Miracle-dz/UniFAR-FTUs huggingface dataset:
Acanthamoeba profilin elicits allergic airway inflammation in mice

Background In previous studies, we suggested that Acanthamoeba is a new aero-allergen and that patients who showed positive results for the skin-prick test response to Acanthamoeba cross-reacted with several pollen allergens. Additionally, patients with common antibodies reacted to the 13–15 kDa Acanthamoeba unknown allergen. Objective We examined whether profilin of Acanthamoeba is a human airway allergic agent because of its molecular weight. Methods We expressed recombinant Ac-PF (rAc-PF) protein using an Escherichia coli expression system and evaluated whether Ac-PF is an airway allergic agent using an allergic airway inflammation animal model. Results Airway hyperresponsiveness was increased in rAc-PF-inoculated mice. The number of eosinophils and levels of Th2 cytokines, interleukin (IL)-4, IL-5, and IL-13 were increased in the bronchial alveolar lavage fluid of rAc-PF-treated mice. The lungs of the rAc-PF-treated mice group showed enhanced mucin production and metaplasia of lung epithelial cells and goblet cells. Conclusion In this study, we demonstrated that rAc-PF may be an allergen in Acanthamoeba, but further studies needed to identify the mechanisms of allergenic reactions induced by Ac-PF.
A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data

Fueled by technological advancement, there has been a surge of human microbiome studies surveying the microbial communities associated with the human body and their links with health and disease. As a complement to the human genome, the human microbiome holds great potential for precision medicine. Efficient predictive models based on microbiome data could be potentially used in various clinical applications such as disease diagnosis, patient stratification and drug response prediction. One important characteristic of the microbial community data is the phylogenetic tree that relates all the microbial taxa based on their evolutionary history. The phylogenetic tree is an informative prior for more efficient prediction since the microbial community changes are usually not randomly distributed on the tree but tend to occur in clades at varying phylogenetic depths (clustered signal). Although community-wide changes are possible for some conditions, it is also likely that the community changes are only associated with a small subset of “marker” taxa (sparse signal). Unfortunately, predictive models of microbial community data taking into account both the sparsity and the tree structure remain under-developed. In this paper, we propose a predictive framework to exploit sparse and clustered microbiome signals using a phylogeny-regularized sparse regression model. Our approach is motivated by evolutionary theory, where a natural correlation structure among microbial taxa exists according to the phylogenetic relationship. A novel phylogeny-based smoothness penalty is proposed to smooth the coefficients of the microbial taxa with respect to the phylogenetic tree. Using simulated and real datasets, we show that our method achieves better prediction performance than competing sparse regression methods for sparse and clustered microbiome signals.
Scoping review of priority setting of research topics for musculoskeletal conditions

Objective Describe research methods used in priority-setting exercises for musculoskeletal conditions and synthesise the priorities identified. Design Scoping review. Setting and population Studies that elicited the research priorities of patients/consumers, clinicians, researchers, policy-makers and/or funders for any musculoskeletal condition were included. Methods and analysis We searched MEDLINE and EMBASE from inception to November 2017 and the James Lind Alliance top 10 priorities, Cochrane Priority Setting Methods Group, and Cochrane Musculoskeletal and Back Groups review priority lists. The reported methods and research topics/questions identified were extracted, and a descriptive synthesis conducted. Results Forty-nine articles fulfilled our inclusion criteria. Methodologies and stakeholders varied widely (26 included a mix of clinicians, consumers and others, 16 included only clinicians, 6 included only consumers or patients and in 1 participants were unclear). Only two (4%) reported any explicit inclusion criteria for priorities. We identified 294 broad research priorities from 37 articles and 246 specific research questions from 17 articles, although only four (24%) of the latter listed questions in an actionable format. Research priorities for osteoarthritis were identified most often (n=7), followed by rheumatoid arthritis (n=4), osteoporosis (n=4) and back pain (n=4). Nearly half of both broad and specific research priorities were focused on treatment interventions (n=116 and 111, respectively), while few were economic (n=8, 2.7% broad and n=1, 0.4% specific), implementation (n=6, 2% broad and n=4, 1.6% specific) or health services and systems research (n=15, 5.1% broad and n=9, 3.7% specific) priorities. Conclusions While many research priority-setting studies in the musculoskeletal field have been performed, methodological limitations and lack of actionable research questions limit their usefulness. Future studies should ensure they conform to good priority-setting practice to ensure that the generated priorities are of maximum value. PROSPERO registration number CRD42017059250.
Research on the Temperature Characteristics of the Photoacoustic Sensor of Glucose Solution

In order to weaken the influence of temperature on photoacoustic (PA) measurements and compensate PA signals with a proposed theoretical model, the relationship of PA signal amplitude with temperature, under the condition of different glucose concentrations and different light intensities, was studied in this paper. First, the theoretical model was derived from the theory of the PA effect. Then, the temperature characteristics of the PA signals were investigated, based on the analyses of the temperature-dependent Grüneisen parameter in glucose solution. Next, the concept of a PA temperature coefficient was proposed in this paper. The result of the theoretical analysis shows that this coefficient is linear to light intensity and irrelevant to the concentration of glucose solution. Furthermore, a new concept of a PA temperature coefficient of unit light intensity was proposed in this paper. This coefficient is approximately constant, with different light intensities and solution concentrations, which is similar to the thermal expansion coefficient. After calculation, the PA temperature coefficient by the unit light intensity of glucose solution is about 0.936 bar/K. Finally, relevant experiments were carried out to verify the theoretical analysis, and the PA temperature coefficient of the unit light intensity of glucose solution is about 0.04/°C. This method can also be used in sensors measuring concentrations in other aqueous solutions.
Hybrid Loss for Learning Single-Image-based HDR Reconstruction

This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean-squared-error (MSE) between the target and reconstructed images, we minimize a hybrid loss that consists of perceptual and adversarial losses in addition to HDR-reconstruction loss. The reconstruction loss instead of MSE is more suitable for HDR since it puts more weight on both over- and under- exposed areas. It makes the reconstruction faithful to the input. Perceptual loss enables the networks to utilize knowledge about objects and image structure for recovering the intensity gradients of saturated and grossly quantized areas. Adversarial loss helps to select the most plausible appearance from multiple solutions. The hybrid loss that combines all the three losses is calculated in logarithmic space of image intensity so that the outputs retain a large dynamic range and meanwhile the learning becomes tractable. Comparative experiments conducted with other state-of-the-art methods demonstrated that our method produces a leap in image quality.
Performance Comparison and Applicability Analysis of Different Armature Structures for a Small-Scale HTS Linear Synchronous Motor

High-temperature superconducting (HTS) coils could bear larger current density and produce higher magnetic field compared to permanent magnets. An HTS linear synchronous motor (LSM) with its secondary excitation system made of HTS coils can provide larger thrust force but unavoidable larger normal force. This paper focuses on the analysis and comparison of different armature structures in an HTS LSM to improve the magnetic flux waveform quality, decrease the normal force, and simultaneously maintain the thrust force. Five alternative armature structures are proposed and investigated, i.e., coreless double layer lap winding (DLLW), yokeless DLLW, coreless and yokeless DLLW, and flat winding with and without iron yoke. Three dimensional models of HTS LSMs are built and verified to compare the magnetic field and force performances of various armature structures using the finite element method. Results indicate that armatures without iron components perform better. Furthermore, the yokeless flat winding is recommended for its 81% reduction of magnetic flux harmonic component and 52% increase of thrust force compared with DLLW.
Momen^et: Flavor the Moments in Learning to Classify Shapes

A fundamental question in learning to classify 3D shapes is how to treat the data in a way that would allow us to construct efficient and accurate geometric processing and analysis procedures. Here, we restrict ourselves to networks that operate on point clouds. There were several attempts to treat point clouds as non-structured data sets by which a neural network is trained to extract discriminative properties. The idea of using 3D coordinates as class identifiers motivated us to extend this line of thought to that of shape classification by comparing attributes that could easily account for the shape moments. Here, we propose to add polynomial functions of the coordinates allowing the network to account for higher order moments of a given shape. Experiments on two benchmarks show that the suggested network is able to provide state of the art results and at the same token learn more efficiently in terms of memory and computational complexity.
Writing Desire: Sixty Years of Gay Autobiography

Exploring nearly sixty years of memoir and autobiography, "Writing Desire" examines the changing identity of gay men writing within a historical context. Distinguished scholar and psychoanalyst Bertram J. Cohler has carefully selected a diverse group of ten men, including historians, activists, journalists, poets, performance artists, and bloggers, whose life writing evokes the evolution of gay life in twentieth-century America. By contrasting the personal experience of these disparate writers, Cohler illustrates the social transformations that these men helped shape. Among Cohler's diverse subjects is Alan Helms, whose journey from Indiana to New York's gay society represents the passage of men who came of age in the 1950s and 1960s, when homosexuality was considered a hidden "disease. " The liberating effects of Stonewall's aftermath are chronicled in the life of Arnie Kantrowitz, the prototypical activist for gay rights in the 1970s and the founder of the Gay and Lesbian Alliance against Defamation. The artistic works of Tim Miller and Mark Doty evoke loss and shock during the early stages of the AIDS epidemic in the 1980s. Cohler rounds out this collective group portrait by looking at the newest generation of writers in the Internet age via the blog of Bryan, who did the previously unthinkable: he "outed" himself to millions of people. A compelling mix of social history and personal biography, "Writing Desire" distills the experience of three generations of gay America.
The distribution of antibiotic use and its association with antibiotic resistance

Antibiotic use is a primary driver of antibiotic resistance. However, antibiotic use can be distributed in different ways in a population, and the association between the distribution of use and antibiotic resistance has not been explored. Here we tested the hypothesis that repeated use of antibiotics has a stronger association with population-wide antibiotic resistance than broadly- distributed, low-intensity use. First, we characterized the distribution of outpatient antibiotic use across US states, finding that antibiotic use is uneven and that repeated use of antibiotics makes up a minority of antibiotic use. Second, we compared antibiotic use with resistance for 72 pathogen-antibiotic combinations across states. Finally, having partitioned total use into extensive and intensive margins, we found that intense use had a weaker association with resistance than extensive use. If the use-resistance relationship is causal, these results suggest that reducing total use and selection intensity will require reducing broadly-distributed, low- intensity use.
Evaluation of the Inhibitory Effects of Genipin on the Fluoxetine-Induced Invasive and Metastatic Model in Human HepG2 Cells

Metastasis of hepatocellular carcinoma (HCC) is usually unrecognized before any pathological examination, resulting in time-taking treatment and poor prognosis. As a consequence, HCC patients usually show symptoms of depression. In order to suppress such psychiatric disorders and to facilitate better treatment outcome, antidepressants are prescribed. Up to present, information about the effect of antidepressants on HCC is still lacking. Therefore, we chose fluoxetine (FXT), one of the top five psychiatric prescriptions in the United States, together with the HepG2 cell model to explore its effect on HCC. Our study found that FXT (5 µM) increased the migratory distance of HepG2 cells by a factor of nearly 1.7 compared to control. In addition, our study also investigated the effect of genipin (GNP), which is an active compound from Gardenia jasminoides Ellis fruit (family Rubiaceae), on the FXT-induced HepG2 cells. Our study found that 30 and 60 µM GNP reduced the migratory distance by 42% and 74% respectively, compared to FXT treatment alone. Furthermore, we also found that FXT upregulated matrix metalloproteinases (MMPs) genes, increased the protein expression of MMPs, urokinase-type plasminogen activator (uPA), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), activator protein 1 (AP-1), phosphorylated mitogen-activated protein kinase (P-p38), phosphorylated protein kinase B (P-Akt), downregulated tissue inhibitor metalloproteinases (TIMPs) genes and decreased the TIMPs proteins expression whereas, GNP fully counteracted the action of FXT. Conclusively, this study has provided valuable information regarding the possible molecular mechanisms through which FXT affects the metastatic invasiveness of HepG2 cells and evidences to support that GNP counteracts such effect via the same molecular mechanisms.
Metaphor processing in middle childhood and at the transition to early adolescence: the role of chronological age, mental age, and verbal intelligence

Abstract Metaphor development in conjunction with verbal intelligence and linguistic competence in middle childhood and at the transition to early adolescence was investigated. 298 individuals between seven and ten years (chronological age) who attended grades two–four (mental age) were tested for metaphor processing by the Metaphoric Triads Task, for linguistic competence (HELD), and verbal intelligence (WISC-III). Chronological age significantly predicted metaphor processing with a breakpoint of 8.2 years regarding identification and comprehension, and 10.2 years regarding preference. Fourth-graders showed highest metaphor processing scores. Verbal intelligence significantly predicted metaphor processing; this effect became stronger with increasing age. Attributional metaphors were best understood and most preferred. Chronological and mental age are associated with metaphor processing in an age span that is seemingly crucial for metaphor development. Verbal analogical reasoning, concept formation, verbal abstraction, and semantic knowledge predicted metaphor comprehension. Understanding facts, principles, and social situations, and resultant inferential verbal reasoning predicted metaphor preference.
Use of MRI and FDG‐PET/CT to predict fixation of advanced hypopharyngeal squamous cell carcinoma to prevertebral space

We evaluated the ability of different (18F)fluoro‐deoxy‐d‐glucose (FDG)‐positron emission tomography (PET)‐based and magnetic resonance (MR)‐based parameters to identify prevertebral space (PVS) infiltration by hypopharyngeal carcinoma.
Using Household Fit Indices to Examine the Psychometric Quality of Food Insecurity Measures

This study focuses on model–data fit with a particular emphasis on household-level fit within the context of measuring household food insecurity. Household fit indices are used to examine the psychometric quality of household-level measures of food insecurity. In the United States, measures of food insecurity are commonly obtained from the U.S. Household Food Security Survey Module (HFSSM, 18 items) of the Current Population Survey Food Security Supplement (CPS-FSS). These measures, in various forms, are used to inform national programs and policies related to food insecurity. Data for low-income households with children from recent administrations of the HFSSM (2012-2014) are used in this study (N = 7,324). The results suggest that there are detectable levels of misfit with Infit mean square error (MSE) statistics ranging from 6.73 % to 21.33% and Outfit MSE statistics ranging from 5.31% to 9.68%. The data suggest for Outfit MSE statistics that (a) male respondents, (b) respondents with lower levels of education, and (c) respondents who did not report participating in SNAP (Supplemental Nutrition Assistance Program, formerly the Food Stamp Program) tend to have more misfit. For Infit MSE statistics, lack of homeownership appears to be a predictor of misfit. The implications of this research for future research, theory, and policy related to the measurement of household food insecurity are discussed.
Integrative analysis of transcriptome‐wide association study and mRNA expression profiles identifies candidate genes associated with autism spectrum disorders

Autism spectrum disorders (ASD) are a group of highly heritable psychiatric syndromes with high prevalence. The genetic mechanism of ASD remains elusive now. Here we conducted a transcriptome‐wide association study (TWAS) of ASD. The GWAS summary data of ASD was driven from the Psychiatric Genomics Consortium (PGC) portal, totally involving 5,305 ASD cases and 5,305 controls. FUSION software was applied to the GWAS summary data for tissue‐related TWAS of ASD considering brain and blood. The ASD associated genes identified by TWAS were further validated by mRNA expression profiling of ASD and the Simons Foundation for Autism Research (SFARI) Gene tool. DAVID 6.8 was used to perform gene ontology (GO) enrichment analysis of ASD associated genes identified by TWAS. TWAS identified 85 genes with TWAS P value <0.05 for ASD. Further comparing the 85 genes with the differentially expressed genes identified by mRNA expression profiling of ASD patients found 5 overlapped genes, including MUTYH (PTWAS = 0.0460, PmRNA = 0.0040), ARHGAP27 (PTWAS = 0.0100, PmRNA = 0.0016), GCA (PTWAS = 0.0480, PmRNA = 0.0063), CCDC14 (PTWAS = 0.0067, PmRNA = 0.0035), and MED15 (PTWAS = 0.0324, PmRNA = 0.0092). Gene Ontology (GO) enrichment analysis of the genes identified by TWAS detected 10 significant GO terms, such as mitochondrion (P = 0.0051), NAD or NADH binding (P = 0.0169), mitochondrial part (P = 0.0386) and 2‐oxoglutarate metabolic process (P = 0.0399). In conclusion, this study identified multiple ASD associated genes and gene sets, providing novel clues for revealing the pathogenesis of ASD. Autism Research 2019, 12: 33–38. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.
Knee joint sagittal plane movement in cerebral palsy: a comparative study of 2-dimensional markerless video and 3-dimensional gait analysis

Background and purpose — Gait analysis is indicated in children with cerebral palsy (CP) to identify and quantify gait deviations. One particularly difficult-to-treat deviation, crouch gait, can progress in adolescence and ultimately limit the ability to ambulate. An objective quantitative assessment is essential to early identify progressive gait impairments in children with CP. 3-dimensional gait analysis (3D GA) is considered the gold standard, although it is expensive, seldom available, and unnecessarily detailed for screening and follow-up. Simple video assessments are time-consuming when processed manually, but more convenient if used in conjunction with video processing algorithms; this has yet been validated in CP. We validate a 2-dimensional markerless (2D ML) assessment of knee joint flexion/extension angles of the gait cycle in children and young adults with CP. Patients and methods — 18 individuals, mean age 15 years (6.5–28), participated. 11 had bilateral, 3 unilateral, 3 dyskinetic, and 1 ataxic CP. In the Gross Motor Function Classification System, 6 were at level I, 11 at level II, and 1 at level III. We compared 2D ML, using a single video camera with computer processing, and 3D GA. Results — The 2D ML method overestimated the knee flexion/extension angle values by 3.3 to 7.0 degrees compared with 3D GA. The reliability within 2D ML and 3D GA was mostly good to excellent. Interpretation — Despite overestimating, 2D ML is a reliable and convenient tool to assess knee angles and, more importantly, to detect changes over time within a follow-up program in ambulatory children with CP.
Effect of melatonin ingestion on physical performance, metabolic responses, and recovery after an intermittent training session.

OBJECTIVES Fatigue is a limiting factor for sport performance. For this reason, optimal recovery after training is just as critical as the training program itself, if not more. Indeed, there is a need for strategies that can facilitate recovery after training, and one such strategy is the ingestion of supplements like melatonin (MEL). This study aimed to evaluate if MEL intake could improve recovery of athletes after an intermittent training session (ITS). METHODS Fifteen elite female athletes (17.4 ± 0.4 years, 76.4 ± 5.6 kg, 1.76 ± 0.04 m; mean ± standard deviation) participated in two testing campaigns. During each period, they performed a battery of physical and cognitive tests before and after an ITS, as well as after ingesting MEL (6 mg tablet) or placebo in a randomized design. The ITS comprised the modified agility T-test, squat jump, counter movement jump, maximum standing ball-throw velocity test, maximum jump ball-throw velocity test, and 20-m sprint. Oral temperature (OT) and vigilance were evaluated before and after the ITS. Rating of perceived exertion (RPE), blood lactate [La], and glucose [Gl] were recorded after each ITS. RESULTS Short-term performance, recovery of physical performance, and OT were not affected by MEL ingestion after the ITS. Moreover, MEL did not affect cognitive performance or RPE scores after the ITS. However, [La] and [Gl] (p < 0.05 for both) were decreased after MEL ingestion. CONCLUSION MEL has no effect on the recovery of physical performance but may affect glucose utilization and lactate metabolism during the team-handball training session.