dataesr/scientific-paragraphs-categorization huggingface dataset:
High-throughput sequencing data analysis Reads were demultiplexed with bcl2fastq 2. 0 software (Illumina), and read quality was assessed using FastQC v0. 11. 3 (http:// www. bioin forma tics. babra ham. ac. uk/ proje cts/ fastqc/). A summary report was constructed with MultiQC  [62]. Reads with an average quality score < 20 were trimmed from the ends (5′ and 3′) and the read was removed if its length was less than 100 bp. Following quality control steps, detection of taxa by the k-mer approach was done using Kraken 2  [63]  with the SILVA 132 database (https:// www. arb-silva. de). The descriptive analyses were performed using phyloseq package in R. Alpha diversity was determined by calculating OTUs number, Chao1, Abundance-based Coverage Estimator (ACE) and Shannon's diversity index. Differences in gut microbiota beta diversity between groups were visualized using non-metric multidimensional scaling (NMDS) plots with the Bray-Curtis dissimilarity index (Additional file 10: Fig.
Statistical analysis Results are expressed as mean ± SEM, except for microbiota. Statistical analyses were performed using Graph-Pad Prism 9. 00 (GraphPad software, San Diego, CA) software package for PC. Multigroup comparisons were performed using a 1-way analysis of variance followed by a Bonferroni correction for multiple tests. Two-group comparisons were performed using an unpaired t test assuming the Gaussian distribution. The Gaussian distribution was tested by Kolmogorov-Smirnov test. A value of P < 0. 05 was considered statistically significant. All P values were 2-sided. Significant variations in bacterial population richness and diversity were assessed using Mann and Whitney test. Statistical significances in bacterial population abundance were determined employing two-way analysis of variance with p values < 0.
Acknowledgements We thank the  ANINFIMIP EquipEx facility  headed by  Professor E Oswald , and supported by the  French government  through the  Investments for the future programme  ( ANR-11-EQPX-0003 ). The authors thank the ICP platform from the  Observatoire Midi-Pyrénées. The  Clean Mouse Facility  is supported by the  Genaxen Foundation, Inselspital  and the  University of Bern. the authors would like to thank  K. Pernet-Gallay  for providing access to the  Grenoble Institut des Neurosciences  transmission electron microscope.
The DEPTQ  [21]  and proton NMR spectra were recorded with a Bruker Avance 400 spectrometer operating at a frequency of 100. 618 MHz for 13 C and 400. 13  MHz for 1 H. The solvent residual peaks of HOD and DMSO-d 6 were used as internal standards at 4. 8 ppm at 298 K and 4. 25 ppm at 353 K for D 2 O, and at 2. 5 ppm for DMSO-d 6 for 1 H and 39. 51 ppm for DMSO-d 6 for 13 C NMR. The proton spectra were recorded with a 4000 Hz spectral width, 65,536 data points, 8. 19 s acquisition times, a 1 s relaxation delay and 16 scans. DEPTQ spectra were recorded using 90-degree pulses, 20,161 Hz spectral width, 65,536 data points, 1. 62 s acquisition time, a 2 s relaxation delay and 6000 scans. The 1 H and 13 C-NMR assignments were based on 1 H-1 H homo-nuclear and 1 H- 13 C hetero-nuclear correlation experiments (correlation spectroscopy, COSY; hetero-nuclear multiple-bond correlation, HMBC; hetero-nuclear single quantum correlation, HSQC).
With that in mind, several areas deserve particular consideration in order to circumvent the limitations of MS-based technologies. First and foremost, one must remember that most of the tools currently used to identify peptides from digesta were developed to meet the needs of proteomics  (Perkins, Pappin, Creasy, & Cottrell, 1999). As such, only peptides in the molecular mass range of 500-3500 Da can be unambiguously and automatically identified by a database search. The identification of shorter peptides would require labour-intensive sample preparation as well as the use of dedicated de novo sequencing software and an optimised liquid chromatography-mass spectrometry (LC-MS) setup for smaller peptides. An additional challenge comes from the fact that di-and tri-peptides resulting from digestion are polar molecules and are as such poorly retained on a C 18 chromatographic column, which is the standard in proteomic studies.
Since bioinformatic analysis plays a crucial role in determining the final list of peptides reported in the digestome dataset, it is of great importance to master these tools and to reach a consensus within the scientific community about which tools are the most appropriate. Within INFOGEST, a subgroup of 10 laboratories collaborated internationally in an interlaboratory ring trial and used their available in-hour equipment and bioinformatics tools to identify and semi-quantify peptides released in the same set of protein-based food digesta. The main objectives of this work were to determine whether peptidomic analyses performed independently in several laboratories using diverse conditions could produce similar conclusions (i. e. identification of the same domains in proteins resistant to digestion) and provide comparable results even when using different bioinformatics tools to handle and compare peptidomic data.
The identification of peptides from MS/MS data was performed with either the UniProt database (https://www. uniprot. org), which was restricted to the expected taxonomies, or in-house databases, each uniquely containing the main proteins of interest. The algorithms of the Mascot or Sequest HT search engines or the de novo sequencing Peaks® Studio software (Bioinformatics Solutions Inc. , Waterloo, Ontario, Canada) algorithm were used. In each case, the technically induced modifications, such as oxidation of methionine, deamidation of asparagine and glutamine, and cyclisation of glutamine to pyroglutamate, were considered. Phosphorylation of serine and threonine (and, for some laboratories, tyrosine) was included (except for Lab 5) as a biological post-translational modification. The post-processing of data was variable between laboratories, and some post-processing tools were based on the statistical program R, using custom scripts.
Statistical analysis and graphical representation Boxplot of peptide size distribution. Peptide size distribution was visualised with a boxplot generated by R programming (version 3. 5. 3) using the ggplot2 package  (Wickham, 2016). Heatmap of peptide patterns. The peptide patterns were generated by summing the number of individual amino acids within all identified peptides  (Egger et al. , 2017). The chosen colour code ranged from red, representing the maximal number of identified amino acids within the corresponding protein and digestion phase, to green, representing medium to low numbers of identified amino acids, to blue, representing the minimal number of identified amino acids. The gaps represent amino acids without identification within any peptide. Peptigram. The visualisation of peptide distributions resulting from the digestion of β-casein, actin and glycinin 1 was mapped to their parent protein amino acid sequence using the free web application Peptigram (https://bioware.
ucd. ie/peptigram/) (Manguy et al. , 2017). Every amino acid covered by the identification of at least one peptide is represented by a vertical green bar. The height of the bar represents the number of times a peptide was identified at this position, and the intensity of green is proportional to the sum of peptide intensities overlapping this position. Sequence logo with R. The sequence logo with the β-casein-derived sequences was obtained using the R package 'ggseqlogo'  (Wagih, 2017). The sequence logo shows the probability of finding a particular amino acid at the N-terminal (1, 2, 3) or C-terminal (-3, -2, -1) end of the sequence, taking into account a set of peptides. In the case of five-length peptides, the amino acid in the 3rd position at both terminal ends was the same. Venn diagram of resistant sequences.
Peptide patterns Two types of post-processing methods were performed to compare different peptide patterns. Both aimed at the reduction in complexity of mass spectrometry data (Fig. 4 ). In the first method (amino acid counting method), instead of comparing individual identified peptides, the frequency of occurrence of the individual amino acids within the identified peptides was counted. In this way (not considering peptide intensity), the properties of individual peptides in mass spectrometry (charge, ionization capacity, and fragmentation behavior) were reduced. In detail, data-dependent MS/MS spectra were first recorded without exclusion list. In consequence, intense peaks eluting over a long time were sequenced several times, leading to a higher identification frequency. Subsequently, an increase in analytical sensitivity was achieved by measuring three overlapping mass ranges (300-1300 m/z with an overlap of 10 m/z).
Cleavage pattern In order to describe the most representative N-and C-terminal sequences of the identified peptides, the R package 'ggseqlogo'  (Wagih, 2017)  was used. This representation provides information about the  cleavage pattern and the enzyme specificities as well as the probability of finding a particular amino acid at the N-terminal (1, 2, 3) or C-terminal (-3, -2, -1) end of the sequence, taking into account the whole list of peptides. A sequence logo with all peptides obtained from β-casein throughout the gastric phase was built (Fig. 5 : proline, valine and leucine represented the most likely amino acids at the N-and C-terminal positions of peptides identified in the majority of laboratories). In general, hydrophobic amino acids (in black) were present to a higher extent at the three C-terminal positions of the peptides.
Protein hydrolysis over time in SMP, tofu and chicken breast The low number of peptide identifications in the intestinal phase (Table  2 , Supplementary Fig. 1 ) could be due to a combination of (i) a lower concentration of peptides, (ii) the presence of fluid components that hinder peptide ionisation or (iii) the fact that digestion arrives to a point where peptides become shorter than five amino acids in length and are therefore too small to be efficiently separated by reverse-phase LC or identified with conventional tandem MS techniques or bioinformatics data retreatment, which relies on specific sequence matches in protein databases. The number of peptides at the end of the gastric phase (120 min) were lower for SMP compared to chicken and tofu, however this difference was no longer observed at the intestinal phase (Fig.
Representation of peptide generation during the digestion process The peptides resulting from digestion can be measured and visualised in different ways. Of course, each peptide could be quantified by MS and presented individuallyhowever, in this study, the goal was to generate an overview of the peptidomics results obtained in the different participating laboratories. It is a general difficulty of peptidomics analyses in digesta to condense the large amount of measured data in a way that will achieve a comprehensible overview of digestion processes over time or, in the case of this collaborative study, per laboratory. With both methods (amino acid counting and Peptigram, Fig. 4 ), digestion processes could be well visualised both over time and between different laboratories. Furthermore, both methods showed regions in the protein that were digestion-resistant or rather labile.
Acknowledgements We thank the  INRAE INFOGEST network  for funding the workshop in Lyon and for giving us the opportunity to conduct this joint project, the  USDA Hatch  project  CA-D-FST-2187-H (D. B. , Y-P. H. ). Project  PID2019-107663RB-I00  funded by  MCIN/AEI/10. 13039/501100011033from  the  Spanish Ministry of Science and Innovation  (P. J. , I. R, L. E. and B. M),  Ege University  funded the Project No. FHD-2021-22816  (S. E. ), the  Alibiotech research programme , which is financed by the  European Union , the  French State  and the  Hauts-de-France region , the French governmental subsidy managed by the  French National Research Agency (ANR)  within the frame of the ' Future Investments' programme  ( ANR-11-EQPX-0037 ) (LC-MS/MS experiments, performed on the REALCAT platform), the  Hauts-de-France region , the  FEDER , the  Ecole Centrale de Lille  and the  Centrale Initiatives Foundation  are also warmly acknowledged for their financial contributions to the acquisition of REALCAT platform equipment (B.
In contrast, individuals with a worse ability to imagine odors would experience weaker cravings that have a low impact on their eating and weight. (d) We predicted that food cue reactivity would mediate the association between odor imagery ability and adiposity change, such that odor imagery indirectly affects adiposity change via a food cue reactivity-dependent mechanism. To test these hypotheses, we collected data from 45 adults (ages 18 -42 years) with a range of BMIs (18. 32 -53. 44 kg/m 2 ). Participants completed three behavioral sessions and an fMRI scan at baseline to quantify odor imagery ability and food cue reactivity. They returned one year later for a follow-up session to assess adiposity change. As predicted, stronger interference effects were associated with better decoding accuracies of imagined, but not real, odors in the piriform cortex.
We restricted our analyses to the right piriform cortex in the 30 individuals with discriminable neural patterns for actual odors to ensure that any effects would not be driven by an inability to decode altogether. We observed a strong positive association between right piriform decoding of imagined odors and our perceptual measure of odor imagery ability (i. e. , the interference effect; Fig. 4f ). Similar analyses using the self-report measures of odor (p = 0. 0571) and flavor (p = 0. 0722) imagery ability approached significance. In contrast, there were no significant associations between the perceptual or self-report measures of odor imagery ability and the fMRI patterns evoked during actual odor presentations (Fig. 4g ) or in the crossmodal datasets (Fig. 4h ). The results remained largely unchanged when including the full sample (n = 44; Supplementary Table  5 ).
These data lead to the hypothesis that information from the coding of imagined odors in the piriform cortex is relayed to the insula and SMA to simulate future interoceptive states and food decisions, promoting the cravings and physiological changes (e. g. , ghrelin release) that trigger subsequent consumption. The direct links between the piriform cortex, insula, and SMA and the general validity of this model warrant future testing. Imagery requires memory systems to pull from past experiences in simulating the future. Though we screened participants for self-reported cognitive deficits or memory loss that could impact mental imagery, we did not explicitly measure memory capacity in the current study. Impaired memory and hippocampal function are hallmark characteristics in the development of obesity 111.
Participants A flow diagram depicting the number of individuals at each stage of the study (e. g. , eligibility, recruitment, completion, analysis) is provided in Extended Data Fig. 3. Participants were recruited from the local New Haven, CT, USA community and university population via flyer and social media advertisements. Individuals interested in this study or other previous studies in our lab filled out an online form using Qualtrics software (Qualtrics, UT, USA) to indicate initial information such as their sex assigned at birth, age, estimated BMI, drug use, etc. We pre-screened subjects in this database to identify individuals free from known taste or smell dysfunction, dieting behaviors, food restrictions, nicotine or drug use, serious medical conditions including metabolic, neurologic, and psychiatric disorders or medications used to treat these, cognitive deficits or memory loss that could impact mental imagery, and any MRIcontraindications (e.
Experimental Procedures The study consisted of three behavioral sessions and one fMRI scan at baseline, along with a follow-up session one year later. Full data collection from the first (baseline) to last (follow-up) sessions spanned 10/6/2020-6/3/2022. The fMRI scan was scheduled between 8:00am-1:00pm, and all other sessions took place between 8:00am-8:00pm. We ensured that food craving and intake were assessed between the hours of 11:30am-7:00pm. Individuals were instructed to arrive to all sessions neither hungry nor full, but at least one-hour fasted.
g. , which tastes sweeter/saltier, is fresher, or has better quality chocolate). They were not explicitly told that the cookies were identical and that the primary aim was to quantify the grams consumed. Data from two participants were excluded from this measure after eating more than 3 SD above the group mean. Following the food craving and intake paradigms, participants also rated their liking on the LHS  33  and frequency of consumption in a typical month on a VAS (labels: 1 or less/month, 2/month, 3/month, 1/week, 2/week, 3-4/week, 5-6/week, 1/day, 2 or more/day) for each stimulus. fMRI Session Participants underwent fMRI scanning while performing a task in an event-related design with six trial types: smell rose, cookie, or clean air; and imagine rose, cookie, or clean air. Each trial began with a 5s auditory cue of "smell" or "imagine" followed by the name of the odor (e.
Follow-Up Session All but one participant returned to the lab approximately one year later (days elapsed from first to last session: M = 363. 17, SD = 7. 33, range = 340 -378) to repeat the adiposity, questionnaire, and food cue reactivity measures. Follow-up data from one participant was excluded after they began a strict diet and lost more than 3 SD above the group mean in weight change from the baseline to follow-up sessions.
Data Analyses Behavioral Analyses   9 ). Sniff Analyses. The spirometer data were preprocessed and analyzed in MATLAB R2020a. The raw airflow traces were separated by scan run and preprocessed (temporally smoothed with a 500ms moving window, high-pass filtered at a cutoff of 0. 02 Hz, and normalized by subtracting the mean and dividing by the SD). Sniff onset for each trial was determined by finding the time of the minimum signal value within a window of ± 0. 75s from the auditory cue end. The time (latency) and value (amplitude) of the proximal maximum signal were identified. Sniff offset was defined as the time (duration) at which the signal returned to its original minimum, which was used in quantifying the area under the curve with the trapezoidal method (volume). Finally, peak and mean airflow rates were assessed using derivatives at each signal point indicating the instantaneous rates of change.
fMRI Analyses Preprocessing. The fMRI data were preprocessed and analyzed using FSL version MVPA was performed using The Decoding Toolbox 145 implemented in SPM12. For the first decoding method (SVM classification), separate voxel-wise patterns were created for smelling and imagining the rose and cookie odors by extracting the parameter estimates from the first level GLMs and subtracting the mean activity across the conditions in each run. Feature selection was used to identify the top class-discriminative voxels in each ROI with an ANOVA, restricted to the number of voxels in each ROI maximally available for all subjects. An SVM from the Library for Support Vector Machines (LIBSVM) package 146 was trained to decode rose versus cookie using patterns of BOLD activation for smelling the odors in four of five scan runs.
ACKNOWLEDGEMENTS This work was supported by the  National Science Foundation Graduate Research Fellowship  under Grant No. 2139841  (E. E. P. ), the  National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health  under Award No. F31DK130556  (E. E. P. ), and the  Modern Diet and Physiology Research Center  (D. M. S. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Science Foundation  or the  National Institutes of Health. We would like to thank  Jason Avery  for advice on the fMRI decoding methods;  Bojana Kuzmanovic  for guidance on the fMRI preprocessing pipeline;  James Howard  for example code to perform the sniffing analyses;  Thomas Hummel ,  Johan Lundström ,  Joel Mainland , and  Paul Wise  for their thoughts in troubleshooting the odor detection threshold testing;  Alain Dagher ,  Ralph DiLeone , and  Barry Green  for their helpful suggestions to the project design; and  Karen Martin  for MR technical assistance.
(  1 )). The model parameters (A and B) were optimized by minimizing the Sum of the Squared Errors (SSE) between experimental and predicted moisture contents (SSE = 3. 90 • 10 -3 and R 2 = 0. 950) using the "fminsearch" optimization function in MATLAB (R2020b, MathWorks, Natick, USA). W = A ( a w 1 -a w ) B (1) where W is the moisture content (g/g d. b. ), a w is the water activity (-), A (0. 105 ± 0. 011 g/g d. b. ) and B (0. 546 ± 0. 068) are the adjusted parameters. The confidence bounds for the adjusted parameters were calculated: parameter ± t Student ̅̅ ̅ S √ (2) where t Student is the inverse cumulative function of Student's t distribution on the confidence level (95 %, bilateral), S is the vector of the diagonal elements from the covariance matrix (σ). For a nonlinear regression, the covariance matrix of the adjusted parameter was calculated by the Jacobian matrix of the fitted parameters (J), its transpose (J T ) and the standard deviation of the residuals (sdr), as presented in Eq.
314 J/mol K), W is the moisture content (g/g d. b. ), k Tref,0 is the inactivation rate constant at initial moisture content of (0. 13 ± 0. (J/mol) are the fitted empirical parameters. The identification of the parameters was carried out in two steps. First, the kinetic parameters (k Tref,W and Ea W ) were estimated by performing nonlinear regression analysis on the relative enzyme activity obtained at different moisture contents. These parameters were identified by minimizing the objective function (Sum of the Squared Errors, SSE) using "fminsearch" from MATLAB software (R2020b, MathWorks, Natick, USA). The confidence bounds (Eqs. (  2 ) and (  3 )) for the fitted parameters (k Tref,W and Ea W ) were calculated in MATLAB (R2020b, MathWorks, Natick, USA). Second, the parameters of the sigmoidal function (Eq.
(  6b )) and linear equation (Eq. (  6c )) were then estimated based on the inactivation rate constants (k Tref,W ) and energy of activation values (Ea W ) that were previously fitted in the first step. The parameters from Eqs. (  6b ) and (6c) were identified using the curve fitting toolbox of MATLAB software (R2020b, MathWorks, Natick, USA).
Thus, it would be of practical interest to assess whether growth over the first days of life is associated with the mortality rate in LBW newborn puppies. The first objective of this study, based on data collected on a national scale, was to describe and compare early growth rates for low-and normal-birth-weight puppies. Then, this study explored the association between early growth (during the first week of life) and mortality rate over the first two months of life, considering the birth-weight category. Finally, specific growth-rate thresholds have been determined to allow breeders and veterinarians to assess the growth of puppies during their first days of life and identify those at higher risk of mortality over the first two months of life.