Explore Sylt Fitness through data from visualizations to datasets, all based on diverse sources.

Summary

This zenodo archive contains the processed data for the publication "Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering". The unprocessed nanopore and illumina data are available via the European Nucleotide Archive (ENA).

Code is available at github.com/Hollfelder-Lab/lrDMS-IRED

If you use this data in your work, please cite the paper.

Description

Fitness scores in this dataset correspond to log-enrichment factors over wild type. For a detailed description of the fitness values, please refer to the paper.

- `srired_active.csv`: Variants with measurable fitness scores.
- `srired_inactive.csv`: Variants with no discernable activity (no output counts despite sufficient input counts).

Abstract

Engineering enzyme biocatalysts for higher efficiency is key to enabling sustainable, ‘green’ production processes for the chemical and pharmaceutical industry. This challenge can be tackled from two angles: by directed evolution, based on labor-intensive experimental testing of enzyme variant libraries, or by computational methods, where sequence-function data are used to predict biocatalyst improvements. Here, we combine both approaches into a two-week workflow, where ultra-high throughput screening of a library of imine reductases (IREDs) in microfluidic devices provides not only selected ‘hits’, but also long-read sequence data linked to fitness scores of >17 thousand enzyme variants. We demonstrate engineering of an IRED for chiral amine synthesis by mapping functional information in one go, ready to be used for interpretation and extrapolation by protein engineers with the help of machine learning (ML). We calculate position-dependent mutability and combinability scores of mutations and comprehensively illuminate a complex interplay of mutations driven by synergistic, often positively epistatic effects. Interpreted by easy-to-use regression and tree-based ML algorithms designed to suit the evaluation of random whole-gene mutagenesis data, 3-fold improved ‘hits’ obtained from experimental screening are extrapolated further to give up to 23-fold improvements in catalytic rate after testing only a handful of designed mutants. Our campaign is paradigmatic for future enzyme engineering that will rely on access to large sequence-function maps as profiles of the way a biocatalyst responds to mutation. These maps will chart the way to improved function by exploiting the synergy of rapid experimental screening combined with ML evaluation and extrapolation.

Description: This dataset contains customer treadmill purchase information

Objective: To explore the dataset to identify differences between customers of each product.

Data fields: Product - the model no. of the treadmill Age - in no of years, of the customer Gender - of the customer Education - in no. of years, of the customer Marital Status - of the customer Usage - Avg. # times the customer wants to use the treadmill every week Fitness - Self rated fitness score of the customer (5 - very fit, 1 - very unfit) Income - of the customer Miles- expected to run

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Includes accelerometer data using an ActiGraph to assess usual sedentary, moderate, vigorous, and very vigorous activity at baseline, 6 weeks, and 10 weeks. Includes relative reinforcing value (RRV) data showing how participants rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10. Includes data on the breakpoint, or Pmax of the RRV, which was the last schedule of reinforcement (i.e. 4, 8, 16, …) completed for the behavior (exercise or sedentary). For both Pmax and RRV score, greater scores indicated a greater reinforcing value, with scores exceeding 1.0 indicating increased exercise reinforcement. Includes questionnaire data regarding preference and tolerance for exercise intensity using the Preference for and Tolerance of Intensity of Exercise Questionnaire (PRETIEQ) and positive and negative outcome expectancy of exercise using the outcome expectancy scale (OES). Includes data on height, weight, and BMI. Includes demographic data such as gender and race/ethnicity. Resources in this dataset:Resource Title: Actigraph activity data. File Name: AGData.csvResource Description: Includes data from Actigraph accelerometer for each participant at baseline, 6 weeks, and 10 weeks.Resource Title: RRV Data. File Name: RRVData.csvResource Description: Includes data from RRV at baseline, 6 weeks, and 10 weeks, OES survey data, PRETIE-Q survey data, and demographic data (gender, weight, height, race, ethnicity, and age).

Raw male and female fitness data for 223 hemiclonal genotypes sampled from the LH_M laboratory adapted population. See Gilks et al (2017; https://f1000research.com/articles/5-2644/v3) for full details on how these lines were established. Assays were designed to measure total adult lifetime fitness for both males and females from each line, under conditions that match as close as possible those experienced by adults in the base population (Chippindale et al., 2001; Rice, 2005; Rice et al., 2006).

Male fitness assay
5 hemiclonal males per line were combined in adult competition vials with 10 competitor bw^- males and 15 virgin bw^- females. After 2 days, each bw^- female was isolated into individual oviposition test-tubes (containing the cornmeal-molasses-agar media but with no additional dried yeast) and left to oviposit for 18 hours. On Day 12, progeny were scored for eye-colour, in two observation rounds to allow ensure that as many eclosing offspring were included. Hemiclonal males were assigned paternity to progeny with wild-type red eyes (progeny of competitors are homozygous for the bw^- allele and therefore have brown eyes), giving an average fitness score (number of offspring sired) for the 5 hemiclonal males that were assayed per line. This assay was independently replicated 5 times, representing data from a total of 25 hemiclonal males per line. Male fitness was calculated as the proportion of offspring sired per assayed male, which accounts for instances where less than 5 hemiclonal males were included (6 out of 1105 assays).

Female fitness assays
Assays of female fitness followed a similar protocol to the male assays, again to match as close as possible the timing and conditions experienced by individuals in the base population. In this case, 5 virgin hemiclonal females were combined in adult competition vials with 10 competitor bw^- females and 15 bw^- males for 2 days. After 2 days, the 5 hemiclonal females were isolated into individual test-tubes and left to oviposit for 18hrs. The tubes were immediately chilled (4°C) to halt embryo development and the number of eggs per female was counted to provide a measure of fecundity. Data was excluded for tubes in which the female was either dead or not present. Since unmated females are known to produce eggs at a low rate, we also excluded data from females where egg counts were 0 or 1 as these are likely to represent output from unmated females (see Supplementary figure 1). By averaging fecundity across all 5 females this provided an average female fitness score for that line. This assay was independently replicated 5 times, representing a total of 25 hemiclonal females per line.

Dataset Column headings:

Male
sex - all male (value = 1)
rep - replicate (values from 1 to 5)
line - hemiclonal line (223 different lines, values from 1 to 230 with 7 lines missing)
red_1 - number of wild-type red-eyed offspring in first round of counting
red_2 - number of wild-type red-eyed offspring in second round of counting
brown_1 - number of brown-eyed offspring in first round of counting
brown_2 - number of brown-eyed offspring in second round of counting
total_red - number of offspring counted with wild-type red eyes (genotype bw⁺/bw^-)
total_brown - number of offspring counted with brown eyes (genotype bw^-/bw^-)
male_density - number of hemiclonal males per vial (value usually 5, but may be less due to missing males)

note: NA - missing value

Female
sex - all female (value = 2)
rep - replicate (values from 1 to 5)
line - hemiclonal line (223 different lines, values from 1 to 230 with 7 lines missing)
f1 - fecundity of female 1
f2 - fecundity of female 2
f3 - fecundity of female 3
f4 - fecundity of female 4
f5 - fecundity of female 5

note: NA - missing value

References

Chippindale, A.K., Gibson, J.R. & Rice, W.R. 2001. Negative genetic correlation for adult fitness between sexes reveals ontogenetic conflict in Drosophila. Proc. Natl. Acad. Sci. 98: 1671–1675.

Gilks WP, Pennell TM, Flis I et al. Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample [version 3; referees: 2 approved]. F1000Research 2016, 5:2644 (doi: 10.12688/f1000research.9912.3)

Rice, W.R. 2005. Inter-locus antagonistic coevolution as an engine of speciation: Assessment with hemiclonal analysis. Proc. Natl. Acad. Sci. 102: 6527–6534.

Rice, W.R., Stewart, A.D., Morrow, E.H., Linder, J.E., Orteiza, N. & Byrne, P.G. 2006. Assessing sexual conflict in the Drosophila melanogaster laboratory model system. Philos. Trans. R. Soc. B Biol. Sci. 361: 287–299.

You're working as a sports journalist at a major online sports media company, specializing in soccer analysis and reporting. You've been watching both men's and women's international soccer matches for a number of years, and your gut instinct tells you that more goals are scored in women's international football matches than men's. This would make an interesting investigative article that your subscribers are bound to love, but you'll need to perform a valid statistical hypothesis test to be sure!

While scoping this project, you acknowledge that the sport has changed a lot over the years, and performances likely vary a lot depending on the tournament, so you decide to limit the data used in the analysis to only official FIFA World Cup matches (not including qualifiers) since 2002-01-01.

You create two datasets containing the results of every official men's and women's international football match since the 19th century, which you scraped from a reliable online source. This data is stored in two CSV files: women_results.csv and men_results.csv.

The question you are trying to determine the answer to is:

Are more goals scored in women's international soccer matches than men's?

You assume a 10% significance level, and use the following null and alternative hypotheses:

The mean number of goals scored in women's international soccer matches is the same as men's.

The mean number of goals scored in women's international soccer matches is greater than men's.

Abstract Functional fitness loss during aging may compromise the quality of life and independence of older subjects. It is important to evaluate and diagnose the functional fitness of the elderly population. This study proposed normative functional fitness scores for a sample of elderly women from southern Brazil and compared values to their counterparts in the US, Extremadura (Spain), Taiwan (China) and Spain. The study sample consisted of 1,783 older women aged 60.0 to 84.9 years (mean 68.7 years; standard deviation 6.3 years) who performed the proposed motor tests of the "Senior Fitness Test" for functional fitness in older women. The percentile values specific to each age group were calculated based on the seven functional fitness components: body mass index, 6-minute walk, arm curl, 30-s chair stand, chair sit-and-reach, backscratch, and 8-feet up-and-go. The non-parametric binomial test compared the 50th percentile value of Brazilian older women with those from other countries. Older women´s performance in the functional capacity tests decreased across age groups. The mean BMI varied among age groups from 29.11 to 26.76 kg/m2, 6-minute walk from 572.94 to 486.95 m, arm curl from 17.51 to 15.11 repetitions, 30-schair stand from 15.62 to 14.30 repetitions, chair sit-and-reach from 1.01 to - 0.47 cm, back scratch from -4.92 to -10.52 cm and 8-feet up-and-go from 5.96 to 6.83 sec. Functional fitness scores among older women in different countries differed significantly. However, the direction and magnitude of differences were specific to the functional fitness component. Significant differences were observed in the normative scores, suggesting that the use of international normative scores in Brazilian older women may underestimate or overestimate potential functional limitations.

Perl and Java code for the automation of promoter motif analysis according to fitness scores. Can also be downloaded from: http://wassist.cs.technion.ac.il/~edwardv/fitnessAnalysis/Sup3.motif_analysis_and_fitness_scripts.tar.gz . (GZ 763 kb)

The National Fitness 100 Physical Certification Center measurement results are business-related data and national fitness measurement result information of the National Fitness 100 Physical Certification Center operated by the Korea Sports Promotion Foundation. It provides information such as age group, age, award type, physical fitness measurement items of the National Fitness 100 Physical Certification Center, and measurement year and month. Physical fitness measurement items include height, weight (kg), body fat percentage (%), waist circumference (cm), blood pressure, grip strength, sit-ups (times), and repeated jumps (times), and users can view the measurement results and exercise prescriptions that match the measurement results.

Abstract Objective: to investigate the association between body mass index (BMI) and functional fitness levels linked to the Elderly in Movement Program of the city of Curitiba, in the state of Paraná, Brazil. Methods: The study is characterized as correlational and cross-sectional. The sample consisted of 1,806 elderly female participants of the Elderly in Movement Program. The short version of the IPAQ was used to evaluate participation in physical activities; body weight (kg) and height (m) were measured to calculate BMI (kg/m²), while the motor tests proposed in the Senior Fitness Test were used to evaluate the functional fitness of the elderly women. Descriptive statistics, the chi-squared test and binary logistic regression were used for data analysis. Results: The results showed that 24.0% of the sample exhibited eutrophic nutritional status, 42.9% were overweight and 33.1% were obese. The elderly women classified as overweight and obese had lower functional fitness scores, based on the rating of Rikli and Jones, while the elderly women classified as eutrophic exhibited levels within the normal range. Obese elderly women were more likely to have low scores in the following functional fitness tests: Walk for 6 minutes, Chair Stand, Chair Sit and Reach, Back Scratch and 8-Foot Up and Go. Conclusion: The study indicated an association between, BMI and functional fitness in the elderly women participating in the program, where the majority of elderly women classified as obese exhibited low fitness in all tests.

Liver steatosis scores (CAP Score) were investigated using data from the National Center for Health Statistics examination survey (NHANES) for 2017-2018 and 2017-March 2020. The datasets include demographics, dietary, examination, laboratory, and questionnaire databases. Each dataset consists of a set of SAS files converted to CSV files using the SAS Viewer software. The selected variables for this analysis are systolic and diastolic blood pressure, total cholesterol, insulin, triglycerides, elasticity score, and CAP scores. Before data cleaning, the total number of records was 8,056 with ten columns. However, after data cleaning, the remaining records used in the analysis were 6,394, with only six columns.

In 2021 in China, based on the analysis of 213,837 children's physical assessment data, nearly 5.5 percent of children's fitness level was rated as very good and 23.5 percent as good. In contrast, around 11 percent of children's fitness level was not qualified according to standards in National Standards for Physical Fitness Measurement published by the General Administration of Sport of China.

Predicting fitness in natural populations is a major challenge in biology. It may be possible to leverage fast-accumulating genomic datasets to infer the fitness effects of mutant alleles, allowing evolutionary questions to be addressed in any organism. In this paper, we investigate the utility of one such tool, called PROVEAN. This program compares a query sequence with existing data to provide an alignment-based score for any protein variant, with scores categorized as neutral or deleterious based on a preset threshold. PROVEAN has been used widely in evolutionary studies, e.g., to estimate mutation load in natural populations, but has not been formally tested as a predictor of aggregate mutational effects on fitness. Using three large, published datasets on the genome sequences of laboratory mutation accumulation lines, we assessed how well PROVEAN predicted the actual fitness patterns observed, relative to other metrics. In most cases, we find that a simple count of the total number of mutant proteins is a better predictor of fitness than the number of variants scored as deleterious by PROVEAN. We also find that the sum of all mutant protein scores explains variation in fitness better than the number of mutant proteins in one of the datasets. We discuss the implications of these results for studies of populations in the wild. Methods We used previously published datasets of growth rates of, and mutations in, mutation accumulation lines in Saccharomyces cerevisiae and Chlamydomonas reinhardtii. We computed the mutated proteins and ran the protein variant, as compared to the laboratory ancestor, through PROVEAN.

We ran PROVEAN on the ComputeCanada cluster. As the program failed to run with the recent BLAST software (version 2.9.0), we configured PROVEAN to run with PSI-BLAST and BLASTDBCMD (Altschul et al. 1997)from BLAST version 2.4.0. We used version 4.8.1 of CD-HIT. We ran our variants with the NCBI nr database from 12/11/2019, which holds 142 GB of non-redundant sequences (229,636,095 sequences). We ran a subset of variants using the 2012 database, on which PROVEAN was developed (the first 5 GB), without radical changes to the PROVEAN scores of variants. The supporting sequence sets used to compute the alignment scores for all proteins were saved.

Sc1 We used the mutations reported in Sharp et al. (2018; Dataset_S2.xlsx). There were 1474 genic mutations in the dataset, occurring in 1219 unique genes across 218 MA lines. We extracted the nucleotide and protein sequence of the genes affected using YeastMine (Balakrishnan et al. 2012). From the same database, we downloaded the location of introns in these genes. The reference nucleotide sequence was then mutated in silicoto represent the mutant sequence, which was then transcribed and translated, using the seqinr package (Charif and Lobry 2007)in R (R Core Team 2019). Additionally, we analyzed VCF files to obtain a table of mutations in the ancestral line as compared to the yeast reference genome (version R64-2-1). In cases where the ancestor and reference strain differed for a mutated gene (126 genes) we separately computed the ancestral protein and used it for comparison to the MA lines. We wrote a script to produce protein variants in the format PROVEAN requires. From 1474 genic mutations, 1126 protein variants were computed (in 961 unique proteins). Two samples (lines 113 and 206) had no nonsynonymous mutations. When an MA line had more than one nonsynonymous mutation in a particular gene both mutations were considered when altering the protein and the number of mutant proteins is reported once. Out of 961 altered proteins, 126 already differed between the S288C reference genome and the laboratory ancestor, in which case the latter was used as the query sequence.

Sc2 We used the mutations reported in Liu and Zhang (2019; Data_S1.xlsx). Additionally, the authors supplied us with a table of mutations in their ancestral line relative to the S288C reference genome. We used the same method as described above for dataset Sc1. There were 1147 genic mutations, occurring in 968 unique genes, across 165 MA lines. From 1147 genic mutations, 877 protein variants were computed (in 754 unique proteins). Out of 754 altered proteins, 16 already differed between the S288C reference genome and the laboratory ancestor, in which case the latter was used as the query sequence.

Cr We received an annotated table of the mutations reported in Ness et al. (2015)as well as VCF files containing the mutations in their six ancestral lines compared to the reference genome. We downloaded an annotated table for all transcripts in the Chlamydomonasreference genome from Dicots PLAZA 4.0 (version 5.5, Van Bel et al., 2018)to identify mutations in coding sequences. Out of the original 6843 mutations, 3889 affected protein sequence, representing 1439 mutated proteins after combining mutations. We found that the majority of transcripts that were mutated during mutation accumulation already had existing variants in the ancestral strain, relative to the reference (table 1). 1397 out of the originally predicted 1439 protein variants remained once ancestral variation had been considered (table 1). As in the other datasets, we use the ancestral protein as the query protein. We found 2 cases in the C. reinhardtiidataset where the reported reference nucleotide deviated from that found in the Dicots PLAZA 4.0 sequence; in each case, the differences between the two reference sequences were synonymous. This discrepancy was likely due to the two different reference genomes used (Ness et al. used v5.3; Van Bel et al. used v5.5). To test the accuracy of our sequence-mutating code, we mutated the coding sequence to the reference nucleotide given by the C. reinhardtiidataset and verified that this produced the reference transcript. We converted the protein variants into the format PROVEAN requires. In cases with alternative transcripts, we treat these as separate proteins in PROVEAN and then report the minimum score given to any protein variant of a gene. This occurred in 42 unique cases, involving all genetic backgrounds. While the difference in scores between transcripts in general was small, we found two cases where the score for one affected transcript was below the default threshold of –2.5 while the other was above it, and six cases where the scores fell above and below zero. Six out of the total 1397 protein variants failed to receive a score from PROVEAN, likely because the changes to the protein were too large to compute alignment scores between the clusters gathered and the mutant protein and were ignored in the analysis (these occurred in six different samples across five ancestral backgrounds).

This data is prepared for CS334 Machine Learning course final project from Emory University. It can also be used and we are very welocome for doing any kind of data analysis and machine learning research by anyone. Data is scraped from vlr.gg: one of the three major websites (vlr.gg, rib.gg, thespike.gg) that give out past Valorant pro matches data. For actual in-game data you can check out rib.gg which may have advanced data for selling. Columns are described in abbreviation and here are the explanation:

name: str
team: str # might be different for the same team
agent: str
rating: float
acs: int # average combat score
k: int # kills
d: int # deaths
a: int # assists
tkmd: int # total kills minus deaths
kast: float # kill, assist, survive, trade %
adr: int # average damage per round
hs: float # headshot %
fk: int # first kills
fd: int # first deaths
fkmd: int # first kills minus first deaths
t # attack side
ct # defend side

Data may have missing values either because they were missing in the original source or the gameplay is so biased (score like 13:0) that one of the team or some of the players did not get to do anything.

Dataset

This dataset was created by Vinuja Lavakumar

Contents

Accurate preoperative risk assessment is of great value to both patients and clinical teams. Several risk scores have been developed but are often not calibrated to the local institution, limited in terms of data input into the underlying models, and/or lack individual precision. Machine Learning (ML) models have the potential to address limitations in existing scoring systems. A database of 1190 elderly patients who underwent major elective surgery was analyzed retrospectively. Preoperative cardiorespiratory fitness data from cardiopulmonary exercise testing (CPET), demographic and clinical data were extracted and integrated into advanced machine learning (ML) algorithms. Multi-Objective-Symbolic-Regression (MOSR), a novel algorithm utilizing Genetic Programming to generate mathematical formulae for learning tasks, was employed to predict patient morbidity at Postoperative Day 3, as defined by the PostOperative Morbidity Survey (POMS). Shapley-Additive-exPlanations (SHAP) was subsequently used to analyze feature contributions. Model performance was benchmarked against existing risk prediction scores, namely the Portsmouth-Physiological-and-Operative-Severity-Score-for-the-Enumeration-of-Mortality-and-Morbidity (PPOSSUM) and the Duke-Activity-Status-Index, as well as linear regression using CPET features. A model was also developed for the same task using data directly extracted from the CPET time-series. The incorporation of cardiorespiratory fitness data enhanced the performance of all models for predicting postoperative morbidity by 20% compared to sole reliance on clinical data. Cardiorespiratory fitness features demonstrated greater importance than clinical features in the SHAP analysis. Models utilizing data taken directly from the CPET time-series demonstrated a 12% improvement over the cardiorespiratory fitness models. MOSR model surpassed all other models in every experiment, demonstrating excellent robustness and generalization capabilities. Integrating cardiorespiratory fitness data with ML models enables improved preoperative prediction of postoperative morbidity in elective surgical patients. The MOSR model stands out for its capacity to pinpoint essential features and build models that are both simple and accurate, showing excellent generalizability.

Insulin resistance has wide-ranging effects on metabolism but there are knowledge gaps regarding the tissue origins of systemic metabolite patterns, and how patterns are altered by fitness and metabolic health. To address these questions, plasma metabolite patterns were determined every 5 min during exercise (30 min, ~45% of V̇O2peak, ~63 W) and recovery in overnight-fasted sedentary, obese, insulin resistant women under controlled conditions of diet and physical activity. We hypothesized that improved fitness and insulin sensitivity following a ~14 wk training and weight loss intervention would lead to fixed workload plasma metabolomics signatures reflective of metabolic health and muscle metabolism. Pattern analysis over the first 15 min of exercise—regardless of pre- vs. post-intervention status—highlighted anticipated increases in fatty acid tissue uptake and oxidation (e.g., reduced long-chain fatty acids), diminution of non-oxidative fates of glucose (e.g., lowered sorbitol-pathway metabolites and glycerol-3-galactoside [possible glycerolipid synthesis metabolite]), and enhanced tissue amino acid use (e.g., drops in amino acids; modest increase in urea). A novel observation was that exercise significantly increased several xenometabolites (“non-self” molecules, from microbes or foods), including benzoic acid/salicylic acid/salicylaldehyde, hexadecanol/octadecanol/dodecanol, and chlorogenic acid. In addition, many non-annotated metabolites changed with exercise. Although exercise itself strongly impacted the global metabolome, there were surprisingly few intervention-associated differences despite marked improvements in insulin sensitivity, fitness, and adiposity. These results, and previously-reported plasma acylcarnitine profiles, support the principle that most metabolic changes during sub-maximal aerobic exercise are closely tethered to absolute ATP turnover rate (workload), regardless of fitness or metabolic health status. Supporting Materials include graphs of blood patterns of metabolites in adult women during a sub-maximal exercise bout and recovery period, and primary data in spreadsheet format on model performance, exercise and recovery, and correlation statistics for metabolites. Journal information -- Am J Physiol, Endo & Metabolism, Exercise plasma metabolomics and xenometabolomics in obese, sedentary, insulin-resistant women: impact of a fitness and weight loss intervention. Resources in this dataset:Resource Title: Supporting Materials 1, exercise plasma metabolite excursions, annotated metabolites. File Name: Supporting Materials 1, exercise metabolite excursions, annotated metabolites, 7-23-19.pdfResource Description: Blood plasma concentrations of known, annotated metabolites in adult women during exercise at ~65W for 30 min, then 20 min cool-downResource Software Recommended: Adobe Acrobat,url: https://acrobat.adobe.com/us/en/acrobat/pdf-reader.html Resource Title: Supporting Materials 2, exercise plasma metabolite excursions, non-annotated (unknown identity) metabolites. File Name: Supporting Materials 2, exercise metabolite excursions, non-annotated (unknown identity) metabolites, 2-7-19.pdfResource Description: Blood plasma concentrations of non-annotated (as yet to be identified) metabolites in adult women during exercise at ~65W for 30 min, then 20 min cool-downResource Software Recommended: Adobe Acrobat,url: https://acrobat.adobe.com/us/en/acrobat/pdf-reader.html Resource Title: Supporting Materials 3, Correlation Stats, Pre & Post exercise plasma metabolite patterns in adults, All Timepoints. File Name: Supporting Materials 3, Correlation Stats, Pre & Post, All Timepoints, 2-16-19 FOR SUBMISSION xls.xlsResource Description: Correlation data for plasma metabolites using data across 30 min of sub-maximal exercise (~65W), then 20 min cool-down, in adult womenResource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Supporting Materials 4, CDS_SA0002 Analysis Results. File Name: Supporting Materials 4, CDS_SA0002 Analysis Results, 2-16-19 FOR SUBMISSION xls.xlsResource Description: Plasma metabolomics data from sub-maximal (~65W) exercise in adult womenResource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

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Objective: Research to date has not provided a clear understanding of how different grades and majors affect the physical fitness of college students. It is postulated that there are significant disparities in physical health among college students of different grades and majors. The purpose of this study was to evidence these health disparities and to engage in an extensive analysis and discussion thereof. Methods: A sample of 8,772 (2,404 boys and 6,368 girls) Chinese college students from freshman to junior years, aged 17-22, including 12 different majors in four colleges, were recruited in Jiangxi Province. All seven physical fitness indicators (body mass index (BMI), forced vital capacity, 50-m dash, standing long jump, sit and reach, upper body muscle strength, and endurance runs) were conducted for all participants. One-way ANOVA and LSD tests were conducted to compare the physical fitness scores of different grades in the same major. Independent sample t-tests were utilized to compare the differences in every physical fitness indicator for different majors. Pearson’s correlations among 12 majors for every grade were conducted to study the significant corrections between the two physical fitness indicators. The body mass index (BMI) and physical fitness indicator (PFI) for college students of different grade were investigated using a nonlinear regression model. Results: The current state of physical fitness among college students is concerning, as the majority of students were barely passing (with a passing rate of 75.3%). Specifically, junior students exhibited lower scores than freshman and sophomore students across all 12 majors. From freshman to junior year, majors of music (78.01±4.58), English (79.29±5.03), and education (76.26±4.81) had the highest scores, respectively, but major art consistently scored the lowest, which were 73.85±6.02, 74.97±5.53, and 72.59±4.84, respectively. Pairwise comparisons revealed more significant differences in individual physical fitness indicators among the three grades in humanities than in sciences. Pearson’s correlations showed significant correlations among seven physical fitness indicators in all three grades. PFI had a parabolic trend with BMI both for boy and girl college students in Jiangxi province. Conclusion: The physical fitness indicators of college students in Jiangxi province significantly differed in grades and majors, showing undesirable phenomena. The physical fitness of senior and humanities major college students was much weaker and needs sufficient attention. The relationship between BMI and PFI presented an inverted “U”-shaped change characteristic. Continued nationwide interventions are needed to promote physical activity and other healthy lifestyle behaviors in China.