Miracle-dz/UniFAR-FTUs huggingface dataset:
Pulmonary Tuberculosis and Drug Resistance in Dhaka Central Jail, the Largest Prison in Bangladesh

Background There are limited data on TB among prison inmates in Bangladesh. The aim of the study was to determine the prevalence of pulmonary tuberculosis (TB), its drug resistance and risk factors in Dhaka Central Jail, the largest prison in Bangladesh. Methods Cross sectional survey with, active screening of a total number of 11,001 inmates over a period of 2 years. Sputum samples from TB suspects were taken for acid- fast bacilli (AFB) microscopy, culture and drug susceptibility testing. Results Among 1,781 TB suspects 245 (13.8%) were positive for AFB on microscopy and/or culture. The prevalence rate of sputum- positive pulmonary TB was 2,227/100,000. Fifty three cases (21.6% of 245 cases) were AFB- negative on microscopy but were found positive on culture. Resistance to isoniazid, rifampicin, streptomycin and ethambutol was 11.4%, 0.8%, 22.4% and 6.5% respectively. No multidrug resistance was observed. The main risk factors of TB in prison were exposure to TB patients (adjusted odds ratio 3.16, 95% CI 2.36–4.21), previous imprisonment (1.86, 1.38–2.50), longer duration of stay in prison (17.5 months for TB cases; 1.004, 1.001–1.006) and low body mass index which is less than 18.5 kg/m2 (5.37, 4.02–7.16). Conclusions The study results revealed a very high prevalence of TB in the prison population in Dhaka Central Jail. Entry examinations and active symptom screening among inmates are important to control TB transmission inside the prison. Identifying undiagnosed smear-negative TB cases remains a challenge to combat this deadly disease in this difficult setting.
Efficient Branching Cascaded Regression for Face Alignment under Significant Head Rotation

Despite much interest in face alignment in recent years, the large majority of work has focused on near-frontal faces. Algorithms typically break down on profile faces, or are too slow for real-time applications. In this work we propose an efficient approach to face alignment that can handle 180 degrees of head rotation in a unified way (e.g., without resorting to view-based models) using 2D training data. The foundation of our approach is cascaded shape regression (CSR), which has emerged recently as the leading strategy. We propose a generalization of conventional CSRs that we call branching cascaded regression (BCR). Conventional CSRs are single-track; that is, they progress from one cascade level to the next in a straight line, with each regressor attempting to fit the entire dataset. We instead split the regression problem into two or more simpler ones after each cascade level. Intuitively, each regressor can then operate on a simpler objective function (i.e., with fewer conflicting gradient directions). Within the BCR framework, we model and infer pose-related landmark visibility and face shape simultaneously using Structured Point Distribution Models (SPDMs). We propose to learn task-specific feature mapping functions that are adaptive to landmark visibility, and that use SPDM parameters as regression targets instead of 2D landmark coordinates. Additionally, we introduce a new in-the-wild dataset of profile faces to validate our approach.
The effect of relational constructs on customer referrals and number of services purchased from a multiservice provider: Does age of relationship matter?

The authors examine the effect of relational constructs (e.g., satisfaction, trust, and affective and calculative commitment) on customer referrals and the number of services purchased, as well as the moderating effect of age of the relationship on these relationships. The research reported, based on data obtained from a large sample of customers of an insurance company, combines archival and survey data. The results provide evidence that supports the moderating effect of relationship age on the relationship between satisfaction, affective and calculative commitment, and the number of services purchased.
Growth and inflation: a cross-country study

This paper examines the effect of inflation on real growth in a Solow growth model using data from a cross section of countries over a 30-year period. The advantage of using a theoretical model is that it reduces the risk that the results will reflect data-mining. The results suggest that the 5 percentage point reduction in inflation from the 1970s to the 1980s would increase the growth rate of real GDP per head by between 0.1 and 0.5 percentage point. This effect would be worth between 15 percent and 140 percent of one year's income. Even the lower of these projections would be larger than most estimates of the costs of bringing inflation down.
Penerapan Model Pembelajaran Inkuiri Terbimbing untuk Meningkatkan Hasil Belajar IPA Siswa Sekolah Dasar

Penelitian ini bertujuan untuk mengetahui penerapan model pembelajaran inkuiri terbimbing untuk meningkatkan hasil belajar siswa kelas II SDN Keper Krembung. Pelajaran ilmu pengetahuan alam di kelas II SDN Keper Krembung ditemukan bahwa, siswa masih belajar secara individual, kurang adanya kerjasama di antara siswa, dan juga guru kurang memberi kesempatan kepada siswa untuk menemukan sendiri konsep-konsep dalam pembelajaran IPA. Penelitian ini dilakukan di SDN Keper Krembung dengan subyek siswa kelas II sebanyak 24 siswa. Rancangan penelitian yang digunakan adalah Penelitian Tindakan Kelas (PTK) yang dikembangkan oleh Kemmis & Mc. Taggart, meliputi empat tahap yaitu 1) planning , 2) acting & observing , 3) reflecting dan 4) revise plan . Sedangkan instrumen yang digunakan adalah lembar tes. Hasil penelitian menunjukkan bahwa penerapan model pembelajaran inkuiri terbimbing pada pembelajaran sumber-sumber energi siswa kelas II SDN Keper Krembung dapat dilaksanakan dengan baik, dalam dua siklus dengan siklus I dan siklus II. Dari hasil analisis data didapatkan rata-rata hasil belajar siswa mengalami peningkatan dari Siklus I ke siklus II sebesar 7,28 (10,72%) dengan peningkatan ketuntasan belajar sebesar 22,23%. Dengan demikian bahwa siklus II dapat dinyatakan berhasil dan terbukti aplikasi model Inkuiri Terbimbing mampu meningkatkan hasil belajar siswa kelas II SDN Keper Krembung.
Scalable link community detection: A local dispersion-aware approach

Real-life systems involving interacting objects are typically modeled as graphs and can often grow very large in size. Revealing the community structure of such systems is crucial in helping us better understand their complex nature. However, the ever-increasing size of real-world graphs, and our evolving perception of what a community is, make the task of community detection very challenging. One such challenge, is the discovery of the possibly overlapping communities of a given node in a billion-node graph. This problem is very common in modern large social networks like Facebook and Linkedln. In this paper, we propose a scalable local community detection approach to efficiently unfold the communities of individual target nodes in a given network. Our goal is to reveal the groupings formed around nodes (e.g., users) by leveraging the relations of the different contexts the nodes participate in. Our algorithm, termed Local Dispersion-aware Link Communities or LDLC, measures the similarity of pairs of links in the graph as well as the extent of their participation in multiple contexts. Then, it determines the ordering that we should group the links in order to form communities. Our approach is not affected by constraints existent in previous techniques (e.g., the need for several seed nodes or the need to collapse multiple overlapping communities to one). Our experimental evaluation using ground-truth communities for a wide range of large real-world networks show that LDLC significantly outperforms state-of-the-art methods on both accuracy and efficiency.
A National Swedish Longitudinal Twin-Sibling Study of Criminal Convictions From Adolescence Through Early Adulthood

Background: Prior twin and adoption studies have demonstrated the importance of both genetic and shared environmental factors in the etiology of criminal behavior (CB). However, despite substantial interest in life-course theories of CB, few genetically informative studies have examined CB in a developmental context. Method: In 69,767 male–male twin pairs and full-sibling pairs with ≤ 2 years’ difference in age, born 1958–1976 and ascertained from the Swedish Twin and Population Registries, we obtained information on all criminal convictions from 1973 to 2011 from the Swedish Crime Register. We fitted a Cholesky structural model, using the OpenMx package, to CB in these pairs over three age periods: 15–19, 20–24, and 25–29. Results: The Cholesky model had two main genetic factors. The first began at ages 15–19 and declined in importance over development. The second started at ages 20–24 and was stable over time. Only one major shared environmental factor was seen, beginning at ages 15–19. Heritability for CB declined from ages 15–29, as did shared environmental effects, although at a slower rate. Conclusions: Genetic risk factors for CB in males are developmentally dynamic, demonstrating both innovation and attenuation. These results are consistent with theories of adolescent-limited and life-course persistent CB subtypes. Heritability for CB did not increase over time as might be predicted from active gene-environmental correlation. However, consistent with expectation, the proportion of variability explained by shared environmental effects declined slightly as individuals aged and moved away from their original homes and neighborhoods.
Generalization of value in reinforcement learning by humans

Research in decision‐making has focused on the role of dopamine and its striatal targets in guiding choices via learned stimulus–reward or stimulus–response associations, behavior that is well described by reinforcement learning theories. However, basic reinforcement learning is relatively limited in scope and does not explain how learning about stimulus regularities or relations may guide decision‐making. A candidate mechanism for this type of learning comes from the domain of memory, which has highlighted a role for the hippocampus in learning of stimulus–stimulus relations, typically dissociated from the role of the striatum in stimulus–response learning. Here, we used functional magnetic resonance imaging and computational model‐based analyses to examine the joint contributions of these mechanisms to reinforcement learning. Humans performed a reinforcement learning task with added relational structure, modeled after tasks used to isolate hippocampal contributions to memory. On each trial participants chose one of four options, but the reward probabilities for pairs of options were correlated across trials. This (uninstructed) relationship between pairs of options potentially enabled an observer to learn about option values based on experience with the other options and to generalize across them. We observed blood oxygen level‐dependent (BOLD) activity related to learning in the striatum and also in the hippocampus. By comparing a basic reinforcement learning model to one augmented to allow feedback to generalize between correlated options, we tested whether choice behavior and BOLD activity were influenced by the opportunity to generalize across correlated options. Although such generalization goes beyond standard computational accounts of reinforcement learning and striatal BOLD, both choices and striatal BOLD activity were better explained by the augmented model. Consistent with the hypothesized role for the hippocampus in this generalization, functional connectivity between the ventral striatum and hippocampus was modulated, across participants, by the ability of the augmented model to capture participants’ choice. Our results thus point toward an interactive model in which striatal reinforcement learning systems may employ relational representations typically associated with the hippocampus.
KOSZUL DUALITY OF AFFINE KAC -

We give a proof of the parabolic/singular Koszul duality for the category O of affine Kac-Moody algebras. The main new tool is a relation between moment graphs and finite codimensional affine Schubert varieties. We apply this duality to q-Schur algebras and to cyclotomic rational double affine Hecke algebras. This yields a proof of a conjecture of Chuang-Miyachi relating the level-rank duality with the Ringel-Koszul duality of cyclotomic rational double affine Hecke algebras.
Zebrafish larvae exposed to ginkgotoxin exhibit seizure-like behavior that is relieved by pyridoxal-5′-phosphate, GABA and anti-epileptic drugs

SUMMARY The etiology of epilepsy is a very complicated, multifactorial process that is not completely understood. Therefore, the availability of epilepsy animal models induced by different mechanisms is crucial in advancing our knowledge and developing new therapeutic regimens for this disorder. Considering the advantages of zebrafish, we have developed a seizure model in zebrafish larvae using ginkgotoxin, a neurotoxin naturally occurring in Ginkgo biloba and hypothesized to inhibit the formation of the neurotransmitter γ-aminobutyric acid (GABA). We found that a 2-hour exposure to ginkgotoxin induced a seizure-like behavior in zebrafish larvae. This seizure-like swimming pattern was alleviated by the addition of either pyridoxal-5′-phosphate (PLP) or GABA and responded quickly to the anti-convulsing activity of gabapentin and phenytoin, two commonly prescribed anti-epileptic drugs (AEDs). Unexpectedly, the ginkgotoxin-induced PLP depletion in our experimental setting did not affect the homeostasis of folate-mediated one-carbon metabolism, another metabolic pathway playing a crucial role in neural function that also relies on the availability of PLP. This ginkgotoxin-induced seizure behavior was also relieved by primidone, which had been tested on a pentylenetetrazole-induced zebrafish seizure model but failed to rescue the seizure phenotype, highlighting the potential use and complementarity of this ginkgotoxin-induced seizure model for AED development. Structural and morphological characterization showed that a 2-hour ginkgotoxin exposure did not cause appreciable changes in larval morphology and tissues development. In conclusion, our data suggests that this ginkgotoxin-induced seizure in zebrafish larvae could serve as an in vivo model for epileptic seizure research and potential AED screening.
Cladistic analysis of the pennatulacean genus Renilla Lamarck, 1816 (Coelenterata, Octocorallia)

The genus Renilla is an interesting taxon for phylogenetic studies, which includes six species endemic to America with an anphiamerican distribution Pacific-Atlantic Ocean). A cladistic analysis of Renilla Lamarck, 1816 using eight characters from external morphology produced one cladogram (length 14, CI = 0.92, RI = 0.87), and the characters were polarized using Echinoptilum macintoshii Hubrecht, 1885 as an outgroup. In the cladogram the following phylogenetic sequence results: ((R. koellikeri (R. muelleri, R. musaica)) (R. octodentata (R. reniformis, R. tentaculata))).
Functional neuroimaging of belief, disbelief, and uncertainty

The difference between believing and disbelieving a proposition is one of the most potent regulators of human behavior and emotion. When one accepts a statement as true, it becomes the basis for further thought and action; rejected as false, it remains a string of words. The purpose of this study was to differentiate belief, disbelief, and uncertainty at the level of the brain.
Trained Named Entity Recognition using Distributional Clusters

This work applies boosted wrapper induction (BWI), a machine learning algorithm for information extraction from semi-structured documents, to the problem of named entity recognition. The default feature set of BWI is augmented with features based on distributional term clusters induced from a large unlabeled text corpus. Using no traditional linguistic resources, such as syntactic tags or special-purpose gazetteers, this approach yields results near the state of the art in the MUC 6 named entity domain. Supervised learning using features derived through unsupervised corpus analysis may be regarded as an alternative to bootstrapping methods.
Public Disclosure by Accredited Doctoral Programs: Prospective Applicants’ Views on Clarity, Helpfulness, and Sufficiency of Information to Make Application Decisions

Little is known about prospective applicants’ perspectives on the required content (e.g., public disclosure data) of doctoral program websites in health service psychology, despite that they are one of the primary audiences of this information. Eighty-seven undergraduate students considering doctoral study in health service psychology reviewed the public disclosure data (PDD) of two hypothetical doctoral program websites. Participants rated the clarity, helpfulness, and sufficiency of the information as well as the likelihood that they would apply to the hypothetical doctoral program. Results indicated that PDD in most, but not all, areas are clear, helpful, and sufficient to undergraduate students, although they are not particularly relevant to their application decisions. In addition, the amount of information included along with required PDD influenced the clarity of some aspects of the PDD. Participants were more likely to want to apply to more selective programs in the presence of additional information, but there were no differences in likelihood of applying to less selective programs. Implications for revision of the PDD requirements as well as ways that undergraduate programs and the larger training community can facilitate prospective applicants in researching graduate programs are discussed.
Correlation Between Serum Uric Acid and Renal Function in Patients With Stable Coronary Artery Disease and Type 2 Diabetes

Background The aim of this study was to investigate the relationship between serum uric acid and renal function, expressed as estimated glomerular filtration rate (eGFR), in patients with stable coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) in China. Methods Serum levels of uric acid and creatinine were determined in 526 enrolled patients diagnosed with stable CAD and T2DM. To assess renal function, eGFR was calculated using a modified MDRD formula suitable for the Chinese population. Patients’ anthropometric measurements were collected using standardized protocols, and 6-month follow-up results were collected and analyzed. Pearson’s correlation coefficient r was calculated and multivariate analysis was performed to evaluate the relationship between uric acid and renal function. Patients with eGFR < 60 mL/min/1.73 m2 were classified as having reduced renal function or chronic kidney disease (CKD) in this study. Results Uric acid levels were negatively associated with eGFR (P = 0.002), especially in patients with CKD (eGFR < 60 mL/min/1.73 m2) (P < 0.001). In patients with reduced renal function, the risk in the highest quartile of uric acid levels was higher than in the lowest quartile (odds ratio 9.18, 95% confidence interval: 4.01 - 21.01, P < 0.001). These associations remained after multiple adjustments for potential confounders and were recapitulated after 6-month follow-up. Conclusions Serum uric acid level is negatively associated with renal function, as assessed by eGFR, and serves as an independent predictor for CKD in patients with stable CAD and T2DM.
Effects of Exposure to Inclusion and Socioeconomic Status on Parental Attitudes Towards the Inclusion of Deaf and Hard of Hearing Children

Abstract The purpose of the study was to investigate the attitudes of parents of normal hearing (NH) children towards the inclusion of deaf and hard of hearing (DHH) children in the educational setting of their child. In particular, it examined the effect of parental socio economic status (SES) and exposure to inclusion (whether their child was in a class together with DHH children or not) on their attitudes. Hundred and thirteen parents of children with NH aged 4–6 years participated in the study. The parents differed in terms of SES and their experience with inclusion. All the parents completed a questionnaire on general attitudes towards inclusion and a background questionnaire. The parents whose children attended a class that included DHH children completed an additional questionnaire on their evaluation of the effects of the inclusion of DHH children in the regular class. The results indicated that in general, all the parents reported positive attitudes towards the inclusion of DHH children in a regular class with NH children. ANOVA revealed a significant interaction between SES and parental exposure to inclusion on parents’ attitudes towards inclusion. Among parents whose child attended a class that included a DHH child, middle-high SES parental attitudes were more positive than those of lower SES parents. By contrast, among parents whose child was not enrolled in an inclusive class the attitudes of middle-high SES parents were less positive than low SES parents. The results suggest that it is important to obtain information on parents’ attitudes toward inclusive programmes and to address their needs.
Text Simplification as Tree Transduction

Lexical and syntactic simplification aim to make texts more accessible to certain audiences. Syntactic simplification uses either hand-crafted linguis- tic rules for deep syntactic transformations, or machine learning techniques to model simpler transformations. Lexical simplification performs a lookup for synonyms followed by context and/or frequency-based models. In this paper we investigate modelling both syntactic and lexical simplification through the learning of general tree transduction rules. Experiments with the Simple En- glish Wikipedia corpus show promising results but highlight the need for clever filtering strategies to remove noisy transformations.
Emotion regulation in mathematics homework: An empirical study

ABSTRACT The author examined 2 distinctive aspects of emotion regulation in mathematics homework, including emotion management and cognitive reappraisal. Participants were 1,799 high school students from 46 classes in China. Two multilevel models were run, 1 with emotion management and another with cognitive reappraisal as the dependent variable. Both emotion management and cognitive reappraisal were positively associated with 5 individual-level variables (monitoring motivation, managing time, learning-oriented reasons, self-concept, and teacher feedback) and 1 class-level variable (self-concept). In addition, at the individual level, emotion management was associated negatively with adult-oriented reasons but positively with arranging the environment and prior mathematics achievement. Meanwhile, cognitive reappraisal was positively associated with parent education at the class level.