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Table S1. Subjects demographic, anthropometric and clinical characteristics: All. Table S2. Brachial artery characteristics and vascular reactivity indexes: All. Table S3. Subjects demographic, anthropometric and clinical characteristics: Reference Intervals subgroup (´European criteria´). Table S4. Brachial artery characteristics and vascular reactivity indexes: Reference Intervals subgroup (´European criteria´). Table S5. Subjects demographic, anthropometric and clinical characteristics: Reference Intervals subgroup (´HUNT-FIT criteria´). Table S6. Brachial artery characteristics and vascular reactivity indexes: Reference Intervals subgroup (´HUNT-FIT criteria´). Table S7. Multiple regression models (with interaction terms between age and sex) as determinant of vascular reactivity indexes: Reference Intervals subgroup (´European criteria´ and ´HUNT_FIT criteria´). Table S8. Age-related and/or sex-related RIs: schematic diagram. Table S9. Age-related mean and standard deviation equations: mathematical model summary (Reference Intervals subgroup: ´European criteria´ and ´HUNT-FIT criteria´). Table S10. ΔDD Peak_Basal [mm] reference intervals: All (European criteria). Table S11. DD Ratio Peak_Basal [%] reference intervals: All (European criteria). Table S12. FMD% [%] reference intervals: All (European criteria). Table S13. TPD FMD% [seconds] reference intervals: All (European criteria). Table S14. ΔDD Peak-Basal/ΔVPeak-Basal [mm/cm/s] reference intervals: All (European criteria). Table S15. FMD/ΔVPeak_Basal [1/cm/s] reference intervals: All (European criteria). Table S16. pFMDv reference intervals: All (European criteria). Table S17. FMD% WSS reference intervals: All (European criteria). Table S18. ΔDPrerelease_Basal [mm] reference intervals: All (European criteria). Table S19. DD Ratio Prerelease_Basal [%] reference intervals: All (European criteria). Table S20. L-FMC% [%] reference intervals: All (European criteria). Table S21. ΔDDPrerelease_Basal/ΔEDVPrelease_Basal reference intervals: All (European criteria). Table S22. L-FMC/ΔVPrerelease_Basal [1/cm/s] reference intervals: All (European criteria). Table S23. pL-FMCv reference intervals: All (European criteria). Table S24. L-FMC%/WSS reference intervals: All (European criteria). Table S25. TVR [%] reference intervals: All (European criteria). Table S26. TVR [%] reference intervals: Female (European criteria). Table S27. TVR [%] reference intervals: Male (European criteria). Table S28. ΔVPeak_Basal [cm/s] reference intervals: Male (European criteria). Table S29. ΔWSSPeak_Basal [dyn/cm2] reference intervals: All (European criteria). Table S30. ΔWSSPeak_Basal [dyn/cm2] reference intervals: Female (European criteria). Table S31. ΔWSSPeak_Basal [dyn/cm2] reference intervals: Male (European criteria). Table S32. ΔRIPeak_Basal reference intervals: All (European criteria). Table S33. ΔRIPeak_Basal reference intervals: Female (European criteria). Table S34. ΔRIPeak_Basal reference intervals: Male (European criteria). Table S35. ΔRI%Peak_Basal [%] reference intervals: Male (European criteria). Table S36. ΔDD Peak_Basal [mm] reference intervals: All (HUNT-FIT criteria). Table S37. DD Ratio Peak_Basal [%] reference intervals: All (HUNT-FIT criteria). Table S38. FMD% [%] reference intervals: All (HUNT-FIT criteria). Table S39. TPD FMD% [seconds] reference intervals: All (HUNT-FIT criteria). Table S40. ΔDD Peak-Basal/ΔVPeak-Basal [mm/cm/s] reference intervals: All (HUNT-FIT criteria). Table S41. FMD/ΔVPeak_Basal [1/cm/s] reference intervals: All (HUNT-FIT criteria). Table S42. pFMDv reference intervals: All (HUNT-FIT criteria). Table S43. FMD%WSS reference intervals: All (HUNT-FIT criteria). Table S44. ΔDPrerelease_Basal [mm] reference intervals: All (HUNT-FIT criteria). Table S45. DD Ratio Prerelease_Basal [%] reference intervals: All (HUNT-FIT criteria). Table S46. L-FMC% [%] reference intervals: All (HUNT-FIT criteria). Table S47. ΔDDPrerelease_Basal/ΔEDVPrelease_Basal reference intervals: All (HUNT-FIT criteria). Table S48. L-FMC/ΔVPrerelease_Basal [1/cm/s] reference intervals: All (HUNT-FIT criteria). Table S49. pL-FMCv reference intervals: All (HUNT-FIT criteria). Table S50. L-FMC%/WSS reference intervals: All (HUNT-FIT criteria). Table S51. TVR [%] reference intervals: All (HUNT-FIT criteria). Table S52. TVR [%] reference intervals: Female (HUNT-FIT criteria). Table S53. TVR [%] reference intervals: Male (HUNT-FIT criteria). Table S54. ΔVPeak_Basal [cm/s] reference intervals: All (HUNT-FIT criteria). Table S55. ΔWSSPeak_Basal [dyn/cm2] reference intervals: All (HUNT-FIT criteria). Table S56. ΔWSSPeak_Basal [dyn/cm2] reference intervals: Female (HUNT-FIT criteria). Table S57. ΔWSSPeak_Basal [dyn/cm2] reference intervals: Male (HUNT-FIT criteria). Table S58. ΔRIPeak_Basal reference intervals: All (HUNT-FIT criteria). Table S59. ΔRIPeak_Basal reference intervals: Female (HUNT-FIT criteria). Table S60. ΔRIPeak_Basal reference intervals: Male (HUNT-FIT criteria). Table S61. ΔRI%Peak_Basal [%] reference intervals: All (HUNT-FIT criteria). Table S62. ΔRI%Peak_Basal [%] reference intervals: Female. (HUNT-FIT criteria). Table S63. ΔRI%Peak_Basal [%] reference intervals: Male (HUNT-FIT criteria). (XLSX)

Workout Data

The dataset provided includes the logged data of my own strength workouts following the 5/3/1 BBB routine. While some insights were derived in an article I published recently, there is an opportunity for the community to benefit from the open sourcing of this data.

Most notably, I haven't found time to come up with a way of training and applying performance metrics against the data which I have labeled; and I'm hoping that the work I've spent to prepare a decent dataset can be picked up by someone looking to try out computer vision but on a dataset that has a clearer use case than some of the toy datasets that are currently open sourced.

The goal is to try to build an ML model that takes either phone images or scans of workout sheets, and automatically transfer them into the more structured Excel format for easier data gathering.

Content

There are 3 folders contained in the dataset, all files within the folder are datestamped by filename as DD-MM-YYYY: Excel Data This is considerable as the labeled data to a matching phone image or scanned image. There is an Excel file for each workout performed. Phone Images These are images of the filled out workout sheets as taken by my Android phone. More recently I have stopped taking phone images of my workout sheets, but about 85% of the Excel data has a matching phone image. While these images represent a harder challenge for computer vision, the ease of taking these images makes them much more practical as a future deployable mobile application. Scanned Images These are scans of the filled out workout sheets as scanned on my HP Deskjet printer. These scans are higher quality than the mobile images, however the lack of quick and easy access to scanners means that it is harder to gain a userbase as a potential future product.

Although new treatments have improved survival for multiple myeloma (MM), quality of life remains poor for people with this incurable cancer. We conducted a multi-site randomized, waitlist-controlled trial of an individualized exercise program for people at all stages of MM (n=60). Compared to the waitlist control group, participants of the 12-week intervention had significant improvement in health-related quality of life, mediated through improved MM symptoms, cardiorespiratory fitness and bone pain, with were mostly maintained at follow-up (up to 12 months). Exploratory plasma metabolomics and lipidomics was conducted to delineate molecular mechanisms and biomarkers

Subjects and MRI protocol

Imported from https://openneuro.org/datasets/ds001378

Nine spinocerebellar ataxia type 2 (SCA2) patients and 16 age-matched healthy controls, were examined twice (SCA2 patients 3.6±0.7 years and controls 3.3±1.0 years apart) on the same 1.5T MRI scanner (Philips Intera, Best, The Netherlands) by acquiring:

• sagittal 3D T1-weighted turbo gradient echo images [repetition time (TR) = 8.1 ms, echo time (TE) = 3.7 ms, flip angle = 8°, inversion time = 764 ms, field of view (FOV) = 256 mm × 256 mm, matrix size = 256 × 256, 160 contiguous slices, slice thickness = 1 mm];
• axial diffusion-weighted images by using a single-shot echo-planar imaging sequence (TR = 9394 ms, TE = 89 ms, FOV = 256 mm × 256 mm, matrix size = 128×128, 50 slices, slice thickness = 3 mm, no gap, number of excitations = 3). Diffusion sensitizing gradients were applied along 15 non-collinear and non-coplanar directions using b-value of 0 (b0 image) and 1000 s/mm2.

Defacing

Pydeface was used on all anatomical images to ensure de-identification of subjects.

How to acknowledge

Please cite: Mascalchi M, Marzi C, Giannelli M, Ciulli S, Bianchi A, Ginestroni A, Tessa C, Nicolai E, Aiello M, Salvatore E, Soricelli A, Diciotti S. DTI histogram analysis reveals progression of pontocerebellar degeneration in SCA2. DOI:10.1371/journal.pone.0200258

The global PVC sport flooring market, valued at $11.47 billion in 2025, is projected to experience robust growth, driven by increasing participation in sports and fitness activities worldwide. A compound annual growth rate (CAGR) of 5.7% from 2025 to 2033 indicates a substantial market expansion, reaching an estimated $18.2 billion by 2033. Key drivers include the rising popularity of various sports, the growing demand for safe and durable flooring in gyms and fitness centers, and increasing investments in sports infrastructure globally. The market is segmented by application (sports arenas, school & gym halls, fitness centers, dance centers, and others) and type (4.5mm, 5mm, 6mm, 7mm, 8mm, and others), offering diverse choices tailored to specific needs and budgets. North America and Europe currently dominate the market, fueled by high disposable incomes and established sports infrastructure. However, rapidly developing economies in Asia Pacific, particularly China and India, are expected to witness significant growth, presenting lucrative opportunities for manufacturers. The increasing preference for environmentally friendly and sustainable flooring solutions will further shape the market landscape, prompting manufacturers to innovate and offer eco-conscious products. Competitive pressures among established players like Tarkett, Armstrong, and Gerflor are driving innovation and price competition, benefiting consumers. Growth will be fueled by several factors including the expansion of the fitness industry, government initiatives promoting physical activity, and the increasing awareness of the importance of injury prevention in sports. The demand for specialized flooring, such as those designed for specific sports like basketball or volleyball, is also expected to drive growth within certain segments. While challenges like fluctuations in raw material prices and environmental regulations exist, the overall market outlook remains positive, with significant potential for expansion in both established and emerging markets. The preference for high-performance flooring solutions that offer superior shock absorption, durability, and aesthetic appeal will continue to shape market trends. The ongoing development of innovative PVC flooring technologies incorporating advanced materials and enhanced features will further drive market growth and create new opportunities for market players.

Since 27.04.2016, I've been wearing Mi Band by Xiaomi every day.

This fitness tracker collects information about physical activity (steps, distance, calories, etc.) and sleep. Below you can find two datasets, one for steps and the other for sleep.

Current datasets are 27.04.2016 - 14.01.2023 (dd.mm.yyyy). In total 2454 days. I update the datasets approximately every 6 months.

For steps, there is almost no empty data. However, data for around 15 days have been corrupted so I changed it into zeroes. Regarding sleep, there are slightly fewer days than for the steps dataset because sometimes I did not sleep at night, plus Mi Band does not record sleep during the daytime (it does since 6 gen). Also, "start" and "stop" timings are changing their format in the second half of 2022.

There are more data features that I may share by demand. The most interesting of them is heart rate, which is measured every 5 minutes. However, this function was implemented since the 4th generation, therefore I have had it for fewer days. Also, I have some occasional body characteristics that were measured by Mi Scale 2. My current Mi Band is 7th gen.

If you are wondering what to do with 'lastSyncTime', be sure to check Notebooks.

Contains a table with the values of eight fish fitness traits. Furthermore, the fish ID number (Fish_ID), the species name (Species), the fish family (Family_ID), the year of dissection (Year), treatment (Treatment) and fish sex (Sex) are given. The fitness traits measured include the length in mm and weight in g of fish, the body condition estimated using the residuals of the linear regression of log10-transformed weight against log10-transformed body length, the head-kidney weight in g, liver weigth in g, the weight of testes in g, the respiratory burst activity and the concetration of sperm motility (number of spermatozoa/µL) (Sperm concentration).

UK Biobank

2 sets of manual segmentations for 20 UK Biobank retinal images are available for download here. The corresponding original retinal images can only be accessed directly from the UK Biobank health resource following successful registration and application.

If you use this dataset in your work please cite the following paper (due to be published in Dec 2017):

Welikala, R.A., Fraz, M.M., Habib M.M., Daniel-Tong, S., Yates, M., Foster, P.J., Whincup, P.H., Rudnicka, A.R., , Owen, C.G., Strachan, D.P., and Barman, S.A. (2017) Automated quantification of retinal vessel morphometry in the UK Biobank cohort. Image Processing Theory Tools and Applications (IPTA), 7th International Conference on. IEEE.

Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g. genetic variation), and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods, and apply them to empirical data from a wild rodent population. While MM show high false positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false positive rates, but have always low power. Indeed, MM analys...

team_won: The name of the team that won the match. For example, values may include paiN, ALTERNATE aTTaX, etc.

team_lost: The name of the team that lost the match. For example, values may include RED Canids, Case, etc.

stars_of_tournament: The level of importance of the tournament, expressed in stars or another rating format. The higher the value, the more prestigious the tournament. Typically, values range from 0 to 5.

shape: The format of the match, such as bo3 or bo5, indicating the number of games required to win. For example, bo3 means "best of 3," meaning the winner is the one who wins two out of three matches.

event_name: The name of the tournament or event, for example, ESL Challenger League Season 48 South America. This indicates the tournament in which the teams participated.

score: The final score of the match, for example, 2 - 0 or 1 - 2. The first element indicates the number of maps won by the first team, and the second indicates the number won by the second team.

time: The date the match took place in the format YYYY-MM-DD, for example, 2024-10-24. This indicates the exact time when the match was played, which is important for analysis and predicting future matches.

team1: The name of the first team in the match. This is important for identifying specific matches and analyzing their outcomes.

team2: The name of the second team in the match. Similar to team1, this is used for identifying matches.

target: The label indicating the match result, for example, 1 if team1 won or 0 if team1 lost.

The selectivity parameters of D25, D30, D35, D40, D45 and D54 were from the previous study by Yang et al. [8].

Comparison of proposed method (PM), circular and elliptical fit.

In this study, the effect of morphological traits on fresh herbage yield of sorghum x sudangrass hybrid plant grown in Konya province, which is the largest cereal production area in Turkey, was analyzed with some data mining methods. For this purpose, Artificial Neural Networks (ANN), Automatic Linear Model (ALM), Random Forest (RF) Algorithm and Multivariate Adaptive Regression Spline (MARS) Algorithm were used, and the prediction performances of these methods were compared. Plant height of 251.22 cm, stem diameter of 7.03 mm, fresh herbage yield of 8010.69 kg da-1, crude protein ratio of 9.09%, acid detergent fiber 33.23%, neutral detergent fiber 57.44%, acid detergent lignin 7.43%, dry matter digestibility of 63.01%, dry matter intake 2.11%, and relative feed value of 103.02 were the descriptive statistical values that were computed. Model fit statistics, including coefficient of determination (R2), adjusted R2, root of mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), Mean Absolution Error (MAE) and Relative Absolution Error (RAE), were used to evaluate the prediction abilities of the fitted models. The MARS method was shown to be the best model for describing fresh herbage yield, with the lowest values of RMSE, MAPE, SD ratio, MAE and RAE (137.7, 1.488, 0.072, 109.718 and 0.017, respectively), as well as the highest R2 value (0.995) and adjusted R2 value (0.991). The experimental results show that the MARS algorithm is the most suitable model for predicting fresh herbage yield in sorghum x sudangrass hybrid, providing a good alternative to other data mining algorithms.

The goal of this study was to assess and compare the relationships between temperature and different fitness components in a modular animal to reveal the mechanisms underlying TPC’s for fitness. We reared replicated clones of the marine bryozoan Bugula neritina across a thermal gradient ranging from 23°C to 32°C, which reflected the upper thermal range of seasonal variation in the field. TPCs were constructed for survival (measured as zooids states within a colony), growth rate, development to reproductive maturity, and reproductive capacity, and measured over the life span.Data of data collection: 2021-Jun-08 - 2021-Aug-15Geographic location of data collection: Dog Island, Florida, USAFile List:Calculate Realized Temperatures.RFigure 1.RFigure 2.RFigure 3.RFigure 4.RFigure 5.RFSUCML Temp.csvPhenotypic Data.csvOvicell Presence Over Time.csvTemperature Logger Data.csvTemperature Summary.csvRelationship between files:Calculate Realized Temperatures.R uses Temperature Logger Data.csvFigure 1.R uses FSUCML Temp.csvFigure 2.R uses Phenotypic Data.csvFigure 3.R uses Phenotypic Data.csvFigure 4.R uses Ovicell Presence Over Time.csv and Temperature Summary.csvFigure 5.R uses Ovicell Presence Over Time.csv and Temperature Summary.csvMetadata:FSUCML Temp.csvDate: Date (MM/DD/YYYY) when temperature was recorded by a loggerTemperature: Temperature (°C) recorded by handheld YSI Multi-parameter Sonde Time Taken: Time (Eastern Daylight Time) when temperature was recorded by a loggerPhenotypic Data.csvcolony: Unique identifier for each colonygenotype: Identifier for each source colony genotype fragment: Identifier for each fragment taken from a genotypetankID: Unique identifier for the tank that was used as a water bath for bowls of algae. Also indicates the tanks’ target temperaturestarget.temp: Temperature (°C) that tanks were assigned to reachrealized.temp: Median actualized temperature (°C) recorded by a temperature loggerincubator: L = left incubator; R = right incubatorzooids.starting: Total number of zooids comprising a colony when placed into their treatmentovicells.count: Number of ovicells zooids.feeding: Number of zooids possessing a visible gut and lophophore tentacles inside the cystidzooids.regressed: Number of zooids with visible signs of regression (brown bodies or tissue lacking lophophore tentacles)zooids.dead: Cystids entirely lacking polypide tissue (visibly empty)zooids.total: Total number of zooids comprising a colony after 30 days of being placed into their treatmentOvicell Presence Over Time.csvcolony: unique identifier for each colonygenotype: identifier for each source colony genotype fragment: identifier for each fragment taken from a genotypetankID: Unique identifier for the tank that was used as a water bath for bowls of algae. Also indicates the tanks’ target temperaturestarget.temp: Temperature (°C) that tanks were assigned to reachincubator: L = left incubator; R = right incubatordate: Date (MM/DD/YYYY) colony.age.days: Days since the 27 source colonies were born days.since.cutting: Days since fragmented clones were cut from source coloniesdays.in.treatment: Days since fragmented clones were placed into their temperature treatmentszooids.starting: Total number of zooids comprising a colony when placed into their treatmentovicells.presence: 0 = ovicells absent on colony; 1 = ovicells present on colonyovicells.count: Number of ovicellszooids.total: Total number of zooids comprising a colony after 30 days of being placed into their treatmentTemperature Logger Data.csvlog: The nth recording made by a temperature loggerdate: Date (MM/DD/YYYY) when temperature was recorded by a loggertime: Time (Eastern Daylight Time) when temperature was recorded by a loggertemp: Actualized temperature (°C) recorded by a temperature loggertarget.temp: Temperature (°C) that tanks were assigned to reachincubator: L = left incubator; R = right incubatorlogger: Unique identifier for each temperature loggertankID: Unique identifier for each tank

IntroductionPhysical fitness is an important prognostic factor in heart failure (HF). To improve fitness, different types of exercise have been explored, with recent focus on high-intensity interval training (HIT). We comprehensively compared effects of HIT versus continuous training (CT) in HF patients NYHA II-III on physical fitness, cardiovascular function and structure, and quality of life, and hypothesize that HIT leads to superior improvements compared to CT.MethodsTwenty HF patients (male:female 19:1, 64±8 yrs, ejection fraction 38±6%) were allocated to 12-weeks of HIT (10*1-minute at 90% maximal workload—alternated by 2.5 minutes at 30% maximal workload) or CT (30 minutes at 60–75% of maximal workload). Before and after intervention, we examined physical fitness (incremental cycling test), cardiac function and structure (echocardiography), vascular function and structure (ultrasound) and quality of life (SF-36, Minnesota living with HF questionnaire (MLHFQ)).ResultsTraining improved maximal workload, peak oxygen uptake (VO2peak) related to the predicted VO2peak, oxygen uptake at the anaerobic threshold, and maximal oxygen pulse (all P

Background: Many observational studies have shown a protective effect of physical activity on cognitive ageing, but interventional studies have been less convincing. This may be due to short time scales of interventions, suboptimal interventional regimes or lack of lasting effect. Confounding through common genetic and developmental causes is also possible. Objectives: We aimed to test whether muscle fitness (measured by leg power) could predict cognitive change in a healthy older population over a 10-year time interval, how this performed alongside other predictors of cognitive ageing, and whether this effect was confounded by factors shared by twins. In addition, we investigated whether differences in leg power were predictive of differences in brain structure and function after 12 years of follow-up in identical twin pairs. Methods: A total of 324 healthy female twins (average age at baseline 55, range 43-73) performed the Cambridge Neuropsychological Test Automated Battery (CANTAB) at two time points 10 years apart. Linear regression modelling was used to assess the relationships between baseline leg power, physical activity and subsequent cognitive change, adjusting comprehensively for baseline covariates (including heart disease, diabetes, blood pressure, fasting blood glucose, lipids, diet, body habitus, smoking and alcohol habits, reading IQ, socioeconomic status and birthweight). A discordant twin approach was used to adjust for factors shared by twins. A subset of monozygotic pairs then underwent magnetic resonance imaging. The relationship between muscle fitness and brain structure and function was assessed using linear regression modelling and paired t tests. Results: A striking protective relationship was found between muscle fitness (leg power) and both 10-year cognitive change [fully adjusted model standardised β-coefficient (Stdβ) = 0.174, p = 0.002] and subsequent total grey matter (Stdβ = 0.362, p = 0.005). These effects were robust in discordant twin analyses, where within-pair difference in physical fitness was also predictive of within-pair difference in lateral ventricle size. There was a weak independent effect of self-reported physical activity. Conclusion: Leg power predicts both cognitive ageing and global brain structure, despite controlling for common genetics and early life environment shared by twins. Interventions targeted to improve leg power in the long term may help reach a universal goal of healthy cognitive ageing.