dataesr/scientific-paragraphs-categorization huggingface dataset:
Factors associated with stress at work, at home, and in life in general Stress at home and in life in general was significantly different according to age groups (p = 0. 004 and 0. 019, respectively), mean stress being higher in both the 31-37 years and 47-63 years age groups Data are expressed as N (%), or mean ± SD. Abbreviations: SD (standard deviation). "-" means that the factor was not included in the model kept. compared to the 21-30 years group. The usual work position significantly impacted the 3 fields of stress, particularly for SAMU workers who had a decreased risk of stress compared with workers who were reassigned (stress at work: p = 0. 015, stress at home: p = 0. 021, stress in life in general: 0. 018). Stress at work and at home were significantly different according to professional experience (p = 0.
In these validations, crack inspections in ferritic steel components are simulated using several different configurations: TOFD  [28]  configurations, which are usually used to detect and characterize cracks from their edge diffraction echoes, and pulse echo configurations with various incidences to study both the reflection (normal incidence) and diffraction (oblique incidence) from the crack. The influence of the flaw height and extension is also evaluated notably for small sizes to determine the improvement provided by the FEM model in 3D configurations. For the simulations, the following mechanical parameters have been considered: longitudinal and shear sound velocities of 5900 and 3230 m/s and a density of 7. 9 g/cm 3. All the input data for simulation are exactly the same for all the used models.
A. Dataset A subset of tweets emitted in 2018 was extracted using the Twitter API. This study focuses on tweets from English countries, the majority of which are in the United States and European countries. The content includes the timestamp of the tweets along with their geographical attributes. In addition, health dataset information for liver diseases and sleep apnea, including diseases, symptoms, and remedies, were extracted from health care websites with a combination of manual and automatic methods using Beautiful Soup, a Python package for web-scraping and parsing HTML and XML documents in Python. Table  I  displays the number of diseases, symptoms, and remedies for chronic liver diseases and sleep apnea in the keyword dataset. The data used in this study were obtained from publicly accessible sources, namely Twitter API.
B. Data Preprocessing The collected tweets were input into the preprocessing steps shown in Fig. 1. The raw tweets were primarily extracted with the attributes of places, timestamps, and tweet texts and fed into another step to remove URLs, retweets, hashtags, and usernames. After the clean tweets were obtained, they were filtered by the chronic liver diseases and sleep apnea keywords, including diseases, symptoms, and treatments, to ensure that the tweets were specifically related to these two diseases. In addition, a list of stop words was also used to filter unnecessary words (viz. , subjects, prepositions, articles, or general words) from the tweet data. The filtered tweet file was then joined with the clean health and non-health-related file and converted into a LIBSVM format. With the Octave support, classifying tweets based on health and non-health-related tweets was conducted using MATLAB.
C. Data Processing on T-ATAM 1) Review of Time-Aware Ailment Topic Aspect Model (T-ATAM): In this study, the previously proposed time-aware model, T-ATAM  [8] , was implemented to predict healthrelated tweets, using time as a variable drawn from a multinominal distribution from the corpus. To begin, the data of mapping tweets to documents with different timestamps and geographical attributes were presented. According to the dataset, a set of posts  = {ρ 1 , ρ 2 ,. , ρ n } is considered, where a post is the unit update of user activity on social media, such as Twitter. In a post, there are several attributes, including place identity, timestamp, and post content. Moreover,  = { 1 ,  2 ,. ,  n } is considered as the geographical attributes for each post. T-ATAM is a time-aware model where the timestamp t of each post is treated as a random variable  [4].
Finally, words were inferred from the diseases associated with the document. However, it is noticeable that in T-ATAM, a timestamp is generated for each document depending on the ailment associated with the considered tweet obtained from the database. As timestamp is an observed random variable, generating one time-distribution per ailment {,  ∈ A} to generate a timestamp for a given tweet is necessary. Subsequently, the timestamp according to a multinomial distribution with parameter   , which was obtained from the Dirichlet distribution defined by the vector  specifically for each ailment, was generated depending on the ailment  connected to the document. The process of the model is illustrated in Fig. 2.
2) Data Processing on T-ATAM by Regions: This part discusses the prediction of diseases, symptoms, and remedies in each region of the tweet dataset. First, the tweet posts ρ were grouped and divided by their region  before providing each data into T-ATAM to generate the distribution vectors of the diseases in each region. In this case, the top ailment in each region with the highest distribution was selected along with other information, including symptoms and remedies. The process is shown in Fig. 3.
4) Data Processing on T-ATAM by Treatments: One essential aspect of analyzing health-related tweets is the ability to answer questions related to what happened before and after a given treatment. For instance, the patients' condition after they receive the CPAP treatment. CPAP is a mode of respiratory ventilation used to treat sleep apnea. In this study, the result of the changes in the diseases, symptoms, and remedies was observed from before and after CPAP was introduced. To address those questions, the dataset was split into three main categories for each region: PRE (before CPAP is mentioned in the tweets), DURING (three months after CPAP is first mentioned), LONG-TERM (later than the three months from when CPAP is first mentioned). Then, T-ATAM was applied to obtain sets, as shown in Fig.
According to the dataset, the word "snoring" appeared in 1084    tweets in 395 regions while "CPAP" in 55 tweets in 41 regions. The study on the impact of CPAP on the users' tweets was also conducted in order to observe the changes in the diseases, symptoms, and remedies of sleep apnea in each region. The different terms which have a similar meaning to "snoring" include "snore", "snores", "snored", "snoring", "wheeze", "wheezing", and "wheeze". At the same time, words that are similar to "CPAP" and its related therapy include "mask", "masks", "positive airway pressure (PAP)", "Philips", "compliance", "humidifier", and "therapy".
Liquid chromatographic conditions: Qualitative and quantitative analyses of lyophilized mamey apple pulp extracts were carried out using an Ultra-Fast Liquid Chromatography (UFLC) Shimadzu Prominence (Kyoto, Japan) analytical chain comprising the following elements: -two LC-20AD Prominence pumps, -an analytical column Lichrocart Lichrospher PR-18 (250mmx4mm, 5μm) (Merck, Darmstadt, Germany) equipped with a pre-column Lichrospher PR-18 (5μm) (Merck, Darmstadt, Germany), a DGU-20A5 Prominence degasser, -SIL-20ACHT Prominence automatic sample changer, a SPD-M20A Prominence Diode Array Detector (DAD), and a LC Solution software acquisition system (Shimadzu, Kyoto, Japan). The compounds to be separated were eluted by a concentration gradient consisting of:.
UPLC-MS-ESI analyses: After analysis of retention times, spectral characteristics and fragmentations acquired by UPLC-MS-ESI, we identifi ed or attempted to identify 33 compounds in mamey apple pulp extracts. The analytical data recorded by MS for each chromatographic peak are presented in Table  2. The compounds identifi ed in mamey pulp extract by UPLC-MS analysis are reported in the Figure  2. Compounds 1 to 33 presented in Table  2. The phenolic classes found in our samples are cited below: • First class: fl avan-3-ols including one monomeric compound and 6 dimers or trimers subform (peaks n°8, 14, 15, 18, 20, 21, 33), • Second class: 10 glycosylated hydroxycinnamic acids and some derivatives (peaks n°  4, 10, 11, 12, 13, 16, 23, 25, 26, 32) , -6 glycosylated hydroxybenzoic acids and some derivatives (peaks n° 2, 3, 5, 9, 17, 24), to a sugar molecule or by an ether, ester or acetal type bond to another molecule.
Experimental Setup A digital piano with a real key action (KAWAI, VPC-1) was used in the experiment to collect data representing the timing, pitch, and velocity of the individual key presses and releases (i. e. MIDI information) with a custom-made LabVIEW (National Instruments) program. The instrumental sound was elicited via a headphone attached on the participant's ears. The surface electromyography (EMG) system with two sets of wirelessly connected electrodes (Trigno Quattro sensors, Delsys Inc. ) was connected to a laptop through an analog-to-digital board (NI USB-6363; National Instruments). Each electrode was placed on the muscle belly of the extensor digitorum communis (EDC) and flexor digitorum superficialis (FDS) of the right hand. The EMG signals were amplified, band-pass filtered (10-500Hz), and sampled at 1kHz using LabVIEW.
Data analysis Movement variables. The MIDI information obtained from the keyboard was used as variables for evaluating the keystroke performance. The Coefficient of Variation (CV) of the inter-onset interval of two successive keystrokes was used as a variable representing stability of the tempo, whereas the CV of the keystroke velocity was used as an index representing the loudness stability. Data of the finger pressure and key motions were cut for each press/keystroke as epochs according to a threshold (three times of the standard deviation of the signals prior to the task performance), which was used for time normalization of the epochs. These data were averaged across the epochs for smoothing, and the diff function in MATLAB (Mathworks Inc. ) was used to compute the first and second derivatives of the vertical position of the key.
Materials and methods We used data from the European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (EPAD LCS) for this study. This cohort was selected as it recruited participants both within and outside of the Mediterranean region, allowing an exploration of differences between dietary associations with cognition by region. Explained in full elsewhere  (Ritchie et al. , 2016; Solomon et al. , 2018) , briefly the EPAD LCS is a pan-European cohort of participants recruited across a spectrum of risk states for AD dementia. Participants were eligible for inclusion in the EPAD LCS if they were aged 50 years or older, were in generally good health, were able to undergo an MRI and lumbar puncture, did not have dementia at baseline and had a study partner. Participants underwent a baseline visit with several assessments and were followed up at 6 months and then once a year for the duration of the project  (2015) (2016) (2017) (2018) (2019) (2020).
Due to the ongoing recruitment throughout the project, participants have varying lengths of follow up (from one visit up to six visits). Participants consented for their data to be used in future research projects and data from the EPAD LCS is fully open access. After completing a data access request, following the EPAD procedures, we accessed the data from the Alzheimer's Disease Drug Foundation (ADDF) platform. We used data from baseline, year 1, year 2 and year 3 for this study. The main focus of the study was to test for cross-sectional associations. Additional analyses were conducted to explore longitudinal cognitive performance and Mediterranean Diet Adherence Screener (MEDAS) scores.
Data We used the EPAD LCS v. IMI dataset for this analysis, following approval of a data access request (ep-ad. org/openaccess-data/overview). EPAD LCS is registered at www. clinicaltrials. gov Identifier: NCT02804789. The dataset includes data from all participants who consented to join the study. Data used in preparation of this article were obtained from the EPAD LCS data set V. IMI, doi:10. 34688/epadlcs_v. imi_20. 10. 30. We excluded participants with missing data in the exposure, outcome, and covariate variables of interest from the analysis. To determine minimum sample size we used G*Power Version 3. 1. A sample size of 148 participants, with 74 participants in each group, was the minimum required for an α of 0. 05 and a power of 0. 95.
Sociodemographic data Participants self-reported sociodemographic data through interviews with research staff. For this analysis we were interested in age (in years), education (in years), family history of dementia, sex and whether the site they were attending was based in the Mediterranean, which was used as a proxy for region of residence. We used the European Commission biogeographical region to classify a site as being Mediterranean or non-Mediterranean (European Commission). A list of site allocation is provided in the Supplementary Material (Supplementary Table  S1 ). Both Toulouse and Perugia were situated near the border and for the main analysis are included in the Mediterranean region following consensus discussion by the analytical team. In addition, we conducted a sensitivity analysis with both sites excluded from the Mediterranean region analysis.
Cognition Participants completed a battery of neuropsychological assessments at the baseline, year 1, year 2 and year 3, combining both validated and experimental tasks. Additionally, participants completed cognitive testing at month 6. However, as no dietary data is available at this visit this data was excluded. For this analysis we selected the Four Mountains Test (FMT)  (Chan et al. , 2016)  and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)  (Karantzoulis et al. , 2013). The FMT is a task assessing allocentric processing, with participants asked to initially study an image of four mountains and after a brief interval select which one of the four images then presented matches the stimulus but shown from a different point of view. We calculated total scores for each participant with higher scores indicating better performance.
Statistical analysis All statistical analyses were done in R (Version 4. 1. 0). Participants with missing data in the variables of interest were excluded from the analysis. Descriptive statistics were calculated for all participants. We initially tested the cohort as a whole and fitted univariate and fully adjusted linear regression models to test for associations between MEDAS and the co-primary outcomes (FMT, RBANS) at baseline. The fully adjusted model included the following variables: sex, age, education, family history, APOE, physical activity, smoking, sleep, BMI, hypertension, hypercholesterolemia, hyperglycaemia, diabetes, stroke, antihypertensive medication use, diabetic medication use, living in the Mediterranean. All covariates were included in the models simultaneously. We tested for interactions between dietary score and region lived in.
Our pre-planned stratified analysis split the dataset into participants living in Mediterranean and non-Mediterranean regions and the previously mentioned analysis was replicated with living in the Mediterranean removed as a covariate from the fully adjusted models. We then repeated the analysis with the MEDAS continuous and then the Pyramid scores to explore whether different definitions of MedDiet adherence impacted the observed associations. Finally, we ran our exploratory analysis repeating the above with the cohort stratified by sex, initially as a whole cohort and then by Mediterranean region. We also analysed the longitudinal associations between diet and cognition across all visits, using linear mixed effect models. We modelled for random effects of participant and visit, and included the same covariates as in previous models, initially running the models in the whole cohort and then splitting by biogeographical region.
Results We included 1826 participants in the RBANS analysis and 1,144 participants in the FMT analysis. Compared to participants who were excluded due to missing data, the participants included in either analysis were more likely to be APOEe4 carriers (RBANS: 38. 9% vs. 21. 6%, p < 0. 001; FMT: 39. 2% vs. 33. 6%, p < 0. 001), were younger in the RBANS, but not the FMT cohort (65. 69 ± 7. 42 years vs. 67. 27 ± 8. 00 years, p: 0. 001) and were more likely to live in the Mediterranean region in either analysis (RBANS: 33. 24 vs. 23. 8%, p = 0. 002; FMT: 38. 2% vs. 24. 8%, p < 0. 001). It may be that the participants with full data were more motivated or able to complete the assessments, however this is just speculative. Full descriptive details of the cohort are presented in Table  1.
These results should be interpreted in the context of a sensitivity analysis with reduced sample sizes We conducted a further sensitivity analysis to remove those with a clinical dementia rating (CDR) score of 0. 5, which is indicative of  MCI (n = 262 in FMT dataset, n = 493 in RBANS dataset). This has no effect on the analysis when the data was considered as a whole cohort on any of the outcome measures. When splitting the data by region there was no longer a significant association between MEDAS score and FMT score for those in the Mediterranean region if people with MCI were excluded (fully adjusted β: 0. 10, SE: 0. 09, p: 0. 24), however the association remained outside of the Mediterranean region (fully adjusted β: 0. 13, SE: 0. 06, p: 0. 02). There were no associations seen in either region with any of the RBANS indexes when those with MCI were excluded from the dataset.
, 2017). This rigorously designed cognitive test battery designed specifically to detect the earliest stages of AD is a key strength of both the EPAD cohort and this study investigating cognitive outcomes. We applied three scoring methods to the dietary data to replicate the analytical approach taken in the EPIC-Norfolk study  (Shannon et al. , 2019). The MEDAS score is one of the most commonly used scores in MedDiet research hence was identified as the primary exposure variable. The two continuous scores were included to provide greater sensitivity which may be important particularly in non-Mediterranean regions where achieving traditional MEDAS cut-offs for each food group may be difficult for participants. Both continuous measures were used as the Pyramid is more widely used, and the MEDAS Continuous has been developed using cohorts recruiting participants in the United Kingdom.
The characteristics of the CISE-LOCEAN seawater isotope dataset (δ 18 O, δ 2 H, referred to as δD) are presented (https://doi. org/10. 17882/71186; Waterisotopes-CISE-LOCEAN, 2021). This dataset covers the time period from 1998 to 2021 and currently includes close to 8000 data entries, all with δ 18 O, three-quarters of them also with δD, associated with a date stamp, space stamp, and usually a salinity measurement. Until 2010, samples were analyzed by isotopic ratio mass spectrometry and since then mostly by cavity ring-down spectroscopy (CRDS). Instrumental uncertainty in this dataset is usually as low as 0. 03 ‰ for δ 18 O and 0. 15 ‰ for δD. An additional uncertainty is related to the isotopic composition of the in-house standards that are used to convert data to the Vienna Standard Mean Ocean Water (VSMOW) scale.
Different comparisons suggest that since 2010 the latter have remained within at most 0. 03 ‰ for δ 18 O and 0. 20 ‰ for δD. Therefore, combining the two uncertainties suggests a standard deviation of at most 0. 05 ‰ for δ 18 O and 0. 25 ‰ for δD. For some samples, we find that there has been evaporation during collection and storage, requiring adjustment of the isotopic data produced by CRDS, based on d-excess (δD -8×δ 18 O). This adjustment adds an uncertainty in the respective data of roughly 0. 05 ‰ for δ 18 O and 0. 10 ‰ for δD. This issue of conservation of samples is certainly a strong source of quality loss for parts of the database, and "small" effects may have remained undetected.
G. Reverdin et al. :  The CISE-LOCEAN seawater isotopic database  The internal consistency of the database can be tested for subsets of the dataset when time series can be obtained (such as in the southern Indian Ocean or North Atlantic subpolar gyre). These comparisons suggest that the overall uncertainty of the spatially (for a cruise) or temporally (over a year) averaged data is less than 0. 03 ‰ for δ 18 O and 0. 15 ‰ for δD. However, 18 comparisons with duplicate seawater data analyzed in other laboratories or with other datasets in the intermediate and deep ocean suggest a larger scatter. When averaging the 18 comparisons done for δ 18 O, we find a difference of 0. 082 ‰ with a standard error of 0. 016 ‰. Such an average difference is expected due to the adjustments applied at LOCEAN to saline water data produced either by CRDS or isotope ratio mass spectrometry (IRMS), but the scatter found suggests that care is needed when merging datasets from different laboratories.
The GEOSECS program  (Östlund et al. , 1987)  provided the first consistent global dataset of seawater isotopes, but with limited data coverage. The Global Seawater Oxygen-18 Database at NASA GISS  (Schmidt et al. , 1999)  has assembled most water isotope data collected prior to 1998, with an effort to homogenize the dataset, when possible, by estimating biases based on multiple measurements of deep-water samples  (Schmidt, 1999; Bigg and Rohling, 1999). A large part of the early analyses was done by isotope ratio mass spectrometry (IRMS) and more recently using cavity ringdown spectrometry (CRDS). Walker et al. (2016)  illustrated that the two measurement techniques can provide equivalent results with no obvious biases. Since 1998, the isotopic platform facility at LOCEAN (later "CISE-LOCEAN") has measured seawater isotopic composition of samples collected on a series of oceanographic cruises or ships of opportunity, mostly in the North Atlantic, the equatorial Atlantic, the southern Indian Ocean, and the Southern Ocean.
This dataset of δ 18 O and δD of marine water covers the period 1998 to 2021 and is ongoing. Most data prior to 2010 (only δ 18 O) were produced using an Isoprime IRMS coupled with a Multiprep system (dual inlet method), whereas most data collected since 2010 (and a few earlier data) were obtained by CRDS, usually with a Picarro L2130-i or less commonly a Picarro L2120-i. Occasionally, some samples were also run on an Isoprime IRMS coupled to a GasBench (dual-inlet method) at the university of Iceland  (Reykjavik). There are also a few pairs of samples measured on both systems. Most of these LOCEAN data are not currently included in the Global Seawater Oxygen-18 Database at NASA GISS  (Schmidt et al. , 1999) , except for the 1998 OISO cruise data (note that earlier datasets measured by coauthor C.
Pierre on other mass spectrometers preceding the current IRMS are included in the NASA GISS database). Subsets of the LOCEAN data have been used in publications  (Akhoudas et al. , 2020 (Akhoudas et al. , , 2021;; Benetti et al. , 2015 Benetti et al. , , 2017a Benetti et al. , , b, 2019;; Reverdin et al. , 2019) , with the subsets corresponding to measurements at LOCEAN over a short period with specific instrumental and analysis protocols. A regional surface North Atlantic subset of the data was also presented in  Reverdin et al. (2018a). Here, we review the errors and uncertainties in this published dataset (Waterisotopes-CISE-LOCEAN, 2021) and the extent to which the overall dataset of δ 18 O, δD, and d-excess (d-excess = δD -8 × δ 18 O) presented as ‰ versus VSMOW is internally consistent.