Miracle-dz/UniFAR-FTUs huggingface dataset:
Mean Value of Median Nerve Cross-sectional Area in Healthy 20s and 30s in Busan Area

This study was designed to investigate the median nerve cross-sectional area of the upper extremity which is the main cause of CTS in the 20s and 30s. The median nerve cross-sectional area (MNC-area) of each part of the upper limb was measured in healthy 20s and 30s females and males without neurological diseases or other diseases. This MNC-area was compared with the hand, wrist, finger, and other body indexes. The research group was divided into 20s female and male groups, and the 30s were also divided into female and male groups. In the comparison between the ages, the hand, and wrist configurations in the 30s were significantly higher than those of the 20s. The mean median nerve cross-sectional area was significantly larger in the male group than in the female group in both 20s and 30s, and it was larger in both men and women than in the 20s. Hand and wrist configurations were also positively correlated with the median nerve cross-sectional area in both 20s and 30s. The median values of hand ratio and wrist ratio were 2.26 and 0.65, respectively. This median value of hand ratio was inversely correlated with the median nerve cross-sectional area. The median nerve cross-area of the 20s was 6.88 ~ 7.38 mm 2 in the male group and 5.69 ~ 6.99 mm 2 in the female group, respectively. The median nerve cross-area of the 30s was 6.32 ~ 8.89 mm 2 in the male group and 6.15 ~ 7.17 mm 2 in the female group, respectively. The mean median nerve cross-sectional area was positively correlated with body mass index in both groups. Most of the variables were higher in their 30s than in their 20s.
Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19–95.25% for training and 89.22–95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.
Two biomass preparation methods provide insights into studying microbial communities of intestinal mucosa in grass carp (Ctenopharyngodon idellus).

Animal digestive tract is habitat for a large number of autochthonous microbiota, which play central roles in multiple biological and physiological processes of the host. In this study, two different micro-biomass preparation methods were employed to evaluate the diversity of intestinal mucosa-associated microbiota in grass carp (Ctenopharyngodon idellus). Genomic DNAs were isolated either directly from intestinal mucosal samples (group A), or from micro-biomass after microbial dissociation (group B). Community richness, diversity and evenness indices were all higher in group B, but differences were not statistically significant (P = 0.97, P = 0.33, P = 0.34 respectively). Furthermore, group B samples exhibited an increased ratio of bacterial DNA in comparison with group A samples, but the difference was also not statistically significant (P = 0.74). In addition, there were no statistically significant differences between the two groups (P > 0.05) at the taxonomic level. Our results support previous findings that there exists a great abundance of the intestinal mucosa-adherent microbiota in the grass carp; among these, Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Spirochaetes and Fusobacteria were the most common phyla. Within these microbiota, Paenibacillus, Bacteroides, Bacillus and Cetobacterium genera comprise the majority of the community, implicating their functional importance (e.g. as probiotics) to their host. Our results contribute towards a better understanding of the intestinal microbial profile of grass carp. Both micro-biomass preparation techniques proved to be feasible for studying mucosa-adherent microbiota of grass carp; however, the second method (group B) provides a protocol that is somewhat more effective than the first method (group A).
Selection probability of multivariate regularization to identify pleiotropic variants in genetic association studies

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.
Association between physical function and perceived stress among U.S. Chinese older adults

Objectives Physical function impairment can cause great stress to older adults. The purpose of the study is to investigate the association between self-reported and directly-observed physical function on perceived stress among U.S. Chinese older adults. Methods Data were from the Population Study of Chinese Elderly in Chicago (PINE) of 3,157 Chinese older adults who were 60 and above in the Greater Chicago Area. Self-reported and directly-observed physical function measures, and Perceived Stress Scale were used. Results Participants had a mean age of 72.8 ± 8.3 years old (range 60–105). Higher scores of Katz activities of daily living impairments (odds ratio [OR]=1.77), Lawton instrumental activities of daily living impartments (OR=1.10, p<0.01), Rosow–Breslau index of mobility scale (OR=1.39, p<0.05), and Nagi index of basic physical activities scale (OR=1.19, p<0.001) were associated with higher levels of perceived stress. In addition, higher scores of directly-observed physical function measurements, including chair stand (OR=0.93), tandem stand (OR=0.71, p<0.05), timed walk (OR=0.73, p<0.001), and the overall measurement (OR= 0.87, p<0.01) were associated with lower level of perceived stress. Discussion Findings suggested that poor physical function was associated with perceived stress among U.S. Chinese older adults. Longitudinal studies are needed to obtain a more comprehensive understanding of the pathways between physical function and perceived stress. Implications for practice Health care professionals could provide personalized physical activity interventions to encourage older adults to engage in regular exercise in order to maintain and promote older adults’ physical function and psychological well-being.
A Review and Evaluation of Planetary Boundary Layer Parameterizations in Hurricane Weather Research and Forecasting Model Using Idealized Simulations and Observations

This paper reviews the evolution of planetary boundary layer (PBL) parameterization schemes that have been used in the operational version of the Hurricane Weather Research and Forecasting (HWRF) model since 2011. Idealized simulations are then used to evaluate the effects of different PBL schemes on hurricane structure and intensity. The original Global Forecast System (GFS) PBL scheme in the 2011 version of HWRF produces the weakest storm, while a modified GFS scheme using a wind-speed dependent parameterization of vertical eddy diffusivity (Km) produces the strongest storm. The subsequent version of the hybrid eddy diffusivity and mass flux scheme (EDMF) used in HWRF also produces a strong storm, similar to the version using the wind-speed dependent Km. Both the intensity change rate and maximum intensity of the simulated storms vary with different PBL schemes, mainly due to differences in the parameterization of Km. The smaller the Km in the PBL scheme, the faster a storm tends to intensify. Differences in hurricane PBL height, convergence, inflow angle, warm-core structure, distribution of deep convection, and agradient force in these simulations are also examined. Compared to dropsonde and Doppler radar composites, improvements in the kinematic structure are found in simulations using the wind-speed dependent Km and modified EDMF schemes relative to those with earlier versions of the PBL schemes in HWRF. However, the upper boundary layer in all simulations is much cooler and drier than that in dropsonde observations. This model deficiency needs to be considered and corrected in future model physics upgrades.
Production of amylase enzymes by filamentous fungi

Fifty-two out of one hundred and forty-four isolates of filamentous fungi were recorded as amylase producer, but with different degrees, on solid plate method. Chemical constituents of potato waste samples (collected from the Chpis’ Factory for Food Industries, Assiut, Egypt) were determined by chemical analysis. Twelve isolates (eight highly producer isolates and four isolates isolated from potato wastes) were screened for amylase production on potato wastes. Aspergillus flavus443 was recorded as the best enzyme producer on potato wastes and synthetic medium. The best environmental and nutritional conditions for amylase production by Aspergillus flavus443 were : [50 gm of potato wastes with 10 ml distilled water , manitol (1%) as a carbon source , casein (1%) as a nitrogen source , pH 5 and the medium was incubated at 40 oC for three days ).
Techno-Economic Study of Castor Oil Crop Biorefinery: Production of Biodiesel without Fossil-Based Methanol and Lignoethanol Improved by Alkali Pretreatment

Castor, a non-edible oil crop that flourishes even under extreme cultivation conditions, can be cultivated in wastewater with a lower cultivation cost than similar plants, e.g., rapeseed and soybean. This plant, containing seeds and lignocellulosic residues, has a promising perspective for biofuel production. The oil extracted from the seeds is inexpensive and can be efficiently converted to biodiesel, while the lignocellulosic parts are suitable for ethanol production after pretreatment with NaOH. Biodiesel typically produced from the fossil-based methanol; however, it can also be produced from the ethanol. In this study, ethanol used for biodiesel production is produced from the lignocellulosic residues (scenario 1), which are more sustainable and environmentally friendly; the process was compared with that of the methanol (scenario 2). In this study, techno-economic analyses were used to compare the technical and economic aspects of producing biodiesel from methanol and the produced ethanol. Simulations of the processes were carried out by Aspen plus software, and economic studies were conducted by Aspen Economic Analyzer. The prices of produced ethanol as a byproduct in scenarios 1 and 2 were USD 0.701 and 0.693 per liter, respectively, which are greater than that of gasoline. The prices of biodiesel produced as a primary product for scenarios 1 and 2 are USD 0.410 and 0.323/L, lower than the price of diesel in the Middle East region. The profitability indices for scenarios 1 and 2 are 1.29 and 1.41, respectively. Therefore, despite environmental benefits, the biorefinery based on producing biodiesel from methanol is more economically feasible than that produced from ethanol.
Laboratory analogs of carbonaceous matter: Soot and its precursors and by-products

Abstract Carbonaceous materials have been prepared in the laboratory by laser-induced pyrolysis of a mixture of hydrocarbons under different conditions and laser ablation of graphite in reactive gas atmospheres. We have investigated the soluble and insoluble parts of the condensed carbon powders with several spectroscopic and chromatographic methods in order to obtain information on the composition of the condensate. The results of these experiments have demonstrated that, at temperatures lower than 1700 K, the pyrolysis by-products are mainly PAHs, whereas at higher temperatures fullerenes and polyyne-based compounds are formed. The experimental findings point to different soot formation mechanisms with variable intermediates and end products. It has been found that soot extracts can contain more than 65 different polycyclic aromatic hydrocarbons (PAHs). Eventually, the study of the condensation pathways of soot particles and their precursors and by-products will permit the prediction of the spectral properties of carbonaceous matter in space.
Leaf shape in Populus tremula is a complex, omnigenic trait

Abstract Leaf shape is a defining feature of how we recognize and classify plant species. Although there is extensive variation in leaf shape within many species, few studies have disentangled the underlying genetic architecture. We characterized the genetic architecture of leaf shape variation in Eurasian aspen (Populus tremula L.) by performing genome‐wide association study (GWAS) for physiognomy traits. To ascertain the roles of identified GWAS candidate genes within the leaf development transcriptional program, we generated RNA‐Seq data that we used to perform gene co‐expression network analyses from a developmental series, which is publicly available within the PlantGenIE resource. We additionally used existing gene expression measurements across the population to analyze GWAS candidate genes in the context of a population‐wide co‐expression network and to identify genes that were differentially expressed between groups of individuals with contrasting leaf shapes. These data were integrated with expression GWAS (eQTL) results to define a set of candidate genes associated with leaf shape variation. Our results identified no clear adaptive link to leaf shape variation and indicate that leaf shape traits are genetically complex, likely determined by numerous small‐effect variations in gene expression. Genes associated with shape variation were peripheral within the population‐wide co‐expression network, were not highly connected within the leaf development co‐expression network, and exhibited signatures of relaxed selection. As such, our results are consistent with the omnigenic model.
Inheritance of resistance to Kyuri green mottle mosaic virus in melon.

Kyuri green mottle mosaic virus (KGMMV) is one of the virus which can reduce the quality and the amount of melon production in Indonesia. Resistance breeding is one of the solution to overcome the disease by crossing of PI 371795 and Andes to study the inheritance of resistance gene to KGMMV in melon. The aims of this research were to study the inheritance of resistance gene to KGMMV and to study various phenotypes of melon fruit produced by crossing between PI 371795 and Andes. Reciprocal breeding experiments were conducted between PI 371795 and Andes to obtain F1 population. Furthermore, F1 individuals were selfed to produce F2 population. On the other hand, another F1 individual was crossed to Andes to produce test cross population. F1, F2 and test cross individuals were then inoculated manually by KGMMV melon isolate (KGMM-YM). Resistance to KGMMV-YM was analyzed by symptom observation and serological detection using double antibody sandwich enzyme linked immunosorbent assay (DAS-ELISA). The DAS-ELISA results of F2 and test cross population were analyzed by chi-square test. Results of this research indicate that melon PI 371795 and F1 are resistance to KGMMV-YM, while Andes is susceptible. F2 and test cross population showed segregation and it follows Mendelian law with ratio F2 segregation is 3:1 (34 resistant, 17 susceptible) while the ratio in test cross is 1:1 (6 resistant, 7 susceptible). The results of this study revealed that the inheritance of resistance gene to KGMMV-YM is controlled by a single dominant gene and it is proposed to be called Krey for the allele symbol (KGMMV-YM resistance gene). Results of these breeding experiments also show four variations of melon fruits which have the potential to become a commercial melons.
Failure mode and effect analysis using multi‐linguistic terms and Dempster–Shafer evidence theory

Failure mode and effect analysis (FMEA) is a useful technique to identify and quantify potential failures. FMEA determines a potential failure mode by evaluating risk factors. In recent years, there are many works improving FMEA by allowing multiple experts to use linguistic term sets to evaluate risk factors. However, it is important to design a framework that can consider both the weight of risk factors and the weight of the experts. In addition, managing conflicts among experts is also an urgent problem to be addressed. In this paper, we proposed an FMEA model based on multi‐granularity linguistic terms and the Dempster–Shafer evidence theory. On the other hand, the weights for both experts and risk factors are taken into consideration. The weights are computed objectively and subjectively to ensure the reasonability. Further, we apply our method to an emergency department case, which shows the effectiveness of the method.
Smartphone indoor positioning based on enhanced BLE beacon multi-lateration

In this paper, we introduce a smartphone indoor positioning method using Bluetooth Low Energy (BLE) beacon multilateration. At first, based on signal strength analysis, we construct a distance calculation model for BLE beacons. Then, with the aims to improve positioning accuracy, we propose an improved Lateral method (Range-based method) which is applied for 4 nearby beacons. The method is intended todesign a real-time system for some services such as emergency assistance, personal localization and tracking, location-based advertising and marketing, etc. Experimental results show that the proposed method achieves high accuracy when compared withthe state of the art lateral methods such as geometry-based (conventional trilateration), Least Square Estimation-based (LSE-based) and weighted LSE-based.
Leukemia-Related Gene Expression of Bone Marrow Cells from Patients with Shwachman-Diamond Syndrome at the Pre-Leukemic Phase.

Background: Shwachman-Diamond syndrome (SDS) is an inherited bone marrow failure disorder characterized by varying degrees of cytopenia and a high propensity for myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) in up to 36% of the patients by the age of 30 years. Although the gene associated with SDS, SBDS, has recently been identified, its function, the link with MDS/AML and the mechanism for the development of MDS/AML in SDS is unclear and the molecular events occurring during transformation haven’t been yet identified. It is likely that several events occur many years before overt transformation occurs, and might be identifiable by comprehensive analysis. Objectives: To use oligonucleotide microarray to identify leukemogenic gene expression before overt transformation, which can explain a propensity for MDS/AML. Methods: Total RNA from marrow cells from 9 SDS patients and 7 healthy age-matched donors of bone marrows for transplantation was extracted, labeled and hybridized to Affymetrix HG_U133_Plus2. 0 GeneChip. Data were pre-processed using robust multichip analysis (RMA) and differentially expressed genes were identified with permutation-based methods. False discovery rate (FDR)-adjusted p-values were used to rank genes and cluster analysis grouped genes and samples. Real-time PCR was performed to confirm differential expression of genes found by microarray. Results: Of the 38,500 genes on the HG_133_Plus2. 0 we analyzed 52 known leukemia-related genes. We identified several genes with small FDR-adjusted p-values. Clustering of arrays resulted in two clusters that clearly separated patients from controls. Interestingly among the leukemia-related genes, the most differentially expressed gene (T=4.2) was ARHGEF12, a member of the Rho GEF family. Rho GEFs are oncogenes; many of them can transform NIH 3T3 cells into a malignant phenotype by altering expression and activation of Rho GTPases. ARHGEF12 is mapped at 11q23, telomeric to MLL, and is a novel MLL fusion partner in acute myeloid leukemia. Real time PCR after normalization against beta-actin confirmed statistically higher expression of the ARHGEF12 (p=0.03) in SDS marrow cells. In addition to ARHGEF12, we have found striking expression changes in several other genes, related to MDS/AML including TAL1, whose differential expression was also confirmed by real-time PCR. Conclusions: SDS marrow cells exhibit abnormal gene expression pattern, which might results in continuous stimulation favoring evolution or progression of malignant clones. Additional molecular and cytogenetic events are likely necessary for the malignant process to be irreversible and complete. Although analysis of whole marrow cells may not enable the detection of genes with lower differential expression between SDS and normal, it may still assist identifying molecular pathways involved in leukemogenesis. This is critically important when studying marrow failure disorders as obtaining sufficient amount of RNA from purified cell population is largely impossible.