dataesr/scientific-paragraphs-categorization huggingface dataset:
0  31  ) and InterProScan v5. 35-74. 0  32. COG  33  , KEGG  34  and CAZy 55 annotations were derived from the eggNOG-mapper results. Differences in annotation coverage and COG functional categories between the core and accessory genes were evaluated with a two-tailed Wilcoxon rank-sum test in R v3. 6. 0 (function 'wilcox. test'). Expected P values were corrected for multiple testing with the Benjamini-Hochberg method. Cohen's d effect sizes were estimated with the function 'cohen. d' from the Effsize 56 R package. To accurately estimate the proportion of each KEGG module in the species pan-genome, we used the compositional data analysis R package CoDaSeq  57. Pseudo counts for zero-count data were first imputed using a Bayesian-Multiplicative simple replacement procedure implemented in the 'cmultRepl' function (method 'CZM').
Acknowledgements We would like to thank  Phelim Bradley  and  Zamin Iqbal  for their help in the BIGSI implementation, and  Dongying Wu  for assistance in the identification of uncultured monophyletic groups. Funding:  European Molecular Biology Laboratory (EMBL) ;  Biotechnology and Biological Sciences Research Council  [ BB/N018354/1  and  BB/R015228/1 ];  European Research Council  (project  ERC-STG MetaPG-716575 ) to N. S.
Data availability Genome assemblies of the UHGG have been deposited in the European Nucleotide Archive under study accession ERP116715. The UHGG, UHGP and SNV catalogues are available in a public FTP server (http://ftp. ebi. ac. uk/pub/databases/metagenomics/mgnify_genomes/) alongside functional annotations and the pan-genome results. These data together with the BIGSI search index of the UHGG can also be accessed interactively on the MGnify website: https://www. ebi. ac. uk/metagenomics/genomes.
Genome collection We compiled all the prokaryotic genomes publicly available as of March 2019 that have been sampled from the human gut. To retrieve isolate genomes, we surveyed the IMG  23  , NCBI  21  and PATRIC  22  databases for genome sequences annotated as having been isolated from the human gastrointestinal tract. We complemented this set with bacterial genomes belonging to two recent culturomics collections: the Human Gastrointestinal Bacteria Culture Collection (HBC)  18  and the Culturable Genome Reference (CGR)  20. To avoid including duplicated entries due to redundancy between reference databases, we combined genomes obtained from the PATRIC and IMG repositories, and added only those without an identical genome in the sets extracted from NCBI, HBC and CGR. Metagenome-assembled genomes (MAGs, i.
e. uncultured genomes) were obtained from Pasolli, et al. 19  (CIBIO), Almeida, et al. 17  (EBI) and Nayfach, et al. 16  (HGM). For the CIBIO set, only those genomes retrieved from samples collected from the intestinal tract were used. Metadata for each genome was retrieved using the API of the various public repositories and combined with that available in each of the original studies.
Hardware The hardware used to collect and analyze the temperature information is a Raspberry Pi 3 model B + with a 16 Go micro SD card. The Raspberry Pi is a low-cost computer of the size of a credit card (approximately 40s, SD card included). The main advantage is that it operates using Linux, which is a common operating system to the scientific community. The official supported operating system (OS) is Raspbian which can be install in micro SD card from the Raspberry Pi foundation (https://www. raspberrypi. org/; using another computer than the Raspberry Pi). Once the OS is installed on micro SD card, the Raspberry Pi can be started. The system should be updated from the terminal using: sudo apt-get update sudo apt-get upgrade In the configuration menu (sudo raspi-config), from "Interfaces", SSH and 1-wire should be enabled.
For the temperature monitoring, DS18B20 sensors (Maxim Integrated Products, Inc) are used. DS18B20 sensors are digital thermometers (slaves) that communicate with a central microprocessor (master: the Raspberry Pi) over a 1-Wire bus. Each DS18B20 has a unique 64-bit serial code which is practical to connect multiple sensors on the same 1-Wire bus. Temperature measurements range from -55 C to +125 C with a AE0. 5 C accuracy over a temperature range from À10 C to +85 C. A complete description can be found on the manufacturer web site (https://www. maximintegrated. com). The sensors can be connected in parallel to the Raspberry Pi using the GPIO4, Ground, and 3V3 power pins, with a 4. 7 k ohm resistor between the GPIO4 and the 3V3 power (see Fig. 1 ). DS18B20 sensors can be connected using jumper wires, but it is advised to solder the sensors to wires for long term usage.
An overall schematic representation of the monitoring system can be found in Fig. 2. Reading temperature from the DS18B20 sensors and store information into CSV files. All the procedures to read the temperature and store the information into a CSV file uses Python 3. 5. 3. The complete script can be found in Supplementary material S1 ("envColTemp_XX. py"). A CSV file is created with the headers corresponding to the data to be stored ("year", "month", "day", "hour", "minute", "second", "sensorId", "temperature"; with sensorId corresponding to the DS18B20 64-bit serial code), and named after the date of initialization. Temperature reading is contained in the "/sys/bus/w1/devices/" directory of the Raspberry Pi, under directories starting with "28-00". The Python script used to retrieve the temperature information was obtained and adapted from a StackOverflow post by "joan" under a CC-BY-SA license (https://raspberrypi.
stackexchange. com/). In this article temperature is measured every minute using "time. localtime() [5] = = 0 00 (line 54 in Supplementary material S1). To reduce the number of writing operations on the micro SD card, the temperature information is stored in the RAM with a default number of reading of 40 that can be changed using "RAMReadNumber" variable (line 21). After 40 reads, the data is transferred to the csv file. To reduce the management of large files by the Raspberry Pi, a new file is created once the current file exceeds 10 Mo, configurable with the variable "fileMaxSizeMo" (line 22). Once uploaded into the Raspberry Pi in the "Documents" directory), from the command line interface of the Raspberry Pi (e. g. , through an SSH connection), a bash file can be created to launch automatically the script at each computer startup using a CRON job.
This is particularly important in case of power failure or when installing the monitoring system: the monitoring will start automatically and power failure can also be monitored. A separate bash script is used to launch the monitoring system automatically (Supplementary material S2, "launcher. sh"), as it allows to maintain different versions of the Python script without having to change the CRON job. nano launcher. sh # create and edit bash file chmod 755 launcher. sh A directory "logs" was created under the "Documents" directory for the logs files. CRON job was created using "sudo crontab -e": @reboot sh /home/pi/Documents/launcher. sh >/home/pi/Documents/logs/launcher_log 2 > &1 Configuring an alert system with emailing. A simple alert system triggered by a temperature over or below a threshold cannot be used in the case of a rearing unit because variations of temperatures that would trigger the alert are expected when opening the rearing unit for insect manipulation.
Thus a more configurable threshold has been developed in which the alert is triggered in case the average temperature over a period of time reach the threshold. A different minimum and maximum threshold can be used for each sensor, like in the case presented here. A Python script was used and uploaded into the directory "Documents" to be able to send emails automatically with a content defined by arguments passing through the script execution. The Python script can be found in Supplementary material S3 ("emailSender_XX. py"). Then, an R script  [5]  was made and uploaded into the directory "Documents" to configure the threshold (Supplementary material S4, " envCheckTemp_XX. R"). In this article the monitoring system had 8 DS18B20 sensors connected to the Raspberry Pi. Maximum temperature thresholds for the 8 sensors are stored in the variable "paramMaxTemp" and minimum thresholds in the variable "paramMinTemp".
The average temperature for triggering alerts was set to 60 min with the variable "paramDuration". All CSV files are merged into a data frame R object, and sensors renamed from the manufacturer 64-bit serial code to a more human readable name (each sensor can be identifiable by sequentially holding them while recording temperature). If the average temperature over the defined period of time reach a threshold, then the python script is launched from R with arguments corresponding to the average temperature and the name of the sensor. In order to use the script, R must be installed in the Raspberry Pi: sudo apt-get install r-base In order to monitor automatically the temperature and check for potential anomalies, a bash script was used and launched automatically every hour using a CRON job (Supplementary material S5, "checker.
sh"). nano checker. sh # create and edit bash file chmod 755 checker. sh CRON job was created using "sudo crontab -e": * * * * sh /home/pi/Documents/checker. sh >/home/pi/Documents/logs/checker_log 2 > &1 The CRON job first five fields correspond to date and time, followed by the command. The date and time fields correspond to minute (0-59), hour (0-23), day of the month (0-31), month (0-12), and day of the week (0-7), respectively. The star symbol "*" represents all available values. Here "0 * * * * *" means that the command will be executed every time the minutes reach 0 (i. e. , every hour). The complete documentation on CRON jobs can be found from any Linux terminal using "man crontab". Additional security measures (optional). A file backup system was used to ensure CSV files security.
The backup is triggered from a server and not from the Raspberry Pi using a CRON job every day at 7. 00 a. m. : 7 * * * python3 /xxx/copyToCommun. py >/xxx/copyFromCommun_log 2 > &1 The backup system consists in a Python script (Supplementary material S6, "copyToCommun. py") which lists the files in the Raspberry Pi and locally in the server. If new files or files with a bigger size found in the Raspberry Pi, they are automatically copied to the server. It can also be triggered manually from the server at any moment. Using a server to store copies of the Raspberry Pi is useful to ensure long term storage, but also in the case of analyses that require a large amount of RAM or other computer resources not available from the Raspberry Pi.
Dashboard (optional). If the information about the temperature monitoring needs to be revised by several people with graphical visualizations, working with copies of the original files on a server rather than the Raspberry Pi itself is advised (see section 2. 3. ). In the case presented here, a dashboard in html is created every day at 7. 30 a. m. in a server using R  [5] , with the packages "flexdashboard"  [6]  and "ggplot2 00  [7]  (Fig. 3 ). The complete script to build the dashboard and to compile it into html can be found in Supplementary material S7 and S8 ("dashboard_MONTH. Rmd", "renderRMDtoHTML. R"). 7 * * * Rscript /xxx/renderRMDtoHTML. R >/xxx/dashboard_log 2 > &1 Fig. 3. Example of a simple dashboard for one rearing unit (named "SNK_Mak_bib_26 00 and set to 25 C) to visualize temperature within the rearing units for the last month.
Conclusion This method article presents a yet powerful system for temperature monitoring at low cost and highly configurable particularly suitable for experiments in laboratory with insect rearing units. It is based on a Raspberry Pi computer and DS18B20 temperature sensors. With eight temperature sensors, the Raspberry Pi, the micro SD card, the power supply, and the wires, the overall cost is below 100s which makes the system affordable and easy to deploy even in laboratories with a large number of rearing units. Automatic reports and email alerts can easily be configured to ensure full control over your experiments together with long term storage of temperature data, and easy access to the information without programming skills. All the files can be found in GitHub (https://github. com/ frareb/raspi_tempMonitor).
Methods Statistical analyses. All analyses have been carried out in the R environment (specific functions within specific package are indicated in parentheses). Data. We used the distribution maps provided by the Amphibian and Mammal Red List Assessment (http://www. iucnredlist. org/) for 5547 and 4616 species, respectively. For birds, breeding range distribution maps were extracted from BirdLife (http://www. birdlife. org/) for 9993 species. Ranges were converted to 100 km × 100 km equal-area grid cells, a resolution previously validated as the finest justifiable for these data globally  22. Grid cells within the distribution range of each species were thus converted to presence points, while those outside their distribution ranges were converted as absence points. We finally focused on 1351 amphibian, 7248 bird and 2896 mammal species after removing species occurring in <20 grid cells, as well as domestic and aquatic species.
We consider 20 presence points the minimum to successfully fit response curves to four different predictor variables. Climatic data. Current climate (1979-2013) was represented by four bioclimatic variables from the CHELSA dataset  23  up-scaled from a 1 km to a 100 km resolution. The chosen variables were as follows: annual mean temperature, annual temperature range, annual sum of precipitation and precipitation seasonality (coefficient of variation in monthly sum of precipitations). Projected future climate variables were taken from five GCMs driven by four scenarios of RCPs in a factorial manner as explained in Supplementary Fig. 1. The five selected models originate from the CMIP5 collection of model runs used in IPCC's 5th Assessment Report (IPCC 2013). The five models from which data were taken are: CESM1-BGC  24  run by National Center for Atmospheric Research (NCAR); CMCC-CMS  25  run by the Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC); CM5A-LR  26  run by the Institut Pierre-Simon Laplace (IPSL); MIROC5  27  run by the university of Tokyo; and ESM-MR  28  run by Max Planck Institute for Meteorology (MPI-M).
Future climatic conditions of the four climatic variables were also taken from the CHELSA dataset  23  , which provides CMIP5 scenarios at a native resolution of 30 arc seconds. Future conditions from coarser resolution GCMs had been achieved using climatologically aided interpolation. We took the difference between selected GCMs from CMIP5 at a 0. 25°grid cell size for current conditions (1979-2013) and the selected future periods (2041-2060, 2061-2080) and interpolated them using b-spline interpolation to the resolution of 30 arc seconds of CHELSA. The resulting difference was then added to (for temperature) or multiplied with (for precipitation) to the CHELSA climatologies of the 1979-2013 baseline period. As our study used 100 km grid cells, the native CHELSA resolution was upscaled by calculating mean within values per each 100 km grid cell.
Species distribution models. An ensemble of projections of SDM was obtained for the 11,495 selected species. The ensemble included projections with Generalized Additive Models, Boosting Regression Trees, Generalized Linear Models and Random Forests. Models were calibrated for the baseline period using 70% of observations randomly sampled from the initial data and evaluated against the remaining 30% data using the true skill statistic (TSS  29  ). Presence data were randomly drawn from the gridded range maps. For absences, we considered data in a reasonable buffer around the presence data to avoid having over-optimistic predictive accuracies  30. To be consistent with assumed realistic dispersal distances (see next paragraph) and in line with previous analyses, we selected absence data in 2000, 3000, and 4000 km buffer around amphibian, mammal and bird species ranges, respectively.
Acknowledgements W. T. thanks the  Technical Support Unit of the IPBES  for the Expert Group on Models and Scenarios for funding. We also thank  H. M. Pereira ,  R. Alkemade  and  P. W. Leadley  for their support and valuable suggestions. W. T. , D. N. K. and N. E. Z. received funding from the  ERA-Net BiodivERsA -Belmont Forum , with the national funder  Agence Nationale pour la Recherche  ( ANR-18-EBI4-0009 ) and  Swiss National Foundation  ( 20BD21_184131/1 ), part of the  2018 Joint call BiodivERsA-Belmont Forum call  (project ' FutureWeb '). N. E. Z. & W. T. further acknowledge support from the  SNF/ANR  grant  310030L-170059 / ANR-16-CE93-004. As part of the  CDP-Trajectories  project, this work was also supported by the  Agence Nationale pour la Recherche  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ).
To complement the TF-DNA binding datasets, there is a need for methods to validate TF-target interactions based on a functional assay that takes into account in vivo context. Perturbation of TFs using knockout or overexpressing transgenic lines to identify regulated targets is standard across microbes and higher eukaryotes  [15] [16] [17]. However, these in vivo approaches are prohibitively time consuming to scale for most eukaryotes. Additionally, it is not possible to distinguish direct from indirect targets in these systems without additional in vivo TF-target binding information, such as ChIP. Moreover, studies across eukaryotes reveal a poor overlap of TF-bound and TF-regulated targets in vivo  [8] [9] [10]. To address the need for TF-target assays based on gene expression, we scale-up the throughput of a cell-based temporal TF perturbation system called TARGET (Transient Assay Reporting Genome-wide Effects of Transcription factors)  18.
In this study, we introduce several innovations to scale-up the throughput of the cell-based TARGET system for TF perturbation to 24 TF assays/day (e. g. , 8 TFs × 3 replicates). These innovations have allowed us within ~2 months to identify the candidate direct regulated genome-wide targets of 33 TFs that collectively target ~88% of the genes in the early nitrogen (N) response in Arabidopsis. We use this validated TF-target dataset to define a network path that connects the direct targets of these N-early response TFs in root cells to indirect targets identified only in planta. To do this, we present a Network Walking approach that combines functionally validated (85,144 edges) and time-inferred TF-target edges to connect TF targets validated in root cells, with indirect targets regulated in planta, as shown in Fig.
This analysis showed that the collective targets of the 33 TFs were enriched in GO terms such as: N compound metabolic process, cellular amino acid biosynthesis, lateral root formation, and response to hormone (Supplementary Data 5). Additionally, enrichment of N-related GO terms increased as the number of TFs with edges to a set of common targets increased (Fig. 3b ). To test whether this increased enrichment is significant, we devised a figure of merit, which we call Focus (see Methods). The Focus for a TF-target network is greater with respect to set of genes (e. g. , GO term) when TFs have more edges to that set of genes. This test determines the probability that the Focus calculated from the edges in the TARGET validated network is higher than we would expect to see by chance. For the network of all validated targets of the N-early response TFs, the Focus for each of the GO terms was significantly greater than for the randomized networks (p value < 0.
Network Walking charts paths from direct to indirect targets. Finally, we performed an analysis that integrates the validated TF-targets edges for 33 N-early response TFs from TARGET with the high-confidence edges for the 116 untested TFs in the pruned GRN and in planta TF perturbation data. In an approach called Network Walking (Fig. 7a ), we used these combined datasets to chart a path for a TF 1 from its direct regulated targets in root cells, to its indirect regulated targets in planta via intermediate TF 2 s. As proof of concept, we demonstrated how Network Walking revealed the network paths and mode of action for two important regulators of the N response in planta-TGA1  [26] [27] [28]  and CRF4  29  (Fig. 7b, c ). The TF perturbation data used in this Network Walking analysis included direct targets that respond to the TF in cells (e.
After overnight incubation, each pool of transfected root protoplasts was treated with the N dose present in standard MS media  64   For transcriptome analysis, cells expressing the candidate TF or EV were collected in triplicate and RNA-Seq libraries were prepared from their mRNA using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina®. The RNA-Seq libraries were pooled (up to 27 libraries per run) and sequenced on the Illumina NextSeq 500 platform. The RNA-Seq reads were aligned to the TAIR10 genome assembly using TopHat2  65  and gene expression estimated using the GenomicFeatures/GenomicAlignments packages  66. The gene counts for every sample were combined and DE genes in the TF transfected samples vs the EV samples were identified using the DESeq2 package  67  with a TF+Batch model and an FDR adjusted p value < 0.
05. We filtered out genes that respond more than 5fold to CHX treatment in transfected protoplasts from the lists of TF targets (Supplementary Data 1 and Supplementary Methods). Genes that are expressed in any of the protoplast experiments (excluding the CHX-responsive genes in Supplementary Data 1) were used as the background for subsequent enrichment analyses. Calculating nitrogen specificity and influence of TF targets. The specificity of each TF to target genes in the N × Time-responsive dataset of Varala et al. 29  was calculated by dividing the N × Time-responsive targets of a TF by the total number of targets regulated by that TF. The influence of a TF on the N × Time-responsive genes is the number of N × Time-responsive genes targeted by a TF divided by the total number of N × Time-responsive targets.
E g ¼ F k F b : ð1Þ To test for the significance of this enrichment, we used the figure of merit Focus. For each gene list (G) we calculate Focus (F g ) by simply adding the E g for all k up to n TFs: F g ¼ X n k¼1 F k F b : ð2Þ To test if the Focus of the validated network is significant for a given GO term, a permutation test was used to determine an empirical p value by comparing the Focus of the validated network to the Focus of 1000 iterations of a randomized network, generated by shuffling the edges within the experimentally validated TF-target network. Cis-motif enrichment and clustering. Enrichment of the cis-binding for motifs in TF target genes was calculated using the AME tool within the MEME package  69. The background used corresponded to the same gene region for all genes expressed in any of the cell-based TARGET experiments and the frequency of bases to the base frequency within the background.
For cis-motif clustering, cis-binding motifs for Arabidopsis transcription factors were collected from DAP-seq 5 , Cis-BP  38  , PBM of Franco-Zorrilla et al. 39  and ChIP-seq from Song et al. 37. PWMs were converted to the MEME motif format  70  and the RSAT matrix-clustering tool  41  was used with the following parameters: hclust_method = average, calc = sum, metric_build_tree = Ncor, lth w 5 lth cor = 0. 6, lth Ncor=0. 45, quick=true. To search for the enrichment of each cis-motif in the TF targets, the consensus PWM for each of the 80 cis-motif clusters was converted to the MEME format and the FIMO tool within the MEME package  69  was used to identify every occurrence of each of the 80 consensus cis-motifs in the 500 bp promoters and gene body of all 20,662 protoplast expressed genes at a p value < 0.