dataesr/scientific-paragraphs-categorization huggingface dataset:
The incidence based on suspected cases only may be overestimated, given the low culture confirmation rate, as was shown previously in other Africhol countries  [9]. There were 23 cholera deaths during the enhanced surveillance period (CFR, 1. 2%), similar to what was found previously in Mozambique (0. 9% during reported outbreaks in the period 2009-2011). Other studies showed the CFR was likely much higher under non-research conditions when immediate rehydration and transportation to hospitals may not be available  [13]. Previous studies in Mozambique using surveillance data showed that 90% of deaths and 70% of cases occurred during the first six weeks of the outbreak  [5]. Case distribution by sex and age group showed common patterns in all zones, except for Beira, where a high proportion of suspected cases occurred in persons under five years of age.
08), and no information on immunosuppression status of enrolled patients was provided in this study  [16]. The different cholera characteristics between zones highlight the need to tailor intervention strategies to the specific local setting and at-risk populations and are useful for evidence-based decision making. The data presented here were used to direct interventions to prevent/treat cholera such as timely and solid cholera surveillance system, improved environmental management in particular continued access to safe water and proper sanitation, and the adequate use of cholera vaccines as a complementary immediate measure. The high-risk zones where cholera outbreaks repeatedly occur (e. g. , Nampula city in Nampula province, Mocuba district in Zambezia, and Pemba city in Cabo Delgado province) are so-called "cholera hotspots" and may benefit from preventive or reactive cholera immunization campaigns in combination with other cholera control activities (such as WASH activities), as recommended by the World Health Organization (WHO)  [17].
There was a high proportion of certain variables missing, which made it difficult to analyze the risk factors associated with confirmed cholera. In addition, data gaps limited our ability to conduct a robust examination of the association of cholera with malaria or HIV, including any differences in associations that may be due to immunosuppression status. The selection criteria for our surveillance included functional surveillance system and functional health structures with access to appropriate case management. Therefore the mortality related estimates cannot be generalized for the entire country which would likely underestimate mortality and CFRs. Moreover, our study did not account for the cholera cases and deaths in the community when the patients did not visit the health facilities. In this analysis we presented cholera cases in Africhol surveillance sites and some cholera outbreaks from 2011-2015.
Conclusion Our study provides the most comprehensive information on cholera in Mozambique available in recent years. This study does not aim to replace the national system but to assess the characteristics of populations at risk of cholera and risk factors in specific sites through an enhanced case-based surveillance. There is a need for continued surveillance, detailed data and stronger laboratory capacity to target prevention and control efforts, including locally adapted WASH interventions and preemptive use of OCV. In order to improve quality and access to safe water and sanitation, the Mozambican government has established investment funds and water supply assets which shall be used for public investment into the water supply system and the management of several companies across Mozambique.
Acknowledgments The authors would like to acknowledge the  Mozambique Provincial Health Directorate  for the collaborative support for the implementation of the Africhol surveillance system. We would like to thank the  Africhol  field team (provincial supervisors, nurses, lab technicians, and data clerks) for their support in the implementation of the activities and the contribution to this manuscript. We are also thankful to the teams that conducted the outbreak investigations. We thank additional  AMP  staff for their contributions to the  Africhol  project ( Delphine Sauvageot ,  Leonard Heyerdahl , and  Lan Ngo ). Finally, we are grateful to  Claire MacDonald  for copyediting this manuscript.
The ion transfer tube temperature was set at 350°C. The injected volume was 5 lL. A positive polarity mode was used. Finally, LC-HRMS profiles were obtained operating at full scan mode (65-1,000 m/z). Relative quantifications of urine amino acids are presented in Figure  3. Retention time and peak area for each amino acid are listed in Table  1. As shown in Figure  3 , the urine sample from the affected heifer exhibited generalized severe aminoaciduria. Indeed, except for proline and L-methionine, which are overrepresented in the control 2 urine sample (Fig  3B ), all amino acid and catabolite concentrations were higher in the patient urine sample as compared to controls. Data obtained for proline and Lmethionine in the control 2 urine sample are notable because the sample originally came from a 3-month-old male Holstein.
ACKNOWLEDGEMENTS This work was supported by funding from by a  National Health and Medical Research Council Career Development Fellowship , a  Club Melbourne Fellowship  and a  Brain Foundation Research Gift  to  S. J. S. , an  NWO Meervoud  and  NWO Food Cognition and Behavior (NWO-FCB) ,  JPI-Nutricog  to A. K. , and funding from the  USDA Intramural, U. S. Highbush Blueberry Council , and  California Strawberry Commission  to B. SH.
Fluorescein solution (4 nM, 150 μL) was mixed with CD-NA (0-5 μM, 25 μL), CD-SA (0-5 μM, 25 μL), Trolox standards (1-18 μM, 25 μL) or blanks, and AAPH (160 mM, 25 μL) in a 96well microplate. The plate was incubated at 37 °C, and fluorescence decay was monitored during 1 h with data taken every minute using an emission/excitation filter of 485-528 nm (i-control™ microplate reader software, TECAN Männedorf, Switzerland). The complex antioxidant activity was determined from its ability to keep the fluorescence signal of the indicator in the presence of peroxyl radicals. The AUC and AUC net were calculated using 3 and 4. Where f 1 is the initial fluorescence reading at 0 min and f i is the fluorescence measured at time i. Final ORAC values were calculated by using the regression equation between the Trolox concentration and the AUC net and were expressed as Trolox equivalents (TEAC) as millimolar per liter.
Fishermen in Uganda like those in other parts of Africa are poor with high rates of infectious diseases, such as human immunodeficiency virus (HIV) infection  [13] [14] [15]. While the proximity to water bodies is a known risk factor  [4, 6, 16] , little is known about people inhabiting these at-risk areas and so far no interventions targeting fishing villages have been proposed. Generally, WHO recommends integrated cholera prevention and control interventions comprising Water, Sanitation and Hygiene (WASH) measures and Oral Cholera Vaccine (OCV) use in endemic settings. For this article we collected and reviewed epidemiological data on cholera with a focus on fishing villages for the period 2011 to 2015. Our aim was to generate information on the burden and characteristics of cholera epidemics so as to make recommendations on how to target cholera control interventions as recommended by WHO.
Data collection We worked through Ugandan Ministry of Health (MOH) which routinely conducts disease surveillance and response to outbreaks of cholera and others diseases/events nationwide  [17]. Based on this surveillance, the health workers at community, health facility, sub-district, district and central MOH levels investigate potential public health threats or outbreaks and take appropriate preventive and control actions. The investigation teams and district level units report to central level in Kampala on actions taken and gaps that need additional support. Delay in reporting and response are determined by the public-health relevance of the reported event. Since 2000, the MOH requires districts to regularly compile weekly reports from their health facilities and aggregate the data into a district summary report that is submitted to the central level in Kampala and used to produce a national level weekly surveillance report  [18].
We also assessed for seasonal pattern of the cholera outbreaks using satellite rainfall data (precipitation) for Uganda that was obtained from Weatherbase at http://www. weatherbase. com/ weather  [19]. We used the long-term average rainfall (precipitation) collected for 101 years from 80 cities/districts in Uganda. We focused on cholera outbreaks that occurred in the fishing villages along the Great Lakes (lakes Victoria, Albert, Edward, George and Kyoga) and along the River Nile (Fig  1 ). In addition to the routine MOH disease surveillance system, Uganda with other eight (8) sub-Saharan African countries participated in the African Cholera Surveillance Network (AFRICHOL) project which carried out cholera case-based surveillance in selected surveillance areas  [20]. In Uganda, these areas included Kasese district in Western Uganda plus Mbale, Busia, Manafwa, Butaleja and Tororo districts in Eastern Uganda.
Data management, analysis and interpretation Data were collected by district health workers after training on study protocol and tools. Data with personal identifiers were coded or made anonymous. The data were entered, cleaned, stored and analyzed using Stata 12 software (StataCorp, Texas, USA) and Microsoft Excel spreadsheets. The differences between groups were tested using chi-square test for proportions (or Fisher exact test for small size). Comparison of age between outbreaks was tested for significance with Kruskall-Wallis test. The analysis of factors associated with cholera deaths was performed using logistic regression models. Variables with p values<0. 20 in the univariate analysis were entered into a multivariate model. Variables were kept in the final model if p < 0. 05. For each outbreak, attack rates (number of cases/population) were calculated and expressed per thousand, using sub-county population data as the denominator.
Sub-county data used was obtained from the Uganda Bureau of Statistics [UBOS], http://www. ubos. org. Population data from the most recent census (2014)  [24]  was used for outbreaks occurring in 2014 or 2015 (adding district growth rates for 2015 data, based on 2014 census: 2. 45%, 4. 27% and 6. 61% for Kasese, Hoima and Wakiso districts, respectively). For outbreaks that occurred in 2011-2013, population projections derived from UBOS census data of 2002 were used  [25]. Information on residence was self-reported by the patients to the health workers at the health facilities. Based on this information, we used Quantum Geographic Information System (QGIS)  [26]  to create maps. The administrative boundary GIS layer was obtained from UBOS on the Humanitarian Data Exchange website  [27]  in ESRI shapefile format.
The unit of geographic analysis was the sub-county (administrative level 5), which was recorded for each suspected cholera case. This was the minimum mapping unit that corresponded to the population census data conducted by UBOS  [24]. Individual sub-counties (SC) on the map were shaded according to the number of cases occurring within them. We applied several amendments to the SC names. In Namayingo district, Buhemba was replaced by Buyinja because, in the GIS layer we used, Buhemba was a parish of the Buyinja SC. Similarly in Wakiso district, Bussi was replaced by Kasanje. For the Hoima 2012 cholera outbreak, one case from Masindi SC (which belonged to Masindi district) did not appear on the Hoima map. There were two cholera cases during the first cholera outbreak in Moyo district in 2014 from Ciforo SC (Adjumani district) who did not appear on the Moyo map.
The qualitative study was instrumental in the development of the quantitative study, since the variables tested by our measurements tools (BSS, BIAG and an adapted food frequency questionnaire (FFQ)) cannot be isolated from their socio-cultural context. Thus, we used the results of this qualitative study to: (i) identify Bamiléké' norms of diet, physical activity practices and body weight with greater depth than in a quantitative study; (ii) help construct the culturally appropriate FFQ; and (iii) interpret the findings of the quantitative study. Hence, we used qualitative data concerning the norms of diet, physical activity practices and body weight, to test for causal links between these three aspects and weight gain, which would have been impossible to establish from cross-sectional quantitative data alone.
Thus, we used the combination of qualitative and quantitative approaches to: (i) demonstrate the relationship between dietary intake, physical activity, body weight perceptions and obesity status; and (ii) assess how these phenomena are impacting in this specific population. Data collection was conducted by three authors of the team specialized in sociocultural anthropology: Nicolas Monteillet for rural Cameroon, Nicolas Monteillet and Emmanuel Cohen for urban Cameroon and Nicolas Monteillet for France.
Relationships between Beliefs, Practices and Body Weight With a binary logistic regression model in the total sample, the relationships (adjusted odds ratios, 95% confidence intervals) between obesity and their possible determinants (sex, age, living area, educational level, high-calorie diet index, social valorisation of stoutness index, and physical activity) were assessed. ANOVA, ANCOVA, Student test and chi 2 were used to compare anthropometric measures, body size perception averages, food consumption, physical activity, and prevalence of obesity and hypertension in the total sample. We used NVivo 7 software (QSR International, Melbourne, Australia) to summarise the main outcomes of the interviews. We used the Statistica 7 (Statsoft Inc. , Tulsa, OK, USA) and IBM-SPSS 21 (IBM Co. , Armonk, NY, USA) softwares to perform descriptive statistics, ANOVA/ANCOVA, MCAs and PCA, and My Stat 12 software (Systat Software Inc.
Acknowledgments: This study was supported by grants from the  UMI 3189  and the  French National Agency for Research  through the  ANTRAC  project ( ANR-08-BLAN-0004 ) led by the  UMI 7206. Emmanuel Cohen  is supported by the  South African DST/NRF Centre of Excellence in Human development. Particular thanks to  Julie Leport ,  Martial Tchetgna  and others investigators in the field.
Methods Ethics statement. All samples were collected according to the ethical and safety regulations of the Countries in which samples were collected as follows. Human biological samples and associated data were obtained from the Austrian CJD Surveillance and KIN-Biobank Medical University of Vienna (Ethics Committe approval: 396/2011). All tissue samples were obtained according to Austrian legislations. The human tissue examined in this study was provided by the MRC Edinburgh Brain Bank and its use was covered by ethical approval from the East of Scotland Research Ethics Service REC 1 (reference number 16/ES/0084). Informed consent for the research use of autopsy tissue was obtained from the relatives of the deceased whenever necessary. Human brain tissue was obtained from the Institute of Neuropathology Brain Bank (HUB-ICO-IDIBELL Biobank, Barcelona, Spain) following the guidelines of Spanish legislation (Real Decreto 1716/2011) and the approval of the local ethics committee.
Reverse transcription and Real Time-quantitative PCR (RT-qPCR). For human frontal cortex and mouse whole brain, cDNA was obtained starting from 3 µg of total RNA with 50 µM Oligo(dT)20, 10 mM dNTP mix, 5X First Strand Buffer, 0. 1 M DTT, 40 U RNAse inhibitor and 200 U SuperScript ® III Reverse Transcriptase (Life Technologies). cDNA from blood samples was obtained using 750 ng RNA, maintaining the same reaction conditions as above. For each sample a negative control was performed by omission of the reverse transcriptase (-RT control). qPCR primers were designed using the online tool Primer-Blast provided by NCBI and, possibly, spanning an exon-exon junction or flanking intron sequence/s, to prevent the amplification of genomic DNA. The primers sequences for human targets were as follows: for GAPDH 5′-CCTGCACCACCAACTGCTTA-3′ and 5′-TCTTCTGGGTGGCAGTGATG-3′; for ACTB 5′-AGAGCTACGAGCTGCCTGAC-3′ and 5′-AGCAC TGTGTTGGCGTACAG-3′; for RPL19 5′-CTAGTGTCCTCCGCTGTGG-3′ and 5′-AAGGTGTTTTT CCGGCATC-3′; B2M for 5′-AGATGAGTATGCCTGCCGTG-3′and 5'-TCATCCAATCCAAATGCGGC-3′; for SERPINA3 5′ TGCCAGCGCACTCTTCATC 3′ and 5′ TGTCGTTCAGGTTATAGTCCCTC 3′; for ALAS2 5′-TGTCCGTCTGGTGTAGTAATGA-3′ and 5′-GCTCAAGCTCCACATGAAACT-3′.
qPCR reaction was performed in technical triplicates for each primer pair and sample. -RT sample for each primer pairs was analyzed to evaluate possible genomic RNA contamination and non-template control (NTC) was added in each plate to exclude presence of contaminating DNA in the qPCR reaction mix. Melting curve analysis of amplicons was performed for each primer pair to verify that artificial products or primer dimers were not responsible for the obtained fluorescence signals. RT-qPCR data analysis. Differential expression of human target genes was normalized to four reference genes (GAPDH, ACTB, RPL19 and B2M) expression. For mouse samples, Gapdh, Actb and Tubb3 were used as reference genes. The absolute expression value (C T ) of a specific human erythrocyte marker, ALAS2, was used to account for blood contamination of brain samples.
For PrP analysis, samples were digested with 50 µg/mL of proteinase-K (Life Technologies) at 37 °C for 1 hour. Densitometric analysis was carried out using UVIBand software. For mouse samples the same procedure as above was followed. Detection of PrP Sc was performed using W226 monoclonal antibody (1:1000) and a goat anti-mouse HRP secondary antibody. Actin beta was detected using monoclonal anti-β-actin-peroxidase antibody (1:10,000 Sigma), SerpinA3n detection was performed using Mouse Serpin A3N Antibody (1:500 R&D Systems) and a rabbit anti-goat HRP secondary antibody. States regarding the protection of animals used for experimental and other scientific purposes). Animal facility is licensed and inspected by the Italian Ministry of Health. Mice were housed in groups of 2-5 animals in individually ventilated cages, daily fed and water provided ad libitum.
Calculation of milk flow. In practice, D 2 O concentration of all mothers' plasma and pups' urine samples was determined by FTIR spectrophotometer and then basal D 2 O concentration was deducted. Plasma and urine D 2 O concentration data were subjected to compartmental analysis to calculate milk flow between the dam and its litter. We used single-compartment models to describe the dynamic aspects of water metabolism both in mother and its litter related by milk flow which led to a bicompartmental model (Fig  1 ). The SAAM II program was used to fit the model to the observed tracer data by a weighed-least-squares approach to find the best fit as previously described  [18]  and to determine the parameters of the model. Implicit in the use of this model is the assumption that each animal remains in steady state with respect to its total body water turnover during the time course of the study.
This condition is justified for the total body water of mother compartment as showed by constant body weight (results not shown). We assumed near-steady state for pups' body water pool as well. The first and second compartments correspond to the turnover of total body water of mother and litter respectively, resulting from both intestinal absorption and metabolic processes. with y axis at time 0. Dam's Total Body Water (TBWd) expressed in % was calculated by dividing this mass of Body Water by dam's mass. Flow constants K21, K01 and K02 of the model are calculable directly from the plasma and urine D 2 O enrichment-time curve. The flow from mother to pups R21 (g/h) was calculated as the product of K21 (h -1 ) and the mass of Dam's Total Body Water (g). In this model, milk flow was associated to milk production by the dam as well as milk consumption by the litter.
Statistics Non-parametric test was used for kinetic parameters obtained from water turnover study and growth parameters. Mann-Whitney tests (GraphPad Prism, version 6. 0) were used to analyze differences between the 2 regimens (NP, LP) for birth weight, pups' daily mass gain between PND 1 and PND 18, dam's food intake from PND8 to PND16, dams' TBW, and milk production. Data obtained using kinetic study and growth are expressed in median and upper and lower quartiles due to small sample sizes. Regarding the WSW method, due to larger sample sizes, we assumed Gaussian distribution, and used unpaired Student tests with Welch's correction (we did not assume equal standard-deviations) to compare milk production between the 2 regimens (software GraphPad Prism, version 6. 0). Mean values and standard deviations are therefore reported.
Acknowledgments The study was supported, in part, by a grant from the  AlimH Department of INRA  ( AlimH-ANSSD 2015 ). The authors would like to thank specifically  Guillaume POUPEAU  and  Thomas MOYON  ( PhAN, INRA, Nantes, France ) for animal care and for statistical analysis supervision, respectively. The authors also acknowledge  Prof. Jean-Franc  ¸ois  HUNEAU (Agro-ParisTech ) for his invaluable advice in setting up the deuterated water dilution method.
Materials and Methods An extended version of materials and methods is presented in SI Materials and Methods. All procedures were performed under the ethical approval both for animal and human cell work. For studies involving mice, all procedures were performed under the United Kingdom Animals (Scientific Procedures) Act, 1986. Human cells were prepared according to an approved protocol (East London & the City Local Research Ethics Committee; no. 06/ Q605/40; P/00/029 ELCHA). The Ethics Committee approved the human research protocol (Clin-icalTrials. gov Identifier: NCT01990716) to collect biopsies and clinical information. Written informed consent was received from participants before inclusion in the study. Sample Extraction and LM Metabololipidomics. Human and mouse gastrointestinal tissues extraction and LM metabololipidomics was conducted as described previously  (25).
ACKNOWLEDGMENTS. This work was funded by the  William Harvey Research Foundation  (T. G. and M. P. ). J. D. received funding from the  European Research Council  under the European Union's  Horizon 2020 Programme for Research and Innovation  (Grant  677542 ) and a  Sir Henry Dale fellowship , jointly funded by the  Wellcome Trust  and the  Royal Society  (Grant  107613/Z/ 15/Z ). C. N. S. is supported by  National Institutes of Health  Grant  P01GM095467. N. V. is supported by the  European Research Council  (Grant  ERC-2012-StG-20111109 ).
The present goal was to examine the consumption of plain drinking water by children and youth in the UK, as distinct from the consumption of caloric beverages  [15] [16] [17]. In this study, the nationally representative NDNS database (National Diet and Nutrition Survey 2008-2011) was used to assess total water consumption among British children aged 4-13 years. The NDNS is the principal national dietary survey for the UK that is jointly funded by Public Health England (PHE), an executive agency of the Department of Health, and by the UK Food Standards Agency (FSA). The NDNS is carried out by a consortium of three organizations: NatCen Social Research (NatCen), MRC Human Nutrition Research (HNR) and the University College London Medical School (UCL)  [18]. Analyses reported below examined water consumption patterns by gender and age, socio-demographics characteristics, and by time and place of consumption.
Additional analyses estimated total water sources (plain water, beverages, and foods) and the water-to-calorie ratios (L/kcal) in relation to the EFSA desirable norms. The NDNS is highly relevant to policy making; the data are used by the UK Government to develop policy and monitor progress on diet and nutrition objectives. This study was intended to inform one of those objectives related to the provision of safe, clean drinking water in schools  [19].