dataesr/scientific-paragraphs-categorization huggingface dataset:
Calculations and coding The variables of interest were ingested, duodenal and absorbed flows, and milk secretion of EFA-all on a daily basis. For each EFA,  the intake (g/d) was calculated by multiplying diet EFA content (g/ g of total FA) by dietary total FA content (g/kg of DM) and DMI (kg/ d). For experiments providing diet EFA content in g/kg of DM, diet EFA content was directly multiplied by DMI to obtain EFA intake. Milk EFA yield (g/d) was calculated by multiplying milk EFA content (g/g of total FA) by milk fat yield (g/d) and by a coefficient of 0. 933, which is the FA content of total milk lipids  (Glasser et al. , 2007). The milk fat depression (MFD) diets were coded in the database, and the other diets were coded as control (CTL) diets. The MFD diets were considered as the diets that alter fermentation in the rumen, leading to the release of specific RBH intermediates such as trans-10, cis-12 conjugated linoleic acid which is known to reduce both lipogenesis in the mammary gland and plasma uptake of preformed FA by the mammary gland  (Gervais et al.
Consequently, between-experiment regressions of daily ingested, duodenal and absorbed EFA flows (EFA input flow ) on BW and daily milk EFA secretion flow (EFA output flow ) were studied according to the following equation: EFA input flow;i ¼ b1 Â BW i þ b2 Â EFA output flow;i þ e i where b1 (g of EFA input flow/d per kg BW) is the coefficient for the BW i , b2 (g of EFA input flow/g of EFA output flow) is the coefficient for the EFA output flow,i and e i is the residual error of the model (i = 1,. , n experiments). All models were coded in R (version 4. 0. 2; R Foundation for Statistical Computing, Vienna, Austria) using the lm function in the ''stats" package (version 4. 0. 2). The residuals of the models were regressed against the main potential quantitative interfering factors of the database to determine how these factors could modify the relationships between input EFA flows, BW and milk EFA flows  (Sauvant et al.
Evaluation of the models The models were evaluated using three different methods: external validation, leave-one-out cross-validation and 10-times-repeated 5-fold cross-validation. These methods were run together and compared for each model. First, an external validation was performed by randomly splitting each initial dataset into a training dataset for model development and a test dataset for model evaluation with the ''caret" package in R (version 6. 0-88) using training-to-test datasets size ratio of 70/30. In accordance with the statistical unit used in this approach (mean value of treatments per experiment), the splitting of the dataset was performed by experiment. The two datasets resulting from the splitting were compared by ANOVA on the main variables of interest (diet composition, animal characteristics, milk composition, ingested, duodenal and absorbed EFA flows, milk EFA flow, EFA content in diet and milk) to ensure homogeneity between the training and test datasets.
Then, leave-one-out cross-validation was performed with the ''caret" package in R using the ''LOOCV" method. Finally, 10-times-repeated 5-fold cross-validation was run with the ''caret" package in R using the ''repeatedcv" method with k = 5 and number of repetitions = 10. The performance of each model to accurately relate input flows of LA and ALA to BW and milk LA and ALA flows was assessed with several model evaluation metrics. For both external validation and cross-validation, the adjusted R 2 validation and the root mean square of prediction error were calculated. For external validation, errors in central tendency, errors due to regression, and errors due to disturbances  (Bibby and Toutenburg, 1977)  were also calculated as the three components of the mean square of prediction error.
Models for linoleic acid flows The models used to study the relationships between input LA flows, BW and milk LA flows are presented in Table  4. Models on the whole dataset (1a-6a) are shown first, followed by models on the training dataset (1b-6b) and then the different validation methods. Significant BW-associated coefficients and milk LA secretion-associated coefficients were obtained for all ingested, duodenal and absorbed LA flow models (equations 1b, 3b and 5b). The residuals of the ingested flow model (1b) were positively correlated with dietary starch and total FA and negatively correlated with milk fat content and sum of milk C4:0-C14:0 FA (Supplementary Table  S5 ). The residuals of the duodenal (3b) and absorbed (5b) flow models were positively correlated with concentrate level, dietary starch and total FA, and negatively correlated   with dietary NDF and milk fat content (Supplementary Table  S5 ).
Models for alpha-linolenic acid flows The models used to study the relationships between input ALA flows, BW and milk ALA flows are presented in Table  5. Models on the whole dataset (1c-3c) are shown first, followed by models on the training dataset (1d-3d) and then the different validation methods. Since data of ALA intake (g/d) were not normally distributed, we log 10 -transformed this variable to obtain normality. Significant BW-associated coefficients and milk ALA secretionassociated coefficients were obtained for all ingested, duodenal and absorbed ALA flow models (equations 1d, 2d and 3d). The residuals of the ingested flow model (1d) were negatively correlated with DMI, milk yield, milk fat, protein and lactose yields (Supplementary Table  S6 ). The residuals of the duodenal (2d) and absorbed (3d) flow models were positively correlated with dietary total FA and negatively correlated with milk protein and lactose yields and sum of milk C4:0-C14:0 FA (Supplementary Table  S6 ).
Assessments of model quality For all models, coefficients were stable between the whole datasets and the training datasets. Moreover, adjusted R 2 and RMSE were similar between the whole and training datasets, indicating a similar share of the data variance explained by the models and a similar error between models. The share of the data variance explained by the models was higher for LA models compared to ALA models based on adjusted R 2 validation, though the training and test datasets used very similar numbers of data. For all LA and ALA models, the root mean square of prediction error was very similar to the RMSE of the models developed. For all LA and ALA models, errors due to disturbances accounted for the largest share of the mean square of prediction error (90-95% of mean square of prediction error for LA models and 74-94% of mean square of prediction error for ALA models), indicating that errors were mainly due to weak correlations between observed and fitted values.
Methodological limitations in reassessing  Prado et al. (2019)  models for dairy cows could also explain the difference in results. Indeed, the number of available data for dairy cows only was lower than in  Prado et al. (2019)  who used data of bovine (beef and dairy cows) and ovine species. In the publications used for building the models, BW variations were almost never reported, and as we related input EFA flows to BW and milk EFA flows, we can wonder if BW and milk EFA flows account for variations in body reserves, as cows were mostly in mid-lactation (mean DIM = 123 d in training LA and ALA subsets) and probably replenished body reserves. Variations in body reserves could be encompassed in either the BW-associated coefficient or milk EFA secretion-associated coefficient. As the BW used in the models was often measured at the beginning of experiments (74 and 76% of treatments in LA and ALA datasets, respectively), it is likely that the BW-associated coefficient does not account for the variations in body reserves.
Towards an improvement in the variables modelled In both the LA and ALA models, errors due to disturbances accounted for the largest part of the mean square of prediction error, indicating that errors were mainly due to weak correlations between observed and fitted values. This weak correlation could be explained by the lack of precision and the uncertainties related to the variables modelled (BW, milk LA or ALA secretion, and ingested, duodenal and absorbed LA or ALA flows). The BW used in the models was often measured at the beginning of experiments (data found in most publications), and this does not always reflect BW at sampling time. Moreover, most papers fail to specify how BW was measured (beginning, middle or end of the experiment; morning or evening measure; single or repeated measure).
Finally, uncertainties around duodenal and absorbed flows of LA and ALA exist since these flows are obtained from prediction equations (see the above-mentioned limitations in reassessing  Prado et al. (2019) models) , which implies that our present models are based on either measured data (ingested flow models) or calculated data (duodenal and absorbed flow models). Ideally, models based on measured data only could lead to more precise relationships between EFA flows, but there are scarce few published trials measuring both duodenal (or absorbed) flows of EFA and their secretion into milk. As both LA and ALA datasets are mostly based on lipidsupplemented diets, it is likely that cows are overfed LA and ALA, and that they oxidise the excess of LA and ALA as general energy sources. Ideally, the inclusion of diets with very low or almost depleted LA or ALA content could allow more accurate relationships between EFA flows, as these EFA are less prone to oxidation.
Conclusion This study was based on a meta-analysis applied to a large dataset of published data from nutrition-trial experiments measuring diet and milk content of LA or ALA in mid-lactation dairy cows. The between-experiment regression approach has been used in accordance with the approach proposed by INRA feed unit systems (2018) in renewing energy requirements. We analysed betweenexperiment relationships between ingested, duodenal, or absorbed flows of LA and ALA, BW and milk LA and ALA flows in midlactation dairy cows. These relationships could be integrated into the renewed INRA feed unit systems for energy and protein, and could be used to improve feed ration formulation in dairy cows.
Dietary intakes from 24 h recall Dietary intakes data were collected by face-to-face interview using a structured questionnaire to probe for all foods consumed over a 24 h period. Multiple pass method was used for data collection. Initially, respondents were asked to list all the foods and drinks they consumed the day before during the waking hours. For each food, respondents estimated the amounts eaten using a photographic atlas of portion sizes. A single 24 h recall was used, with data collected on weekdays. Energy and nutrient intakes were calculated using a customised version of the NutriSurvey for Windows 2007. First, the latest 2017 version of the Indonesian Food Composition Table  (19)  was manually entered into the software. The Indonesian FCT consists of 1128 foods (including cooked foods/ menu), categorised into 13 food groups and provides data for energy, water (moisture content) and 19 nutrients.
Plan of data analysis The survey sample had more young urban males (18-24 years), more urban females (30-39 years) and more rural females (ages 20-39 years) than the national averages. The sample was then weighted by population density, by urban rural location, age and sex to make it representative of the Indonesian population. Univariate tests of differences across socio-demographic groups were tested using one-way ANOVA. Associations between intake of protein and protein diversity and socio-demographic variables (e. g. age, gender, wealth, modernisation, urbanisation) and cultural variables (ethnicity, area) were assessed based on one-way ANOVA and χ 2 tests. Associations between protein diversity and adequacy of protein and essential amino acid (EAA) intakes were assessed with logistic regression models.
g. , those involving digestive disorder, cellulitis, osteitis, pneumonia, and endocarditis) led to a life-threatening event (septic shock), amputation, or death in 36% of patients. Physicians may need to add vibriosis to their list of infections to assess in patients with associated risk factors. This study received a favorable opinion from the Committee of Expertise for Health Research, Studies, and Evaluations (registration no. TPS 1170745). It was authorized by the Commission Nationale de l'Informatique et des Libertés (decision no. DR-2020-125). A letter explaining the study and the patients' rights regarding the use of their data was sent to the last known address of the patients. This study was registered on clinicaltrials. gov (identifier NCT04451707).
Variables and Statistical Methods We retrieved sociodemographic data, sea-related activity, and clinical and therapeutic data from patient medical records. We used means (+SD) to describe continuous variables, and percentages and 95% CIs to describe categoric variables. We explored associated factors with sepsis. We used Mann-Whitney tests to compare continuous data of independent samples where appropriate. We used the Fisher test of homogeneity for categoric variables. We used an α level of 0. 05 for statistical tests, for which we also calculated SDs and 95% CIs.
Ensuring adequate B 12 intake among those adhering to vegan diets The British Dietetic Association (BDA) advises that those following a vegan diet should use good manufacturing practice (GMP) certified vitamin B 12 supplements and consume B 12 fortified foods  [7]. GMP certified status supplements prove that supplements meet the highest standards for manufacturing products regulated by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Data from the Quadram Institute Food Databanks National Capability from studies of the macro/micronutrient composition of available vegan products in the UK supermarkets have shown that most do not commonly or adequately fortify their products with vitamin B 12 [Unpublished data: Zhang L, Langlois E, Nikolaeva A, et al. (2021) The macro and micronutrient composition of vegan diet and links to health benefits].
Other reports from the Food Databanks National Capability (2019) have shown that currently available animal produce, specifically pork-derived products, have significantly lower vitamin B 12 and iodine content (around one-third less) compared with the early 1990s  [64]. This is most likely due to pigs no longer being fed animal offal, i. e. , B 12 status in animals is lower when provided with less animal-based food. Using supplements within a well-planned vegan diet or plant-based diet is likely to provide a more effective and sustainable method to prevent overt vitamin B 12 deficiency  [65]. A few studies have investigated the rate of vitamin B 12 supplementation practices among sub-groups following vegan diets; this is an area for future investigation as these sub-groups are most at risk.
Data collection and analysis First, we reviewed existing literature, including unpublished documents, maps and reports. Second, we conducted a participative diagnosis on people's living conditions and water issues and selected the communities for our case studies (Tab. 1); four communities were chosen, reflecting a diversity of situations: water services (individual, collective), community organization (with active association, without association), and type of water resources (surface and groundwater) and infrastructures (collective and individual networks, wells, storage dams and cisterns). Third, we undertook a historical analysis of the trajectory of the RWSS in the different cases through participant observation, developing live narratives and semi-structured interviews with key stakeholders (Tab.
Acknowledgements. This work was carried out as part of a  joint PhD program  at  UFC (Federal University of Ceará ) and  Institut Agro, Montpellier. The research was supported by  Funceme (Ceará Foundation for Meteorology & Water Resources)  and  CIRAD (Montpellier)  through the  Pacte and Sertões projects , funded by the  French Development Agency (AFD)  and FUNCEME. Part of the field work and analyses in Brazil were carried out jointly with  Elie Boillot.
Recruitment and screening Participants will be recruited via a variety of routes, for example, study databases, webpages, social media, posters and flyers. Potential participants will be prescreened using an online or telephonic prescreening questionnaire in accordance with the inclusion and exclusion criteria. Candidates passing prescreening will be invited to attend an information session, either online or in person, where they will be given detailed information about the study and invited to participate in a question and answer session. Candidates who wish to participate in the study will provide written informed consent and sign a general data protection regulation (GDPR) form before being fully screened. The screening session will be performed in person or online, and will consist of anthropometric measurements (height, weight, waist and hip circumference; all confirmed in person at CID1 for participants being screened online); eligibility questionnaires (EAT-26  28  and short sweet food frequency questionnaire); baseline questionnaires (a sociodemographic questionnaire, a questionnaire to assess habitual sweet food consumption, including regular and S&SE sweet foods (SWITCH sweet food frequency questionnaire),  29  a questionnaire to assess habitual physical activity (International Physical Activity Questionnaire  30  and a consumer perspective questionnaire); an eligibility taste test of the control intervention product where participants rated the pleasantness of the product on a 100 mm VAS after taking one bite and chewing for 5 s (a score of >40 mm was required for inclusion into the study).
Statistical analysis plan Per-protocol analysis will include participants who completed all 6 CIDs and had a level of adherence to the product consumption >80%. The main evaluations for this trial will be to investigate differences between the intervention products (two no added/reduced sugar reformulated S&SE products and one sucrose-sweetened control). Where this is not appropriate for some of the secondary outcomes, descriptive analyses will be used to interpret differences. Data will be pooled across the splitsite (Leeds and Lyon) study using the biscuit matrix. Data will be presented as means and SD. Outcome variables will be checked for normality and transformed where necessary. To account for any missing data, analyses will be conducted using linear mixed models. Models will compare S&SE product conditions versus sucrose control in a 3 (S&SE1, S&SE2, sucrose control) × 2 (exposure day 1 and exposure day 14) within-subject design.
ETHICS AND MONITORING Each intervention site has obtained ethical approval from their local ethical committee. The following details the specific ethical committees and the reference numbers: University of Leeds School of Psychology (PSC-127, approved 19  All study procedures will be conducted in accordance with the Helsinki Declaration and the study protocol has been registered in a public database ( ClinicalTrials. gov NCT04633681; online supplemental material 6). To the extent relevant and reasonable International Council for Harmonisation Good Clinical Practice guidelines will be used, and standard operating procedures will be Open access developed to facilitate the same performance and compliance with the protocol in each centre. All personal data are handled confidentially and stored in accordance with applicable law, GDPR and local laws (see online supplemental material 7).
Which data will be collected and how? The data collected includes information about health and personal data e. g. name, [civil registration number if relevant, include name/type], contact information, gender and ethnicity and biological material (i. e. blood[,urine and faeces]). The data is registered in a personal participant folder and/or in an electronic database. Data and biological material that is sent from the intervention site to other laboratories or researchers will contain a participant number but never your name or any other personal identifying data. The investigators and other data processors will ensure that the information collected about you is not accessed by unauthorized persons and that your identity is protected when the results of the study are published. The online questionnaire delivery platform collecting body sensations and other measures will only use basic cookies to enable the proper functioning of the program.
How will my data be stored? The investigators and the data manager will take all necessary security precautions to ensure that any identifying information about you is kept confidential and stored securely in accordance with local law [include name/references] and EU Regulation. You will be assigned a unique ID number which your data will be identified by throughout the study. All electronic forms of data will be protected with a password which can only be accessed by the researchers. All data recorded on paper will be locked in study-specific storage cabinets accessible only to the researchers on the project.
Appendices: Informed Consent Materials and GDPR only sent from the intervention site to other laboratories with your participant number and never your name or other personal identifying data. Pseudo-anonymised data may also be shared with other researchers who are not working on this study for future research purposes in the public interest. After full anonymisation (destruction of the participant ID-log) the data collected in this study will be made available to the public (open access) by depositing it in an open access repository or other related archive. As a result, other external organisations or researchers will be also able to apply to access these fully anonymised data.
How will my data be destroyed? The ID-log for all data will be stored for a maximum of 5 years after the study has ended. After that point, the ID-log will be destroyed and all data will be fully anonymized. After full anonymisation data will be available to the public (open access). Any excess biological material will be destroyed by the handling laboratories after the analyses have been completed [keep/remove: unless you have chosen to donate some to a biobank in your local country. If this is the case, biobank material will be destroyed after 15 years following the termination of the study (i. e. by 2035)]. I confirm that I have read and agree to the above information about the handling, processing and storage of my personal information in this study.
Trial registration data Data category Information Primary registry and trial identifying number ClinicalTrials. gov NCT04633681 Date of registration in primary registry November 2021 Secondary identifying numbers N/A Source(s) of monetary or material support European Union Horizon 2020 Program Primary sponsor European Union Horizon 2020 Program Secondary sponsor(s) N/A Contact for public queries Professor Graham Finlayson (G. S. Finlayson@leeds. ac. uk) Contact for scientific queries Professor Graham Finlayson (G. S. Finlayson@leeds. ac. uk) Public title Impact of Sweeteners on Behaviour, Physiology & Health ( SWEET-WP2-P2 ) Scientific title Acute and Repeated Impact of Sweeteners and Sweetness Enhancers on Food Behaviour, Physiology & Health (SWEET Work Package 2 Phase 2) Countries of recruitment Denmark, France, Spain, United Kingdom Health condition(s) or problem(s) studied Eating Behaviour Intervention(s) Consumption of food product with sweetener and sweetness enhancer Consumption of sucrose-sweetened control food product Key inclusion and exclusion criteria Ages eligible for study: ≥ 18 ≤60 years ; Sexes eligible for study :both; Accepts healthy volunteers: yes Inclusion criteria: BMI 25-35kgm2; Simultaneous participation in other relevant clinical intervention studies; Previous university or college training related to eating behaviour research; Self-reported eating disorders; Diagnosed anaemia; Diagnosed diabetes mellitus; Abnormal G.