dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Data are presented as means ± standard error of the mean (SEM). Statistical analysis was performed using GraphPad Prism version 5. 00 for Windows (GraphPad Software, La Jolla, CA, USA). For the immunohistochemical experiment, the Kolmogorov-Smirnov test revealed parametric (normal) distribution for the c-Fos data for most brain regions. Therefore, differences between groups were assessed using unpaired one-sided t-tests. The level of significance was set at p < 0. 05.
The FADS1 and FADS2 genes, which code D5-desaturase and D6-desaturase respectively, form a group of genes with FADS3 on human chromosome 11 (11q12-Q13. 1) (Nakamura and Nara, 2004 ). In the NCBI SNP database, more than 3285 simple nucleotidic polymorphisms (SNPs) are referenced on FADS2 for Homo sapiens. Although the first studies on FADS gene polymorphisms are recent  (Schaeffer et al. , 2006) , several significant associations have been confirmed between FADS genotypes and long-chain polyunsaturated fatty acids, in diverse types of human tissue (erythrocytes, plasma, skin, breast milk, etc. ) showing that the polymorphisms on the FADS gene cluster are major regulators of the synthesis of this type of fatty acid. Several studies have shown a close correlation between several SNPs in the FADS1 and FADS2 genes and the concentration of omega-3 and À6 fatty acids.
Methods We searched the literature on systematic reviews and metaanalyses using the search engine Pubmed (http://www. pubme d. nl), Embase and the Cochrane databases. To meet the inclusion criteria, a review had to: be a systematic review; have micronutrient status (zinc, ferritin, vitamin A, vitamin B12, folate and iodine) or report on anemia or micronutrient deficiency as a primary outcome; include children of 0-5 years old; and focus on low or middle income countries. The search process is shown in Fig. 1. The first search was performed to identify articles that reported on intervention strategies with micronutrient status as an outcome. [tiab]. In addition to these search terms, the filter was set to only select systematic reviews. Subsequently, abstracts were reviewed and selected by two authors (MCP and CD), after which the full text was read to make the final selection based on the inclusion criteria.
The following search terms were entered into Pubmed, Embase and the Cochrane database in September 24, 2014 (and was repeated in May 2016): Micronutrients"[Mesh] OR micronutrient*[tiab] OR multimicronutrient*[tiab] OR multi-micronutrient*[tiab] OR folic acid[tiab] OR folate[tiab] OR "vitamin a"[tiab] OR retinoic acid[tiab].
Acknowledgements The authors would like to thank  Eline Freriks  and  Mirthe Klein Haneveld  for their help in the May 2016 search. This study was performed in the context of the SMILING (" Sustainable Micronutreint Interventions to controL deficiencies and Improve Nutritional status and General Health in Asia" ) project that was funded by the European commission in the 7th framework programme for research and technological development (GA 289616). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Total selected: 42 systemaƟc reviews The following search terms were entered into Pubmed, Embase and the Cochrane database in September 24, 2014 (and was repeated in May 2016): Micronutrients"[Mesh] OR micronutrient*[tiab] OR multimicronutrient*[tiab] OR multi-micronutrient*[tiab] OR folic acid[tiab] OR folate[tiab] OR "vitamin a"[tiab] OR retinoic acid[tiab] MN intervenƟons Author search Via references MN intervenƟons MN intervenƟons 24 sept 2014 Sept 2014 Sept 2014 Update May 2016 Update February 2018 Search for Search of all authors Search of references Search for micronutrient Search for micronutrient micronutrient for new or addiƟonal of intervenƟon: intervenƟon: intervenƟon: studies exisƟng arƟcles 643 possible arƟcles 1343 possible arƟcles 6754 possible arƟcles SelecƟon based SelecƟon based SelecƟon based SelecƟon based SelecƟon based on Ɵtle: on Ɵtle: on Ɵtle: on Ɵtle: on Ɵtle: 219 selected 3 selected 6 selected 40 selected 94 selected SelecƟon confirmed SelecƟon confirmed SelecƟon confirmed with SelecƟon confirmed with SelecƟon confirmed with with abstract: with abstract: abstract: abstract: abstract: 84 selected 3 selected 4 selected 15 selected 45 selected SelecƟon confirmed SelecƟon confirmed SelecƟon confirmed with SelecƟon confirmed with full SelecƟon confirmed with full with full text: with full text: full text: text: text: 23 selected 2 selected 4 selected 5 selected 8 selected Fig.
Human TTR crystals were soaked directly in metal-containing cryoprotectant solutions. The duration, pH and metal salt concentration were optimized to allow any conformational changes to occur with minimal deterioration to the X-ray diffraction quality. In order to unambiguously identify metal sites, X-ray diffraction data were collected at two X-ray wavelengths just prior and just beyond the X-ray absorption K-edge of the corresponding metal (see Supplementary Information). The phased anomalous difference Fourier maps were used as a guide to identify and position the specific metal ions (see Supplementary Fig. S2 ). Three key inter-residue distances can be used to describe the conformational changes induced by metal binding to TTR. Differences in the A-A′ and B-B′ Leu-110-Leu-110 separation (see Supplementary Table  S3 ) measures the asymmetric rotation of the dimers relative to each other.
3 Å, b = 85. 8 Å, c = 63. 9 Å, as observed for the TTR-Mn complex, and typical for most human TTR-ligand complexes in the PDB  32  , to a = 42. 8 Å, b = 83. 1 Å, c = 65. 3 Å, for the TTR-Fe complex (Table  1 , PDB code: 5N5Q). The TTR-Fe structure with the strongest anomalous signal in the phased anomalous difference Fourier map, out of 35 successful soak experiments, is reported here (see Supplementary Table  S1 ). The observed conformational changes surpass those induced by rhenium  30. The magnitude of these changes, combined with an increased total absorbed dose at the X-ray absorption K-edge of Fe  33  and the higher multiplicity to improve the anomalous signal  34  , results in a structure with a crystallographic resolution limit lower than that for TTR-Re. The largest anomalous features for the TTR-Fe complex are located in proximity of Glu-51.
On A monomers, the electron density can be fitted by a three-atom Fe-cluster (see Supplementary Fig. S2B ) and on B monomers by two Fe atoms (Fig. 2C ). An additional Fe site at the entrance of the T4 binding site involves Glu-54 from symmetry related B and B′ monomers belonging to the same tetramer. Some uninterpreted electron density extends beyond the phased anomalous difference density for the Fe, suggesting the presence of a chelating residue, probably Glu-7 from the disordered N-terminus. An additional unidentified peak, too strong be explained by a water molecule, but without any anomalous signal and thus can not be an Fe atom, is located close to His-88 (see Supplementary Fig. S2D ). The TTR tetramer loses the symmetry of the T4-TTR complex (PDB code: 1ICT  22  ) matching the asymmetric conformation of the TTR-Re-complex  30.
Manganese binding to TTR. Although manganese is of minor interest for either AD  35  or TTR amyloidogenesis because the Mn concentrations are 1000-fold lower than either Cu or Fe in the CSF  36  , its position in the periodic table relative to Fe and Re warranted the determination of its complex. When TTR crystals grown at pH 6. 0 were soaked in 5 mM MnCl 2 under conditions similar to those carried out with Fe, Mn was found bound to Glu-66 and Asn-98 in monomer B and to Asp-99 and Glu-66 in monomer A, with minor local changes. The residues involved in Mn binding are not involved in Fe or Re binding (see Supplementary Fig. S3 , PDB code: 5N62). More globally, Mn may influence an A-A′ and B-B′ asymmetry. The separation between the central Leu-110 residues (B-B′ Leu-110-Leu110 separation of only 6.
73 Å) suggests a rotation of the TTR dimers in a direction opposite to that observed for Fe and Re-TTR structures (see Supplementary Table  S3 ). The conformation of the TTR-Mn complex with a distance Asp38-Asp38 of 19. 2 Å is within the range for distances observed for wt-TTR with ligands (e. g. 5EZP  37  ) and even a wide selection of mutant TTR structures including those subjected to heating (PDB code: 2QEL  38  ) or to a pH as low as 4. 6 (PDB code: 2G4G  39  ). Copper binding to TTR. When TTR crystals grown or soaked in 30 mM CuCl 2 the conformation of the TTR-Cu complex yields a distance Asp-38-Asp-38 of 25. 0 Å, a Thr-123-Gly-83 distance of 25. 7 Å and a Leu-110-Leu-110 asymmetry, which are comparable in magnitude but opposite to that of the TTR-Mn complex. This suggests a more nuanced response to metal binding (see Supplementary Table  S2 , PDB code: 5N7C).
Similarly, crystals grown in the presence of Aβ and CuCl 2 also failed to incorporate the peptide in an ordered manner. However, it is noteworthy that when soaked with CuCl 2 (conditions as for the TTR-Cu complex PDB code: 5N7C; this work), the crystals grown in the presence of Aβ show a reduced binding of copper and an incomplete transformation, with B-values higher than those for other complexes (PDB code: not deposited, see Fig. 3F ). Based on the magnitude of the three key distances, the extent of the conformational changes is intermediate between the TTR-Mn and the TTR-Cu complexes (see Supplementary Table  S3 ). The electron density for the 72-92 zone in proximity of His-90 suggests the coexistence of modified and unmodified B monomers. The conformation of the unmodified monomers matches that of the A monomers, while the B monomers adopt a conformation different from that found in the TTR-Cu complex (Fig.
Although the binding of various metals provokes similar changes, the changes induced differ in their extent. Three key distances can be used to evaluate the magnitude of the conformational change (see Supplementary Table  S3 ): the separation between the four Ile-110 residues in the tetramer (to monitor the rotation of the dimers relative to each other), the distance between Gly-83B on the E-F loop from Thr-123A (to measure the shift of the E-F helix), and the separation between two Asp-38 residues (to assess the relationship between the monomers that form the dimer). This last measure provides a distinctive signature of metal binding. The Asp-38-Asp-38 separation, typically 21. 5 Å in the absence of metals, shortens to 20. 3 Å in the TTR-Mn complex and to 17. 8 Å for the TTR-Zn complex (PDB code: 3DGD  28  ), while in the TTR-Cu complex it increases to 25.
7 Å and 26. 3 Å in the TTR-Re complex (PDB code: 5K1N  30  ) up to a maximum of 27. 0 Å for the TTR-Fe complex (see Supplementary Table  S3 ). Metal chelation by proteins often involves the repositioning of certain residues. When considering TTR-Aβ complexes it is important to take into account that both partners in the interaction may alter their conformation in a dynamic and polymorphic manner. Fe promotes Aβ aggregation  48  that interferes with the dynamics of amyloid formation  49  , and its metal binding is pH-dependent and affects oligomerization  50. Morphological differences may affect toxicity. Aβ aggregates containing Zn, Fe or Cu are neurotoxic  51, 52. Studies in vitro are focused on Aβ interactions with Cu and Zn, even if Mn also binds Aβ peptides  31  and plays a role in neurodegeneration.
The data from these two last steps were used to estimate differences in interference caused by binding of TTR to the Aβ loaded tip. A blank run was carried out with buffer A for calibration purposes. A series of 5 runs was performed with CuCl 2 at constant 1. 56 mM, the first without TTR followed by 4 runs with increasing amounts of TTR (0, 0. 20, 0. 39, 0. 78, 1. 56 μM. Another series of 8 runs was performed with TTR at constant 1. 56 μM, the first without CuCl 2 followed by 7 runs with increasing amounts of CuCl 2 (0, 188, 375, 750, 1. 56 μM and 1. 56, 3. 12, 6. 25, 12. 5 mM. Affinity (K D ) was calculated using the FortéBio Data Analysis HT v10. 0. 1. 7 software. Crystal preparation and structure determination. TTR crystals were grown by sitting drop vapor diffusion as previously reported  59  using lyophilized human TTR (Calbiochem, Merck Millipore, Darmstadt, Germany) dissolved, 1 mg in 100 mL of 0.
02% (w/v) NaN 3 and dialyzed overnight against 0. 1 M NaCl, 50 mM sodium acetate, pH 5. 5. The reservoir solution consisted of 21% polyethylene glycol 4,000 (PEG4K), 0. 14 M imidazole malate, pH 6. 0 and the second one from 21% polyethylene glycol 4,000 (PEG4K), 0. 14 M imidazole malate, pH 6. 0, 3. 6% polyethylene glycol monomethylether (MPEG5K), 30 mM sodium acetate, pH 5. 5. Streak seeding was used to induce nucleation. The cryoprotectant solution was composed of 40% of SM2 (12. 5% ethylene glycol, 12. 5% glycerol, 12. 5% 1,2-propanediol, 25% DMSO and 37. 5% 1,4-dioxane) 25% PEG 8K and 30 mM of CuCl 2 , MnCl 2 or FeCl 2 , to which 2. 5% H 2 O 2 or 0. 8 mM Methylene Blue (MB) was added in some experiments. The combination of H 2 O 2 and MB resulted in the loss of the blue color of MB. Crystals were flash cooled by in liquid nitrogen and data were collected on beamline Proxima-2A at the Soleil storage ring in Saint Aubin, France, on a Dectris Eiger 9M detector.
Data processing was carried out at the synchrotron facility using XDS  60  with the xdsme script  61. The structure was solved by molecular replacement using Phaser 62 followed by refinement using REFMAC5  63  starting with a TTR model without inhibitor. The electron density maps were viewed and fitted in COOT  64. The structures were subjected to over ten cycles of rebuilding and refinement with REFMAC5  63  and PHENIX  65. The phased anomalous difference Fourier maps were calculated with the program ANODE  66. Data processing and refinement statistics are given in Table  1. The coordinates for the TTR wild-type and various mutants, see Supplementary Table  S3 , were retrieved from the Protein Data Bank  32. The figures were made with PyMOL  67. Structures deposited into the PDB Data Bank: 5N5Q, 5N62 and 5N7C.
Data Collection In a face-to-face interview, trained interviewers completed a detailed data collection form for each subject. This questionnaire included information pertaining to the participants' medical history (all diseases that are associated with BPA and medications), diet through a food frequency questionnaire (FFQ) as well as lifestyle habits (smoking, alcohol, coffee, and physical activity), and socio-demographic information (age, gender, residence and previous travel, education, occupation, and income), physical exams (anthropometric data, weight, height, BMI (body mass index), waist circumference, and blood pressure), and a collection of urine and blood samples. Blood samples were collected after an overnight fast. The FFQ was an 80-item, semi-quantitative questionnaire, referring to subjects' dietary intake 12 months prior to the interview  [17].
The model tested for both a linear and a squared term for the parameters. For the fatty acids, which were significantly correlated with BMI and Waist, we also assessed the assumptions of linearity using the linearity test, which showed that the analyzed correlations demonstrated a linear distribution; hence, we proceeded with statistical tests of linear correlation. Statistical analyses were also run in relation to percentage of body fat (data not shown). Adjustments were performed for the following factors: age (continuous variable), menopausal status in women (pre-and postmenopause), physical activity (total MET minutes/week), smoking status (non-smokers, current smokers, and ex-smokers), education (none, incomplete primary, complete primary, complete secondary, and complete high school), alcohol consumption (g/day), energy intake (kcal/day), and analytical batch.
It is important to check three points before a child is fed with its mother's own fresh milk or with donated milk from a human milk bank. Firstly, we need to consider whether any medical treatment the mother is receiving is compatible with breastfeeding. The number of drugs that are contraindicated during breastfeeding is low and clinicians should refer to reliable and updated databases for Abbreviations information. Secondly, clinicians should consider the results of serological tests for viral infections, such as the human immunodeficiency virus, hepatitis B, hepatitis C and human T-lymphotrophic virus. Finally, the traceability from collection to the administration of human milk is important. This includes clear and precise labelling to identify the date and time of collection, which is required to prevent any risk of bottle exchanges between different children  (4).
METHODS The MEDLINE database and Cochrane Library were used to carry out a bibliographic search up to June 2017. We used the terms presented in Table  1 , together with colostrum, breastfeeding, human milk composition, neonatal infection, cytomegalovirus and Bacillus cereus. The search was restricted to papers published in English and French. Prospective, retrospective and clinical studies were reviewed. Recommendations published by French authorities, such as the National Agency for Food, Environmental and Occupational Safety, and the American Centers for Disease Control, were collected. Guidelines from societies in different countries were also compiled. For example, we considered the guidelines produced by the American Academy of Pediatrics, the United Kingdom National Institute for Health and Care Excellence, the Swedish Milknet, French guidelines and the latest recommendations from Europe  (1, 3, (8) (9) (10) (11) (12) (13).
Knowledge about breastfeeding in preterm infants should be updated and team practices should be harmonised so that parents receive clear and scientific information that is delivered without ambiguity. The rate of premature infants that are breastfed at the end of the neonatal care period is a good indicator of the quality of care, and these data should be collected and monitored by each neonatal unit. Ensuring fresh mother's own milk meets the nutritional needs of very preterm infants Enteral feeding should start as soon as possible, to shorten the exposure to the intravenous catheters required for parenteral nutrition, and this is the same for fresh mother's own milk and pasteurised human milk. The nutrients supplied by human milk depend on its composition and on treatments such as refrigeration, freezing, pasteurisation and fortification.
Two variables measured health workers' perceptions regarding their capacity to influence decision-making processes and control work at their facility. Both variables elicited answers on a Likert scale with values ranging from 0 to 10. Five variables measured facility-level characteristics, specifically: rural vs. urban location; facility type (1 = primary facility; 2 = secondary facilityincluding the few regional hospitals included in the sample; 3 = private); district; number of clinical staff assigned as personnel; and proportion of very poor people residing in a specific district. This last variable was computed using data from the parallel household survey conducted in the catchment areas of all facilities included in our sample. We computed this variable by looking at how many people were listed in the lowest wealth quintile in each of the districts.
To generate this variable, we first computed an asset-based wealth index for the overall sample (i. e. , pooling data from all 24 districts) and then checked what proportion of very poor resided in each district. We treated this as a continuous district-level variable, with district-specific values ranging from 0 to 1. In addition to exploring what factors caused variation in knowledge and application of exemption policies, we included a secondary outcome, asking health workers to identify what proportion of the total population should be considered ultra-poor and therefore be exempted from paying user fees. Five categorical options were provided (1%, 5%, 10%, 20%, 25%).
Acknowledgements The authors wish to thank  Donna Riley  for translation and editing services. The authors express their gratitude to the members of the PBF technical service, the district-level managers, and participants for their contribution to this study. We also want to acknowledge the work of  Aurelia Souares ,  Nobila Jean Marc Ouedraogo ,  Julia Lohmann , and  Muraz Research Centre team  for their contribution to data collection and basic data management.
A combination of de novo and reference based OTU identification was carried out, clustering sequences at 97% similarity and aligning a representative sequence to the Greengenes database (version gg_13_8). A non-parametric statistical analysis between treatment groups was conducted with R (v 3. 4. 2). Results & discussion Overall archaeal and bacterial population diversity differed between CH HRFI and LRFI steers (P<0. 05). This was accompanied by increased diversity coverage and dominance in LRFI steers. VFA results showed that the LRFI CH steers had reduced butyrate concentration (P<0. 1) and an increased acetate:propionate ratio (P<0. 05). No difference in diversity was observed between HF HRFI and LRFI steers. There was an increase in acetate, propionate, isobutyrate, valerate and total VFA concentrations in LRFI in comparison to HRFI HF steers (P<0.
9. 1 were used to filter reads and determine Operational Taxonomic Units (OTUs) and the Usearch algorithm was used to cluster the reads OTUs with a 97% cutoff, and to assign the taxonomy using the Ribosomal Database Project (RDPII) version 10. Statistical analyses were performed with the assistance of R software, differences were considered significant at p < 0. 05 using the Wilcoxon test. Results A mean of 3131 ± 589 sequences per sample were produced. Total OTUs (467±51), Chao (518. 47±34) and Simpson (0. 96±0. 01) indices were not affected by EN supplementation (P > 0. 05). However, comparison of the archaeal communities by principal coordinate analysis (PCoA) using the weighted Unifrac distance (Figure  2 ), explains 56. 14% of the variation in the data and confirmed a separation between Control and EN groups.
The feeding standards calculate that eMCP varies from 130 to 170 g MCP/kg digestible organic matter (DOM)  (SCA 2007)  under adequate RDP but tropical forages promote low values often <130 g MCP/kg DOM  (Bowen et al 2016). The interaction between RDP and rumen fluid dilution rate was investigated to explain and develop better predictive equations. Material & methods Databases were searched and papers that yielded data about rumen water retention time (RWRT, or dilution rate), levels of RDP intake and digestible organic matter (DOM) intake and eMCP were used in the subsequent analyses. Information was also collected on crude protein, organic matter, neutral detergent fibre, estimated RDP/CP, organic matter digestibility, NDF digestibility, dry matter intake and live weight for both sheep and cattle.
Material & methods A total of 334 Holstein dairy cows (parity = 3. 08 ± 1. 14, DIM = 159 ± 34) housed at a commercial dairy farm were selected. Cows were fed a same diet with a ratio of forage-to-concentrate at 45:55. Milk composition and milk yield were recorded. Rumen contents of all the cows were collected by an oral stomach tube for analysis of volatile fatty acids (VFA) and 16S rRNA gene sequencing of V3 and V4 regions. Operational taxonomic units (OTUs) were clustered with a 97% identity threshold, and taxonomy was assigned using the latest Greengenes database. The core rumen bacterial microbiota was defined as genera observed in 100% of the samples, and pan-microbiota was defined as a total observed richness in all the samples. The contribution of bacterial genera to microbiome variation was plotted on the two first principal coordinates analysis dimensions.