dataesr/scientific-paragraphs-categorization huggingface dataset:
Code and data availability. The general description of this model can be found at https://cmc. ipsl. fr/ipsl-climate-models/ipsl-cm4/. The code used in this study is provided with the link http://www. lmd. jussieu. fr/~li/LMDZ4_code. tar. gz (Li et al. , 2020). Configuration files, boundary conditions and job-launching scripts are provided with the link http://www. lmd. jussieu. fr/~li/LMDZ4_data. tar. gz (Li et al. , 2020).
Lactobacilli are exploited extensively for food fermentation and biotechnology. Some food and gut isolates have been developed as probiotics, for which species that may be commensal to the human host are considered desirable. However, the robustness of defining original niches for lactobacilli -food, environment, the gut -is questionable, and culture-independent analyses of prevalence in different human populations is lacking. Here we analyzed the abundance of lactobacilli in 6,154 subjects from a database of highly curated fecal shotgun metagenomics data spanning 25 nationalities, with ages ranging from infancy to 102 years. Twenty-five species were detected, which we assigned into low, medium, and high prevalence groups. The microbiome of apparently healthy individuals could be categorized into 6 clusters or Lactobacillotypes (LbTypes), with three of the Lbtypes being dominated by L.
23 In reality, commensalism or autochthony is not an a priori requirement for an ingested microbe to have a beneficial effect on the host, and because so many lactobacillus species are naturally found in raw or fermented foods, isolating a given species from human stool does not reliably guarantee that species is autochthonous. Finally, many of the older culture-based literature relied on ambiguous phenotypic traits or since-altered species assignments,  24  making it hard to combine published datasets reliably to generate larger subject numbers for determining prevalence. When administered as probiotics to humans, selected species and strains of lactobacilli have conferred benefits including alleviation of infant colic,  25  amelioration of lactose intolerance  26  and reduced symptoms of atopic dermatitis.
The availability of a large number of metagenomic datasets from globally distributed cohorts, apparently healthy controls and case studies, has allowed us to dissect the interaction of location, age, health, and disease with the abundance of commensal lactobacilli. We observe that the Lactobacillus composition in gut microbiomes displays distinct associations with geographical location, age, BMI, and gender of the individuals. Lactobacillus-microbiome configurations in western countries may represent recent reconfiguration of a primordial intestinal ecotype, among which a specific configuration of lactobacilli is positively associated with not only age and BMI, but also with multiple diseases and disease marker taxa.
Lactobacillus prevalence in the human gut and Lactobacillus-specific microbiome configurations Lactobacilli present in human stool may be either autochthonous species being shed, or allochthonous strains transiently acquired from food or the physical environment. We reasoned that transient carriage would be less of a factor in a very large dataset derived by culture-independent methods. The curatedMetagenomicData repository provides such a resource, comprising more than 5,700 fecal shotgun metagenome datasets from 34 studies. Of these, 21 are disease-microbiome studies, with 17 containing paired control and diseased samples  46  (detailed in Supplementary Table  S1 ). The remaining 13 study cohorts included only apparently healthy individuals, either from specific nationalities/ethnicities or age-groups.
These datasets have all been collated and analyzed in a uniform manner which virtually eliminates bioinformatic-analysis-related variations across studies. We supplemented this dataset with 408 fecal microbiome profiles from our own ELDERMET project and a recently published case-control dataset comprising of IBD patients. 47, 48 e first explored the prevalence of various Lactobacillus species across the 6,155 collated fecal microbiome datasets. Overall, 2141 of the 6155 samples harbored at least one Lactobacillus species, detected with a relative abundance of 0. 01% (See Methods for the selection of this threshold of detection). We next used a linear regression-based strategy that quantified the association of various hostassociated demographic factors with the Lactobacillus detection rate (that is the number of Lactobacillus species detected per sample) after taking into account the study-specific (technical) variations (by especially taking the study name as a confounder) (Supplementary Table  S2a ).
The detection rates were significantly associated with geography (country), age-group and the studyconditions of the individuals. Study condition refers to the clinical status of the individual from whom the corresponding gut microbiome sample was collected (as part of the original study and then collated in the curatedMetagenomicData repository). Study condition indicates whether an individual is an apparently healthy control, or is suffering from a specific disease or has undergone a specific treatment like antibiotics or fecal microbiome transplantation (FMT). Thus, the above result indicates that even after adjusting for study-specific factors, the overall prevalence rates of Lactobacilli showed significant variation, not only with respect to the country or the age-group of the individuals, but also with clinical status.
In addition to age, we also probed the association of Lactobacillus prevalence rates with BMI and gender separately within each geographical region. BMI data was available for 2092 subjects belonging to 16 studies (Supplementary Table  S1 ). Increased lactobacillus abundance in obesity has been reported by one study,  53  but meta-analysis of the response of the gut microbiota to successful weight management interventions indicated that reduced lactobacillus abundance associated with weight loss. 54 We examined the prevalence of LbTypes as a function of Body Mass Index (BMI) in the gut metagenome data. For this we applied similar logistic regression models that checked the association of Lactobacilli prevalence first with BMI (and then with gender), while accounting for the country-specific variations as confounder (Supplementary Table  S7 ).
In this context, a key challenge is the accurate species-level identification of the Lactobacilli in the gut microbiomes. Given their close phylogenetic relatedness, species assignment of lactobacilli is challenging based on 16S rRNA amplicon data (specifically those using short-read sequencing technologies) and so we relied on shotgun metagenomic sequencing data for the current study. The thresholds for the detection of each species has been validated for the Metaphlan software,  57  but our thresholds erred on the side of caution, so we acknowledge the possibility of under-detection of some species. Despite this technical caveat, the rank order of species prevalence aggregated across the subjects analyzed is broadly in line with older culture-based analyses (and reviewed in Introduction).
Thus, it is likely that the lactobacillus-disease associations are reflective of the overall gut ecological changes in these diseases. However, at least one medical case report exists where in injection of multiple Lb species including L. salivarius and L. fermentum was linked with primary biliary cirrhosis (in line with the results obtained in the current study albeit at the gut microbiome level). 64 Furthermore, these hitherto unreported associations between LbTypes and the known disease markers indicate the need to consider the baseline state of an individual's microbiome in addition to other anthropometric factors prior to specific Lb-based probiotic administrations. Different kinds of host-associated and technical factors are likely to affect the results of such metaanalyses that combine multiple datasets.
We attempted to systematically account for a majority of these factors. First, we show that all the major host-associated factors (geography, age-group and disease) investigated in this study show significant associations with Lactobacillus detection rates even after adjusting for study-specific technical factors (the data for which was available for the curatedMetagenomicData repository). In the disease-association analyses, we further restricted our analyses to the study cohorts specific to each disease. This precludes biases in the results originating from varying inclusion/exclusion criteria as well as variations in experimental methodology. However, there were other factors which could not be accounted or investigated because of the nomavailability of these metadata in the curatedMetagenomicData repository.
Collation of fecal microbiome datasets The curatedMetagenomicData is available as a R package, downloadable from Bioconductor. The curatedMetagenomicData package (as on September 2019) was downloaded, filtered and processed as discussed in a previous study. 48 This created a repository of 5746 gut (fecal) microbiome profiles. To this, we added a further 189 and 219 from the ELDERMET cohort and a previous IBD case-control dataset. 47, 48 The samples from the later two datasets were processed using the same approach as used for the samples in the curatedMetagenomicData repository, using Metaphlan2 and humann2. 66, 67 The details of the datasets included in this study along with their geographical locations, the distribution availability of the age and BMI of the subjects, as well as the total number and the number of 'control' (apparently healthy) and 'non-control' (diseased and other conditions) have been listed in Supplementary Table  S1.
The details of the subject-specific metadata (including BMI, age, etc) for each study were already collected in the curatedMetagenomic repository from the individual studies (and used directly in this analysis). The collated fecal microbiome profiles corresponded to samples from 36 studies, spanning 25 nationalities across Europe, North America, Asia and Africa/South America/Oceania (grouped together as Others), distributed across five major age groups ranging from infancy to 102 years of age. The non-industrialized versus industrialized terminology was adopted from a recent study published recently by our group that used the same curatedMetagenomicData repository,  62  where in designations of industrialized and nonindustrialized status were based on the classifications of the different nationalities by the United Nations Industrial Development Organization (UNIDO).
Determination of Lactobacillus composition of the fecal microbiome The first challenge here was to determine an appropriate abundance threshold to report a given Lactobacillus species as detected in a given metagenome. Using simulated metagenomes, previous studies on the validation of Metaphlan had observed that for abundance values greater than 0. 01%, there was a linear relationship between the Metaphlan-calculated and the actual abundance values. 57 Therefore, we used this abundance threshold to identify a given Lactobacillus as being a present in each metagenome. Using this threshold, we identified the number of times a given Lactobacillus was detected across the different gut microbiome samples as well as the number of Lactobacilli detected in each sample. We specifically focussed on the non-diseased control individuals (tagged as 'control' in the study-condition metadata in the curatedMetagenomicData) for this purpose.
These Lactobacillotypes and Enterotypes were further performed and validated visually using heatmaps (heatmap. 2 function of the gplots package in R and the cutree function of the dendextend package) showing the clustering of the samples based on the Euclidean distances of the PCoA axes. For the Lactobacillotypes, we performed an additional round of validation using Random Forest models wherein we again performed 100 iterations, where in each iteration, we trained models on 50% of the samples (for predicting the associated Lactobacillotype from the Lactobacilli composition) and tested the model on the rest 50%. The average classification accuracy per LbType as well as the full accuracy of classification was measured. Associations between Enterotypes and the different regions and age-groups were performed using a combination of Fishers' exact test and logistic regression models.
The region-stratified logistic regression models (separate models for each region) associating Lactobacillus prevalence with host anthropometrics were obtained as follows: glm(LactobacillusDetected (1: Detected and 0: Not Detected) ~ Country + Age/BMI/Gender, family = "binomial") These models considered the country-wise variations as a confounder. The association of Lactobacillotypes to host anthropometrics were computed as: glm(Lactobacillotype X (1: If sample belongs to LbType X and 0: If it does not belong to LbType X) ~ Region + Age/BMI/Gender, family = "binomial") These models considered the region-wise variations as a confounder. We next probed the association of the overall prevalence or the different Lactobacilli with disease. First, we removed those disease datasets from this analysis that contained fewer than 20 diseased subjects.
This resulted in a list of 11 diseases, namely, adenoma (or polyps), atherosclerosis (AS), Clostridium difficile infection (CDI), cirrhosis, colorectal cancer (CRC), hypertension, inflammatory bowel disease (IBD), otitis, premature-born, type-I diabetes (T1D) and type-II diabetes (T2D). Out of these 11 diseases, for AS and premature born individuals, there were no matched controls sequenced as part of the same study (Supplementary Table  S1 ), thereby restricting us to focus on the remaining nine diseases for this analysis. This included 15 matched case-control study datasets. For each disease, we constituted disease-specific bins by collating 'control' and 'case' samples from the 'casecontrol' study datasets corresponding to that disease. Subsequently, only within the disease-specific bins, we utilized logistic regression models (as follows): glm(LactobacillusDetected~Age+BMI+gender +DiseaseStatus, method = "binomial") (for overall prevalence) glm(Lactobacillus Species 'X' Detected~Age+BMI +gender+DiseaseStatus, method = "binomial") (for overall prevalence) (for a specific Lactobacilli X) The list of multiple disease markers were obtained from a previous study by our group.
Virtual rice model A previously developed Functional-Structural model of rice  [2] , implemented in the modelling platform GroIMP  [20]  was used in this study. The rice model integrates major physiological functions together with processes describing vegetative and generative morphology in a simplified way. Moreover, a separate parameter module contains the general settings of the system and the environmental parameters. The data and model code, plus instructions to run the model, can be found in this GitHub repository: https://github. com/lfxu1/riceSpacingOpt. The environment parameters (PAR, temperature, day length) are mainly used for the calculation of light interception by the modelled leaves and for the corresponding biomass produced by photosynthesis. The processes involving the amount of biomass as state variable can be subdivided into three parts: growth (including growth respiration), maintenance respiration, and biomass distribution.
There are other studies combing optimization algorithms with growth models  [25, 26]. The focus in this study was to optimize plant spacing for rice individuals within a functional-structural rice model. The simulation results from this study indicated that the optimized spacing of virtual rice plants was 18 cm. Though we did not validate our model with an independent dataset, a comparison with the recent literature shows that this result is within the range of optimal row spacing suggested by other studies, i. e. 15-20 cm of row spacing was recommended to produce maximum yields by Hardke et al. [27] , and 18-27 cm by Dunn et al  [28]. It is understood that this result was obtained under a number of simplifications, e. g. fixed number of tillers and fixed distribution of plant height to internode length.
ChAP-Seq --sequencing and peak discovery The obtained DNA fragments of each sample (up to 2 g) were used for library preparation and indexing using the TruSeq DNA PCR-free sample preparation kit according to the manufacturer's instruction, yet skipping fragmentation of the DNA and omitting the DNA size selection steps (Illumina, Chesterford, UK). The resulting libraries were quantified using the KAPA library quant kit (Peqlab, Bonn, Germany) and normalized for pooling. Sequencing of pooled libraries was performed on a MiSeq (Illumina) using paired-end sequencing with a read-length of 2 × 150 bases. Data analysis and base calling were accomplished with the Illumina instrument software and stored as fastq output files. The sequencing data obtained for each sample were imported into CLC Genomics Workbench (Version 9, Qiagen Aarhus A/S, Aarhus, Denmark) for trimming and base quality filtering.
RNA-Seq --analysis Sequencing reads quality was explored with the FastQC (https://www. bioinformatics. babraham. ac. uk/projects/ fastqc/) tool. Since reads appeared to be of a good quality and did not harbor significant fraction of adapters or over-represented sequences, no pre-processing was undertaken. Identical reads were collapsed with a custom script in order to prevent gene levels' misquantification caused by PCR overamplification. Reads were mapped to the C. glutamicum genome (NC 003450. 3) with Bowtie2  (45). Bowtie2 was run with the following parameters: bowtie2 -1 [path to the reads, 1st mate] -2 [path to the reads, 2nd mate] -S [path to the mappings] -phred33 -sensitive-local -local -score-min C,90 -rdg 9,5 -rfg 9,5 -a -no-unal -I 40 -X 400 -no-mixed -ignore-quals. The reads mapped to multiple locations were split proportionally between parental genes.
5 mM EDTA and 0. 005% (w/v) Triton X-100. After incubation for 20 min at room temperature, the reaction mixtures were loaded onto a 10 % native polyacrylamide gel and subsequently separated and documented using a Typhoon TrioTM scanner (GE healthcare). The band intensities of unbound DNA were quantified using Image Studio Lite (Licor, Bad Homburg, Germany). The band intensities were normalized to the lane containing no DNA and plotted against the HrrA concentration in log 10 scale. Apparent K d values were calculated based on at least three gels each using a sigmoidal fit and the software GraphPad Prism 8. For the sigmoidal fit, Y = 0 and Y = 1 were set as top and bottom constraints. The turning point of the curve was defined as the apparent K d.
(B) A binding motif was deduced from the sequences of the top 25 peaks (T 0. 5 ) using MEME v. 5 analysis (http://meme-suite. org). (C) Pie chart presenting HrrA targets, which can be attributed to known functional categories (total of 272 genes, among which 128 encode proteins of unknown function, e. g. target genes within the CGP3 prophage region were excluded). For a complete overview of HrrA targets, see Supplementary Table  S3. (D) Proportional behavior of the HrrA regulon. For each peak that passed the threshold (distance of <800 bp to the closest downstream or <200 to the closest upstream TSS) at time point A, the highest peak in the same region (±50 nt from the center of the peak) was selected for time point B and vice versa. Thus, 'paired' peaks for these two time points were obtained, and the Pearson correlation of the intensities of all paired peaks was calculated for all six time points.
The HrrSA TCS coordinates heme homeostasis by integrating the response to oxidative stress and cell envelope remodeling Our dataset confirmed the binding of HrrA to all previously known targets, including genes encoding components of heme biosynthesis (hemE, hemH and hemA), degradation (hmuO) and export (hrtBA) pathways and heme-containing complexes of the respiratory chain (ctaE-qcrCAB operon and ctaD). A comprehensive overview of all identified HrrA targets is presented in Supplementary Table  S3 ; selected target genes are listed in Table  1. Among the more than 180 novel targets identified in this study, we observed HrrA binding upstream of ctaB, which encodes a protoheme IX farnesyltransferase that catalyzes the conversion of heme b to heme o (26) and upstream of ctaC, which encodes subunit 2 of the cytochrome aa 3 oxidase.
HrrA activates the expression of genes encoding components of both branches of the quinol oxidation pathway To evaluate how HrrA binding affects the expression of individual target genes, we analyzed the transcriptome (RNA-Seq) of the C. glutamicum wild-type strain (ATCC 13032) as  b The corresponding peak intensity. c Relative ratio of the transcript levels of the hrrA deletion mutant compared to the wild-type (log 2 fold change). The values are derived from a comparison between the two strains 0. 5 and 4 h after hemin addition. The log 2 ( hrrA/wt) value for was not determined for the deleted hrrA gene (n. d. ). This table summarizes results from the HrrA ChAP-Seq analysis of the C. glutamicum strain ATCC 13032::hrrA-C-twinstrep and the transcriptome analysis of C. glutamicum wildtype and strain hrrA (complete datasets are provided in Supplementary Tables  S3  and 4 , respectively).
From networks to function Genome-wide analysis of regulatory networks may provide important hints toward the physiological function of genes. An example is provided by the HrrA-dependent regulation of cg2079 (hemQ), described in this study (Table  1 ). In actinobacteria, it was recently proposed that these proteins inherit an essential role in heme biosynthesis  (49, 67). The finding that HrrA binds to the promoter of this gene and represses its expression supports a role of HemQ in heme biosynthesis in C. glutamicum. Among the direct targets of HrrA are many further targets encoding proteins of unknown function, including several ABC transport systems with a potential role in heme uptake or export. Therefore, this dataset provides guidance for further functional analysis of these HrrA targets to decipher their role in heme homeostasis.
g. MAR as a starting point)expressed via the mean and variance of log IMORs. Then, the adjusted log ORs are pooled using inverse-variance weighting  [7, 10]. Albeit being more straightforward to apply, the twostage PM approach has several shortcomings inherent to the within-trial normal approximation assumption. By fixing the within-trial results to the assumed mean and variance of log IMOR, the two-stage PM approach does not allow the observed data to contribute to the estimation of log IMOR to gain further insights on the missingness process in the collected trials  [4]. Furthermore, the adjusted within-trial treatment effects and variances the latter assumed known, although estimated, under the normal distributioncomprise the dataset for the second stage of the two-stage PM approach. In the presence of zero cells, continuity correction is thus required a suboptimal approach that has been criticised for leading to biased results  [11, 12].
Factors that may affect within-trial normality approximation We used the database of 29 networks from several health-related fields considered in previous work  [6]. Detailed information on the MOD per network can be found elsewhere  [6]. For this study, we considered a sample size of fewer than 50 participants to represent small trials, and event risk below 5% to be low. We characterised a network as 'susceptible' to within-trial normality approximation (hereinafter called 'susceptible' network) when there was at least one trial with a sample size less than 50 participants and/ or at least one trialarm with observed event risk less than 5%. Otherwise, the network was characterised as 'non-susceptible' to within-trial normality approximation (hereinafter called 'non-susceptible' network). We acknowledge that these two categorisations of the networks may not be universally accepted.