers were 71.4%, 50%, and 7.7% respectively, and the difference was significant (p<0.01). Forty-one percent of the consumers drank at public places and workplaces, which may be socially harmful. About 38% of the dependents purchased alcohol from unlicensed liquor shops. Only 16% expressed concerns for their drinking habit mainly to the past illness. The proportion of the concerned respondents was higher in the hazardous and harmful drinking patterns than in the non-hazardous non-harmful drinking pattern, and the difference was significant (p<0.05). About 62% of the dependents had clinical signs of chronic alcohol consumption. The presence of a considerable proportion of alcohol-dependents, the low mean age at initiation of drinking alcohol, and the habit of drinking in public places and workplaces are the main areas that need special emphasis by intervention programmes.
The Timing of Births: A Marriage Market Analysis

We argue that one of the key channels linking the labor and marriage markets is the decision of when to become a parent. We develop art equilibrium model of marriage, divorce, and human capital accumulation that allows for differential timing of fertility. We calibrate the model to US panel data and analyze the effects of raising women's wages relative to men's, and increasing the rate of return to experience for women. We find that an increase in the returns to experience for women is causes an increase in fraction of children born to women over age 30, and that raising women wages reduces marriage rates.
Modulating the neural bases of persuasion: why/how, gain/loss, and users/non-users

Abstract Designing persuasive content is challenging, in part because people can be poor predictors of their actions. Medial prefrontal cortex (MPFC) activation during message exposure reliably predicts downstream behavior, but past work has been largely atheoretical. We replicated past results on this relationship and tested two additional framing effects known to alter message receptivity. First, we examined gain- vs. loss-framed reasons for a health behavior (sunscreen use). Consistent with predictions from prospect theory, we observed greater MPFC activity to gain- vs. loss-framed messages, and this activity was associated with behavior. This relationship was stronger for those who were not previously sunscreen users. Second, building on theories of action planning, we compared neural activity during messages regarding how vs. why to enact the behavior. We observed rostral inferior parietal lobule and posterior inferior frontal gyrus activity during action planning (“how” messages), and this activity was associated with behavior; this is in contrast to the relationship between MPFC activity during the “why” (i.e., gain and loss) messages and behavior. These results reinforce that persuasion occurs in part via self-value integration—seeing value and incorporating persuasive messages into one's self-concept—and extend this work to demonstrate how message framing and action planning may influence this process.
Microtask Available, Send us your CV!

Most current micro task crowd sourcing platforms do not exploit the individual expertise of workers, which becomes extremely relevant for knowledge-intensive micro tasks in human computation scenarios. In this paper, we discuss work in progress on worker profiling within micro task platforms to increase both the quality of the work and the satisfaction of the users. We analyse the issue of profiling workers and propose the introduction of a crowd worker CV as a comprehensive means to describe a worker's expertise and interests. We discuss several important dimensions that should be included in such a CV and analyse their benefits.
Pendidikan Karakter Berbasis Budaya Lokal pada Masyarakat Muslim

Masita Sekolah Tinggi Agama Islam Muhammadiyah Bima Email: echodeh@yahoo.com Abstract Formation of character is one of the objectives to be achieved in the implementation of national education. In Article 1 of Law No. 20 Year 2003 on National Education System, stated that among the goals of national education is to develop the potential of learners to have intelligence, personality and noble character. Mandate of the law intended that education is not only a smart Indonesian human form, but also the personality or character, so that later generations will be born a nation that grew up breathing character noble values of the nation and religion. This paper is to reveal the implementation of character education based on local wisdom in a mid-level Islamic education institution. Keywords character education, local wisdom, madrasah Abstrak Pembentukan karakter merupakan salah satu tujuan yang ingin dicapai dalam pelaksanaan pendidikan nasional. Pada Pasal 1, Undang-Undang Nomor 20 Tahun 2003 tentang Sistem Pendidikan Nasional, dinyatakan bahwa di antara tujuan pendidikan nasional adalah mengembangkan potensi peserta didik untuk memiliki kecerdasan, kepribadian dan akhlak mulia. Amanah undang-undang tersebut bermaksud agar pendidikan tidak hanya membentuk insan Indonesia yang cerdas, namun juga berkepribadian atau berkarakter, sehingga nantinya akan lahir generasi bangsa yang tumbuh berkembang dengan karakter yang bernafas nilai-nilai luhur bangsa serta agama. Tulisan ini ingin mengungkap pelaksanaan pendidikan karakter yang berbasis kearifan lokal di suatu lembaga pendidikan Islam tingkat menengah. Kata Kunci pendidikan karakter, kearifan lokal, madrasah