dataesr/scientific-paragraphs-categorization huggingface dataset:
This would be anticipated from the known sources of cohesins and dockerins in both cellulosome-producing species as well as non-producers  (Peer et al. , 2009). Finally, the 61 putative cohesin sequences were aligned with those from the database of known cohesins to examine sequence conservation. The metagenome-derived cohesin sequences were classified according to two major types (Supporting Information Figure  3S ). Eight cohesin modules classified as type I and 23 as type III. The rest, 30 (49. 18%) cohesin modules, could not be classified, and no significant similarity to type II cohesins was found. The unclassified cohesins exhibited similarity in their length and sequences to known cohesins. However, when the tree was collapsed by 70% statistical significance (Supporting Information Figure  3S ), and no significant branching with any of the types was observed.
Interaction studies of cohesins and dockerins Cohesin-dockerin interactions are essential for the cellulosomal machinery to assemble. We, therefore, sought to A. Phylogeny of the retrieved cohesins. A set of 61 cohesin modules was aligned using the CLUSTAL-Omega tool in the EBI-website, which served to reconstruct a phylogenetic tree by MEGA6. 0 software. The cohesins distributed into four major putative species by BLAST comparison of the cohesin modules, and the rest were defined as unknown or less prevalent species that were grouped as 'others'. The displayed tree was condensed whereby each branch with less than 70% statistical significance was collapsed, and the numerical values above the nodes indicate the bootstrap percentiles. The putative species are coloured according to the legend.
B. Phylogeny of the retrieved dockerins. A set of 649 dockerin modules was aligned using the MUSCLE tool in the EBI-website, and the alignment served to reconstruct a phylogenetic tree by MEGA6. 0 software. The dockerins distributed into five major putative species by BLAST comparison of the dockerin modules, and rest were defined as unknown, uncultured bacteria unknown or less prevalent species that were grouped as 'others'. The displayed tree was condensed in which each branch with less than 70% statistical significance was collapsed. The putative species are coloured according the legend. The seven selected dockerins are shown with their full annotations (contig and ORF numbers). The asterisk indicates possible lateral gene transfer events between Firmicutes and Bacteroidetes phyla. examine whether we could locate cohesin and dockerin modules among the identified putative cellulosomal components which would actively interact with each other in a specific manner.
, 2001) ]. with dockerin 4b and cohesin 3a exhibited significant interaction with dockerin 3a and dockerin 4b (Fig. 4B  and Supporting Information Figure  5S ). In addition, cohesin 2b also showed somewhat weaker interaction with dockerin 4b (Fig. 4B ). Figure  4B  presents the interactions between the modules according to ELISA results. When verifying Eighteen contigs containing cellulosomal proteins, with both dockerin and cohesin modules have been found. The total number of cohesin and dockerin modules along with the putative species for each contig, based on BLAST searches of the full contig, is shown. The selected contigs for cloning are in boldface font, highlighted in purple. C. Non-denaturing gels confirming positive interactions between dockerin 4b and (i) cohesin 3a and (ii) cohesin 4a that showed positive interaction by ELISA.
The structures were modeled on dockerin type I (2ccl), which was one of the selected templates. Calcium ions are represented in pink in A an E. Clustal Omega alignment of these and additional putative dockerins against known type 1 dockerins is presented on the following pages. Established type-1 dockerins whose 3D structures from Clostridium thermocellum XynY(10B) (PDB Code 2CCL) and Cel48S (PDB Code 2MTE), Clostridium cellulolyticum Cel5A (PDB Code 2VN5) and Acetivibrio cellulolyticus ScaB (PDB Code 4UYP) were used as standards in the alignment. Calcium-coordinating residues (dockerin positions 1, 3, 5, 9 and 12) are highlighted in cyan, and reputed recognition residues (dockerin positions  10, 11, 17, 18 and 22)  are highlighted in yellow. Some of the putative dockerins are abridged at the C terminus, owing to incomplete sequencing.
Fig. 2S. Ratio of taxonomical composition between the rumen cellulosomal components to the fibre adherent rumen microbiome. The ratios within a common phylum were calculated and represented as blue bars, displayed in relation to a ratio of 1. 0. Fig. 3S. Predicted phylogeny and types of the retrieved cohesins. A set of 61 cohesin modules was aligned using the CLUSTAL-Omega tool in the EBI-website, which served to reconstruct a phylogenetic tree by MEGA6. 0 software. The cohesins distributed into 2 major cohesin types, I and III (pink and purple respectively). Thirty cohesins had no significant similarity to known cohesin types, and therefore are marked as unknown (green). The displayed tree was condensed in which each branch with less than 70% statistical significance was collapsed, and the numerical values above the nodes indicate the bootstrap percentiles.
The numbers represent the predetermined molar-ratio estimates of the cohesin and dockerin modules. Table  S1 : List of primers. Cloned protein names and designed primers. Restriction sites appear in red upper case fonts. Each protein includes its name based on the contig number (1 to 5) and the original name (in parentheses) as provided in the metagenome database. Table  S2 : Five selected contigs containing one or more cohesins and dockerins. The contigs are numbered from 1 to 5 and the names of the cellulosomal components on each of them start with this number. The letters after the numbers distinguish between the cohesin and dockerins. Where relevant, the sequence of the N-terminal signal peptide appears in yellow font. Putative calcium-binding residues (D,N,S in positions 1, 3, 5, 9 and 12 of the duplicated calcium-binding loop) of the dockerin are highlighted in cyan.
Acknowledgements The research described in this communication was supported by grants from the  Israel Science Foundation  (No. 1313/13  to I. M. and  1349/13  to E. A. B. ), by the  Israeli Chief Scientist Ministry of Agriculture and Rural Development Fund  (No. 362-0426 ) to E. A. B. and I. M. , by the  Israeli Chief Scientist Ministry of Science Foundation  (No. 3-10880 ) to E. A. B. and I. M. , by the  European Research Council  under the European Union's  Horizon 2020 research and innovation program , project number  640384  to I. M. and E. A. B. , by the  United States-Israel Binational Science Foundation (BSF) , Jerusalem, Israel and by the establishment of an  Israeli Center of Research Excellence (I-CORE  Center No. 152/11 , to E. A. B. ) managed by the  ISF.
Central Composite Design The yoghurt whey was considered as the basal medium, and used for optimization by response surface methodology (RSM) using Central Composie Design (CDD). The level of three variables: sugars concentration, yeast extract concentration and inoculum size chosen for this study were optimized by the experimental plan. Design of the experiments was done with Minitab Statistical Software version 16 (Minitab Inc, State College, PA). As shown in Table  2  the three factors were designated as X 1 , X 2 , X 3 and prescribed into five differents levels (-α, -1, 0, +1, +α). A set of 20 experiments were carried out. All variables were taken at a central coded value considered as zero. The minimum and the maximum ranges of variables were investigated and the full experimentatl plan with respect of their values in actual and coded forme is listed in Table  3.
Statistical analysis Statistical analyses were restricted to completed questionnaires with evaluable results. Data were analysed using Minitab 13. 3 software (Minitab Inc, State College, Pennsylvania, USA). General frequency responses to all survey items were determined and then used to test for associations among the categorical variables. When needed, data were split to cross tabs with respect to various grouping variables. Comparisons were made by Pearson χ 2 test of independence. A p value ≤0. 05 was considered statistically significant.
Western blotting Samples were treated with proteinase K, subjected to sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and blotted onto membranes according to the Prionics AG (Schlieren, Switzerland) Check ® Western BSE test protocol  [33] , using Sha31 monoclonal antibody (1:8000; Spibio A03212), the Bio-Rad VersaDoc imaging system and Quantity One 1-D Analysis Software (Bio-Rad) for visualization.
Data analysis Vacuolation, PrP Sc deposition, astrocytosis and microgliosis in the CNS were evaluated in the stained sections and scored on a scale ranging from 0 to 5 (0 = not detectable; 1 = occasional; 2 = mild; 3 = moderate; 4 = severe; 5 = extreme). A possible correlation between lesions and immunolabeling in the CNS was evaluated using the non-parametric Spearman's rho measure. P < 0. 05 was considered significant, P < 0. 01 very significant and P < 0. 001 highly significant. Lesion and immunolabeling scores were plotted as a function of the anatomical area, and data expressed as the mean ± standard deviation. In peripheral tissues, positive results were scored according to the intensity of PrP Sc labeling, and were classified as negative (-), minimal-to-mild (+), moderate (++) or strong (+++).
Acknowledgements This work was financed by the  Fondo Europeo de Desarrollo Regional (FEDER) ,  proyecto de Cooperación Transpirenaica en Seguridad de los Alimentos de Origen Ovino y Caprino (COTSA and ConCOTSA) ,  Proyecto de Consolidación de la Cooperación Transpirenaica en materia de Seguridad Alimentaria   EFA 282/13  (Transprion) and by the  Government of Aragon. The authors also thank  Silvia Castejón ,  Silvia Ruiz ,  Sonia Gomes  and  Nuria Segovia  for their technical assistance.
Growth and body composition The primary efficacy variable was the body weight measured on the DES. Growth was also assessed by calculating the increase in body weight (g/kg$d), length (cm/wk), and head circumference (HC) (cm/wk) during this period. Weight for length, weight for age, length for age, and HC for age Z-scores, and changes from baseline Z-scores were calculated at birth, upon study inclusion, on the DES, at discharge, and at the end of the study's TT, using Olsen's reference curves  [17]. At a corrected GA of 41 weeks, a whole-body dual-energy X-ray absorptiometry (DEXA) scan (Hologic QDR 4500A, Infant WB software; Hologic Inc. , Waltham, MA, USA) was performed to assess bone mineral content, soft tissue composition. As the study took place in three different units, quality control of the bone mineral content and soft tissue measurements was carried out using our all-solid DEXA phantoms  [18, 19].
Data collection and management Nurses collecting the data, the doctors involved in the follow-up, and attending physicians were blinded with respect to which intervention groups the infants had been allocated. Parental characteristics and antenatal data were extracted from the infants' medical records. Body weights were recorded daily, and crownheel lengths and HC were recorded from the time the infants were included in the study to the time of their discharge from the hospital at the TT using an electronic diary.
Any other protocol deviation was considered to be minor. The third analysis set was the safety population, which was defined as infants who had received at least one dose of the placebo or probiotic and from whom safety data were collected. The results were analyzed by a statistician who was blinded to the treatment allocation, using SAS 9. 2 (SAS Institute Inc. , Cary, NC, USA) for Windows. Statistical tests were two-sided and conducted at the 5% level. Bonferroni corrections for multiplicity were planned in case of a significant global treatment effect conditioning analyses of the effect of the three subgroups of probiotics as planned in the protocol. It was emphasized, subsequently to the design of this study, that probiotics cannot be studied as a whole and that any positive or negative effects on the selected outcome should be investigated in relation to each probiotic species  [21].
Furthermore, we accounted for the low level of Bifidobacterium implantation. Lastly, the number of infants included in our study approached or even exceeded the numbers included in previous nutritional studies that reported positive effects on postnatal weight gain. Our population was representative of a typical population of preterm infants, because we also included very immature infants who are at risk of severe growth restrictions. The absence of effect of probiotics on short-term growth could also be related to an insufficient effect on gut microbiota in the participating neonatal units. The proportion of infants with bifidobacteria in stools has tripled but the basal level was low as it was close to 10%, in agreement with our previous findings and data from other authors  [6, 30]. Some authors reported higher proportions  [31, 32].
We carefully collected data on actual energy and protein intakes during the supplementation period and took these intakes into account during the analyses, which is rarely undertaken in studies of this type. Almost all infants received human milk, but the human milk composition was not measured in our study, which could have introduced an error. Human milk was assumed to contain 64 kcal/100 mL and 1. 4 g of protein/100 mL, which are commonly used average values. By encoding carefully and prospectively the real amount of parenteral fluids intake and of milk intake during the study period, we minimized the risk of error in nutrient's intakes calculations. However, such an error is randomly distributed in all the feeding groups and it is very unlikely that it could have significant influenced our results.
Calculations and Statistical Analyzes The AID of P and Ca was determined using the following equation: AID (%) = 100 -[100 × (T i D iet × X D igesta ) / (T i D igesta × X D iet )] where Ti Diet is the titanium concentration of dietary intake, X Digesta the P or Ca concentration of ileal digesta, Ti Digesta the titanium concentration of ileal digesta, and X Diet the P or Ca concentration of dietary intake. Data were analyzed using the MIXED procedure of SAS (SAS Inst. , Inc. , Cary, NC) and the individual bird as experimental unit, except for feed intake and feed conversion ratio which were on a pen basis. Results were analyzed using a factorial effects model. For data obtained up to 21 d, the model included dietary Ca, dietary NPP, and their interaction as the main fixed effects, with block as a random effect.
Data obtained up to 35 d were analyzed using a model including grower dietary Ca, grower dietary NPP, finisher diet, and their interactions as the main fixed effects, with block as a random effect. Least-square means were compared using the PDIFF option of SAS. Differences were considered significant at P < 0. 05 and trends were considered at P < 0. 10. Means of six treatments were compared using orthogonal polynomial contrasts for linear and quadratic effects within (1) H 2 H, L 1 H, and L 1 L, (2) H 2 L, H 2 H, and L 1 L, and (3) H 2 H, H 1 H, and H 1 M. These contrasts were chosen to study the capacity of the birds to compensate for (1) early P and Ca restriction according to P and Ca levels in the finisher diet (H 2 H vs. L 1 H vs. L 1 L), (2) delayed P and Ca restriction occurring during the finisher period according to P and Ca levels in the grower diet (H 2 L vs.
Data Sources This study used data from the Cambodia Demographic Health Surveys (CDHS) conducted in 2000, 2005, 2010, and 2014 (Macro international Inc. , Opinion Research Corporation (ORC Macro), Caverlton, Maryland, USA) on women of reproductive age from 15 to 49 years old  [1, [18] [19] [20]. The DHS surveys collect information on household demographic and socioeconomic characteristics and on child anthropometry, child feeding practices, and child health in a nationally representative sample. The surveys were based on stratified samples selected at two stages, and each reporting domain was separated into rural and urban areas.
The analysis of inequality was carried out between subgroups of the population constructed according to different socio-demographic factors usually implicated in inequalities and defined by DHS survey. Age was analyzed in three categories: younger than 20 years old, from 20 to 34 years old, and from 35 to 49 years old. The education level was categorized as "none" for women who never attended school, "primary" for women who have attended some primary school without necessarily having completed it, and "secondary+" for women who have attended some secondary school or more. The living area was categorized as urban or rural. The household wealth index was constructed using principal component analysis, as described before  [23] , with assets that were consistently available over time. This included data concerning accessibility and type of water, sanitation facility, materials used for housing construction, type of fuel used for cooking, and ownership of selected assets such like a radio, television, refrigerator, etc.
Acknowledgments: This secondary analysis was supported by  UNICEF Cambodia. The collection of the blood samples was supported by several organizations ( UNICEF, IRD, ILSI, World Vision , and WFP) and collected by the  Fisheries Administration of Cambodia. The development of the paper was supported by  UNICEF National committees (Australian, Korean , and  Hong Kong ). We would like to thank the  Fisheries Administration of Cambodia and its team  for their support.
6 he issue of misdiagnosis and its consequences has been previously explored in case studies, post hoc medical record reviews, and country-specific patient registries. 11, 12, 14, 16 To better characterize this issue, the clinical aspects and trends of C1-INH-HAE misdiagnosis need to be investigated within a large, diverse, multinational, real-world patient population. The Icatibant Outcome Survey (IOS) (ClinicalTrials. gov Identifier: NCT01034969) is an ongoing, Shire-sponsored, international, prospective, observational registry that collects demographic, medical history, and clinical outcomes data in patients eligible for treatment with icatibant, a subcutaneously administered bradykinin B2 receptor antagonist, in the treatment of C1-INH-HAE. 18, 19 Analyses of data from patients participating in the IOS were conducted to help determine trends in C1-INH-HAE misdiagnosis.
Patients attended regular follow-up visits, recommended every 6 months according to physician routine clinical practice. Data were collected from electronic forms completed by physicians during routine patient visits. Additional details of the IOS registry have been previously described. 20 t enrollment, patients reported any misdiagnoses (based on HAE symptoms) received before the diagnosis of C1-INH-HAE. Descriptive retrospective analyses of any reported misdiagnosis were performed using the IOS data collected from July 2009 to January 2016. Descriptive statistics were used to compare patients who had 1 or more misdiagnoses with patients who had no misdiagnoses. Statistical testing was considered exploratory in this observational study and no adjustment for multiplicity was performed. Patients with a previous misdiagnosis also were compared by type of misdiagnosis, C1-INH-HAE type I or II, sex distribution, and familial history of C1-INH-HAE using descriptive statistics.
Discussion This study found that patients with C1-INH-HAE commonly receive misdiagnoses, frequently allergic angioedema or appendicitis. The present analysis is the first using IOS data to show that an initial misdiagnosis is a major driver of the long delays that patients    It is well recognized that patients with C1-INH-HAE can be subject to an initial misdiagnosis, but analyses of misdiagnosis patterns and trends in a real-world setting are scarce. 4, 5 7e9 Moreover, angioedema is a widely variable disease with different types and subtypes, including acquired angioedema and idiopathic histaminergic angioedema. 4, 5 It is understandable, therefore, that clinical suspicion for C1-INH-HAE may be low, particularly among physicians who are not familiar with the disorder or its clinical management.
Patients with misdiagnoses included those with frequent attacks. Given the age at onset of symptoms, delay in diagnosis is likely to lead to severe disruption of education and early working life and may account for many of the economic, social, and psychological consequences of C1-INH-HAE described elsewhere. 23e27  In a recent study by Bygum et al,  24  misdiagnosis was mentioned by patients as an important burden of their disease and was included in a conceptual model of C1-INH-HAE burden in patients. Considering that published data regarding C1-INH-HAE misdiagnosis are scarce and consist largely of case reports and countryspecific analyses, the relatively large real-world multinational database used in this analysis substantially contributes to the understanding of this issue. Limitations of this analysis necessarily include those related to patient recall, the number of patients analyzed, and the fact that the IOS currently includes only patients from centers in Europe, South America, and the Middle East.
Acknowledgments Medical writing support was provided by  Stephanie Eide  and  David Lickorish, PhD , CMPP, of  Excel Scientific Solutions  and funded by  Shire. We thank the following IOS investigators for their contributions: Austria: W. Aberer; Brazil:  A. Grumach ; Denmark:  A. Bygum ; France:  C. Blanchard Delaunay ,  L. Bouillet ,  B. Coppere ,  O. Fain ,  B. Goichot ,  A. Gompel ,  S. Guez ,  P. Jeandel ,  G. Kanny ,  D. Launay ,  H. Maillard ,  L. Martin ,  A. Masseau ,  Y. Ollivier ,  A. Sobel ; Germany:  J. Arnolds ,  E. Aygören-Pürsün ,  M. Bas ¸ ,  A. Bauer ,  K. Bork ,  I. Martinez ,  M. Maurer ; Greece:  E. Papadopoulou-Alataki , F. Psarros; Israel:  Y. Graif ,  S. Kivity ,  A. Reshef ,  E. Toubi ; Italy:  F. Arcoleo ,  M. Cicardi ,  P. Manconi ,  G. Marone ,  V. Montinaro ;  Spain: M.
Study population EPIC is an on-going multicenter prospective cohort study investigating the role of diet, lifestyle, genetic and environmental factors on the risk of cancer and other chronic diseases. A detailed description of the methods employed has previously been published  [17, 18]. Briefly, 521,448 participants aged 25-70 years were recruited between 1992 and 2000, from 23 study centers in 10 European countries: Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom. Participants with previous cancer (n  = 23,243) , CVD (n = 7,007 myocardial infarction, n = 8,335 angina and n = 4,156 stroke) or diabetes (n = 13,844) diagnosis were excluded from this analysis. All study participants provided written informed consent. Ethical approval for the EPIC study was obtained from the review boards of the International Agency for Research on Cancer and local participating centres: National Committee on Health Research Ethics (Denmark); Comité de Protection des Personnes (France); Ethics Committee of the Heidelberg University Medical School (Germany); Ethikkommission der Landesärztekammer Brandenburg Cottbus (Germany); University of Athens Medical School (Greece) Comitato Etico Indipendente, Fondazione IRCCS Istituto Nazionale dei Tumori (Italy); Human Genetics Foundation Torino Ethics Committee (Italy); Medical Ethical Committee (METC) of the University Medical Center Utrecht (the Netherlands); Regional Ethical Committee for Northern Norway and the Norwegian Data Inspectorate (Norway); Comité de Ética de Investigación Clínica (Spain); Ethics Committee of Lund University (Sweden); Umea Regional Ethical Review Board (Sweden); Norwich District Ethics Committee (UK); Scotland A Research Ethics Committee (UK); and the Imperial College Research Ethics Committee (UK).