dataesr/scientific-paragraphs-categorization huggingface dataset:
Subfamily GH31_2 contains a broad-spectrum α-glucosidase The gene encoding the GH31_2 subfamily member RaGH31 (QDL53937. 1) from R. aquaticus sp. nov. isolate Gr-4 T (GenBank: CP036282. 1) was synthesized in a codon optimized form for E. coli, expressed in this host and purified to homogeneity. The enzyme was initially screened against a range of chromogenic substrates. H NMR of the initially formed product revealed the formation of α-glucose, and thus that the enzyme is a retaining αglucoside hydrolase (and not an α-glucan lyase). The effects of pH on the hydrolytic activity and temperature stability test of RaGH31 were evaluated using PNPGlc as a substrate (Fig. 5 ). The pH dependence on k cat /K M gave a bell-shaped curve, with an optimum at pH 6 and pK a1 and pK a2 values of 5. 9 ± 0. 4 and 5.
Kinetic parameters (k cat /K M ) reveal a mild preference for sucrose over maltose and nigerose, while trehalose was the poorest of the four disaccharide substrates (Fig. 5 , D-G and Table  4 ). The AlphaFold 3D structure prediction of RaGH31 shows excellent structural similarity to an experimentally determined X-ray structure of Chaetomium thermophilum GIIα-D556A mutant with nigerose (PDB: 5DKZ, green, a member of subfamily 1) (Fig. S9 ).
Conclusion We describe a subfamily classification of the mechanistically and functionally diverse family GH31 using SSN analysis. This classification collates sequence, biochemical, mechanistic, and structural data on characterized members and supports more refined bioinformatic predictions, AlphaFold predictions, and provides a guide for experimental studies. The identification of subfamilies and unclassified sequences with no functionally characterized members offers an opportunity for future enzyme discovery. To support future sequence annotation and experimental design, the GH31 subfamily classification is now publicly available in the CAZy database (http://www. cazy. org/ GH31. html).
Experimental procedures Data acquisition 13,464 GH31-containing Genbank sequences were extracted using the SACCHARIS Perl script cazy_extract. pl (https:// github. com/DallasThomas/SACCHARIS) based on CAZy database lists (June 2021)  (54). The GH31 module was manually defined for biochemically characterized GH31 members on the basis of multiple sequence alignment in the program MAFFT using the G-INS-I strategy. This was used to conduct an hmmsearch in HMMER3-3 to identify the GH31 modules for all family members. Sequences shorter than 40% of query/reference modules were eliminated from the SSN analysis (0. 03 in total%) resulting in 13,473 GH31 modules, saved as a FASTA-format file.
SSN analysis 13,473 GH31 modules were introduced to the SSNpipe tool  (10) , with default settings to obtain all-versus-all pairwise local alignments of all GH31 modules by BLAST+ 2. 2. 31  (55). BLAST E-values ranging from 10 -60 to 10 -140 by steps of 10 -5 allowed the definition of a series of 17 SSNs. In each SSN, every connected component (set of nodes connected to each other by any path) was considered as a candidate subfamily if it either (i) contained at least one characterized member and sufficient sequence diversity (15 proteins for prokaryotes, 4 for eukaryotes -less populated in CAZy due to incompleteness of eukaryotic assemblies) or (ii) contained at least 20 proteins. The remaining sequences were considered unclassified (uc). A Python script was used to down sample the number of nodes for an SSN to reduce the number of edges.
Performance analysis using HMM For each SSN E-value threshold, a library of HMMs for the corresponding GH31 subfamilies, along with one extra HMM for the unclassified GH31 sequences were generated using the four following steps. Each sequence set (subfamily or the unclassified group) was subjected to the sequence redundancy reduction using the CD-hit (56) online platform with default parameters except a clustering percentage tuned to 75%. The resulting low-redundancy sets were aligned using the G-INS-i strategy in MAFFT  (57). HMMs were built using the hmmbuild command in HMMER-3. 3 (58) software after defining boundaries for each multiple sequence alignment using Jalview. The cat bash command in HMMER-3. 3 was used to concatenate all HMM profiles (all subfamilies plus the uncharacterized) into a single HMM library for each SSN E-value.
The HMM library was used to predict subfamilies in the 13,473 GH31 modules. Hits were obtained using the hmmscan command from HMMER3-3 software with default parameters. The modules were assigned to a subfamily based on two aspects: a minimal per-domain HMM E-value of 10 -100 and a required margin between the first/assigned and second-best hits of 10 -20. Using a confusion matrix, true positives, false positives, and false negatives were calculated for each SSN E-value and used to deduce the overall precision and recall for each SSN E-value.
Abbreviations-The abbreviations used are: CAZy, Carbohydrate Active enzyme; CBM, carbohydrate-binding module; EC, enzyme commission; GH, glycoside hydrolase (family); 6GT, 6-α-glucosyltransferase; HMM, hidden Markov model; PDB, protein databank; SI, sucrase-isomaltase; SSN, sequence similarity network.
Intestinal Morphology Hematoxylin and eosin (HE) staining was performed on four-micron paraffinembedded tissue sections. A total of 8 male pups per group were selected for histological analyses (n = 2-4 males per litter per group). The selections were based on the males' body weight at PN20 to represent the average body weight and standard deviation of their litter. At PN21, the ileal villus height and crypt depth, as well as the colonic mucosa thickness and colonic crypt depth, were determined using ImageJ software v1. 53t (NIH, USA) in a blinded manner by two independent observers (10 well-oriented villi or crypts per section, 9 sections from 3 segments per tissue, 8 mice per group).
The targeted metagenomic sequences were analyzed using a bioinformatics pipeline based on the 'find, rapidly, operational taxonomic units (OTUs) with Galaxy solution' (FROGS) guidelines  [24]. The taxonomic assignment was performed using BLAST (v2. 2. 30) against the SILVA 138 Parc database to determine the bacterial profiles from phylum to genus and, when reachable, to species level. Alpha (Shannon and Simpson indexes) and beta diversity (Unifrac) analyses were conducted using the OTU table.
Statistical Analysis Statistical analyses, except for the microbiota, were performed with GraphPad Prism 8. 0 Software (San Diego, CA, USA). Variables were expressed using the mean and standard error of the mean (SEM) or standard deviation (SD). Statistical analyses were conducted based on values obtained for each individual. Outliers were excluded using Grubb's test. The variables' normality was assessed using the D'Agostino-Pearson test. If necessary, a log10 transformation was applied to reach normality. Thus, depending on the variable normality test results, statistical differences were tested via mixed-effects analysis (Tukey's post-test), one-way ANOVA (Tukey's post-test) or the Kruskal-Wallis test (Dunn's post-test) for group comparisons. For the microbiota analysis, the normal distribution of the values was verified with the Shapiro-Wilk test.
Study population The inclusion criteria in the present study were: 1) Patients aged 75 years and over hospitalized in the geriatric acute care unit of Angers University Hospital, France, between October 2020 and March 2021; 2) Data available on ionized calcium and total calcium concentrations; 3) COVID-19. diagnosed with RT-PCR and/or chest CT-scan; 4) No objection from the patient and/or relatives to the use of anonymized clinical and biological data for research purpose. (Figure  1 ).
Biological data and COVID-19 diagnostic All biological samples were analyzed at the Biology department of the University Hospital Angers. COVID-19 was diagnosed with RT-PCR and/or chest CT-scan. Ionized calcium was collected with a blood gas syringe and analyzed by blood gas machine from the first of October 2020 to the 29 th of November then by direct potentiometry (ABL 90 Flex plus or ABL 800) from the 30 th of November 2020 to the 31 st of March 2021. This measurement was systematically adjusted to pH. An immunoassay method (Automate LiaisonXL, DiaSorin) was used to measure PTH and 25(OH)D. C-Reactive Protein (CRP) and albumin were measured by immunoturbidimetric method (Advia Chemistry XPT, Siemens). Albumin was measured on the same blood sample than calcium. Colorimetric method was used to measure total calcemia.
Study selection was restricted to investigations presenting data for potatoes cooked by boiling or baking in a conventional oven (i. e. not in a microwave) without the addition of any cooking oils or fats. More details of the data-sets considered in these calculations are provided in Supplementary Table  1. As it can be observed in Table  1 , the glycemic response to boiled sweet potato is almost half of that to boiled white potato (average GIs of 66 ± 4 vs. 113 ± 26, respectively) (Perceval S. Bahado-Singh, Riley, Wheatley, & Lowe, 2011; P. S. Bahado-Singh, Wheatley, Ahmad, Morrison, & Asemota, 2008; Henry, Lightowler, Strik, & Storey, 2005; Leeman, Östman, & Björck, 2005; Soh & Brand-Miller, 1999). However, the baked versions of these foods seem to elicit similar responses (GIs of 126 and 127 ± 7 for baked white and sweet potatoes, respectively) (Perceval S.
Total starch Prior to analysis, cooked potato samples were freeze-dried in a FreeZone 8 L -50 • C Benchtop Freeze Dryer (Labconco Corporation, USA) and then manually milled at room temperature. The starch content was then determined using the total starch assay kit. This analytical method, based on AOAC Official Method 996. 11 and AACC Method 76. 13, relies on the use of thermostable amylase and amyloglucosidase to fully hydrolyze all the starch in the sample into glucose  (Mccleary, Gibson, & Mugford, 1997). All samples were analyzed in triplicate. The a Bread was preferred over glucose as a reference food because its starch-rich composition is more representative of the composition of potatoes and because it is considered to be a more physiological standard  (Wolever, 1990). In studies where data were reported using glucose as the reference food, the GI was divided by 0.
70 air objective and a 63×/1. 4 oil immersion objective and maintaining the pinhole diameter at 1 Airy Unit. Calcofluor White was excited at 405 nm using a diode laser, and the corresponding emission filter was set at 415-470 nm. FITC was excited at 488 nm using an Argon laser, and the corresponding emission filter was set at 525-593 nm. Leica LAS AV software (v 2. 7. 3. 9723) was used to acquire digital images of 1024 ×1024 pixels in size.
Gastric digestion. After the digestion vessel was prepared, the bolus was added and the chyme was briefly stirred (30-45 s, 20 rpm) before the first sample was collected. This sample corresponded to the end of the oral phase and start of the gastric phase. Stirring was resumed and gastric digestion started with the addition of pepsin and HCl. Gastric digestion lasted 150 min. During this period, 12. 8 mL of pepsin solution were added at a rate of 0. 085 mL/min (syringe pump NE-4000, New Era Pumping Systems Inc. , USA or ref. 78-8100, KD Scientific, USA) to achieve a pepsin activity of 2,000 U. mL -1 of digesta at the end of gastric digestion. HCl was added using Titrando 842 or Titrando 902, both controlled via the software TIAMO® (both titrators and software from Metrohm, Ireland). Gastric pH was continuously monitored in TIAMO® and the flow rate of HCl was adjusted as required to maintain a steady acidification rate of the digesta from its native pH (at t 0 ) to pH 2 in min  (Dressman et al.
Calculations and statistical analysis A total of 140 samples have been collected during the digestion experiments. Digestion material from two gastric emptying points (t 100 of baked white and sweet potatoes) has been lost due to laboratorial error. This has made the analysis of the third repetition at this time point impossible as well as the subsequent intestinal phases (t 110 and t 220 of I2). Cumulated starch release and hydrolysis into oligosaccharides throughout digestion have been calculated and normalized as a percentage of the total starch in the test food at t 0. Starch and oligosaccharide release were expressed as the percentage of total starch in the initial sample over time. The incremental area under the starch and oligosaccharide release curves (AUC) was calculated using GraphPad Prism 9 (GraphPad Software, USA) using the trapezoid rule.
With the exception of time points for which the third repetition was missing (t 100 , t 110 and t 220 of baked white and sweet potatoes) all data points were presented as means ± SDs. The statistical analyses were performed in R (version 1. 1. 463) (RCoreTeam, 2013) using the Rstatix package  (Kassambara, 2020). Statistical significance was accepted at P ≤ 0. 05. The normality of the results was assessed using the Shapiro-Wilk's test and the homogeneity of variances was assessed using Levene's test. Both data normality and homogeneity of variances have been confirmed for all data sets (P > 0. 05). Normally distributed data sets were analyzed using a two-way mixed ANOVA with "time" as within-subject factor and "sample type" (either cooking method or potato type) as between-subject factor. Significant effects were observed for "time", "sample type" and their interaction.
Starch digestion Starch release and hydrolysis during digestion were monitored by determining the concentrations of solubilized starch and ethanol-soluble oligosaccharides in the supernatant of digested samples. The results are presented in Fig. 4. By considering data from each time point, cumulative levels of starch release (filled areas) and hydrolysis (stripped areas) have been calculated and plotted as a percentage of the total starch available in the food sample against time. There are four groups of charts in this figure, one for each potato type and cooking method combination. Within each group, the larger chart on top contains data from the end of the oral phase (t 0 ) and from the gastric phase (from t 0 to t 150 ) and the three bar charts correspond to the intestinal digestions (I1, I2 and I3).
25 and 10 ms activation time. The raw data processing of peptide MS/MS spectra was performed in Progenesis QI software (version 4. 2, Nonlinear Dynamics, Newcastle upon Tyne, UK) consisting in an automatic alignment with respect to a reference run defined by the software considering a maximum allowable ion charged set to 5. The mass generating function list containing the detected ions was directly exported to Mascot v2. 5 (https://www. matrixscience. com) interrogation engine and searched against a Bos taurus decoy database (i. e. ref_bos_taurus, 23 971 sequences -Update 08/17/2017). The following parameters were considered for the search: precursor mass tolerance of 10 ppm and fragment mass tolerance of 0. 5 Da, a maximum of two missed cleavage sites of trypsin, carbamidomethylation (C) and oxidation (M) set as variable modifications.
Protein identification was validated when at least two peptides originating from one protein showed statistically significant identity above Mascot scores > 29 with a False Discovery Rate of 1% (adjusted significance threshold P < 0. 0125). Ions score is À10 log(P), where P is the probability that the observed match is a random event. Individual ions scores > 29 indicate identity or extensive homology. The identified peptides from the database search were imported back to Progenesis QI. All unique validated peptides of an identified protein were included, and the total cumulative abundance was calculated by summing the abundances of all peptides allocated to the respective protein. The resulting UniProtKB protein identifiers were converted to corresponding gene names (GN) using the Retrieve/ID mapping tool of the UniProt web service.
Statistical analysis Statistical analyses were performed with R Studio environment  (R version 4. 2. 1 (2022-06-23) ) in order to compare dairy cows either fed with the HPO or COS diets. Data were obtained from six cows per diet (COS and HPO diets), reared over two periods (P3 and P4 differing by 28 d of lactation). DMI, milk production and composition were statistically evaluated by a linear model testing the diet, the period (as fixed effects) and their interaction using the model <-lm (data $ treatment * period). The P-values <0. 05 were considered as statistically significant. The diet and the period effects were evaluated on the protein abundances determined from the MFGM, the SM and the plasma of cows. Raw pro-teomics data with less than 50% of samples missing intensities were deleted, and data with more than 50% of samples missing intensities were imputed by the k-neighbour imputation method with k = 5 (impute package, version 1.
5 were considered as differentially abundant. A supervised sparse partial least square discriminant analysis (sPLS-DA) was applied on proteomics data corrected for the period effect using the mixOmics package in R (version 6. 20. 0, Rohart et al. , 2017). For each milk fraction and plasma, we used 30% of protein datasets to build the sPLS-DA based on two components, which correspond to 30, 48 and 20 proteins from plasma, MFGM and SM datasets. The multivariate sPLS-DA analysis then selects the most predictive or discriminative proteins to separate the two groups of cows, some of them were more abundant in HPO, other in COS and their contribution represent weights assigned by the sPLS-DA to each protein on a component (Supplementary Figures). The Lists of proteins composed by the result of both univariate and multivariate analysis were mined for biological meaning by a gene ontology (GO) enrichment analysis, performed using ProteINSIDE webservice online tool version 2.
org/10. 15454/AL30HS). The 12 proteins were annotated by terms within the biological process (BP) category that emphasised the regulation of proteolysis; immune system process for SERPING1, LOC107131209, APOA4, FN1, GPLD1, ADIPOQ; acutephase response for FN1 and AHSG; cholesterol efflux or regulation of lipid transport for APOA4 and ADIPOQ; regulation of cellular response to insulin stimulus for GLPD1. Moreover, LOC107131209 and AHSG were annotated by terms from the Reactome database related to the insulin pathways: Regulation of IGF transport and uptake by IGF Binding Proteins (IGFBPs) and Regulation of Complement cascade. Milk fat globule membrane proteins Among the 158 proteins identified from the MFGM fraction (Supplementary Table  S1  available in the Data INRAE repository at https://doi.
Bioinforma cs workflow GO analysis of 21 MFGM proteins related to HPO diet (Supplementary Table  S3  available in the Data INRAE repository at https://doi. org/10. 15454/AL30HS) was annotated by GO terms within the BP category or terms provided by the Reactome database that were mainly related to the lipid metabolism. The proteins ACSL3, ALOX15, GPAM, GPAT4, DHRS3, and LSS were annotated by terms such as glycerolipid biosynthetic process, and metabolism of lipids. Of these, three proteins (ACSL3, ALOX15, and LSS) are localised in the milk fat globule membrane and were annotated by the GO term lipid droplet in the cellular component (CC) category, and two (GPAM and GPAT4) were annotated by the term glycerol-3phosphate O-acyltransferase activity within the molecular function (MF) category. To sum up, the GO analyses of the MFGM proteins that discriminate the COS and HPO diet highlighted their biological functions within the lipid or glycerolipid biosynthetic process as well as the lipid droplet organisation.
Skimmed milk proteins Among the 70 proteins identified in SM fraction (Supplementary Table  S1  available in the Data INRAE repository at https://doi. org/ 10. 15454/AL30HS), when considered one by one in a univariate analysis, 14 proteins (LPO, JCHAIN, F1MLW7, G3N0V0, IGK, TF, INA, FABP3, SERPINA3-7, CD36, CREG1, QSOX1, M91_09155, and SERPING1) were significantly more abundant (FC between 3. 6 and    6  and Fig. 2C ). Furthermore, the sPLS-DA has selected 10 proteins that discriminated the COS-and HPOfed cows, all were differentially abundant between the COS and HPO groups (Table  6  and Supplementary Fig. S3 ). According to GO analysis of the 14 proteins (Supplementary Table  S4  available in the Data INRAE repository at https://doi. org/10. 15454/AL30HS), seven proteins (G3N0V0, JCHAIN, SERPING1, TF, LPO, CD36, and F1MLW7) were annotated by GO terms within the BP category related to response to bacterium and the immune function with terms such as immune system process or humoral immune response.
Acknowledgments The experimental cow model is issued from PhD thesis of  Hélène Fougère  ( 2020 ), https://tel. archives-ouvertes. fr/tel-02094454). The authors gratefully acknowledge the staff of  Unité Expérimentale Herbipôle de Theix (INRAE, Saint-Genès-Champanelle , France; https://doi. org/10. 15454/1. 5572318050509348E12 )for their diligent care of the experimental animals;  Sylvie Bardou-Valette ,  Martine Tourret  and  Arnaud Delavaud  from  UMR 1213 Herbivores (INRAE)  for their assistance with the experiment management, sample collection and laboratory analysis;  Christophe Chambon  from the  Plateforme d'Exploration du Métabolisme composante Protéomique (INRAE, Saint-Genès-Champanelle , France; https://doi. org/10. 15454/1. 557239511356972E12 )for the proteome analysis;  Andréa Rau  and  Maxime Guilleton  for their help in coding of statistical analysis.
Data and model availability statement The dataset generated for this study is publicly available with a CC-BY licence and can be found in the Data INRAE repository (https://entrepot. recherche. data. gouv. fr/dataverse/inrae): Delosière, Mylène; Bernard, Laurence; Viala, Didier; Fougère, Hélène; Bonnet, Muriel, 2023, ''Datasets reporting s-PLSDA results and gene ontology enrichment of proteins identified in plasma and milk of dairy cows fed lipid supplements. ", https://doi. org/10. 15454/ AL30HS, Recherche Data Gouv, V2, UNF:6:YvW3DJfQTtFb kY9hpgXqRg== [fileUNF].
Vertebrate pigmentation patterns are amongst the best characterised model systems for studying the genetic basis of adaptive evolution. The wealth of available data on the genetic basis for pigmentation evolution allows for analysis of trends and quantitative testing of evolutionary hypotheses. We employed Gephebase, a database of genetic variants associated with natural and domesticated trait variation, to examine trends in how cis-regulatory and coding mutations contribute to vertebrate pigmentation phenotypes, as well as factors that favour one mutation type over the other. We found that studies with lower ascertainment bias identified higher proportions of cis-regulatory mutations, and that cis-regulatory mutations were more common amongst animals harbouring a higher number of pigment cell classes.
For this purpose, we used Gephebase (https://www. gephebase. org/)a knowledge base dedicated to the compilation of literature on genes and mutations underlying natural and domesticated organismal variation in eukaryotes  (Courtier-Orgogozo et al. , 2020). Gephebase gathers published data on evolutionarily relevant mutations, with each entry representing a causal association between an allelic change at a given locus and trait variation between individuals or species. Each entry includes information relating to the species/population, the type of trait, the gene, the nature of the mutation(s), whether they represent null alleles, and the study that identified it. Using the vertebrate pigmentation entries in this database, we focused on the relative abundance of cis-regulatory and coding mutations contributing to vertebrate pigmentation evolution.