Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach to the CLPsych 2018 Shared Task Using Top-Down Text Representation and Simple Bottom-Up Model Selection

The Computational Linguistics and Clinical Psychology (CLPsych) 2018 Shared Task asked teams to predict cross-sectional indices of anxiety and distress, and longitudinal indices of psychological distress from a subsample of the National Child Development Study, started in the United Kingdom in 1958. Teams aimed to predict mental health outcomes from essays written by 11-year-olds about what they believed their lives would be like at age 25. In the hopes of producing results that could be easily disseminated and applied, we used largely theory-based dictionaries to process the texts, and a simple data-driven approach to model selection. This approach yielded only modest results in terms of out-of-sample accuracy, but most of the category-level findings are interpretable and consistent with existing literature on psychological distress, anxiety, and depression.
Beyond Necrotizing Enterocolitis: Other Clinical Advantages of an Exclusive Human Milk Diet

Abstract Objective: Articles previously published by Sullivan et al. and Cristofalo et al. were reanalyzed using the proportion of cow milk-based nutrition received to determine whether that affected clinical outcomes during hospitalization for infants birth weight 500–1250 g. Abrams et al. showed in the same cohort incidences of necrotizing enterocolitis (NEC), NEC requiring surgery and sepsis increased proportionally to the amount of dietary cow milk. Methods: The data from the two studies conducted under essentially the same protocol were combined yielding a cohort of 260 infants receiving a diet ranging from 0% to 100% cow milk. Data analysis utilized negative binomial regression which mitigates differences between subjects in terms of their time on study by incorporating that number into the statistical model. The percent of cow milk-based nutrition was the only predictor investigated. Results: For all outcomes the larger the amount of cow's milk in the diet the greater the number of days of that intervention required. A trend toward statistical significance was seen for ventilator days; however, only parenteral nutrition (PN) days and days to full feeds achieved statistical significance. Conclusions: Incorporation of any cow milk-based nutrition into the diet of extremely premature infants correlates with more days on PN and a longer time to achieve full feeds. There was a nonstatistically significant trend toward increased ventilator days. These represent additional clinical consequences of the use of any cow milk-based protein in feeding EP infants.
Effectiveness and cost of radiofrequency ablation and stereotactic body radiotherapy for treatment of early‐stage hepatocellular carcinoma: An analysis of SEER‐medicare

For early‐stage hepatocellular carcinoma (HCC) patients, ablative strategies are potentially curative treatment options. Stereotactic body radiotherapy (SBRT) has emerged as a promising ablative therapy, although its comparison with radiofrequency ablation (RFA) remains confined to a single institution retrospective review. We sought to characterize the comparative outcomes and cost between the two treatment strategies.
Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health

Mental health problems represent a major public health challenge. Automated analysis of text related to mental health is aimed to help medical decision-making, public health policies and to improve health care. Such analysis may involve text classification. Traditionally, automated classification has been performed mainly using machine learning methods involving costly feature engineering. Recently, the performance of those methods has been dramatically improved by neural methods. However, mainly Convolutional neural networks (CNNs) have been explored. In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health. We show that this architecture improves overall classification results as compared to previously reported results on the same data. Benefitting from the attention mechanism, it can also efficiently select text elements crucial for classification decisions, which can also be used for in-depth analysis.
Challenges and opportunities in Gen3 embedded cooling with high-quality microgap flow

Gen3, Embedded Cooling, promises to revolutionize thermal management of advanced microelectronic systems by eliminating the sequential conductive and interfacial thermal resistances which dominate the present “remote cooling” paradigm. Single-phase interchip microfluidic flow with high thermal conductivity chips and substrates has been used successfully to cool single transistors dissipating more than 40kW/cm2, but efficient heat removal from transistor arrays, larger chips, and chip stacks operating at these prodigious heat fluxes would require the use of high vapor fraction (quality), two-phase cooling in intra- and inter-chip microgap channels. The motivation, as well as the challenges and opportunities associated with evaporative embedded cooling in realistic form factors, is the focus of this paper. The paper will begin with a brief review of the history of thermal packaging, reflecting the 70-year “inward migration” of cooling technology from the computer-room, to the rack, and then to the single chip and multichip module with — and liquid-cooled coldplates. Discussion of the limitations of this approach and recent results from single-phase embedded cooling will follow. This will set the stage for discussion of the development challenges associated with application of this Gen3 thermal management paradigm to commercial semiconductor hardware, including dealing with the effects of channel length, orientation, and manifold-driven centrifugal acceleration on the governing behavior.
Quality control of riboflavin-treated platelet concentrates using Mirasol® PRT system: Polish experience.

BACKGROUND The quality of platelet concentrates (PCs) is affected by preparation, storage, the type of container, and pathogen reduction technology (PRT). The Mirasol® Pathogen Reduction Technology (PRT) system (Terumo BCT Inc., Lakewood, USA), which uses riboflavin and ultraviolet (UV) light, has recently been proven effective against bacteria, viruses, parasites, and leukocytes. OBJECTIVES The aim of the study was to evaluate the effect of the Mirasol® PRT system, based on riboflavin and UV light exposure, on the most common in vitro platelet quality parameters of PCs prepared from whole blood-derived buffy coats. MATERIAL AND METHODS The study included 15 trials (n = 15). For each trial, 2 PCs were used: 1 for treatment with the Mirasol® PRT system (M) and 1 for a control (C). In the M group, PCs were illuminated. In the C group, saline solution was added. PCs from groups M and C were stored at 20-24°C, with agitation. Samples were collected on days 1, 3 and 5 to determine platelet concentration, total platelet count/unit, mean platelet volume (MPV), power of hydrogen (pH), glucose and beta-thromboglobulin concentration (BTG), hypotonic shock response (HSR), aggregation, CD42b and CD62P expression, pCO2, and pO2. RESULTS No significant differences in HSR or CD42b expression were observed between groups M and C. All pH values were stable during the whole storage period (7.1-7.5). On storage day 1, CD62P expression in group C was significantly higher than in group M. In the Mirasol® group, significantly higher glucose consumption was noted on storage days 3 and 5. On day 5, a 2-3-fold increase in BTG was observed in both groups as compared to day 1; on day 5, BTG concentration was 32% higher in group M than in group C. On all storage days, pCO2 was comparable in groups M and C; lower pO2 values were reported for group M. CONCLUSIONS In vitro results demonstrated that pH, HSR, aggregation, CD42b antigen expression, and MPV and platelet count parameters were comparable in groups M and C.
Editorial: de novo inflammatory bowel disease following bariatric surgery—potential implications for research and clinical practice. Authors’ reply

tric surgery) impact the risk of Intestinal inflammation? Inflamm Intest Dis. 2016;1:129-134. 5. Ahn LB, Huang CS, Forse RA, et al. Crohn’s disease after gastric bypass surgery for morbid obesity: is there an association? Inflamm Bowel Dis. 2005;11:622-624. 6. Ungaro R, Fausel R, Chang H, et al. Bariatric surgery associated with increased risk of new-onset inflammatory bowel disease: case series and national database study. Aliment Pharmacol Ther. 2018;47:11261134. 7. Braga Neto MB, Gregory M, Ramos GP, et al. De-novo inflammatory bowel disease after bariatric surgery: a large case series. J Crohns Colitis. 2017; https://doi.org/10.1093/ecco-jcc/jjx177 [Epub ahead of print]. 8. Bernstein GR, Pickett-Blakely O. De novo inflammatory bowel disease after bariatric surgery: a case series and literature review. Dig Dis Sci. 2017;62:817-820.
Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder

Identifying customer segments in retail banking portfolios with different risk profiles can improve the accuracy of credit scoring. The Variational Autoencoder (VAE) has shown promising results in different research domains, and it has been documented the powerful information embedded in the latent space of the VAE. We use the VAE and show that transforming the input data into a meaningful representation, it is possible to steer configurations in the latent space of the VAE. Specifically, the Weight of Evidence (WoE) transformation encapsulates the propensity to fall into financial distress and the latent space in the VAE preserves this characteristic in a well-defined clustering structure. These clusters have considerably different risk profiles and therefore are suitable not only for credit scoring but also for marketing and customer purposes. This new clustering methodology offers solutions to some of the challenges in the existing clustering algorithms, e.g., suggests the number of clusters, assigns cluster labels to new customers, enables cluster visualization, scales to large datasets, captures non-linear relationships among others. Finally, for portfolios with a large number of customers in each cluster, developing one classifier model per cluster can improve the credit scoring assessment.
Genetic Heterogeneity and Gene Diversity at ABO and Rh Blood Group Polymorphisms in Seven Pashtun Populations of Upper Khyber Pakhtunkhwa, Pakistan

Pashtun population in the North-West Pakistan is ethnically, culturally and compositionally very distinct from the rest of Pakistanis. Pashtuns are important for the understanding of genetic affinities of Pakistanis with other populations like Afghans, Central Asians and Aryans. To get an overview of their genetic structure, we have assembled the ABO and Rh blood group polymorphisms data of 83,000 subjects from seven Pashtun populations of upper Khyber Pakhtunkhwa, namely Buner, Mardan, Dir-Lower, Swabi, Swat, Nowshera, and Peshawar. We establish that these populations despite their linguistic and ethnic affinities, exhibit ample variation at the blood group markers. The frequencies of alleles p(A), q(B) and r(O) ranged from 18.7—23.2%, 22.0—26.5%, and 53.9—57.7%, respectively, while allele Rh(d) remained 21.5—31.4%. The average heterozygosities ranged from 0.458 to 0.517 in Swabi and Swat populations, respectively. Comparison of genotypic frequencies, homogeneity tests for allelic frequencies, and heterozygosity analyses reveal that a straightforward clustering of these populations was not possible. Furthermore, gene diversity analyses yielded total heterozygosity, HT =0.4901, and DST =0.0012, while coefficient of gene differentiation, GST was calculated to be 0.0024. Nei's measure of genetic distance (DA) was found to be highest between Swabi and Swat samples, and lowest between Dir-Lower and Mardan. These results established substantial differentiation and substructuring among these populations. Further investigations by employing highly polymorphic molecular markers would be indispensable in getting a deeper insight into the genetic diversity of Pashtuns and identifying the underlying evolutionary force causing this differentiation.
Premarital sexual practice and its predictors among university students: institution based cross sectional study

Introduction Adolescents are susceptible to different social, peer and cultural pressures that Drives them into earlier sexual experimentation. Despite the fact that delaying sexual activity until marriage reduces the spread of HIV/AIDS and various sexually transmitted infections (STI), sexual activities among youths have been reported to be increasing worldwide. Methods Institution Based cross-sectional survey was conducted between January 2016 to March 2016. A total of 604 students were included in the study using multi-stage sampling technique. Mixed Quantitative and Qualitative approach was applied. Bivariate and multivariate logistic regression model was fitted to identify factors associated with premarital sexual practice. Results The proportion of premarital sexual practice in the study area was found to be 54.3%. The mean age of first sexual debut was 18.7±1.96. Half (50.6%) of these sexual activities were performed because of student's interest to meet their sexual desire. Being Male, watching pornography and High academic performance were the factors significantly associated with premarital sexual practice. Conclusion The study had revealed that more than half of the participants were sexually active. Being male, watching pornography and high academic performance were predictors of premarital sexual practice. Therefore, Institute of Medicine and health science, gender office and HIV resource center have to organize different programs targeting at bringing behavioral change to minimize the prevailing prevalence of premarital sexual practice as well as its common consequences.
Hexokinase 2 Is Targetable for HK1-Negative, HK2-Positive Tumors from a Wide Variety of Tissues of Origin

Although absent in most adult tissues, hexokinase 2 (HK2) is expressed in most tumors and contributes to increased glucose consumption and to in vivo tumor 18F-FDG PET signaling. Methods: Both HK2 knockdown and knockout approaches were used to investigate the role of HK2 in cancer cell proliferation, in vivo xenograft tumor progression, and 18F-FDG tumor accumulation. BioProfiler Glycolysis analysis monitored cell culture glucose consumption and lactate production; 18F-FDG PET/CT monitored in vivo tumor glucose accumulation. Cancer Cell Line Encyclopedia data were analyzed for hexokinase 1 (HK1) and HK2 expression. Results: Neither cell proliferation in culture nor xenograft tumor progression are inhibited by HK2 knockdown or knockout in cancer cells that express HK1 and HK2. However, cancer subsets from a variety of tissues of origin express only HK2, but not HK1. In contrast to HK1-positive/HK2-positive (HK1+HK2+) cancers, HK2 knockdown in HK1-negative (HK1−) HK2+ cancer cells results in inhibition of cell proliferation, colony formation, and xenograft tumor progression. Moreover, HK1-knockout (HK1KO)HK2+ cancer cells are susceptible to HK2 inhibition, in contrast to their isogenic HK1+HK2+ parental cells. Conclusion: HK1 and HK2 expression are redundant in tumors; either can provide sufficient aerobic glycolysis for tumor growth, despite a reduction in 18F-FDG PET signal. Therapeutic HK2 inhibition is likely to be restricted to HK1−HK2+ tumor subsets, and stratification of tumors that express HK2, but not HK1, should identify tumors treatable with emerging HK2 specific inhibitors.
Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged

We study how digital service firms can develop an active customer base, focusing on two questions. First, how does the way that customers use the service postadoption to meet their own needs (personal usage) and to interact with one another (social usage) vary across customer acquisition methods? Second, how do firm-to-customer and customer-to-customer communications promote different types of usage? We study these questions using two data sets and by developing a multivariate hierarchical Poisson hidden Markov model (HMM), which fits the data significantly better than univariate and latent class approaches. We indeed find that postadoption behavior varies depending on customer acquisition method and dynamic states. At the total usage level, in one context (an annotation and note-taking service), customers who heard about the service through search and mass-invite exhibited significantly higher usage compared to those who joined through word of mouth (WOM), whereas in another context (a cloud-based file s.
Lumbosacral Transitional Vertebra-Related Low Back Pain: Resolving the Controversy

Study Design Case control study. Purpose The association of lumbosacral transitional vertebra (LSTV) with low back pain (LBP) is controversial, as is the role of occupational physical activity and radiological spinal abnormalities suggestive of other spinal disorders (OSDs) such as spinal degeneration and instability. This study aimed to determine if any association of LSTV with LBP exists. If so, the association of the level of physical activity and presence of OSD with LSTV-related LBP was determined. Overview of Literature The cause of LBP has been linked to proximal level disc degeneration, arthritic pseudoarticulation between LSTV and the sacral ala, facet joint degeneration, and nerve root compression due to a broadened transverse process. LSTV associated with LBP is present among individuals who are involved in high-level physical activity, including military recruits and athletes. Methods This was an unmatched study comprising 372 cases and 224 controls consecutively recruited with clinical and radiographic documentation. The relationship between LSTV and LBP was analyzed, and the effects of LSTV and OSD on this relationship were also assessed and statistically controlled. Results The presence of LSTV (p =0.039) was significantly associated with LBP, and the presence of OSD was associated with LTSV-related LBP, after statistically controlling for the level of physical activity (p =0.024). The level of physical activity was not associated with LBP. Demographic analysis revealed female predominance with an advanced age (>45 years) among those with LSTV-related LBP who have OSD. Conclusions The presence of LSTV was associated with an increased prevalence of LBP. This association was probably due to the confounding effect of OSD. The level of occupational physical activity was not associated with LSTV-related LBP. We speculate that advanced age and female sex caused the spurious association of LSTV with LBP in our study, rendering LSTV-related LBP controversial in published literature.
A Study of Feature Selection Algorithms for Predicting Students Academic Performance

The main aim of all the educational organizations is to improve the quality of education and elevate the academic performance of students. Educational Data Mining (EDM) is a growing research field which helps academic institutions to improve the performance of their students. The academic institutions are most often judged by the grades achieved by the students in examination. EDM offers different practices to predict the academic performance of students. In EDM, Feature Selection (FS) plays a vital role in improving the quality of prediction models for educational datasets. FS algorithms eliminate unrelated data from the educational repositories and hence increase the performance of classifier accuracy used in different EDM practices to support decision making for educational settings. The good quality of educational dataset can produce better results and hence the decisions based on such quality dataset can increase the quality of education by predicting the performance of students. In the light of this mentioned fact, it is necessary to choose a feature selection algorithm carefully. This paper presents an analysis of the performance of filter feature selection algorithms and classification algorithms on two different student datasets. The results obtained from different FS algorithms and classifiers on two student datasets with different number of features will also help researchers to find the best combinations of filter feature selection algorithms and classifiers. It is very necessary to put light on the relevancy of feature selection for student performance prediction, as the constructive educational strategies can be derived through the relevant set of features. The results of our study depict that there is a 10% difference of prediction accuracies between the results of datasets with different number of features.
A model for postzygotic mosaicisms quantifies the allele fraction drift, mutation rate, and contribution to de novo mutations

The allele fraction (AF) distribution, occurrence rate, and evolutionary contribution of postzygotic single-nucleotide mosaicisms (pSNMs) remain largely unknown. In this study, we developed a mathematical model to describe the accumulation and AF drift of pSNMs during the development of multicellular organisms. By applying the model, we quantitatively analyzed two large-scale data sets of pSNMs identified from human genomes. We found that the postzygotic mutation rate per cell division during early embryogenesis, especially during the first cell division, was higher than the average mutation rate in either male or female gametes. We estimated that the stochastic cell death rate per cell cleavage during human embryogenesis was ∼5%, and parental pSNMs occurring during the first three cell divisions contributed to ∼10% of the de novo mutations observed in children. We further demonstrated that the genomic profiles of pSNMs could be used to measure the divergence distance between tissues. Our results highlight the importance of pSNMs in estimating recurrence risk and clarified the quantitative relationship between postzygotic and de novo mutations.
Dimensionality-Driven Learning with Noisy Labels

Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs). We propose a new perspective for understanding DNN generalization for such datasets, by investigating the dimensionality of the deep representation subspace of training samples. We show that from a dimensionality perspective, DNNs exhibit quite distinctive learning styles when trained with clean labels versus when trained with a proportion of noisy labels. Based on this finding, we develop a new dimensionality-driven learning strategy, which monitors the dimensionality of subspaces during training and adapts the loss function accordingly. We empirically demonstrate that our approach is highly tolerant to significant proportions of noisy labels, and can effectively learn low-dimensional local subspaces that capture the data distribution.