Miracle-dz/UniFAR-FTUs huggingface dataset:
A genome-wide association study to identify candidate genes for erectile dysfunction

Erectile dysfunction (ED) can be caused by different diseases and controlled by several genetic networks. In this study, to identify the genes related to ED, the expression profiles of normal and ED samples were investigated by the Gene Expression Omnibus (GEO) database. Seventeen genes were identified as associated genes with ED. The protein and nucleic acid sequences of selected genes were retrieved from the UCSC database. Selected genes were diverse according to their physicochemical properties and functions. Category function revealed that selected genes are involved in pathways related to humans some diseases. Furthermore, based on protein interactions, genes associated with the insulin pathway had the greatest interaction with the studied genes. To identify the common cis-regulatory elements, the promoter site of the selected genes was retrieved from the UCSC database. The Gapped Local Alignment of Motifs tool was used for finding common conserved motifs into the promoter site of selected genes. Besides, INSR protein as an insulin receptor precursor showed a high potential site for posttranslation modifications, including phosphorylation and N-glycosylation. Also, in this study, two Guanine-Cytosine (GC)-rich regions were identified as conserved motifs in the upstream of studied genes which can be involved in regulating the expression of genes associated with ED. Also, the conserved binding site of miR-29-3p that is involved in various cancers was observed in the 3' untranslated region of genes associated with ED. Our study introduced new genes associated with ED, which can be good candidates for further analyzing related to human ED.
Self-catalysis growth of zinc oxide nanopillar array

Zn nanodot array was prepared by using a nanomasking with porous alumina membrane as mask. Based on such a nanodot array, a self-catalysis method was developed for fabricating ZnO nanopillars on Si substrate. The resultant nanopillars show a twodimensional, and regular array with uniform size and orientation.
Two-Phase Evaluation of a Community-Based Lifestyle Intervention for Palestinian Women in East Jerusalem: A Quasi-Experimental Study Followed by Dissemination

Palestinian-Arab women are at increased risk of cardiovascular disease due to high prevalence of diabetes and other risk factors. The current study investigates the effectiveness of an intensive group-based intervention on lifestyle habits that can prevent diabetes and cardiovascular disease. To that end, we conducted a group-based intervention based on the diabetes prevention program in two consecutive phases. The first phase consisted of a quasi-experimental study and the second phase included community-wide dissemination, with a 6-month follow-up. Findings from the quasi-experiment indicate increased consumption of fruit, vegetables and whole grains, weight reduction (−2.21 kg, p < 0.01), and a significant increase in the average daily steps in the intervention group (from 4456 to 6404). Findings from the dissemination indicate that average daily vegetables consumption increased from 1.76 to 2.32/day as did physical activity and average daily steps (from 4804 to 5827). There was a significant reduction in blood pressure, total cholesterol and LDL. These gains were sustained over 6 months following the intervention. This community-based, culturally adapted, health-promotion intervention led to improved nutrition and physical activity which were maintained after 6 months. Collaboration with community centers and local community partners created an effective channel for dissemination of the program to pre-clinical individuals.
Factors Associated with Survey Non-Response in a Cross-Sectional Survey of Persons with an Axial Spondyloarthritis or Osteoarthritis Claims Diagnosis

Non-response in surveys can lead to bias, which is often difficult to investigate. The aim of this analysis was to compare factors available from claims data associated with survey non-response and to compare them among two samples. A stratified sample of 4471 persons with a diagnosis of axial spondyloarthritis (axSpA) and a sample of 8995 persons with an osteoarthritis (OA) diagnosis from a German statutory health insurance were randomly selected and sent a postal survey. The association of age, sex, medical prescriptions, specialist physician contact, influenza vaccination, hospitalization, and Elixhauser comorbidity index with the survey response was assessed. Multiple logistic regression models were used with response as the outcome. A total of 47% of the axSpA sample and 40% of the OA sample responded to the survey. In both samples, the response was highest in the 70–79-year-olds. Women in all age groups responded more often, except for the 70–79-year-olds. Rheumatologist/orthopedist contact, physical therapy prescription, and influenza vaccination were more frequent among responders. In the logistic regression models, rheumatologist/orthopedist treatment, influenza vaccination, and physical therapy were associated with a higher odds ratio for response in both samples. The prescription of biologic drugs was associated with higher response in axSpA. A high Elixhauser comorbidity index and opioid use were not relevantly associated with response. Being reimbursed for long-term care was associated with lower response—this was only significant in the OA sample. The number of quarters with a diagnosis in the survey year was associated with higher response. Similar factors were associated with non-response in the two samples. The results can help other investigators to plan sample sizes of their surveys in similar settings.
In Vivo Functions of Mouse Neutrophils Derived from HoxB8-Transduced Conditionally Immortalized Myeloid Progenitors

Key Points HoxB8 progenitor–derived neutrophils can repopulate irradiated mice. HoxB8 chimeras are useful for the in vivo analysis of neutrophil functions. Visual Abstract Although neutrophils play important roles in immunity and inflammation, their analysis is strongly hindered by their short-lived and terminally differentiated nature. Prior studies reported conditional immortalization of myeloid progenitors using retroviral expression of an estrogen-dependent fusion protein of the HoxB8 transcription factor. This approach allowed the long-term culture of mouse myeloid progenitors (HoxB8 progenitors) in estrogen-containing media, followed by differentiation toward neutrophils upon estrogen withdrawal. Although several reports confirmed the in vitro functional responsiveness of the resulting differentiated cells (HoxB8 neutrophils), little is known about their capacity to perform in vivo neutrophil functions. We have addressed this issue by an in vivo transplantation approach. In vitro–generated HoxB8 neutrophils showed a neutrophil-like phenotype and were able to perform conventional neutrophil functions, like respiratory burst, chemotaxis, and phagocytosis. The i.v. injection of HoxB8 progenitors into lethally irradiated recipients resulted in the appearance of circulating donor-derived HoxB8 neutrophils. In vivo–differentiated HoxB8 neutrophils were able to migrate to the inflamed peritoneum and to phagocytose heat-killed Candida particles. The reverse passive Arthus reaction could be induced in HoxB8 chimeras but not in irradiated, nontransplanted control animals. Repeated injection of HoxB8 progenitors also allowed us to maintain stable circulating HoxB8 neutrophil counts for several days. Injection of arthritogenic K/B×N serum triggered robust arthritis in HoxB8 chimeras, but not in irradiated, nontransplanted control mice. Taken together, our results indicate that HoxB8 progenitor–derived neutrophils are capable of performing various in vivo neutrophil functions, providing a framework for using the HoxB8 system for the in vivo analysis of neutrophil function.
Effects of Sodium Bicarbonate Ingestion on Measures of Wingate Test Performance: A Meta-Analysis.

The review aimed to perform a meta-analysis of studies exploring the acute effects of sodium bicarbonate on Wingate test performance. Ten databases were searched to find studies that examined the effects of sodium bicarbonate on single and repeated Wingate tests. Meta-analyses were performed using the random-effects model. Ten studies were included in the review. There was no significant difference between the sodium bicarbonate and placebo trials for mean power in Wingate test 1 (standardized mean difference [SMD] = 0.02; 95% confidence interval [CI]: -0.07, 0.11) and test 3 (SMD = 0.21; 95% CI: -0.16, 0.58). There was a significant effect of sodium bicarbonate on mean power in Wingate test 2 (SMD = 0.09; 95% CI: 0.03, 0.16), and test 4 (SMD = 0.62; 95% CI: 0.15, 1.08). When considering studies that used shorter rest intervals between repeated Wingate tests, a significant effect of sodium bicarbonate was found on mean power in Wingate test 3 (SMD = 0.40; 95% CI: 0.01, 0.80). There was no significant difference between the sodium bicarbonate and placebo trials for peak power in Wingate test 1 (SMD = -0.01; 95% CI: -0.06, 0.04), test 2 (SMD = 0.02; 95% CI: -0.10, 0.13), or test 4 (SMD = 0.29; 95% CI: -0.13, 0.71). There was a significant effect of sodium bicarbonate on peak power in test 3 (SMD = 0.09; 95% CI: 0.00, 0.17). The results of this review suggest that sodium bicarbonate may provide an ergogenic effect on measures of repeated Wingate test performance. Key Teaching Points Sodium bicarbonate is a popular ergogenic aid. The Wingate test is commonly used to evaluate high-intensity exercise performance. While several studies explored the effects of sodium bicarbonate ingestion on Wingate test performance, the findings are conflicting. In this meta-analysis, 10 studies that examined the acute effects of sodium bicarbonate on single and/or repeated Wingate test performance were included. There was no significant difference between sodium bicarbonate and placebo trials for mean or peak power in a single Wingate test. However, sodium bicarbonate was ergogenic for mean power in repeated Wingate tests. Specifically, an ergogenic effect was found in test 2 and test 4 (standardized mean difference: 0.09 to 0.62). When considering only studies that used shorter rest intervals between repeated Wingate tests, an ergogenic effect was found in test 3 (standardized mean difference: 0.40). Sodium bicarbonate was also ergogenic for peak power in Wingate test 3, but with small effects (standardized mean difference: 0.09).
Statistical Analysis on Transition of Actual Fuel Consumption by Improvement of Japanese 10 15 Mode Fuel Consumption

The aim of this study is to analyse the diffusion effect of passenger vehicles that achieved the fuel consumption standard under the Top Runners Approach towards the improvement of stock-based fuel consumption in actual use (actual fuel consumption). In order to investigate the actual fuel consumption with high statistical reliability, we have put focus upon voluntary reported fuel consumption log data collected from all over Japan for 54 months by a certain mobile phone contents provider and have established an actual fuel consumption database of passenger vehicles. The results of statistical analyses using the database show that actual fuel consumption can be explained by vehicle weight and that actual fuel consumption of gasoline-fuelled passenger vehicles is significantly improving every year compared with that of FY2001. The validity of the actual fuel consumption obtained from the database is confirmed by comparing with the actual fuel consumption estimated from existing transport statistics.
Isothermal crystallization kinetics and crystal structure of poly(lactic acid): Effect of triphenyl phosphate and talc

In this article, kinetics of neat poly(lactic acid) (PLA) and its blends with triphenyl phosphate (TPP) and/or talc crystallized isothermally in the temperature range of 113–128°C was analyzed by differential scanning calorimetry (DSC) and described by the Avrami equation. The results showed that the average value of Avrami exponents n is around 2.0 for neat PLA and the PLA/15TPP blend, and around 3.0 for the PLA/1.2Talc blend and the PLA/15TPP/1.2Talc blend. The crystallization rate constants k for neat PLA and its blends were increased in the following order: the PLA/15TPP blend, neat PLA, the PLA/15TPP/1.2Talc blend, and PLA/1.2Talc blend. The half-time of crystallization t1/2 was increased with an increase of crystallization temperature Tc and the presence of TPP, and was decreased sharply with the incorporation of talc. Further investigation into crystallization activation energy ΔEa of neat PLA and its blends with 15 wt % TPP and/or 1.2 wt % talc showed that the values of ΔEa were increased with the incorporation of TPP and/or talc compared to that of neat PLA. It was verified by wide angle X-ray diffraction (WAXD) that neat PLA and its blends with TPP and/or talc crystallized isothermally in the temperature range of 113–128°C, and all formed the α-form crystal after enough annealing time. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
Factors That Affect the Accuracy of Raman Shift Measurements on Multichannel Spectrometers

Raman shift measurements have been made on cyclohexane, by using linear interpolation between bracketing neon line positions to establish Raman band wavelengths. Factors that affect the precision of these measurements have been examined in detail. These include change of slit width, grating line spacing, neon line intensity, distance of separation of neon lines, and effects of small grating movements. All these have some effect, but the last factor is particularly important. Techniques that give standard deviations of 0.03 cm−1 when measurements are made without moving the grating are degraded by about an order of magnitude when the grating is moved. This result is attributed to adventitious partitioning of signal power between adjacent detector elements. By deliberately moving the grating a small amount between otherwise replicate measurements, one can control this effect. Values for the Raman shifts for eleven cyclohexane bands, based on approximately 800 Raman spectra and an equal number of neon emission spectra, are presented. The values are compared with previous measurements. They are generally within the ±σ range of the earlier values, but the standard deviations of these results are about an order of magnitude smaller.
Dynamic Viscosity Study of Binary Mixtures Triethylamine + Water at Temperatures Ranging from (283.15 to 291.35) K

Densities, ρ, and dynamic viscosities, η, of triethylamine + water (TEA−W) binary mixtures have been determined experimentally in the single phase region and over the whole composition range at T = (283.15 to 291.35) K. The experimental measurements of these properties were carried out at atmospheric pressure. Results have been fitted to obtain the adjustable parameters and standard deviations between the measured and fitted values, respectively. The viscosity data have been also correlated with the equations of Grunberg−Nissan, Hind et al., Katti−Chaudhri, Heric−Brewer, McAllister, and Auslander.
Antidepressant Response in Major Depressive Disorder: A Genome-wide Association Study

Importance: Antidepressants are a first line treatment for depression. However, only a third of individuals remit after the first treatment. Genetic variation likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. Objective: Gain insight into underlying biology of antidepressant response, characterize SNP-based heritability and genetic overlap with related outcomes, and evaluate out-of-sample prediction using polygenic scores. Design: Genome-wide meta-analysis of antidepressant response measures, Remission and Percentage Improvement in depression scores. Setting: Multiple international recruitment sites, including clinical trial and open label studies. Participants: Diagnosed with Major Depressive Disorder and assessed for depressive symptoms before and after prescription of an antidepressant medication. Main Outcome(s) and Measure(s): Antidepressant response measured as Remission and Percentage Improvement. Results: Genome-wide analysis of Remission (Nremit=1,852, Nnon-remit=3,299) and Percentage Improvement (N=5,218) identified no genome-wide significant variants. The heritability from common variants was significantly different from zero for Remission (h2=0.132, SE=0.056), but not Percentage Improvement (h2=-0.018, SE=0.032). Polygenic score analysis showed better antidepressant response was associated with lower genetic risk for schizophrenia, and higher genetic propensity for educational attainment. Polygenic scores for antidepressant response demonstrated weak but statistically significant evidence of out-of-sample prediction across cohorts, though results varied in external cohorts. Conclusions and Relevance: This study demonstrates antidepressant response is influenced by common genetic variation, has a genetic overlap with schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.
A General Framework for a Joint Calibration of VIX and VXX Options

We analyze the VIX futures market with a focus on the exchange-traded notes written on such contracts, in particular, we investigate the VXX notes tracking the short-end part of the futures term structure. Inspired by recent developments in commodity smile modeling, we present a multi-factor stochastic local-volatility model able to jointly calibrate plain vanilla options both on VIX futures and VXX notes. We discuss numerical results on real market data by highlighting the impact of model parameters on implied volatilities.
Deep learning based classification of unsegmented phonocardiogram spectrograms leveraging transfer learning

Objective. Cardiovascular diseases (CVDs) are a main cause of deaths all over the world. This research focuses on computer-aided analysis of phonocardiogram (PCG) signals based on deep learning that can enable improved and timely detection of heart abnormalities. The two widely used publicly available PCG datasets are from the PhysioNet/CinC (2016) and PASCAL (2011) challenges. The datasets are significantly different in terms of the tools used for data acquisition, clinical protocols, digital storages and signal qualities, making it challenging to process and analyze. Approach. In this work, we have used short-time Fourier transform-based spectrograms to learn the representative patterns of the normal and abnormal PCG signals. Spectrograms generated from both the datasets are utilized to perform four different studies: (i) train, validate and test different variants of convolutional neural network (CNN) models with PhysioNet dataset, (ii) train, validate and test the best performing CNN structure on the PASCAL dataset, as well as (iii) on the combined PhysioNet-PASCAL dataset and (iv) finally, the transfer learning technique is employed to train the best performing pre-trained network from the first study with PASCAL dataset. Main results. The first study achieves an accuracy, sensitivity, specificity, precision and F1 scores of 95.75%, 96.3%, 94.1%, 97.52%, and 96.93%, respectively, while the second study shows accuracy, sensitivity, specificity, precision and F1 scores of 75.25%, 74.2%, 76.4%, 76.73%, and 75.42%, respectively. The third study shows accuracy, sensitivity, specificity, precision and F1 scores of 92.7%, 94.98%, 89.95%, 95.3% and 94.6%, respectively. Finally, the fourth study shows a precision of 96.98% on the noisy PASCAL dataset with transfer learning approach. Significance. The proposed approach employs a less complex and relatively light custom CNN model that outperforms most of the recent competing studies by achieving comparatively high classification accuracy and precision, making it suitable for screening CVDs using PCG signals.
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Monocular Depth Completion

Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate the depth completion as a one-stage end-to-end learning task, which outputs dense depth maps directly. However, the feature extraction and supervision in one-stage frameworks are insufficient, limiting the performance of these approaches. To address this problem, we propose a novel end-to-end residual learning framework, which formulates the depth completion as a two-stage learning task, i.e., a sparse-to-coarse stage and a coarse-to-fine stage. First, a coarse dense depth map is obtained by a simple CNN framework. Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with coarse depth map and color image as input. Specially, in the coarse-to-fine stage, a channel shuffle extraction operation is utilized to extract more representative features from color image and coarse depth map, and an energy based fusion operation is exploited to effectively fuse these features obtained by channel shuffle operation, thus leading to more accurate and refined depth maps. We achieve SoTA performance in RMSE on KITTI benchmark. Extensive experiments on other datasets future demonstrate the superiority of our approach over current state-of-the-art depth completion approaches.
Pushing automated morphological classifications to their limits with the Dark Energy Survey

We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-type galaxies (LTGs); and (b) face-on galaxies from edge-on. Our convolutional neural networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≲ 17.7 mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well-determined classifications would look like if they were at higher redshifts. The CNNs reach 97 per cent accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalogue comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ∼87 per cent and 73 per cent of the catalogue for the ETG versus LTG and edge-on versus face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ϵ), and spectral type, even for the fainter galaxies. This is the largest multiband catalogue of automated galaxy morphologies to date.