dataesr/scientific-paragraphs-categorization huggingface dataset:
Data-Saving Mechanism There are two ways to save data: either to have a data storage device on-board or sending it directly to the computer via WiFi.  Using a SD card It is possible to buy on the Internet SD card adaptors directly connectable on Arduino boards. They are provided with computer code directly implementable on Arduino. The data can be saved in a. txt format file that is easy to use for signal processing (see Figure  12 ). The Arduino code can be found on the official Seeed Studio Wiki and also on the Arduino website  [83, 84]. (a) (b).
• Using WIFI It is possible to set up a WiFi communication between a laptop and an Arduino WiFi. Here, we used the library WiFiNINA. The Arduino board creates an access point (Figure  13 ), and if a computer connects to it, it can receive data under the form of html code. We can access these data with any web browser and the Arduino network SSID and password (Figure  14 ). the access point creation sometimes failed, however, which we cannot yet explain.
Sensors 23, 3036 16 of 24 It is possible to set up a WiFi communication between a laptop and an Arduino WiFi. Here, we used the library WiFiNINA. The Arduino board creates an access point (Figure  13 ), and if a computer connects to it, it can receive data under the form of html code. We can access these data with any web browser and the Arduino network SSID and password (Figure  14 ). the access point creation sometimes failed, however, which we cannot yet explain. The basic code can be found in web tutorials on the Arduino website  [85, 86].
 Storing the experimental data The Arduino writes all the data in a. txt document. In the case the Arduino R3 is used, we need to open the Arduino software console at the beginning of the experiment and to unplug the USB cable at the end of it. It will be possible then to copy-paste the plotted data into the txt document. In the case we use the Arduino WiFi, we plot the data on the html page accessible with a web browser, and copy and paste it into the. txt document. It is possible to set up a WiFi communication between a laptop and an Arduino WiFi. Here, we used the library WiFiNINA. The Arduino board creates an access point (Figure  13 ), and if a computer connects to it, it can receive data under the form of html code. We can access these data with any web browser and the Arduino network SSID and password (Figure  14 ).
 Storing the experimental data The Arduino writes all the data in a. txt document. In the case the Arduino R3 is used, we need to open the Arduino software console at the beginning of the experiment and to unplug the USB cable at the end of it. It will be possible then to copy-paste the plotted data into the txt document. In the case we use the Arduino WiFi, we plot the data on the html page accessible with a web browser, and copy and paste it into the. txt document. The basic code can be found in web tutorials on the Arduino website  [85, 86].
• Storing the experimental data The Arduino writes all the data in a. txt document. In the case the Arduino R3 is used, we need to open the Arduino software console at the beginning of the experiment and to unplug the USB cable at the end of it. It will be possible then to copy-paste the plotted data into the txt document. In the case we use the Arduino WiFi, we plot the data on the html page accessible with a web browser, and copy and paste it into the. txt document.
The analysis was done in whole testis. As a marker database source, we used single-cell sequencing data  (Green et al, 2018). In F3 thia-derived mice, we observed that spermatocyte-specific genes such as Piwil1, Ptgg1, and Rad51 had increased expression (1. 9, 1. 7, and 1. 5 times, respectively) compared with controls. Genes specific for the spermatid fraction, Prm1, Spaca1, and Tssk1, had increased expression (1. 3, 1. 3, and 1. 7 times, respectively). These data suggest a possible impact of thia exposure on gene expression in both meiotic and postmeiotic populations of cells  (Fig 1H). Thus, gestational exposure to thiacloprid leads to changes in testis morphology, affects testosterone levels, alters testis-specific gene expression in adults, and leads to spermatozoa decline, suggesting that ancestral thia exposure has a deleterious effect on reproductive function in third-generation males.
We performed a functional annotation of the down-and upregulated genes separately by using the Gene Ontology program DAVID (Fig 4B ), with genes which are expressed in embryonic testis as a background list. In F1, among the down-regulated genes, we observed enrichment in germ cell development, male meiosis, and transcription repressor-related genes. Among up-regulated genes, the strongest enrichment was found in the genes related to sensory perception of the sound, muscle contraction, cell differentiation, and skeletal system development  (Fig 4B). In F3, we identified less variations between replicates compared with F1 (Fig  S5D  and E ). We determined that 2,277 genes were differentially expressed in F3 (FDR < 0. 1)  (Figs 4C and S5F). In F3, among DEGs, we identified 882 genes that were down-regulated and 1,395 that were up-regulated.
Next, we analysed the DNA methylation state of 45 GRRs. We found that DNA methylation changed at these genes in all three generations of males. Out of 45 known GRRs, we observed that DNA methylation changed in 18, 35, and 26 genes in the F1, F2, and F3 generations, respectively (Table  S3 ), suggesting that most of the GRR regions had perturbed DNA methylation. For example, we detected changes in DNA methylation in the promoters of Ddx4 and Sycp2 (Fig S10 ). Superenhancer (SE) elements are other regions that could be important for germ cell population development, as they control the gene expression essential for cell identity establishment  (Whyte et al, 2013). SEs were first identified in pluripotent embryonic stem cells  (Whyte et al, 2013). A database of SEs was created  (Jiang et al, 2019)  and recently updated  (Wang et al, 2023).
It suggested that DNA methylation is involved in the regulation of SE activity. Variable levels of DNA methylation have been found at superenhancers (SEs), and in experiments using allele-specific reporters, it has been reported that the SE DNA methylation state could be dynamically switched  (Song et al, 2019). To gain insight into the possible impact of neonicotinoids on these regions, we extracted superenhancer coordinates from the https://www. asntech. org/dbsuper/ database for E15. 5 brain, adult testis, and ES cells. We chose to analyse SE that are active in germ cells (testis) and somatic cells (brain) and in precursors of both tissues (ES). First, we identified superenhancers that overlap DNA methylation regions in control samples (Table  2 ). We identified that 199 (~50%) out of 400 brain SEs, 123 (~59%) out of 210 testis SEs and 84 (37%) out of 231 ES cell SEs had DNA methylation marks in unexposed sperm, P < 10 × 10 -6 , hypergeometric test (Tables  S4, S5 , and S6).
Cells were enumerated and divided by the total area of the tubule. We counted cells in a minimum of seven tubules using four biological replicates. For the analyses of each testis section, stages with all cell types (spermatocytes, round spermatids, and some elongated spermatocytes) of tubules were selected, and the total area of each tubule was measured in square micrometers using ImageJ. Cells were counted in a quarter of the section of each tubule, and the result was expressed as the number of cells per 1,000 µm 2 , obtained by applying the formula: n= (number of cells counted*4/total area of the tubule) *1,000. The data were averaged and plotted compared with the control group (±SD). Statistical significance was assessed with a nonparametric test. For the lumen size, we measured the diameter in the centre of all tubules.
We used round and unbroken tubules for analysis. We measured diameters in all tubules found in each section disregarding their stage. The data were averaged for each replicate and presented as lumen size compared with control. Statistical significance was assessed with a nonparametric test. For immunofluorescence, the epitopes were unmasked in 0. 01 M citrate buffer, pH 6, at 80°C for 20 min. After the sections were blocked in BSA containing 1X PBS-0. 05% Tween (PBS-T), they were stained with antibodies against mouse anti-γH2AX (1:500, 05-636; Millipore) or mouse anti-PCNA (1:500, ab29; Abcam). The sections were incubated with a primary antibody overnight at 4°C in a humidified chamber. After washes in PBS-Kodak (0. 04%), the sections were incubated with the appropriate fluorescent Alexaconjugated secondary antibody (1:1,000; Invitrogen) for 1 hour in a humidified chamber at room temperature.
The sections were mounted using Vectashield solution containing 0. 001% (vol/vol) 4,6-diamidino-2-phenylindole dihydrochloride (DAPI). Images were taken using an AxioImager microscope equipped with an AxioCam MRc5 camera and AxioVision software version 4. 8. 2 (Zeiss) with a 20, 40 or 63X objective lens. We analysed cells in the pachytenediplotene stage where γH2AX localized mainly in sex chromosomes, in leptotene-zygotene (strong staining all over the nucleus), and in round spermatids without γH2AX staining (round cells). We analysed a minimum of 10 tubules from four control and four "6" mg/ kg/day thia-exposed animals. Cells at the corresponding stage were enumerated and presented as the average number of cells per tubule ±SD. Statistical significance was assessed with a nonparametric test.
Spermatozoa extraction and quantification Epididymis collected from 60-d-old mice were used for spermatozoa quantification. Briefly, epididymides from 10 control mice and 10 thia-derived mice were suspended in 1 ml of 0. 05% Triton buffer, incised using small scissors, and homogenized for 45 s using a homogenizer (PT 2500 E; POLYTRON). The homogenate was transferred to a 15-ml conical tube and stored on ice. One milliliter aliquot of 0. 05% Triton-X100 buffer was added individually to the original homogenizing vessel, homogenized, and added to the original homogenate volume. This process was repeated five times for each sample to completely obtain all spermatozoa in a final volume of 6 ml. Spermatozoa from each sample were then enumerated using a hemocytometer. Data were obtained by taking the average of 10 squares from each biological replicate, plotted in Excel, and presented as the average number of spermatozoa per epididymis compared with the control.
Cells were examined and scored accordingly. Normal chromosomes were those that were fully synapsed, and SYCP1 and SYCP3 were fully colocalized, except for sex chromosomes, where only partial colocalization was detected. Cells containing more than one type of defect were scored in each defect category present. The statistical significance was assessed using a Mann-Whitney test. Anti-rabbit H3K9me3 (1:500, ab8898; Abcam) and anti-mouse SYCP3 (1:200, sc-74569; Santa Cruz) were also used for the examination of H3K9me3 intensity over the chromosomes. The images were taken using an AxioImager microscope equipped with an AxioCam MRc5 camera and AxioVision software version 4. 8. 2 (Zeiss) with a 63x objective lens using the same exposure time  (DAPI, 350/ 442, Alexa Fluor 488, 488/525, Alexa Fluor 594, 594/617).
RNA was then washed with RW1 and RPE from the RNeasy Plus Mini Kit (QIAGEN) to remove impurities. Purified RNA was then eluted in 50 µl of RNase-free water. Six to eight biological replicates of control or treated groups were used for RT-qPCR. RT was performed using 1 µg of total RNA with iScript (1708891; Bio-Rad) adhering to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines  (Bustin et al, 2009). A no-RT reaction was performed and analysed by qPCR for all the tested genes. The reactions showed no presence of PCR products. We used Rpl37a as a housekeeping gene because it showed no variation between replicates based on RNA-seq data. The data are presented as the fold change compared with the control ±SD. Primers for this study were selected using the Primer-Blast program from https://www.
RNA sequencing and data processing We used three biological replicates from the control group and three for each thia group of mice. Each replicate contained the pool of testes from three embryos because of the small size of this organ. DNA was extracted using an RNeasy kit as described above. One microgram of total RNA was used for a strand-specific library preparation protocol using NEBNext Ultra II Directional RNA Library according to the protocol provided by the manufacturer. Quality control and genome-wide sequencing were performed at the GenomEast platform at the Institute of Genetic, Molecular and Cellular Biology (IGBMC), Strasbourg, France. The sequencing was performed in massive parallel sequencing using paired-end mode, and the size of the sequencing tag was 100 bp. Reads in FASTQ format were processed for quality control using the FastQC tool (http://www.
bioinformatics. babraham. ac. uk/projects/ fastqc/). An average of 50 million sequencing reads per sample was processed. The exact number of reads per sample is provided in Table  S13. The reads were mapped to the reference genome [Mus musculus Ensembl mm10 sequence] using the Hisat2 alignment program  (Kim et al, 2015)  with the following parameters: reference genome file mm10, paired-end mode, reverse strand. The alignment files were generated as BAM files. These files were used as the input for FeatureCounts  (Liao et al, 2014)  to calculate the gene abundance using the gene annotation file mm10. ref-Gene. gtf from UCSC. Differential gene expression was assessed using the package DESeq2 (2. 11. 40. 7)  (Love et al, 2014)  with the option filtering genes with low counts. Functional annotation of DEGs was performed with the DAVID program using "Biological process" mouse gene sets using as background the list the genes that express in embryonic E15 testis.
ChIP-qPCR Equal amounts of precipitated DNA and input samples were used for the qPCR analysis. Quantitative PCR was performed using 0. 4 ng of DNA of immunoprecipitated or Input DNA and 6 biological replicates. Normalized expression values were calculated with CFX Manager program using a region located far from promoter as a reference gene, we used a region in Rplp0 for H3K4me3-ChIP normalization. Primers used in this study are listed in Table  S14. Enrichment of each target in the precipitated DNA was evaluated by calculating the ratio between the average of the normalized ChIP DNA copies and the average of the normalized DNA copies in the inputs.
For MEDIP-qPCR, enriched DNA and input were diluted to equal concentrations, and qPCR was performed. Primers were designed using the coordinates of differential peaks (Table  S14 ). The sequences were retrieved from USCSC repeat masks, and Primer-Blast was used to design primers. Equal amounts of enriched DNA and input were used for PCR. The background was normalized to the Rplp0 unmethylated region. We used six replicates for the control and six for thia, and each replicate contained DNA from E15. 5 testes. The data for each gene were normalized and compared with the control.
2. 7. 1) algorithm  (Zhang et al, 2008) ; the following parameters were applied: a shift-size window of 73 bp, no model, and a q-value threshold <0. 05. For comparison of the control datasets of the thia-exposed and control samples, differential peaks were identified using counting reads at each peak using bedtools MultiCovBed (Version 2. 30. 0)  (Quinlan & Hall, 2010). Statistical significance was calculated using Limma v3. 50. 1 with filtering peaks with low counts. We performed functional annotation of the differential peaks using the web-based tool GREAT v3. 0. 0 (default parameters). For the visualization of ChIP-seq tracks, each bam file was converted to BedGraph tracks by using Genome Coverage 2. 30. 0. IGV was used to visualize the tracks. The superenhancer datasets for testis, ES cells, and embryonic day 14.
After another three ten-minute washes with 1X TBS, Western blotting detection reagents Amersham ECL Prime Western Blotting Detection Reagent (RPN2232; Amersham) were used to coat each membrane. Specific protein expression for the antibody was then detected and measured using a molecular imager (ChemiDocTM XRS+ System with Image LabTM Software). Ponceau Red-stained bands were used to assess protein loading and normalize the levels of H3K9me3 for each sample. The intensity of the bands was measured using Fiji: ImageJ software.
Testosterone measurement by ELISAs Blood was taken from the hearts of deeply anesthetized animals (~800-1,000 µl). The blood was allowed to clot by leaving it undisturbed at room temperature. This process usually takes 15-30 min. We removed the clot by centrifuging for 10 min in a refrigerated centrifuge. The resulting supernatant was preserved at -80°C until use. We used a mouse testosterone ELISA kit (orb340113-960; CliniSciences) for testosterone measurement. We followed the instructions provided by the manufacturer. The data were averaged, plotted, and presented as the concentration of testosterone in ng/ml.
Acknowledgements Sequencing was performed by the GenomEast platform, a member of the " France G énomique " consortium ( ANR-10-INBS-0009 ). We would also like to thank the  Galaxy platform  for bioinformatics support (Galaxy Community, 2022). We would also like to thank the H2P2 platform ( UMS Biosit Inserm UMS 018 ,  CNRS   UMS3480 ) for assistance with the preparation and analysis of paraffin testis sections. We are grateful to  Christian Jaulin  for critical reading and suggestions for the article. This work was supported by  ANSES  funding ( R20155NN ) to  F Smagulova.
This model is more flexible and fits the data better than the Gamma model usually used for SPI calculation. It allows us to estimate the threshold below which precipitation can be qualified as extreme, as well as the threshold value defining drought categories according to severity. The great advantage of this approach is that this set of thresholds is estimated rather than imposed and is specific to each geographical location. Another advantage of our method is that it provides an estimation of the drought thresholds in the same unit as the raw data, allowing for the implementation of an operational drought monitoring system. Finally, the Drought Hazard Index can be measured more precisely. By construction, its value varies from one location to another, depending on the specific characteristics of each rainfall distribution.
To test the relevance of our approach to drought hazard estimation, we work on a gridded set of rainfall data covering West Africa from January 1901 to December 2020. Although the study area is limited, it includes a wide range of climatic zones. To catch potentially disastrous droughts and to facilitate comparisons with the standard approach of drought classification and DHI evaluation, we focus on meteorological droughts measured from cumulated annual rainfall levels. For ease of interpretation, most of the results are provided in the form of maps. The remainder of the paper is organized as follows: Section 2 describes the studied area and the database, Section 3 presents the extreme value mixture model, Section 4 provides the results, and Section 5 concludes.
Data and Study Area The region under study is located between latitudes 0 and 18 • N and longitude 20 • W and 20 • E (Figure  1 ). This area is centered in West Africa but also includes a part of Central Africa. The rainfall patterns in this region are determined by the monsoon system and topography. The region encompasses three major climatic areas and several climate transition zones that cross the region from east to west, forming virtually parallel strips from the Tropic of Cancer to the Equator. Gabon, which are characterized by mountainous relief. By contrast, the Dahomey Gap, which includes parts of Ghana, Togo, and Benin and has an annual rainfall total of 1000 to 1200 mm, is abnormally dry for the region (see Figure  1 ). During the twentieth century, the region experienced several periods of intense drought  [7].
To catch droughts with potentially damaging consequences on the economies and populations of the region studied, we calculate annual rainfall totals. In the Sudano-Sahelian area where rainfall is concentrated over June to September, the annual rainfall accumulation corresponds to the seasonal accumulation. In case of a deficit, the entire year's harvest is at risk. In the southern part of the area, close to the equator, rains are bimodal so that a "normal" (equal to mean) annual rainfall level can mask a drought during one of the two rainfall seasons. Without ignoring the potential compensation between the rainy seasons, we assume that when the annual rainfall deficit is high, the year's production is compromised, whatever the geographical area or intra-annual rainfall distribution. The rainfall data come from the Climatic Research Unit at the University of East Anglia (https://www.
uea. ac. uk/groups-and-centres/climatic-research-unit, accessed on 28 March 2022). We use the gridded time-series of monthly rainfall totals at 0. 5 • × 0. 5 • latitude/longitude resolution for the period January 1901-December 2020. The data were extracted from the revised January 2021 version of the dataset referred to as CRU TS4. 05. The construction of the CRU dataset is detailed in  [38]. Before proceeding to the estimations, the database had to be cleaned. Prior to 1930, numerous annual rainfall data that repeat for two or more consecutive years were detected, especially for cells located in the northern part of the region. These observations were dropped from the dataset, as well as other repetitions detected after 1930. As a consequence, the starting date of the rainfall series and the number of observations in each series may vary from cell to cell.
We use for estimations the maximum likelihood estimator in the Evmix R package developed by Hu, Y. and Scarrott, C. [45]. As an illustration, Figure  2  depicts the density of a Gaussian law and of a mixture with two Weibull distributions for the tails and a normal for the bulk. In this figure, the density is continuous at the thresholds, although it is not a necessary condition. In estimations, we do not impose a continuity constraint at the thresholds in the probability density function. Imposing continuity induces a dependence between the bulk and tail estimates and may have an adverse effect on model identification if the bulk model is poorly specified  [45]. 𝐺 𝑥|𝑢, 𝜎 , 𝜉 = Pr 𝑋 < 𝑥|𝑋 > 𝑢 = ⎩ ⎪ ⎨ ⎪ ⎧ 1 -1 + 𝜉 𝜎 , if 𝜉 ≠ 0 1 -exp - 𝑥 -𝑢 𝜎 , if 𝜉 = 0 (4) for all 𝑥 ∈ 𝑢 ; 𝑢if 𝜉 < 0 ; for all 𝑥 > 𝑢 if 𝜉 ≥ 0.
We use for estimations the maximum likelihood estimator in the Evmix R package developed by Hu, Y. and Scarrott, C. [45]. As an illustration, Figure  2  depicts the density of a Gaussian law and of a mixture with two Weibull distributions for the tails and a normal for the bulk. In this figure, the density is continuous at the thresholds, although it is not a necessary condition. In estimations, we do not impose a continuity constraint at the thresholds in the probability density function. Imposing continuity induces a dependence between the bulk and tail estimates and may have an adverse effect on model identification if the bulk model is poorly specified  [45].
Acknowledgments: The authors are grateful to the members of the project " Modélisation des aléas climatiques en Afrique de l'Ouest ", specifically to  Nourddine Azzaoui  and  Arnaud Guillin  ( LMBP, University of Clermont Auvergne ) for insightful discussions on methodological aspects, and to  Olivier Santoni  ( CERDI, University of Clermont Auvergne ) for assistance in data collection.
Data Availability Statement: The data were from the revised January 2021 version of the dataset referred to as CRU TS4. 05. They are available at: https://crudata. uea. ac. uk/cru/data/hrg/ cru_ts_4. 05/ accessed on 28 March 2022. The main software used is the Evmix R package developed by  [45] : https://cran. r-project. org/web/packages/evmix/index. html accessed on 28 March 2022.