dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomic Diversity In addition to the allelic variation between genes conserved among strains (i. e. , core genes), a second level of biodiversity related to the gene content shared by a few strains or specific to one strain (i. e. , accessory genes) can be described. This accessory genome provides a given strain with wide adaptability and extending capacities such as the ability to colonize different ecological niches. The increasing amount of lactococcal genome sequences is undoubtedly a powerful tool to highlight these capacities. In the NCBI genome database (Table  1 ), the mean genome size of the two subspecies is quite similar (2504 kb ranging from 2245 kb to 2744 kb for the subspecies lactis and 2537 kb ranging from 2000 kb to 2862 kb for the subspecies cremoris). It should be noted that about a 30% difference between the smallest and the largest genomes has been found for the cremoris subspecies, indicating high fluctuation in strain-to-strain coding capacity.
7% and 8. 4% of the genome within the lactis and cremoris subspecies respectively. Recent sequence data from NCBI identified 96 completely sequenced lactococcal plasmids. Examination of the plasmid content of 150 dairy starters revealed a mean of seven plasmids per strain  [45]. Genomic analysis of the L. lactis subsp. cremoris UC509. 9 strain revealed that extrachromosomal DNA represents more than 200 kb distributed in eight plasmids  [46]. In several cases, plasmids have been shown to be transmissible by conjugation, a natural process of DNA transfer that could be exploited in the dairy industry to improve strains by conferring new desirable phenotypes. In contrast, in non-dairy isolates, the average was only two plasmids per strain with larger plasmids (>10 kb)  [45]. Plasmid-encoded genes can harbor important technological traits such as proteinase activity, lactose utilization, bacteriophage resistance and bacteriocin production  [47] [48] [49] [50] [51] [52].
Prior to injection, all samples were filtered through a 0. 2 lm cellulose acetate filter. Mixtures of formic acid/water (solvent A), and formic acid/acetonitrile/water (solvent B) were prepared for the mobile phase at the ratios of 0. 3/99. 7 (v/v), and 0. 3/30/69. 7 (v/v/v), respectively. Elution gradient was as follows: 0 min: 30% (B), 30 min: 90% (B), 32 min: 90% (B), 35 min: 30% (B), using a flow rate of 150 ll/min. MS conditions were as follows: sheath gas (N 2 ) flow rate: 20 ml/min, spray voltage: 4. 5 kV, capillary temperature: 270 °C, capillary voltage: 26 V, and collision energy: 25-35 V. Data acquisition was performed using X-Calibur (version 2. 1) software, and analyzed in positive spray ionization mode. Series of two scans were used: a full mass and MS-MS (MS 2 ) of the most intense ions determined using relative collision energy of 20 V.
Elution gradient was performed as follows: 0 min: 10% (B), 15 min: 30% (B), 50 min: 60% (B), 51 min: 100% (B), 55 min: 100% (B), 56 min: 100% (B), and 60 min: 10% (B), with a flow rate of 800 ll/min. MS conditions were as follows: sheath gas (N 2 ) flow rate of 5. 83 ml/min, spray voltage of 3 kV, capillary temperature of 110 °C, capillary voltage of 128. 5 V, collision energy of 25-35 V. Data acquisition was performed using X-Calibur (version 2. 1) software, and analyzed in negative spray ionization mode. Series of two scans were used: a full scan and MS 2 of the most intense ions determined using relative collision energy of 20 V.
Treatment of multiple responses and selection of optimal conditions were based on desirability function D  (Hurst et al. , 2010). The experimental design and multiple linear regression analysis were performed using Design-Expert v. 8 software (Stat-Ease, Minneapolis, Minnesota, USA). The verification and validation of the generated mathematical model were performed by extracting polyphenols under the optimal conditions (temperature, pH, and ethanol concentration) found using the experimental design. Experimental and predicted values were further compared.
Acknowledgments Authors appreciate the financial support from  International Cooperation Research Funding  granted by  Ministry of Science and Technology of the People's Republic of China  ( 2014DFG32310 ). The authors also acknowledge the support of the  Natural Science Foundation of Hubei Province, China  ( 2014CFB891 ), the  Science and Technology Support Program of Hubei Province, China  ( 2015BHE015 ) and the  National Natural Science Foundation of China  ( 31371727 ).
EngU hydrolyzed β-1,4 linkages in carboxymethyl-cellulose, but displayed its highest activity with mixed linkage (β-1,3-/β-1,4-) glucans such as barley β-glucan and lichenin, where in contrast to characterized lichenases cleavage occurred predominantly at the β-1,3 linkages of C4-substituted glucose residues. EngU and numerous related enzymes with previously unknown function represent a new GH family of biomass-degrading enzymes within the GH-A clan. The name assigned to the new GH family is GH148. Based on amino acid sequence and in consequence also structural similarity, glycoside hydrolase (GH) enzymes are grouped into GH families, as comprehensively documented in the carbohydrate-active enzymes (CAZy) database (http://www. cazy. org/). The related enzymes within a GH family share structural features and the basic catalytic mechanism (retaining or inverting), but some GH families display a remarkably broad diversity of substrate specificities among their members.
Domain organization of EngU and expression of engU fragments. The domain architecture of the EngU protein sequence was analyzed by searching against the Pfam and Superfamily databases and, in order to increase sensitivity, against a Pfam subset which contained only models of glycoside hydrolases and carbohydrate binding modules. These searches revealed an unusual mosaic-like module arrangement of EngU (Fig. 1 ). Two non-contiguous segments within the 905 residue EngU primary structure displayed partial similarity to the Pfam seed representing GH family 42 and to the β-glycanases family in the Superfamily database (family 51487), which suggested EngU belongs to the GH-A clan of glycoside hydrolases (Fig. 1 ). A GH42-like region of EngU (GH-A1, also designated as A 1 in the names of expression vector constructs mentioned below and in Fig.
5  and Supplementary Figure  3 ). These experiments were performed also with the purified mixed-linkage β-glucanase (lichenase) LicB from Clostridium thermocellum (UniProt accession number Q84C00), for which the cleavage specificity with β-glucan is well known  24, 25  , in order to aid the interpretation of the results with EngU. The first detectable intermediate products of β-glucan cleavage by EngU were oligosaccharides with degree of polymerization (DP) ranging from 6 to 9 hexose moieties and larger, suggesting an endo-mechanism for β-glucan degradation (Fig. 5a ). The endo-mode is corroborated by the activity on CMC and HEC. After prolonged incubation, the major final product had a mobility similar to cellotriose (G 3 ) of the G 1-6 standard. However, the end-products of β-glucan cleavage by EngU and LicB differed in their mobility in TLC and HPAEC analysis.
Site directed mutagenesis. EngU was aligned with sequence profiles from the GH-A clan (families GH5 and GH42) as well as with the CBM4 Pfam family using the program hmmscan from the HMMER 3. 1 package  26. Based on this alignment, acidic residues possibly involved in catalysis were identified and substituted with alanine in order to investigate their role in the ability of EngU to cleave polymeric substrates. All mutants and their activities towards CMC are listed in Table  4. In particular, short sequence regions surrounding the two conserved catalytic glutamate residues in the Pfam GH42 model (E136 and E295) could be aligned to E239 and E581 in EngU (Fig. 1 ). E239A (predicted acid/base catalytic residue) and E581A (predicted nucleophile) mutants showed a complete loss of activity compared to the wild type protein.
Interestingly, most of the hits with full-length similarity to EngU were from single-cell sequencing or metagenomics data. EngU did not show sufficient and clear similarity for inclusion into an existing GH family from the CAZy database. Based on the very limited similarity to known CAZy members, the distinct cleavage pattern of the enzyme (see below), and the relatively broad phylogenetic distribution of homologs, we propose that the catalytic domain of EngU (part A1 + A2) is the founding member of a new GH family, GH148. Like all CAZy families, this new family should be defined solely by the sequence of its catalytic domain. To determine which other sequences can be included in family GH148, we constructed an artificial sequence corresponding to EngU without the CBM4 part and used it for a Blast search against the non-redundant protein sequence databank of the NCBI.
Substrate sequence had significant hits with hundreds of sequences, 80 of which are present also in GenBank as finished entries and now displayed in the CAZy database as members of GH148 (http://www. cazy. org/GH148. html). At residues 293-430 in the EngU protein, a CBM4 module separates GH-A1 from GHA-2 and thus splits the proposed new GH module in two. Other examples of GH polypeptides with insertions in the catalytic module can be found in sequence databases (for instance in GH10: GenBank ADD61481. 1 or CCO21036. 1) but further biochemical analysis is lacking. The CBM4 type of CBMs is often found in bacterial enzymes which can attach to xylan, β-1,3 and mixed-linkage glucans, and amorphous cellulose  27. In known NMR or crystal structures of CBM4 representatives the N-and C-terminal ends of the CBM supersecondary structures are typically close to each other (PDB entries: 1ULO, CBM4 from Cellulomonas fimi CenC; 1CX1, CBM4 from Cellulomonas fimi EngC; 1K42, 1K45, CBM4-2 from Rhodothermus marinus xylanase; 3K4Z, CBM4 from Clostridium thermocellum CbhA; 3P6B, CBM4 from Clostridium thermocellum CelK; 1GUI, CBM4 from Thermotoga maritima Lam16A)  [27] [28] [29] [30] [31] [32].
While the closest homologs of EngU also have an inserted CBM4 domain between stand β-4 and helix α-4 of the predicted (β/α) 8 barrel, other sequences have a shorter insertion at the same place, but with no detectable similarity to CBM4 (for example ORF Igag_0510 of Ignisphaera aggregans, ADM27348. 1 or ORF B2J77_11410 of Pseudomonas parafulva, AQW68778. 1). Thus, different members of GH148 carry different insertions at identical positions of their catalytic domain. Interestingly, in the available databases we could not find any proteins belonging to the new GH148 family that lacked an inserted sequence between the two parts (A1 and A2) of the catalytic domain. The reason for this is currently unknown but it can be speculated that apparently the inserted additional domains in GH148 generally play important roles for the enzymes' physiological function(s).
5 ). Presumably a trisaccharide is the smallest unit that the enzyme can process, because the disaccharide products were not degraded further. According to the CAZy database, lichenase (EC 3. 2. 1. 73) activities can be found in 9 different GH families, which do not share significant primary structure similarity. In plants, endo-β-1,3/β-1,4-glucanases are represented by GH families 12 and 17, while these enzymes from the bacteria are mostly members of GH5 and GH16. In conclusion, EngU is the first characterized representative of a new type of β-1,3/β-1,4-glucanase displaying a novel scissile linkage preference and represents the first characterized member of a new GH family within the GH-A clan. Highly unusual, the enzyme carries a CBM4-type carbohydrate-binding module inserted between the catalytic glutamate residues of its catalytic module.
Potential signal peptides were predicted using the signalP software  40  , blastP  41, 42  and the nr database were used for the detection of putative EngU homologs. The Pfam database (version 30)  43  and the HMMER program (version 3. 1)  44  were used to generate sequence alignments of EngU with selected Pfam families. Secondary structure-based alignments were performed with T-Coffee  45  and ENDScript  46.
The reactions were diluted tenfold, 25 µL were injected and the analysis was performed at 30 °C at a flow rate of 1 mL/min. Separation was achieved in 100 mM sodium hydroxide using an eluent gradient profile based on sodium acetate (0-45 min: a linear gradient from 0 to 100 mM sodium acetate; 45-47 min: a linear gradient from 100 to 500 mM sodium acetate; 47-49 min: 500 mM sodium acetate; 49-50 min: a linear gradient from 500 to 0 mM sodium acetate; 50-60 min: 0 mM sodium acetate). Detection was done using the carbohydrate standard quad waveform. Accession codes. Nucleotide and amino acid sequence data for EngU are available in the DDBJ/EMBL/ GenBank databases under the accession number LT622840.
Acknowledgements We gratefully acknowledge the help of  Rolf Daniel  and  Jörg Schuldes  ( University of Göttingen, Germany ) for access to a metagenomic library from an Avachinsky crater sample. We thank  Wolfgang H. Schwarz  and  Vladimir V. Zverlov  ( Department of Microbiology, Technical University of Munich ) for providing purified LicB enzyme as well as  Reinhard Sterner  ( University of Regensburg ) for the helpful comments on the manuscript. The authors are grateful for financial support from the  German Federal Ministry of Education and Research (BMBF)  in the collaborative project ExpresSys within the funding measure  GenoMik-Transfer , and from the  Faculty Graduate Center Weihenstephan of TUM Graduate School  at the  Technical University of Munich, Germany. This work was supported by the  German Research Foundation (DFG)  and the  Technical University of Munich (TUM)  in the framework of the  Open Access Publishing Program.
Data on the nutritional content of food products were obtained from the French Food Quality Observatory (See www. oqali. fr/oqali_eng/) (Oqali). The Oqali database is brand-specific and is specifically designed to follow, over time, the changes in the nutrient contents of products sold on the French market. Depending on the food groups, data were first collected between 2008 and 2010, and the same protocol was repeated in 2011 or 2013 so that the observation period covered two or 3 years. Key nutrients were selected for each food category: sugar, fats, saturated fats (SFA), fiber, and sodium for breakfast cereals; sugar, fats, saturated fats, and fiber for biscuits and cakes; fats, saturated fats, and sodium for potato chips; and sugar for soft drinks. Table  1  displays the number of products considered in each food category, the covered market shares and the initial and second dates of data collection.
Within each food category, the sampled food products were partitioned into three subgroups: products removed from the market between t 0 and t 1 (Group X), paired products present on the market at both t 0 and t 1 (Group C), and new products launched before t 1 (Group N). Food composition data were matched with purchase data from Kantar Worldpanel (The Kantar Worldpanel database provides details on the quantities bought and the corresponding food expenditures by a representative panel of 20,000 households in France) to compute the market share of each product at dates t 0 and t 1 (The use of nutritional data was approved by the steering committee of Oqali. We did not have to ask for an ethical agreement as the food purchase data were anonymous).
Discussion In this study, we aimed to quantify the contribution of food reformulation to changes in the nutritional quality of consumer food purchases. We compared the contribution of food reformulation to that of other factors affecting: the renewal of products on the market and substitutions made by consumers among existing products. This study clearly has some limitations. The most important one is linked to the non exhaustive coverage of the products available on the market in the Oqali data set. The algorithm used to decompose the variation of the nutritional quality of food purchases into the three effects presented above, imposed to have a full coverage of the market at t 0 and t 1. As it was not the case in our data set, we had to make some assumptions about the nutritional content of non-collected items.
Acknowledgements We want to thank the steering committee of  Oqali  for providing the data used in the article. They also thank  Florence Stevenin  and  Oualid Hamza  for their contributions to the matching of the data sets and preliminary treatments. Both authors are responsible for the design and implementation of the study, as well as the writing and final content of the article. This research was funded by the  EU   Horizon 2020 Program  under Grant Agreement no. 633692  (SUSFANS).
A meta-analysis  9  of randomised controlled feeding trials indicated that total PUFA consumption (predominantly linoleic acid) improves both glycaemia and insulin resistance. However, whether such short-term benefits translate to primary prevention of type 2 diabetes remains unclear. Most longitudinal studies  10  of linoleic acid and incident type 2 diabetes have relied on self-reported dietary estimates of intake that might be affected by errors or bias in recall. Linoleic acid cannot be synthesised by human beings, and thus biomarker measurements of linoleic acid can provide objective assessments that are free of memory errors, recall bias, or inaccuracies of food databases. 11 Biomarker measurements are also crucial for studying the effects of arachidonic acid, for which levels are tightly regulated and less correlated with dietary intake.
12 However, only a handful of prospective studies  10  have evaluated associations between linoleic acid or arachidonic acid biomarkers and type 2 diabetes, resulting in potential limitations of publication bias and inadequate power to assess interactions by demographic, medical, or genetic characteristics. Thus, the potential effects of omega-6 PUFAs, including linoleic acid and its metabolite arachidonic acid, on type 2 diabetes remain unresolved and are of considerable clinical, scientific, and public health importance. To address these questions, we did a pooled analysis of new, harmonised, individual-level data within the Fatty Acids and Outcomes Research Consortium. 13  Our primary aim was to assess the associations of linoleic acid and arachidonic acid biomarkers with incident type 2 diabetes, with additional aims to assess factors that might modify these associations.
Study population In this pooled analysis, we identified prospective cohort studies that had assessed circulating or tissue biomarkers of linoleic acid and arachidonic acid, and incidence of type 2 diabetes. Studies were identified by contacting experts, manual searches of reference lists of previous original publications and systematic reviews, and online searches of MEDLINE from inception to Feb 10, 2016, using the search terms "omega-6", "linoleic acid", "arachidonic acid", "diabetes mellitus", "cohort studies", "prospective studies", and "nested case control studies". Participants included in the analysis were aged 18 years or older, with available data for linoleic acid and arachidonic acid biomarkers at baseline. Participants with type 2 diabetes at baseline were excluded. Each cohort receieved institutional review board approval from their respective institutions and written consent was obtained from all participants.
Data pooling and meta-analysis We used HRs and ORs to approximate relative risks (RRs) and pooled the data to generate summary results using inverse-variance weighted meta-analysis. We also used random effects models in sensitivity analyses. 18 Because fatty acids were measured in different lipid compartments (phospholipids, plasma, cholesterol esters, and adipose tissue) using differing methods, linoleic acid and arachidonic acid were evaluated continuously per study-specific interquintile range (the distance between the midpoint of the first and fifth quintiles) to facilitate pooling. We pooled results separately for each lipid compartment and across all studies. For studies with multiple measures, we prioritised the overall pooled analysis on the basis of the biomarkers that would best reflect long-term intake, as specified in the following ordered list: adipose tissue, erythrocyte phospholipids, plasma phospholipids, total plasma or serum, and cholesterol esters.
The association between linoleic acid and type 2 diabetes was assessed in multivariable models for each cohort, and the results were pooled using inverse-variance weighted fixed effects meta-analysis. If multiple biomarkers were available within a study, one was chosen for the overall analysis on the basis of its ability to reflect long-term dietary intake (in the following order of preference): adipose tissue, phospholipids, total plasma, and cholesterol esters. Similarly, data for erythrocyte phospholipids were preferred over plasma phospholipids if both were available from a cohort. References for all studies are shown in the appendix. RR=relative risk. AGES-Reykjavik=Age, Gene/Environment Susceptibility Association between arachidonic acid and type 2 diabetes was assessed in multivariable models for each cohort, and the results were pooled using inverse-variance weighted fixed effects meta-analysis.
If multiple biomarkers were available within a study, one was chosen for the overall analysis on the basis of its ability to reflect long-term dietary intake (in the following order of preference): adipose tissue, phospholipids, total plasma, and cholesterol esters. Similarly, data for erythrocyte phospholipids was preferred over plasma phospholipids if both were available from a cohort. References for all studies are shown in  Association of linoleic acid and arachidonic acid levels with type 2 diabetes was assessed in multivariable models for each cohort, and results were pooled using inverse-variance weighted meta-analysis. The lowest quintile was used as the reference group. For studies in which multiple biomarkers were available, one was chosen for the overall analysis on the basis of its ability to reflect long-term dietary intake (in the following order of preference): adipose tissue, phospholipids, total plasma, and cholesterol esters.
Here, using a robust statistical approach coupled with manual curation, we analyse the ABR data set generated from the 3006 mutant lines and identify 67 that show elevated hearing thresholds compared to wild-type control mice, matched for strain, gender and age. Our findings identify a significant number of candidate hearing loss genes that have not previously been reported, which not only increases our knowledge of the auditory genetic landscape and the molecular mechanisms required for hearing, but also provides a fund of new genes for the many unidentified loci in the human population.
Results Auditory phenotyping. In order to identify genes required for hearing function, the consortium uses an ABR test in the adult pipeline at week 14 that assesses hearing at five frequencies-6, 12, 18, 24 and 30 kHz. The consortium aimed to analyse a minimum of four mutant mice for each gene and, in most cases, both mutant males and females were analysed. Data from each IMPC centre is uploaded to the IMPC Data Coordination Centre, where it is subject to data validation and quality control (QC) procedures prior to statistical analysis and public access (www. mousephenotype. org). Statistical analysis. Statistical analysis for a high-throughput project such as IMPC requires a robust generic analysis pipeline to produce statistically significant and biologically relevant results. IMPC uses the 'PhenStat' package  13  , which employs a number of different statistical techniques appropriate to the phenotype data being examined.