dataesr/scientific-paragraphs-categorization huggingface dataset:
WhAt Is AlreAdy knoWn on thIs topIc Animal studies suggest omega 3 polyunsaturated fatty acids (n-3 PUFAs) have beneficial effects on kidney function, but evidence from human studies is limited Most previous studies on chronic kidney disease (CKD) assessed self-reported intake of n-3 PUFAs using dietary questionnaires, which are subject to errors related to misreporting and inaccuracy of food composition databases.
Secondly, the use of self-reported n-3 PUFA intake is subject to errors related to misreporting and inaccuracy of food composition databases. 20 1] [22] [23] [24]   biomarkers reflect the dietary intake and also the underlying metabolism, and therefore indicate the bioavailable n-3 PUFA intake; this might explain why n-3 PUFA biomarkers have been reported to be superior to self-reported n-3 PUFA intake in predicting outcomes such as cardiovascular disease and all cause mortality. 25 Thirdly, self-reported intake could not distinguish accurately between individual n-3 PUFAs, which might differ in their biological properties and effects on CKD. 26  Finally, studies evaluating the association between α linolenic acid (ALA) biomarker level and incident CKD are lacking, even though ALA is the major dietary n-3 PUFA obtained from specific plant foods and oils that has been associated with lower cardiovascular outcome risk.
Methods study population The study was conducted within the Fatty Acids and Outcomes Research Consortium (FORCE) (http://force. nutrition. tufts. edu), 9] [30] [31] [32]  Studies were identified and study investigators were asked to participate based on previous FORCE projects, expert contacts, and literature searches. All prospective studies of adult participants with n-3 PUFA biomarkers measured in blood fractions or adipose tissue and with data on the primary outcome of incident CKD were eligible. A total of 50 potentially relevant studies were identified in May 2020, but 22 (44%) were not eligible because of lack of exposure or outcome data, and 5 (10%) studies were not included because of a limited number of patients with incident CKD (supplementary table  S1 ). Of the remaining 23 cohorts, 19 (83%) participated in this project, while 4 (17%) declined.
Outcome measurement All outcomes were determined using estimated glomerular filtration rate (eGFR) at baseline and follow-up visits, with the same definitions in all the 19 participating cohorts. The primary outcome was incident CKD using the same definition as used by the Chronic Kidney Disease Genetics (CKDGen) Consortium. 33 Briefly, incident CKD was defined as an eGFR that decreased to <60 mL/min/1. 73 m 2 during follow-up among participants with baseline eGFR≥60 mL/min/1. 73 m 2. eGFR was estimated using the 2009 Chronic Kidney Disease Epidemiology Collaboration equation with calibrated serum or plasma creatinine. 34 If creatinine levels were not calibrated and were obtained with a Jaffé assay before 2009, then data were corrected by multiplying creatinine levels by 0. 95 before eGFR calculation, as described previously.
cohort analyses A prespecified and harmonised protocol with the same participant inclusion and exclusion criteria, exposures, outcomes, covariates, and analytical methods were sent to all cohorts to perform de novo analyses of individual data. For the outcomes of incident CKD and ≥40% decrease in eGFR, Cox proportional hazards models were used to estimate hazard ratios, considered to approximate relative risk, for cohorts with eGFR available in two or more followup visits. In cohorts with data available for only one follow-up eGFR, Poisson regression models were used to estimate relative risks. For the outcome of annual absolute change in eGFR from baseline to the last follow-up, multivariable linear regression was used. All analyses estimated robust standard errors. 0] [31] [32]  To assess potential nonlinear associations, n-3 PUFA levels were also evaluated as variables (in fifths) using study specific cut-off points.
In the primary multivariable model (model 1), analyses were adjusted for age, sex, race, clinical centre or field site, education, occupation, body mass index, smoking, alcohol intake, physical activity, prevalent coronary heart disease, and use of lipid lowering drugs at baseline, when applicable. In model 2, additional covariates that could confound or mediate the association of n-3 PUFAs with incident CKD were considered, including prevalent diabetes mellitus, urine albumin-creatinine ratio, systolic blood pressure, and use of antihypertensive drugs at baseline (see supplementary methods for definitions of all covariates). Participants with missing data on n-3 PUFAs were excluded from the analysis. Missing data on covariates were handled according to the usual practice of each cohort and study investigators (see supplementary methods for cohort specific details).
Our study also has some limitations. The n-3 PUFA biomarkers and covariates were only measured once at baseline and this might lead to increasing random misclassification over time, which would tend to bias the associations towards the null. However, n-3 PUFA biomarkers have good long term reproducibility as determined in a previous study with repeated within person measurements across 13 years. 63 While we standardised our methods across cohorts as much as possible, fatty acid assays, determination of outcomes, and covariate measurements varied by cohort and might contribute to the heterogeneity in our findings. Among different prespecified covariates, many participating cohorts did not have data for urine albumin-creatinine ratio. However, in cohorts that measured urine albumin-creatinine ratio, association of n-3 PUFA levels with incident CKD was similar whether it was adjusted for or not (results not shown).
Study procedures Screening Participants will be identified through screening from health centres' e-PPGBM (elektronik Pencatatan dan Pelaporan Gizi Berbasis Masyarakat) nutritional status database. Data from e-PPGBM will be validated with the most updated data from community health workers in the respective health centre capture areas. Children with moderate and severe wasting and those with growth faltering or severe underweight will be invited to a screening at the nearest health post or visited in their homes. After parental consent for screening, they will be checked for the initial eligibility criteria.
Data collection and management All study participants will be assigned a study identification number. Data to be collected include demographic data (living conditions, ethnicity, sex, caregiver age/ education, food security), morbidity, anthropometry (weight, height, MUAC, oedema, skinfolds), dietary data (breast feeding, food frequency and dietary diversity), birth weight and method. Cognitive development using CREDI will also be monitored and recorded. Biospecimens (stool and DBS via finger prick) will be collected from the participants, labelled with the participant ID number and study visit number, and frozen at -20°C in freezers at the basecamps. All DBS cards will be shipped to CSU for metabolite analysis. Two stool samples will be collected from participants at each respective time point, and one sample will be shipped to CSU for fecael microbiome analysis.
The second stool sample will remain in Indonesia at Bogor Agricultural University for future use. During screening and baseline visits, data will be recorded on paper by fieldworkers, and input into the KoboToolbox application on an offline tablet. On return to basecamps, all data input will be monitored and verified by the field supervisors and uploaded to the KoboToolbox online cloud. Paper documents from children not enrolled in the study will be destroyed, while the others will be stored in the Savica main office. KoboToolbox is a data collection, management and visualisation platform ensuring frequent system backups are stored in encrypted locations. All data are accessible only behind password-protected accounts, and only primary investigators and designated study members will have access to data via user-specific login with a password.
Data analysis A mixed model to capture repeated measures will be employed to measure primary outcomes. Fixed effects include treatment (RUTF, RUTF-Rice Bran), time (baseline, weeks 4, 8, 12 and 16), sex and age, and interactions. Child ID will be used as a random effect to capture correlated responses on the same child. Physiological clinical primary outcomes, including body composition, weight gain, anthropometric indices and morbidity, will be assessed between the two intervention arms. The number of missing data will be given for each variable and missing data will not be imputed on primary outcomes. All endpoints will be analysed on the ITT population. Secondary outcome analysis of biospecimens from each time point will be performed to elucidate changes in microbiota and metabolites intraindividually over time and between treatment arms at each time point.
Safety Prior to the fieldwork, the PIs and researchers will train the field team on research ethics, guidelines, and data collection tools, practice in anthropometric measurements, blood and stool sampling, appetite test and familiarisation with computer tablets and digitalised study tools. A field simulation will be conducted at the end of the training. The field team and the representatives of the selected health centres will also receive training on the Indonesian national guidelines for Integrated Management of Acute Malnutrition. Overall, the study design has minimal risks associated with the dietary intervention. Data on adverse events will be assessed and collected weekly through a recall questionnaire at any visit or as soon as reported by children's guardians. Intervention for the child will stop for significant reactions.
Acknowledgements We thank the children and families who have and will participate in this study. We would like to thank the  Savica  field staff and team: Assistant Field Coordinators,  Fajar Abdillah  and  Fadhillah Dzaky ; Field Assistant  Nita Septianingrum ; Monitoring team Field Supervisors  Anisah Hamidah ,  Metha Feriska ,  Aisha RahmaTsania ,  Niza Puspita Arimukti ; Screening team Field Supervisors  Adam Al Farabi  and  Mochamad Faris Firdaus ; Logistics team  Anita Deviani  and  Soya Maulida Ayu Seka ; Screening team  Ensitia Mei Ardhia ,  Isabela Rachma Agustin ,  Alivia Hania Syah Putri ,  Sharifah Anjani ,  Septimike Yourintan Mutiara ,  Safira Putri Mahendra ,  Destha Putri Amellindha ,  Inayatus Sofia ,  Amirah MaulidaRohmah ,  Diyah Ayu Puspitasari ,  Dieniar Arikho Arumbina  and  Nanda Laras Syati ; Monitoring team  Rizky Fitriani Syahbana ,  Avirilia Pradani Putri Yuvinda ,  Disha Luthfiany Rachmawati ,  Erwanda Maharani ,  Astrid Wahyu Prihashinta ,  Chyntia Cahyawardani ,  DeaIntan Permatasari ,  Shouqi Aji Wiranata ,  Febrina Shinta Nurrachma ,  Luluk Mufidah Rizqiyawati ; Team doctors:  Dr.
, 2021). There are no data describing the changes in RVA properties induced by the durum wheat pasta manufacturing process. Interactions between water and wheat constituents in excess water can be described by measuring heating-induced endothermic phenomena, using differential scanning calorimetry (DSC) methods. The crystallinity state of starch granules was evaluated by the measurement of starch gelatinization endotherms. DSC methods were used for native semolina and intermediate products during pasta extrusion (  Vansteelandt and Delcour, 1998; Masato et al. , 2021)  and for dry pasta  (Güler et al. , 2002; Bruneel et al. , 2010; Tagliasco et al. , 2021). The diversity of DSC protocols makes difficult to consolidate the values among reported works. The molecular nature of interactions and their strength between wheat constituents and water molecules can be investigated by Fouriertransform infrared spectroscopy (FTIR) or low field nuclear magnetic resonance spectroscopy (NMR).
Pasting properties Pasting properties were investigated using a RVA (Newport Scientific, Warriewood, AUS) following the protocol: 10 s at 25 • C and 960 rpm; 120 s at 25 • C and 160 rpm; 300 s at 160 rpm with heating until 95 • C; 180 s at 95 • C; 300 s at 160 • C with cooling until 25 • C; 300 s at 25 • C and 160 rpm. The RVA software determines pasting characteristics: initial viscosity (cP), pasting temperature ( • C), peak viscosity (cP), breakdown viscosity (cP) and final viscosity (cP). Measurements were done in triplicate.
The acquisition of NMR signals was carried out with the Bruker software. Five samples were analyzed for each product. NMR signal intensity was corrected by sample mass. NMR signals were processed using SigmaPlot software (version 13. 0). Mathematical modeling (using a model composed of 1 Gaussian and 4 exponentials) was defined using the Levenberg and Larquardt adjustment method. The mathematical treatment allowed the identification of 5 different proton relaxation peaks, characterized by their relaxation time and intensity.
Fourier transform infrared spectroscopy (FTIR) The FTIR spectra were collected in the 800-4000 cm -1 wavenumber range on a Bruker Vertex 70v Fourier Transform spectrometer (Bruker Optik GmbH, Germany) operating with a Globar source, in combination with a KBr beamsplitter and a DigiTect DLaTGS detector with integrated preamplifier. The optical cell was a Golden Gate diamond ATR system. Spectra were recorded with a resolution of 4 cm -1 , automatically adding 128 repetitive scans in order to obtain a good signal-to noise ratio and a high reproducibility. Scans were corrected for the air contribution and were preprocessed using OPUS software (version 7. 0) by performing a baseline correction, a 9 points smoothing and a vector normalization for taking into account the effective number of absorbers.
Statistical Treatments For the laboratory scale testing, a Tagushi experimental design was chosen, as it allowed the evaluation of numerous variables with relatively few runs  [34, 35]. The statistical analyses relied on the measurement of signal-to-noise ratios with XLSTAT software (Addinsoft_2020; XLSTAT, Paris, France). For pilot-scale experiments, each concentration corresponds to a mean value, and its standard deviation is derived from at least triplicate measurements. The significance was obtained by assessing the ν value according to a non-parametric Mann-Whitney test.
The amount of water that was filtrated during this period was 22. 22 kg. Two filtrates were collected, one (filtrate 1) before pressure stabilization at 5 bars and the other (filtrate 2) when the pressure was kept at its maximum (Figure  S4A  in Supplementary Materials). The flow was maintained at a constant level of 300 kg/h for 6 min. A mass balance based on measurements performed before and after filtration was carried out for IF concentrations. The data seen in Figure  S4B  show that 83. 33% of all IFs were trapped on the cartridge filter, representing 83. 42% for DAI and 82. 56% for GEN, specifically. In total, the 22. 22 L of treatment water left 45. 8 mg of IFs on the cartridge. In parallel, the suspended matter was reduced from 2158 mg/L to 25 mg/L, leaving a mass of 47,300 mg of solids on the filter.
The second is a factor of extrapolation between animals and humans. The third is a factor counting for sensitivity differences among humans. If the commonly admitted values are applied, this brings out a total of 300, even if 184 could be safe enough, as argued by Kodell & Gaylor  [48]. Regarding soybeans, the NTP assessed only GEN toxicity, thus the question regarding other estrogenic IFs remains unanswered. Because soybean also contains DAI that can be metabolised into a more potent estrogen, i. e. , equal  [50] , it would be safer, in the authors opinion, to consider the toxicity of total IFs. Since no data are available on DAI toxicity, it seems adequate to apply the highest safety factor to total IF measurements, i. e. , 300. Such a decision will bring the IF limit for an adult weighing 60 kg to 20 mg/day, and for a 20 kg child to 6.
Acknowledgments: We thank the  Pôle Aliments et Santé, Bordeaux University (ARNA) ,  BIOPRESS  and its Agen subcontractors (Agrotec,  I2M ,  IFTS, INRAE) ,  INSERM  and  Bordeaux Science Agro. We also thank  Gabriele Peruzzi  for his contribution to the funding acquisition and valuable contribution to the work. This research was possible thanks to the support of the  Regional Council of Nouvelle Aquitaine.
Acknowledging that one century of development of sewerage to manage human excreta has still not led to truly effective sanitation, i. e. , a barrier between human feces and the environment, the second option consists in developing source separation of human excreta in a paradigm that would be safe by design  [44]. In countries that are poorly equipped with sewerage, engineers are starting to consider on-site technologies not only as long-term viable options, but possibly as a more sustainable alternative in many ways, compared to sewer-based systems. Onsite technologies can represent viable and more affordable options, but only if the entire service chain, including collection, transport, treatment and safe end ues or disposal, is managed adequately  [45]. Very little research has been conducted in this field in countries already equipped with sewerage.
Acknowledgements This review is part of the Ph. D. thesis work (https://hdl. handle. net/20. 500. 11811/10149 )carried out by  Arash Veshkini , within the  European joint doctorate in Molecular Animal Nutrition (MANNA)  under the  Marie-Skłodowska Curie Innovative Training Network programme. The authors would like to thank the staff of the  Research Institute for Farm Animal Biology (FBN), INRAe, University of Bonn , and the  University of Milan  for their support with the experimental study design and data collection.
For each EQ-5D-5L, a single index value based on the validated French-specific value set (calculated between -0. 5, worst health statement, to 1, best health statement) was derived  [2]. In addition, patients were asked to indicate thanks to the EQ Visual Analogic Scale (VAS) their overall health on the day of questionnaire completion (from 0 = the worst health patient could imagine to 100 = the best health patient can imagine) (Additional file 2: EQ Visual Analogic Scale). The Beck Depression Inventory second version (BDI-II) was used to assess depression  [3]. The scores vary between 0 and 63, with the following cut-off: 0-9: minimal or no depression, 10-18: mild depression, 19-29: moderate depression, 30-63: severe depression. Other data recorded were the usuals demographics data (age, gender), the disease phenotype and duration, and the treatments of WD.
Materials and methods First, a list of available species of traditional fruit trees, leafy vegetables, and pulses in Ethiopia and Kenya was created based on existing plant databases and was confirmed by local experts. The aim was to fill the gap concerning easily accessible country-specific species, particularly using accepted scientific names in the World Flora Online (WFO) database  (22). The rich biodiversity of traditional food plants in the two countries was apparent at first glance. The species list that was generated a great help in tackling the nomenclature confusion in the literature caused by use of different scientific synonyms for the same plant species. This documentation database also enabled identification of floristic regions (Ethiopia) or agroecological zones (Kenya) with the highest abundance of traditional fruit trees, leafy vegetables, and pulses  (23).
Methods This work has been designed and planned following the structure of a survey and used data previously available to the GCCR  2, 3  that analyzed the closed-ended responses. More specifically, we used data acquired by means of open-ended questions included in those surveys. The existing protocol complies with the revised Declaration of Helsinki and was approved as an exempt study by the Office of Research Protections at The Pennsylvania State University (PennState) in the United States (STUDY00014904).
Type 'none' if this is not applicable". The data was first processed by means of a concept-driven quantitative content analysis, as described by  29. Comments recorded at both time points (n=2543) were coded manually by eight different coders for the presence of anosmia, hyposmia, parosmia, phantosmia, whether the person indicated that they had recovered, not at all, partially or fully, and for mentioning food and non-food odorous items according to a predetermined coding scheme (see Coding scheme S1 in the Supplementary material, SM). Briefly, coders were first trained on the coding scheme by developing a set of rules, and a small subset of comments were coded followed by a discussion of comments that were difficult to code. Data was divided into eight roughly equal groups of around 350 comments.
Aim 2 Smell and taste disorders can lead to emotional distress (e. g. 1, 14  ). Our first sub-aim was to determine if longhaulers demonstrated a greater reporting of emotional and psychological distress compared to non-longhaulers. Our second sub-aim was to examine if the emotional distress was linked with specific olfactory dysfunctions (e. g. , phantosmia, parosmia). We therefore trained an aspect-based sentiment classification algorithm (LCF-ATEPC) from PyABSA  31  (version 1. 16. 15) on a dataset of restaurant reviews available in PyABSA ("Restaurant16") and then used the trained algorithm to first extract so-called aspects, i. e. , keywords in each comment. The model examines what sentiment (negative, positive or neutral) is attributed to these aspects. Thus, more than one sentiment classification can be contained in one comment.
Aim 2 A chi-square test was conducted to examine the relationship between sentiment classifications and longhauler status of the aspect-based sentiment classification algorithm. A mixed-effects logistic regression model was used to examine the relationship of sentiment, longhauler status, and olfactory dysfunction type. All analyses were conducted with Python, version 3. 8. 8, and the packages Scipy, version 1. 9. 3, and Statsmodels, version 0. 13. 5.
Aim 3 This aim was approached by creating word clouds, and conducting a relative frequency analysis. For the word clouds, the extracted words describing food and non-food objects were converted to lower case unigrams, bigrams, and trigrams, using the R package RWeka (version 0. 4. 44) and TM (version 0. 7. 8). Using the R package wordcloud2 (version 0. 2. 1) and RColorBrewer (version 1. 1. 3), word clouds were created where the frequency of an n-gram determines the size within the 'cloud'. For the relative frequency analysis, we preprocessed and aggregated each group's comments into a corpus based on longhauler status. Preprocessing steps included splitting the plain text comments into tokens. Tokens were lowercased, and numerals and punctuation removed. Commonly used stopwords were removed, and the text was lemmatized using Wordnet  32.
Although multiple hypotheses on the causes of phanstomia have been proposed, it may very well be that phanstomic sensations have varying causes  37. The present data consists of a wide range of self-reports of these phanstomic sensations, and are therefore suitable for a follow-up analysis on specific contexts that evoke a phanstomic sensation to further the understanding of phantosmic experiences and the mechanisms that underlie them. This also highlights the value of the internet in medical research, which allows for a collection of large datasets on patients' self-reports that may add to the understanding of olfactory dysfunctions. The difficulty people face in discussing smells and olfactory experiences is a well-known phenomenon (e. g. 12  ). In this study, we analysed open-ended comments to gain insight into the experiences of individuals suffering from olfactory dysfunctions, that are not entirely understood or correctly named by patients suffering from these.