Miracle-dz/UniFAR-FTUs huggingface dataset:
Depression, obesity, and metabolic syndrome: prevalence and risks of comorbidity in a population-based representative sample of Mexican Americans.

INTRODUCTION We examined the prevalence of depression, obesity, and metabolic syndrome and associations between them in a population-based representative cohort of Mexican Americans living on the United States-Mexico border. METHOD The sample in this cross-sectional analysis consisted of 1,768 Mexican American adults (≥ 18 years of age) assessed between the years 2004 and 2010, with whom we tested our central hypothesis of a significant relationship between obesity and depression. Depression was measured using the Center for Epidemiologic Studies-Depression scale (CES-D) with a cutoff score of ≥ 16 for depression and a cutoff score of ≥ 27 for severe depression. We categorized body mass index (BMI) values as obese (≥ 30kg/m(2)) and later subdivided the obese subjects into obese (30-39 kg/m(2)[inclusive]) and morbidly obese (≥ 40 kg/m(2)). Metabolic syndrome was defined using the American Heart Association definition requiring at least 3 of the following: increased waist circumference, elevated triglycerides, reduced high-density lipoprotein (HDL) cholesterol, elevated blood pressure, and elevated fasting glucose. Weighted data were analyzed to establish prevalence of depression, obesity, and metabolic syndrome. Univariate and multivariable weighted regression models were used to test potential associations between these disorders. RESULTS Using weighted prevalence, we observed high rates of depression (30%), obesity (52%), and metabolic syndrome (45%). Univariate models revealed female gender (P = .0004), low education (P = .003), low HDL level (P = .009), and increased waist circumference (P = .03) were associated with depression. Female gender (P = .01), low education (P = .003), and morbid obesity (P = .002) were risk factors for severe depression and remained significant in multivariable models. CONCLUSIONS In this large cohort of Mexican Americans, obesity, female gender, and low education were identified risk factors for depression. These indicators may serve as targets for early detection, prevention, and intervention in this population.
What You Seek Is What You Get: Extraction of Class Attributes from Query Logs

Within the larger area of automatic acquisition of knowledge from the Web, we introduce a method for extracting relevant attributes, or quantifiable properties, for various classes of objects. The method extracts attributes such as capital city and President for the class Country, or cost, manufacturer and side effects for the class Drug, without relying on any expensive language resources or complex processing tools. In a departure from previous approaches to large-scale information extraction, we explore the role of Web query logs, rather than Web documents, as an alternative source of class attributes. The quality of the extracted attributes recommends query logs as a valuable, albeit little explored, resource for information extraction.
Modeling the dynamics of domain specific terminology in diachronic corpora

In terminology work, natural language processing, and digital humanities, several studies address the analysis of variations in context and meaning of terms in order to detect semantic change and the evolution of terms. We distinguish three different approaches to describe contextual variations: methods based on the analysis of patterns and linguistic clues, methods exploring the latent semantic space of single words, and methods for the analysis of topic membership. The paper presents the notion of context volatility as a new measure for detecting semantic change and applies it to key term extraction in a political science case study. The measure quantifies the dynamics of a term's contextual variation within a diachronic corpus to identify periods of time that are characterised by intense controversial debates or substantial semantic transformations.
A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable.

Purpose Measurement error is an important source of bias in epidemiological studies. We illustrate three approaches to sensitivity analysis for the effect of measurement error: imputation of the 'true' exposure based on specifying the sensitivity and specificity of the measured exposure (SS); direct imputation (DI) using a regression model for the predictive values; and adjustment based on a fully Bayesian analysis. Methods We deliberately misclassify smoking status in data from a case-control study of lung cancer. We then implement the SS and DI methods using fixed-parameter (FBA) and probabilistic (PBA) bias analyses, and Bayesian analysis using the Markov-Chain Monte-Carlo program WinBUGS to show how well each recovers the original association. Results The 'true' smoking-lung cancer odds ratio (OR), adjusted for sex in the original dataset, was OR = 8.18 [95% confidence limits (CL): 5.86, 11.43]; after misclassification, it decreased to OR = 3.08 (nominal 95% CL: 2.40, 3.96). The adjusted point estimates from all three approaches were always closer to the 'true' OR than the OR estimated from the unadjusted misclassified smoking data, and the adjusted interval estimates were always wider than the unadjusted interval estimate. When imputed misclassification parameters departed much from the actual misclassification, the 'true' OR was often omitted in the FBA intervals whereas it was always included in the PBA and Bayesian intervals. Conclusions These results illustrate how PBA and Bayesian analyses can be used to better account for uncertainty and bias due to measurement error.
Two rank approximations for low-rank based subspace clustering

Rank approximation and minimization problem is widely applied in machine learning and computer vision. As the minimum convex envelope of the rank function, the nuclear norm is often used for rank approximation and has achieved satisfactory results in different tasks. However, the nuclear norm may not be an appropriate rank approximation especially when there are large singular values. In this paper, we propose two different functions to more accurately approximate the rank function. Then based on the low-rank representation model, we use these approximations for robust subspace clustering with desirable low rank and robustness to noise. Experimental results show the effectiveness of our proposed methods.
Survival based radiographic-grouping for esophageal squamous cell carcinoma may impact clinical T stage

Most patients diagnosed with thoracic esophageal squamous cell carcinoma (ESCC) have progressed beyond surgical resection as a therapeutic option. Difficulties in the proper assessment of tumor invasion depth before treatment complicate determination of the type and extent of therapy. Therefore, accurate tumor clinical staging is a necessity for identifying treatment options and aiding in patient prognosis. We investigated radiographic factors as prognostic indicators for survival in ESCC. Between July 2006 - July 2010, 324 thoracic ESCC patients who underwent surgery were selected. All patients received contrast enhanced preoperative chest CT scans and esophageal barium swallow examinations. Measurement of maximal lesion cross-sectional area, the largest long diameter, largest short diameter, CT-indicated lesion length, barium-indicated lesion length and the length of pericardial fat reduction were performed. Relationships between these indicators and post-surgical survival time and the cutoff values of related factors were analyzed. Maximum long diameter, maximum lesion area and lesion length, as measured by CT imaging, were correlated with survival. Survival effects were clearly associated with group intervals, calculated by a genetic algorithm, and tumor stages. Risk-stratification intervals of esophageal lesions from radiographic imaging included: maximum long diameter < 28.7, 28.7-34.6mm, 34.6-41.4mm and >41.4mm; maximum lesion area < 355.8mm2, 355.8-568.0mm2, 568.0-907.3mm2 and >907.3mm2; and CT-indicated lesion length <30.9mm, 30.9-57.3mm, 57.3-70.6mm and > 70.6mm. The reasonable stratification of maximum esophageal lesion area, largest long diameter and lesion length measured in CT is valuable for clinical T staging of ESCC. Radiographic parameters may have prognostic clinical value in the staging of esophageal carcinoma.
Comparison of clinical utility between diaphragm excursion and thickening change using ultrasonography to predict extubation success

Background/Aims Both diaphragmatic excursion and change in muscle thickening are measured using ultrasonography (US) to assess diaphragm function and mechanical ventilation weaning outcomes. However, which parameter can better predict successful extubation remains to be determined. The aim of this study was to compare the clinical utility of these two diaphragmatic parameters to predict extubation success. Methods This study included patients subjected to extubation trial in the medical or surgical intensive care unit of a university-affiliated hospital from May 2015 through February 2016. Diaphragm excursion and percent of thickening change (Δtdi%) were measured using US within 24 hours before extubation. Results Sixty patients were included, and 78.3% (47/60) of these patients were successfully extubated, whereas 21.7% (13/60) were not. The median degree of excursion was greater in patients with extubation success than in those with extubation failure (1.65 cm vs. 0.8 cm, p < 0.001). Patients with extubation success had a greater Δtdi% than those with extubation failure (42.1% vs. 22.5%, p = 0.03). The areas under the receiver operating curve for excursion and Δtdi% were 0.836 (95% confidence interval [CI], 0.717 to 0.919) and 0.698 (95% CI, 0.566 to 0.810), respectively (p = 0.017). Conclusions Diaphragm excursion seems more accurate than a change in the diaphragm thickness to predict extubation success.
Author retrospective for increasing the instruction fetch rate via multiple branch prediction and a branch address cache

"Increasing the Instruction Fetch Rate viaMultiple Branch Prediction and a Branch Address Cache" was the first paper to propose a highly accurate hardware mechanism for predicting and fetching multiple non-contiguous basic blocks using leading-edge aggressive branch predictors of the time. Prior to this paper, the methods to increase fetch bandwidth relied on software and compiler mechanisms to increase the size of the basic blocks themselves. The publication of our paper inspired an explosion of research to further improve the accuracy of multiple branch prediction, reduce complexity of fetching multiple basic blocks, or increase the fetch bandwidth in other ways. Our HPS research group defied conventional wisdom in a 1991 ISCA paper [1] that demonstrated instruction level parallelism (IPC) can be greater than two even for non-scientific workloads. This flew in the face of numerous "proofs" at the time. We showed that there was enough parallelism for an aggressive superscalar out-of-order execution engine to exploit to dramatically improve performance, provided that the penalties caused by incorrect branch predictions, cache misses, and TLB misses could be improved.
Acylhydrazones as Antifungal Agents Targeting the Synthesis of Fungal Sphingolipids

ABSTRACT The incidence of invasive fungal infections has risen dramatically in recent decades. Current antifungal drugs are either toxic, likely to interact with other drugs, have a narrow spectrum of activity, or induce fungal resistance. Hence, there is a great need for new antifungals, possibly with novel mechanisms of action. Previously our group reported an acylhydrazone called BHBM that targeted the sphingolipid pathway and showed strong antifungal activity against several fungi. In this study, we screened 19 derivatives of BHBM. Three out of 19 derivatives were highly active against Cryptococcus neoformans in vitro and had low toxicity in mammalian cells. In particular, one of them, called D13, had a high selectivity index and showed better activity in an animal model of cryptococcosis, candidiasis, and pulmonary aspergillosis. D13 also displayed suitable pharmacokinetic properties and was able to pass through the blood-brain barrier. These results suggest that acylhydrazones are promising molecules for the research and development of new antifungal agents.
tRNAscan-SE On-line: integrating search and context for analysis of transfer RNA genes

High-throughput genome sequencing continues to grow the need for rapid, accurate genome annotation and tRNA genes constitute the largest family of essential, ever-present non-coding RNA genes. Newly developed tRNAscan-SE 2.0 has advanced the state-of-the-art methodology in tRNA gene detection and functional prediction, captured by rich new content of the companion Genomic tRNA Database. Previously, web-server tRNA detection was isolated from knowledge of existing tRNAs and their annotation. In this update of the tRNAscan-SE On-line resource, we tie together improvements in tRNA classification with greatly enhanced biological context via dynamically generated links between web server search results, the most relevant genes in the GtRNAdb and interactive, rich genome context provided by UCSC genome browsers. The tRNAscan-SE On-line web server can be accessed at http://trna.ucsc. edu/tRNAscan-SE/.
Autologous breast reconstruction in sickle cell trait: A fine line between success & failure

Autologous breast reconstructions are increasingly being performed. While increased risks for flap loss after free tissue transfer (FTT) in sickle cell patients are known to most plastic surgeons, risks for patients with sickle cell trait (SCT) are less known. Unfortunately, recommendations to prevent complications after FTT in SCT are still scarce. We present additional and novel surgical measures that we took aiming to enhance probabilities for successful FTT in a SCT patient undergoing autologous breast reconstruction. This is only the second case report describing a successful FTT without flap necrosis/loss in a patient with SCT, and the first successful report involving FTT in the breast.
Feature Scale Simulation of PECVD of SiO2 in SiH4/N2O Mixture

In this paper,to simulate the process of PECVD(plasma enhanced chemical vapor deposition) of SiO2,the plasma chemistry and plasma density of SiH4/N2O mixture have been studied with an inductive coupled plasma model,and the level set methodology has been used to obtain the feature scale variation during the process.In this simulation,the goal is to fill a trench.We studied how ion sputtering and chamber pressure affect the feature scale model.After the simulation,we found that the trench will close up at the top after a few steps,and if we add the ion sputtering into the surface reactions,the trench top will close up a little later.When the chamber pressure is improved,the plasma density will increase,so the trench top will close up earlier.In semiconductor device manufacture,people can control the trench’s feature scale through adjusting these two parameters.
Opposing roles of Toll-like receptor and cytosolic DNA-STING signaling pathways for Staphylococcus aureus cutaneous host defense

Successful host defense against pathogens requires innate immune recognition of the correct pathogen associated molecular patterns (PAMPs) by pathogen recognition receptors (PRRs) to trigger the appropriate gene program tailored to the pathogen. While many PRR pathways contribute to the innate immune response to specific pathogens, the relative importance of each pathway for the complete transcriptional program elicited has not been examined in detail. Herein, we used RNA-sequencing with wildtype and mutant macrophages to delineate the innate immune pathways contributing to the early transcriptional response to Staphylococcus aureus, a ubiquitous microorganism that can activate a wide variety of PRRs. Unexpectedly, two PRR pathways—the Toll-like receptor (TLR) and Stimulator of Interferon Gene (STING) pathways—were identified as dominant regulators of approximately 95% of the genes that were potently induced within the first four hours of macrophage infection with live S. aureus. TLR signaling predominantly activated a pro-inflammatory program while STING signaling activated an antiviral/type I interferon response with live but not killed S. aureus. This STING response was largely dependent on the cytosolic DNA sensor cyclic guanosine-adenosine synthase (cGAS). Using a cutaneous infection model, we found that the TLR and STING pathways played opposite roles in host defense to S. aureus. TLR signaling was required for host defense, with its absence reducing interleukin (IL)-1β production and neutrophil recruitment, resulting in increased bacterial growth. In contrast, absence of STING signaling had the opposite effect, enhancing the ability to restrict the infection. These results provide novel insights into the complex interplay of innate immune signaling pathways triggered by S. aureus and uncover opposing roles of TLR and STING in cutaneous host defense to S. aureus.
Fast bootstrapping‐based estimation of confidence intervals of expression levels and differential expression from RNA‐Seq data

Summary This note presents IsoEM2 and IsoDE2, new versions with enhanced features and faster runtime of the IsoEM and IsoDE packages for expression level estimation and differential expression. IsoEM2 estimates fragments per kilobase million (FPKM) and transcript per million (TPM) levels for genes and isoforms with confidence intervals through bootstrapping, while IsoDE2 performs differential expression analysis using the bootstrap samples generated by IsoEM2. Both tools are available with a command line interface as well as a graphical user interface (GUI) through wrappers for the Galaxy platform. Availability and implementation The source code of this software suite is available at https://github.com/mandricigor/isoem2. The Galaxy wrappers are available at https://toolshed.g2. bx.psu. edu/view/saharlcc/isoem2_isode2/. Contact imandric1@student.gsu. edu or ion@engr.uconn. edu Supplementary information Supplementary data are available at Bioinformatics online.
[Establishment of reference ranges of sex hormones for healthy children in Shenzhen, China based on chemiluminescence].

OBJECTIVE To study the reference ranges of six sex hormones, i.e., luteinizing hormone, follicle-stimulating hormone, progesterone, prolactin, estradiol, and testosterone, for healthy children aged 0-18 years in Shenzhen, China. METHODS Stratified cluster sampling was performed to select 2 178 healthy children aged 0-18 years in the districts of Futian, Luohu, Nanshan, Bao'an, and Longgang in Shenzhen between September 2015 and September 2016. There were 1 219 boys and 959 girls, including 81 neonates, 335 infants, 346 young children, 469 preschool children, 419 school-aged children, and 528 adolescents. The American Beckman DXI800 chemiluminescence meter was used to measure the levels of luteinizing hormone, follicle-stimulating hormone, progesterone, prolactin, estradiol, and testosterone. RESULTS There were significant differences in the levels of luteinizing hormone, follicle-stimulating hormone, progesterone, prolactin, estradiol, and testosterone between different age groups (P<0.05). There were also significant differences in the levels of these sex hormones between boys and girls in the same age group (P<0.05). The reference ranges of six sex hormones were established for healthy children aged 0-18 years in Shenzhen based on the levels of these hormones in different age groups. CONCLUSIONS There are significant differences in sex hormones between different age groups or sex groups. The reference ranges of six sex hormones established for different sexes or ages have great significance in the diagnosis and treatment of endocrine diseases in children.
Complete Genome Sequence of Pseudomonas sp. Strain NC02, Isolated from Soil

ABSTRACT We report here the complete genome sequence of Pseudomonas sp. strain NC02, isolated from soil in eastern Massachusetts. We assembled PacBio reads into a single closed contig with 132× mean coverage and then polished this contig using Illumina MiSeq reads, yielding a 6,890,566-bp sequence with 61.1% GC content.
Genetic analysis reveals efficient sexual spore dispersal at a fine spatial scale in Armillaria ostoyae, the causal agent of root-rot disease in conifers

Armillaria ostoyae is a fungal species causing root diseases in numerous coniferous forests of the northern hemisphere. The importance of sexual spores for the establishment of new disease centers remains unclear, particularly in the large maritime pine plantations of south-western France. An analysis of the genetic diversity of a local fungal population distributed over 500 ha in this French forest showed genetic recombination between genotypes to be frequent, consistent with regular sexual reproduction within the population. The estimated spatial genetic structure displayed a significant pattern of isolation by distance, consistent with the dispersal of sexual spores mostly at the spatial scale studied. Using these genetic data, we inferred an effective density of reproductive individuals of 0.1 to 0.3 individuals/ha, and a second moment of parent-progeny dispersal distance of 130 to 800 m, compatible with the main models of fungal spore dispersal. These results contrast with those obtained for studies of A. ostoyae over larger spatial scales, suggesting that inferences about mean spore dispersal may be best performed at fine spatial scales (i.e. a few kilometers) for most fungal species.
Overview of research center for information technology innovation in Taiwan Academia Sinica

Founded in February 2007, the aim of the Research Center for Information Technology Innovation (CITI) at Academia Sinica is to integrate research and development efforts in information technologies by various organizations in Academia Sinica, and also to facilitate and leverage IT-related multidisciplinary research. As a integral part of CITI, Taiwan Information Security Center (TWISC) to conduct researches on security with funding support from Ministry of Science and Technology. TWISC serves as a platform for security experts from universities, research institutes and private sector to share information and to explore opportunities to collaborate. Its aim is to boost research and development activities and promote public awareness regarding information security. Its research topics cover data/ software/ hardware/ network security and security management. TWISC has become the hub of security research in Taiwan and have been making significant impact through publishing and creating of toolkits. Recently privacy also becomes one of the main focuses of TWISC. The research team at CITI, Academia has been working on a viable way to assess the disclosure risk of synthetic dataset. Preliminary research result will be presented in this paper.