dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions There is much interest in the potential to replace added sugar in foods and drinks with non-nutritive sweeteners or sweetness enhancers for consumer health benefits. However, at present, there is insufficient information about the environmental impact of NNSs to understand the ramifications of making such changes at food product or dietary levels. Most LCA studies are for steviol glycosides, with other NNSs under-represented. Therefore, this study aims to redress that imbalance with the first LCA of sucralose, one of the most popular NNSs. The environmental LCA of producing 1 kg sucralose from chlorination of cane sugar grown in the USA has been presented in this study. It was not possible to engage an industrial producer in data provision. Therefore, life cycle inventory data for chlorination were derived from patent literature, and data for other input materials were taken from background LCA processes.
| Recruitment An invitation-only online survey was developed, and this invitation was supplemented with follow-up phone calls to ensure that the invitation has been received and potentially increase the response rate  (Ball, 2019). Recruitment occurred between January and July 2022. The weblink was sent via email along with a description of the study rationale and aim. Data were collected through the Survey Monkey tool. The survey was initially sent in English to the whole mailing list, followed by two reminders. The survey invitation was sent by the EMBA secretariat to all European HMBs included in the EMBA mailing list and performing donor recruitment (first round). Afterwards, the working group researchers were actively involved in the survey delivery management. Three strategies were applied to increase the response rate during the second round.
| Data analysis All returned questionnaires were reviewed separately by two researchers (S. G. and A. B-. J). Since one reply only was accepted from each HMB, in the case of multiple responses from the same HMB if the answers were identical, one answer only was included in the analysis. If the answers differed, the HMB was contacted via email/phone and asked to provide a definitive answer and explanation for the different replies previously received. Returned questionnaires with no clear HMB identification details were excluded from the analysis. Data were analysed by using IBM SPSS Statistics for Windows, Version 27 (IBM Corp. ). Descriptive data were reported as frequency (percentage) for categorical variables. All calculated percentages were rounded up to the nearest integer. Depending on the distribution of the quantitative variables (the dosages of recommended supplementation), median and inter-quartile range were used.
| Sample A total of 188 European HMBs in 26 European countries were contacted, and 129 answers were received. Following data cleaning, the response rate was 63% (118/188) (Figure  1 ). Replies were received from 22 countries. The major contributors (accounting together for 57% of total replies) were Italian, French, and German HMBs (Figure  2 ). The response rate per each question ranged from 93% (110/118) to 99% (117/118).
Reverse transcription and real-time quantitative PCR The primers used in this study as well as reaction efficiencies are listed in our previous paper  [28]. PCR-primer pairs were designed from mRNA sequences of the studied genes provided by the National Center for Biotechnology Information (NCBI) gene database using PrimerBlast. Results were retained if the reaction efficiency was between 80% and 110%. The optimal Tm at 60°C and exon junction span were selected. The primers were ordered at 100 µM concentration from Eurofins Genomics, Ebersberg, Germany. One microgram of extracted RNA was retrotranscribed using the SuperScript TM VILO TM cDNA synthesis kit (Thermo Fisher, Waltham, MA, USA) according to the manufacturer's instructions. For Fluidigm PCR, cDNA was preamplified according to Fluidigm's protocol (quick reference PN 100-5875 B1).
Preamplified cDNA was then diluted fivefold with Tris-EDTA buffer. Gene expression levels were measured on 48 × 48 GE Dynamic Array IFC using the Fluidigm BioMark TM HD System. Fold changes were calculated by the ∆∆Ct method using 'Fluidigm Real-Time PCR Analysis' software. The reference genes used were GAPDH, CPR2, and HPRT1  [30]. Each expression value was normalized to one reference sample -mammary tissue from the first lactation with inflammation. This allowed for a more reliable reference for genes whose expression is activated by inflammation.
Bioinformatics and statistical analyses Bioinformatics and statistical analyses were performed using a home-made pipeline according to Perrier J-P et al. [32]  and Costes et al. [34]. Quality checks and trimming were performed using TrimGalore v0. 4. 5, which removed adapter sequences, poor-quality bases and reads (Phred score below 20), and reads shorter than 20 nucleotides. High-quality reads were aligned to the mouse reference genome GRCm38 primary assembly (Ensembl 100) using Bismark_v0. 20. 0  [35] , which uses Bowtie 1. 2. 1. 1 alignment software  [36]. The bisulphite conversion rate was estimated from the unmethylated cytosine added in vitro during the end-repair step and was ≥99. 5%. The CpGs were then selected based on their coverage by uniquely mapped reads. Around 1,273,950 CpGs covered by at least 10 uniquely mapped reads (CpG10) were retained for subsequent analyses.
A DMR was constituted by a minimum of three DMCs with a maximum inter-DMC distance of 100 bp. The annotation of the DMCs, DMRs, and the 'background' was performed as described in  [33, 33]  relative to gene features, CpG density, and repetitive elements using an in-house pipeline. The reference files were downloaded at the following sites http://ftp. ensembl. org/pub/release100/gtf/ mus_musculus/Mus_musculus. GRCm38. 100. gtf. gz, http://apr2020. archive. ensembl. org/biomart/mart view/db614f58c20a42c0d2cb13bcedf364f8 [Ensembl Genes 100, mouse genes (GRCm38. p6)], http:// hgdownload. soe. ucsc. edu/goldenPath/mm10/data base/cpgIslandExt. txt. gz and http://hgdownload. soe. ucsc. edu/goldenPath/mm10/database/rmsk. txt. gz. The following criteria were applied: transcription start site (TSS), -100 to +100 bp relative to the TSS; promoter, -2000 to +100 bp relative to the TSS; transcription termination site (TTS): -100 to +100 bp relative to the TTS; shore, up to 2000 bp from a CpG island (CGI); and shelf, up to 2000 bp from a shore.
A site/fragment was considered to belong to a CGI (respective shore and shelf) if an overlap of at least 75% was observed between the site/fragment and the CGI (respective shore and shelf). A site/fragment was considered overlapped by a repetitive element, regardless of the extent of this overlap. DMCs were subjected to enrichment analyses of the Database for Annotation, Visualization, and Integrated Discovery (DAVID) using the 'background' as a reference genome  [38, 39].
| Imaging Images were acquired using a Yokogawa W1 Spinning Disk confocal microscope (Zeiss) with a 63x oil objective (1. 4 numerical aperture). The imaging conditions and acquisition parameters were the same for all slides. The experimental point spread function was obtained using carboxylate microsphere beads (diameter: 170 nm; Invitrogen/ ThermoFisher Corp. ). Except for DAPI, all channels were deconvoluted with Huygens Essential software (version 19. 04, Scientific Volume Imaging, The Netherlands; http://svi. nl), using the classic maximum likelihood estimation algorithm and a signal-to-noise ratio of 50 (for the immunofluorescence channel) or 20 (for the FISH channel), a quality change threshold of 0. 01, and 150 iterations at most.
| FISH quantification by AstroDot and AstroStat All procedures are detailed in our previous report  (Oudart et al. , 2020). AstroDot and AstroStat are respectively java and R scripts. They are open-sources and available on github. Codes can be downloaded freely from https://github. com/pmail ly/Astro cyte_ RNA_Analyze and https://github. com/rtort uyaux/ astro Stat. Briefly, AstroDot is an ImageJ plug-in analyzing the localization of mRNAs at the level of astrocyte intermediate filaments immunolabeled against GFAP in soma, large and fine processes. Astrocytes define individual domains and GFAP processes do not intermingle  (Bushong et al. , 2002). Thus, previous to the analysis, regions of interest (ROI) corresponding to individual astrocytes were selected manually by assessing the GFAP and DAPI (nuclei) staining on the Z projection of image stacks and by defining the stack of confocal plane for each selected cell.
| AstroDot statistics Each analysis was carried out on groups of 3 mice. Table  S2 summa  Computing, Vienna, Austria (https://www. r-proje ct. org/). We used the Student's t-test for normally distributed data and equal variances; the Welch-Satterthwaite's test for a normal data distribution and unequal variances; the Wilcoxon's test for non-normally distributed data. Histograms were generated using the ggplot2 package (https://github. com/hadley/ggplot2-book/blob/master/index. Rmd). The central limit theorem was applied for groups with more than 30 cells. The threshold for statistical significance was p < 0. 05. No sample calculation or test for outliers was conducted and no data points were excluded. No preliminary statistical analysis was performed for AstroDot concerning the size sample.
Funding information Grant/Award Number:  ANR-16-TERC-0016-01 ;  Association France Alzheimer ;  Fondation Bettencourt Schueller ;  Fondation pour la Recherche Médicale , Grant/Award Number:  AJE20171039094  and  FDT201904008077 ;  Journées de Neurologie de Langue Française ;  Fondation Vaincre Alzheimer , Grant/ Award Number: FR/21011.
ACK N OWLED G M ENTS This work was funded by the  Fondation pour la Recherche Médicale  to  M. Cohen-Salmon  ( AJE20171039094 ) and  N. Mazaré  ( FDT201904008077 ), the  Fondation France Alzheimer  to  M. Cohen-Salmon  and  C. Escartin , the  Fondation Vaincre Alzheimer  to  K. Avila  ( FR-21011 ), the ED3C doctoral school to  K. Avila ,  N. Mazaré  and  M. Oudart  and the  "Journées de Neurologie de Langue Française"  to  R. Tortuyaux. The creation of the  Center for Interdisciplinary Research in Biology (CIRB)  was funded by the  Fondation Bettencourt Schueller. C. Escartin 's work was funded by the  French national research agency   ANR-16-TERC-0016-01. All experiments were conducted in compliance with the ARRIVE guidelines.
Nationally Representative Data of Four Populations in Lebanon This analysis used UNICEF's Household Survey data, collected between December 2015 and January 2016. The survey employed specific sampling approaches to obtain nationally representative samples of four respective populations living in Lebanon: Lebanese, Palestinian refugees living in Lebanon, Palestinian refugees from Syria, and Syrian refugees. In the absence of an updated Lebanese sampling frame for residents of Lebanon, a two-stage stratified cluster sample approach (geographical sampling) was used to collect data on various indicators for Lebanese women and children and included a total sample of 10,053 Lebanese households with data on 38,390 individuals. For the Palestinian refugees living in Lebanon, a sample was extracted from the Palestinian Central bureau of statistics' (PCBS) most recent census database using a one-stage 1 3 Inequalities in Wellbeing in Lebanese Children and Different… cluster sampling approach and included a total sample of 4,707 households with 20,282 individuals (Lebanese Palestinian Dialogue  Committee et al.
, 2017). As for Palestinian refugees from Syria, UNICEF referred to the United Nations Relief and Works Agency for Palestine Refugees (UNRWA) list of Palestinian from Syria households and sampled a total of 1,442 households including 8,133 individuals. Lastly, a two-stage stratified sampling was used for Syrian refugees using the United Nations High Commissioner for Refugees (UNHCR) registration database with a total sample of 2,523 households and included 12,661 individuals.
Housing deprivation was defined as overcrowding (greater than three people sharing a sleeping room). As child labor and exposure to violence data were only collected for one randomly selected child per household, the dimension was converted into a household level variable assuming that any child living in a household where another child is exposed to child labor or violence would be at high risk of child protection concerns. Although we assume that an index child's siblings may undergo the same treatment or will at least be affected by the violence their sibling is subjected to, this indicator may be an underestimate of exposure if the index child is not reported to be exposed to violence and non-index children (on whom data are not available) are. All dimensions were given an equal implicit weight as the international children's rights framework does not prioritize particular child rights over others.
METHODS STUDY DESIGN AND SETTING A cross-sectional survey with retrospective data collection and projections based on approaches adapted from the WHO Global taskforce on tuberculosis patient cost surveys 20 was conducted between February 2021 and March 2021. The survey included caregivers (parents/guardians) of children enrolled in the CONTACT study. The CONTACT study [NCT03832023] was a pragmatic, multicentre, cluster-randomised controlled trial (cRCT) that evaluated the impact of child contact tuberculosis investigation and TPT management in Cameroon and Uganda. Cameroon and Uganda are high tuberculosis incidence, resource-limited (gross national income per capita of $3,990 in Cameroon and $2,360 in Uganda) sub-Saharan African countries with different programs for the delivery of tuberculosis services.
SAMPLE SIZE The sample size was calculated assuming catastrophic costs affect 50% of households in our recruited cohort (conservative from the perspective of power), a design effect for this quantity of 2 and an absolute precision of 20% resulting in a sample size of 48 households across the two countries. text (facility types, currencies, socio-economic questions) including translation to the French language in Cameroon. We incorporated previously published country-specific questions on household asset ownership and dwelling characteristics. 23  The survey was done during the COVID-19 pandemic, so we also developed and included a set of questions to assess the impact of the pandemic on TPT care and the economic welfare of the households. Data were collected and managed using REDCap electronic data capture tools hosted at The University of Sheffield.
24  The data collection instrument was piloted during the training of data collectors. Trained data collectors conducted face-to-face interviews at the facility in the control or in the community as part of the home visits in the intervention arm of the CON-TACT study. Enrolled caregivers were interviewed to gather information on expenditures and time losses (opportunity costs) experienced during seeking health care and caring for the children. Data on health care utilisation, household asset ownership, coping mechanisms (e. g. dissaving, borrowing, selling assets), and perceived social and financial impacts of TPT were collected. Participants also provided information on socio-economic characteristics (e. g. , the income of the participant and of household), and COVID-19 impact. Clinical and demographic information of children included in the survey was extracted from the CONTACT study database before the interview.
DEFINITIONS (PATIENT COSTS, INCOME, CATASTROPHIC COSTS, COPING MEASURES) We estimated direct medical, direct non-medical, and indirect costs incurred before and during TPT from a patient perspective. We collected data on direct medical costs (consultation fees, hospital admissions, medicines, laboratory tests), direct non-medical costs (transport and food), and indirect costs (opportunity costs representing lost productivity by a caregiver seeking and receiving care for their child or children) incurred up to the time of interview. The human capital approach was used to estimate the indirect costs by summing up the reported time lost due to receiving and waiting for care, hospitalisation, transportation, and lost working days for the caregiver to get an estimate of the total time lost. The total time lost was multiplied by the minimum hourly wage in each country.
To minimise recall bias, for households with more than one child on TPT, data were collected only for a single child contact and the TPT treatment month the child was in at the time of the interview. We estimated the entire treatment cost per month by extrapolating the costs incurred up to the time of the interview. Costs for the other months of TPT were estimated using the median reported costs collected from caregivers interviewed when their children were in that treatment month. We assumed that direct medical costs and some direct non-medical costs (e. g. accommodation, food and transport) would scale up with the number of contacts. In order to estimate the total costs due to TPT in the household, we multiplied estimated direct medical and non-medical costs in that household by the number of contacts screened (for costs incurred before TPT) and the number of children initiated on TPT (for costs incurred after TPT initiation).
To assess the impact of this choice, we also assessed income losses using the output-based approach in the sensitivity analysis resulting in increases in costs and incidence of catastrophic costs. In our analysis, it was not possible to disentangle the possible impacts of COVID-19 on healthcare seeking, household income, and income losses. Although data collection for this survey did not coincide with the COVID-19 pandemic-related lockdowns (27 March 2020 to 28 August 2020 in Cameroon and 18 June 2021 to 3 August 2021 in Uganda), the pandemic could have impacted healthcareseeking and household income in ways we were not able to quantify in this study. In particular, transport costs were increased due to the reduction of passengers per transport imposed by the restrictive measures. It was also not possible to isolate the effects of the index tuberculosis patients  and/or multiple concurrent tuberculosis illnesses in the same household on household income and income losses.
NM, SM, BT, DA, BTY, BS, RO, SL, JC, AV, MC, MB, PJD, and the CON-TACT study group contributed to the study design and reviewed the protocol. NM and PJD drafted the manuscript. NM and PJD had full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors have read and approved the final version of the manuscript.
ACKNOWLEDGEMENTS We thank the parents/guardians of the children included in this study for spending their time to respond to the survey questions, the CONTACT study research assistants for diligently collecting the data, and the  CONTACT study group  for their support and collaboration on this study. We are very grateful to the  Ministries of Health and National TB programs  in the project countries for the support provided to the CaP-TB project and the CONTACT study. We also acknowledge  WHO  for providing an example R code that was used for the analysis.
Methods Data from the Global School-based Student Health Survey (GSHS) were analyzed. Details on this survey can be found at https://www. who. int/teams/noncommunicablediseases/surveillance/data and http://www. cdc. gov/gshs (accessed on 20 June 2023). Briefly, the GSHS was jointly developed by the WHO and the US Centers for Disease Control and Prevention (CDC), and other UN allies. The main aim of this survey was to examine and quantify risk and protective factors of major non-communicable diseases. The survey used a standardized two-stage probability sampling design for the selection process within each participating country. For the first stage, schools were selected with probability proportional to size sampling. The second stage involved the random selection of classrooms, which included students aged 13-15 years within each selected school.
All students in the selected classrooms were eligible to participate in the survey regardless of age. Thus, the sample could have included adolescents who were not 13-15 years old. Data collection was performed during one regular class period. The questionnaire was translated into the local language in each country and consisted of multiple-choice response options. Students recorded their response on computer scannable sheets. All GSHS surveys were approved, in each country, by both a national government administration (most often the Ministry of Health or Education) and an institutional review board or ethics committee. Student privacy was protected through anonymous and voluntary participation, and informed consent was obtained as appropriate from the students, parents, and/or school officials.
Data were weighted for non-response and probability selection. From all publicly available data, we selected all nationally representative datasets that included the variables pertaining to our analysis and for which data on at least two waves were available from the same country. A total of 31 countries were included in the current study. The characteristics of each country, including the survey year, country income level, response rate, and sample size, are provided in Table  A1  of Appendix A. The surveys included in the current study were conducted between 2003 and 2017, and were mainly from LMICs.
Results Data on 193,388 students aged 12-15 years [mean (SD) age 13. 7 (1. 0) years; 49. 0% boys] were analyzed. Across all surveys, the mean prevalence of any food insecurity was 52. 2%, while that of moderate and severe food insecurity were 46. 5% and 5. 7%, respectively. At the individual survey level, the prevalence of any food insecurity ranged from 18. 7% (Uruguay in 2006) to 81. 2% (Samoa in 2011). High prevalence of severe food insecurity was observed in Samoa in 2011 (36. 0%), Benin in 2009 (19. 0%), Seychelles in 2007 (16. 6%), and Yemen in 2008 (15. 4%). The trends in prevalence of any food insecurity are shown in Table  1  and Figure  1. Of the 31 countries included in the study, significant increasing and decreasing trends of any food insecurity were found in seven countries each.
Strengths and Limitations The large representative sample of school-going adolescents from 31 countries, and the use of standard methodology across surveys are clear strengths of the present study. However, findings must be interpreted in light of the study's limitations. First, food insecurity was self-reported, potentially introducing some level of bias (e. g. , recall bias, social desirability bias) into the findings. Second, our study results are only generalizable to school-going adolescents, as only students were included in the study. However, it is worth noting that school attendance rates are known to be generally high in the countries included in our study. Finally, surveys were undertaken in different years depending on the country, and more data points were available in some countries than others.
Seven stimuli with increasing luminance were presented to the animals. To test the cone contribution to the scotopic ERG responses, double flashes with 1 log cd•s/m2 intensity and an interstimulus interval of 1 second were applied. A 10 second interval was left between stimuli in the case of dim flashes (-5 to -0. 6 log cd•s/m2), and up to 20 seconds for the brightest flashes (0 to 1 log cd•s/m2). ERG records were amplified and band-pass filtered (1-1000 Hz, without notch filtering) using a DAM50 data acquisition board (World Precision Instruments, Aston, UK). A PowerLab system (AD Instruments, Oxfordshire, UK) was used for the presentation of stimuli and data acquisition (4 kHz). The a-wave amplitude was measured from the pre-stimulus baseline to the most negative trough. The b-wave amplitude measurement was taken from the trough of the a-wave to the peak of the b-wave.