dataesr/scientific-paragraphs-categorization huggingface dataset:
Operational use The main objective of our work was to develop a predictive mapping tool of Ae. albopictus mosquito populations that can be easily used by public health stakeholders for targeting their surveillance and control actions. Several models have been developed to predict mosquito population dynamics (e. g. [9, [14] [15] [16] [17] [18] ), but few of them lead to the development of operational tools  [19, 28]. ALBOMAURICE aimed at filling the needs of the Mauritian Ministry of Health and Wellness by adapting an existing mosquito population model to the geographic context of this tropical island. It was successfully transferred to agents of the Vector Biology and Control Division and other departments involved in mosquito surveillance and control activities. Of note, the users have to be familiar to Geographic Information Systems and mapping softwares to exploit and interpret the outputs of ALBOMAURICE.
Conclusions The ALBOMAURICE software contributes to address the pressing need for operational solutions to control the spread of mosquito-borne diseases, including Dengue Fever, into new areas where mosquitoes can thrive due to environmental changes. It encapsulates scientific knowledge of the Ae. albopictus mosquito lifecycle with vector control requirements in a given geographical environment (here Mauritius Island) to predict mosquito abundance in space and time. These predictions come in the form of maps and indicators that health services can use operationally for identifying priorities and optimizing field intervention efforts. An interesting feature of ALBOMAURICE software is that the impact of various control measures can be estimated beforehand. Scenarios of different control strategies can be simulated and compared prior to decision-making.
Acknowledgements This work was partially funded by the  European Regional Development Fund (ERDF)  through the  INTERREG FEDER   TROI  project ( 2018-2021 ), the  International Atomic Energy Agency (IAEA)  through the inter-regional project  INT5155 , and the  Mauritius Ministry of Health and Wellness , under the platform in partnership One Health Indian Ocean (www. onehealth-oi. org). Article processing charge was funded by  ANISETTE , a project supported by the  French Space Agency ,  Centre National d'Etudes Spatiales (CNES)  ( DAR 4800001029 ). The authors thank  Jeremy Bouyer  and  Montana Romero Lopez de Tejada (IAEA) , for facilitating missions to Mauritius, and the  Mauritius Meteorological services  for providing the needed meteorological data. The authors are also grateful to  Mon Loisir Rouillard ,  Queen Victoria ,  Riche En Eau  and  St Felix Sugar Estates  for granting access to set up mosquito traps.
(2004) ,  Diallo et al. (2018)  and  Tao et al. (2019). Besides, we used satellite observations from Aura Microwave Limb Sounder (MLS) to assess the reliability of each simulation. The remainder of the paper is organized as follows. In Sect. 2, we present the domain-filling technique, the data and the configuration of the different experiments. In Sect. 3, we evaluate the different experiments in simulating LS water vapour and how they capture the variability of the water vapour signal over the Asian and North American monsoon regions. Finally, in Sect. 4, we discuss the relevance of the processes to simulate the water vapour signal and also the differences in water vapour found using the domain-filling technique and the standard version of CLaMS. It should be noted that we do not aim to provide a most realistic model here but rather carry out simplified sensitivity experiments to present estimates of the effects of various processes to be potentially included in models simulating water vapour in the monsoon UTLS.
Aura MLS observations Satellite observations of water vapour mixing ratios in the LS from Aura Microwave Limb Sounder  (Waters et al. , 2006)  are used to further assess the results. We use version 4. 2 of the water vapour data from MLS  (Lambert et al. , 2015) , which has been fully described in  Livesey et al. (2018). These water vapour products have been validated in several studies and recently have been part of a climatological overview of the Asian Monsoon Anticyclone  (Santee et al. , 2017). Here, Aura MLS data and CLaMS data have been compiled on the same regular latitude-longitude grid as the one used by the experiments. In particular, we use the MLS data on 100 and 82 hPa pressure levels and compare them to the simulated water vapour distributions at 100 and 80 hPa, respectively.
Therefore, our estimated cloud top altitudes may be low biased compared to theirs. Finally, note that the ISCCP B1 has slightly lower horizontal resolution (∼ 8 km vs. 4 km) than the dataset of  Ueyama et al. (2018)  but similar temporal resolution (3-hourly). Figure  6  shows the water vapour distributions at 100 hPa of TRAJ (Fig. 6a ), CONV (Fig. 6b ) and the differences between both of them. The two experiments result in a very similar water vapour distribution, with a slight moistening effect caused by convection, mainly at mid and high latitudes. These results indicate that even when a convective event occurs, the water vapour is set by temperatures experienced by the air parcels after convection. This result is in agreement with  Randel and Park (2006) ;  Randel et al. (2015)  but not in line with  Ueyama et al.
Acknowledgements. We would like to thank  Bernard Legras  for performing an experiment with TRACZILLA and sharing with us his work. We thank the  Institute of Climate from the Research Center of Jülich  and, especially,  Martin Riese , for their scientific, technical and financial support. Financial support. This research has been supported by the  Spanish Ministerio de Economía y Competitividad  (grant no. CGL2016-78562-P ). Review statement. This paper was edited by  Jianzhong Ma  and reviewed by two anonymous referees.
Study Design We conducted a prospective longitudinal exploratory open study. We collected qualitative and quantitative data over a 6-month period to assess acceptability of the app. Acceptability has several aspects. In digital technology, it may be reduced to 4 dimensions: (1) usefulness, (2) usability, (3) reliability, and (4) risk  [10]. Usefulness reflects intent to use, which is a good predictor of usage behavior. Acceptability encompasses several concepts, for example, in ISO standards [20], usability comprises 3 dimensions: effectiveness, efficiency, and satisfaction. To assess usefulness and intent to use, our primary outcome was overall response rate, R = (number of dates with responses) / (possible instances with responses over the 6-month period). In this case, there were 60 possible instances.
Acknowledgments This research was funded by  Institut de Recherche en Santé Publique  with additional funding provided by  Agence Régionale de Santé des Pays de la Loire. Our study received funding from the  Regional Health Agency  and the  Autism Resource Centre of the Loire Valley , as well as a grant from the  National Center for Autonomy/Research Institute of Epidemiology and Public Health. We thank  Aude Doudard, MD , and  Philippe Duverger, MD , for their help in the early stages of the project, and  Jean-Christophe Guilbaud  assistance with English editing.
1A ,B, RRC filter; https://support. neuralynx. com/hc/en-us/articles/360054937932-Hybrid-DC-Coupling-and-Getting-Back-to-Unity-Gain). Digital inverse filtering has further been proposed for reconstruction of full-band signals from recordings obtained using such a hybrid AC/DC-divider filter (Hybrid_Input_compensation_ v. 1. 0, https://neuralynx. com/software/hybrid-inputcompensation, hereafter referred to as IF NLX. However, the quality of signal reconstruction of biological signals has not yet been assessed. Further, the versatility of the NLX routine is limited by the standard set of inverse filter coefficients, which may vary between channels. Here, we propose a digital inverse filter based on a mathematical model describing the RRC filter, which provides high computational accuracy and versatility, and procedures for the evaluation of the inverse filter coefficients, adapted for each recording channel to minimize the error caused by deviation of the real values of the RRC filter elements from the nominal values.
3 mM Na GTP, and 10 mM HEPES (pH 7. 3). Whole-cell signals were recorded using an Axopatch 200B amplifier and acquired using a Digidata 1440A (Molecular Devices), and/or Digital Lynx SX (Neuralynx). All recorded signals were replayed using the Multiclamp 1440A built-in DAC and acquired using a Digital Lynx SX.
Data analysis Data analysis was performed using custom-written and built-in functions in MATLAB (The MathWorks). Amplitude ratios and phase shifts of sinusoidal signals were estimated using fast-Fourier transform. Signal amplitudes were calculated as the most negative value of LFP after baseline subtraction. Slopes of the signals were calculated from the first LFP derivate. The values in the text are presented as mean value 6 standard deviation. To estimate the quality of reconstruction the percentage root mean square difference (PRMSD) was calculated as %PRMSD ¼ 100% Á ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X N n¼1 ðV DC À V rec Þ 2 X N n¼1 V 2 DC v u u u u u t : (10) For ideal reconstruction, PRMSD = 0.
The MATLAB code used for reconstruction is available as Extended Data 1 and online at https://github. com/ Nasazat/Inverse-filter. For SD reconstruction, the analysis intervals included a short period of control before the SD, the SD itself, and a short fragment after it, in total 100 s for each SD. Data were down-sampled to 100 Hz. SDs from all recordings alternating with zero-containing 30-s periods were merged into one long signal to estimate DAC and ADC offset fluctuations. Also, 3 min of zero-valued signal was added to the beginning of the dataset to eliminate the transient process of the filter. For SD-initiated NUP reconstruction, each recording consisted of a control period, 1 h of ET-1 application and at least 1 h of ET-1 washout. Data were down-sampled to 10 Hz; 5 min of zero-valued signal was added to the beginning of the dataset to eliminate the transient process of the filter.
SDs At the next stage, we investigated the filter with real SD waveforms. SDs were induced by epipial high-potassium solution application at a distance of 4. 2 6 1. 1 mm from a recorded site in the barrel cortex. SDs appeared firstly in the superficial channels and propagated into the deep layers in keeping with previous studies  (Nasretdinov et al. , 2017; Zakharov et al. , 2019). SDs recorded at a depth of 430 6 110 mm (L2/3) were used for analysis. DC reconstructions were tested on an SD-containing dataset replayed by DAC and recorded by the Neuralynx amplifier simultaneously at two different channels in the DC and AC RRC modes (Fig. 3 ). Method verification was performed by comparing real-waveform reconstructed signals to signals recorded in DC mode. An example of SD reconstruction is shown on Figure  3A.
Discussion In the present study, we developed a procedure for fullband, high quality reconstruction of signals recorded using hybrid AC/DC-divider RRC filters by virtue of inverse digital filtering based on a mathematical model of RRC filters. Our procedure also involves calibration of individual channels to minimize the error caused by deviation of the elements of RRC filters from their nominal values. Through validation in a number of datasets including extracellular recordings of high-potassium-induced cortical SDs, endothelin-induced ischemic NUPs and whole-cell recordings of the membrane potential we demonstrate that this approach enables near 99% reconstruction quality of the original signal in full-band that is superior to the IF NLX routine which provides reconstruction quality with ;10% error from the original signal.
In this context, RNA-seq presents several advantages compared to the DNA-seq. First, the number of RNA-seq data sets publicly available is much higher than the number of DNA-seq data sets, for many species (chicken, pig, cow, and other non-model species) since these data have accumulated over the past several years and continue to accumulate in different populations and within populations. Moreover, within populations, different conditions are studied, increasing the number of studied animals, allowing to better detect, in a given population, variants with low frequencies. Second, RNA-seq data allows studying coding region variations that have potential functional impacts. Some of these SNPs can induce a loss of the protein function. These loss-of-function variants are extensively studied because of their possible contribution to phenotypes  (Genome Aggregation Database Consortium et al.
, 2020). In addition, they represent a powerful source of information to understand gene functions (Genome Aggregation Database  Consortium et al. , 2020). However, these loss of function SNPs are rather rare because purged by negative selection in natural populations but can be detected with a certain number of samples. In well-known model-species or human, these coding region variants are accessible using whole exome sequencing (WES), as shown by the recent work of the Genome Aggregation Database (gnomAD)  (Lek et al. , 2016). This consortium analyzed 125,748 human exomes (and much fewer whole genomes: 15,708) from public sources and identified 443,769 high-confidence predicted loss-of-function variants, defined in the work of gnomAD as being either gain of stop (non-sense variants), frameshift or splice site variants.
, 2013)  for read mapping and GATK (Van der Auwera et al. , 2013) for variant detection. Three of the above-mentioned studies were interested in determining the concordance of SNP and genotype detection between RNA-seq and DNA-seq, the latter being the gold standard for SNP detection. However, these studies used only few samples (from 1 to 4) and had not at their disposal both RNA-seq and DNA-seq data on the same tissues of the same individuals. In this context, this work aims at detecting SNPs from RNAseq data in chicken. The first goal was to set up a procedure allowing SNP detection and genotype (GT) calling from RNA-seq data using reference tools (STAR for read mapping and GATK for SNP detection). We tested the SNP reliability according to three filters suggested by the GATK team and compared the detected SNPs with those obtained using DNA-seq data.
(B) RNA-seq data offers a heterogeneous coverage of the genome depending on the expression of the genes harboring the SNPs. At the population level, 4 SNPs having a sufficiently high DP are detected by GATK. At the individual level, SNP 1 shows good read coverage across all samples whereas SNP 3 is on a gene that has a lower expression, in particular in the stress (ST) condition compared to the control (CT). SNP 4 is on an overall very lowly expressed gene. In terms of genotype (GT) per individual, some cannot be provided by GATK (noted ". /. ") because of a too low DP (i. e. , 5 reads, see brown GT and DP) and are not considered for the GT call rate. For SNP 3, most of the individuals from the ST condition have no GT and for SNP 4, only one GT is called whereas in both case the SNP is detected at the population-level.
RNA-Seq and DNA-Seq Data Raw data of both DNA-seq and/or RNA-seq are available on the ENA and SRA archives under accession numbers: PRJEB28745 (RpRm DNA-seq and RNA-seq, Novo1 and Novo2, RNA-seq); PRJEB43829 (FLLL, DNA-seq); PRJNA330615 and PRJNA248570 (FLLL, RNA-seq); PRJEB26695 (red jungle fowl, RNA-seq); PRJEB34341 (Naked neck, RNAseq); PRJEB34310 (Fayoumi, RNA-seq); PRJEB27455 (FrAg, RNA-seq); PRJEB43662 (Cobb, RNA-seq); PRJNA612882 (HerX, RNA-seq)  (Fu et al. , 2015). RNA sequencing was conducted on all samples using an Illumina HiSeq (Illumina, California, United States) system, with 2 × 150 bp or 100 bp. Libraries were prepared following Illumina's instructions by purifying poly-A RNAs (TruSeq RNA Sample Prep Kit). Illumina adapters containing indexing tags were added for subsequent identification of samples.
For the comparison of SNPs detected by RNA-seq versus DNA-seq, we used two populations for which both data types were obtained from same liver samples collected on the same birds. The population A was composed of 15 birds from an experimental layer population (RpRm, PRJEB28745) composed of birds diverging for feed efficiency (Rp and Rm) after a 40-year diverging selection  (Bordas et al. , 1992). The population B was composed of 8 birds from an experimental broiler population (FLLL, PRJNA330615) composed of birds diverging for body fat content (FL and LL)  (Roux et al. , 2015). For the rest of the work, we used RNA-seq data from 11 populations (see Additional File 1 for the detail of the number of birds, the tissues and the number of samples): a red jungle fowl population (called RJFh with 36 birds and 3 tissues); 3 broiler populations, the FLLL presented previously but here extended with 32 birds and 2 tissues) and two commercial ones, the Cobb 500 (Cobb Vantress, named Cobb with 48 birds and 2 tissues) and a 3-way cross produced by Heritage Breeders, LLC (named HerX, 23 birds and 1 tissue), 6 layer populations with 2 commercial brown-egg subpopulations from the Novogen company, Novo1 with 32 birds and 1 tissue and Novo2 with 40 birds and 2 tissues, 2 experimental brown-egg populations with the RpRm presented previously but here extended (with 88 birds and 5 tissues) and an experimental dwarf chicken layer line homozygous for the Naked Neck mutation (named LSnu with 16 birds and 2 tissues) and 2 other layer populations with a leghorn breed (FrAg) with 4 birds and 2 tissues) and the Fayoumi (FAyo), an Egyptian breed with 16 birds and 2 tissues; finally an experimental population (Rmx6) issued from crosses between 2 experimental lines  (Frésard et al.
RNA-Seq Read Mapping and Variant Detection For all samples, RNA-seq variants were detected using the snakemake  (Koster and Rahmann, 2012)  pipeline, available at this reference:  (GitLab, 2019). For each population, samples were analyzed by tissue. FASTQ files were trimmed for Illumina adapter using TrimGalore version 0. 4. 5  (Krueger, 2021). STAR v. 2. 5. 2b  (Dobin et al. , 2013)  was used with default parameters for the read mapping on the Gallus_gallus-5. 0 reference genome, after the multi-sample 2-pass mapping procedure, with a GTF file enriched in long non-coding genes [available on http://www. fragencode. org (LNChickenAtlas); Section: Galgal5-Ensembl v94; Genome annotation: LNCextendedEns94. gtf. gz; (Jehl et al. , 2020) ]. Uniquely mapped reads (selected on a mapping quality score equal to 255) were then post-processed following the GATK best practices for RNA-seq data [duplicates were marked, reads overlapping intron were split and mapping quality score were reassigned, indel were realigned and base were recalibrated thanks to the known variants from Ensembl v94's dbSNP  (Ensembl, 2018) ].
Gene and Exon Expression Quantification Gene expression was quantified with RSEM  (Li and Dewey, 2011) v. 1. 3. 0, at the gene-level, using the GTF file LNCextendedEns94. gtf. gz available on http: //www. fragencode. org (LNChickenAtlas; section Galgal5) and corresponding to the genes from the Ensembl annotation used as reference, extended with lncRNAs loci available in other public databases (NCBI, NON-CODE, etc. )  (Muret et al. , 2017). To compute expression at the exon level, we used FeatureCount v1. 6. 2  (Liao et al. , 2014)  with options -t "exon" and -g "exon_id. " We defined for each exon a metric called RpKb (Read per Kilobase) as the mean number of reads mapped at the exon divided by its length in kilobases. To define an expression threshold, we compared the expression of exons to the expression of a set of randomly selected loci in the genome as done previously in  Jehl et al.
Hierarchical Clustering Analysis The hierarchical clustering was performed on a set of 67,341 SNPs obtained using liver RNA-seq data from the 10 populations presented in Table  1  (liver unavailable for Rmx6). This set corresponds to the SNPs common to the 10 populations and passes the GT criteria (see "Results and discussion") for each population. The analysis was produced by using the function "snpgdsHCluster" of the R (R Core Team, 2019) package SNPRelate v1. 8. 0  (Zheng et al. , 2012).
First, we observed that a large proportion of RNA-seq specific SNPs (46. 6%) and DNAseq specific SNP (40. 0%) belonged to a "SNP cluster" (i. e. , 3 or more SNPs in a sliding window of 35 bp, as per GATK definition) (Figure  3C ). This filter is one of the three filters proposed by GATK for RNA-seq SNP detection, but not for DNA-seq detection and the GATK team notes that these filters are not definitive and should be validated by users. Therefore, in the light of these observations, we decided not to remove the "SNP clusters" from our RNA-seq dataset as for DNA-seq dataset, but only to flag them as belonging to a so-called SNP cluster. Indeed, this filter removed 39,783 true SNPs (i. e. , True positives detected by both DNA-seq and RNA-seq methods) and consequently the benefit of the precision increase (from 90.
Hence, we propose for our subsequent analysis on different RNA-seq datasets to select SNPs within the red surface of Figure  4B  with a (5. reads. DP) genotype CR ≥ 20% ensuring a concordance (precision) of almost 95% and a CR ≥ 50% ensuring a sufficient number of GT per SNP to calculate the allelic frequencies. We can note that most of the SNPs on this surface have a genotype concordance of more than 97%. We can also note in most of the populations analyzed in the next section that more than 98% of SNPs with (5. reads. DP) genotype CR ≥ 20% have a CR ≥ 50% (Additional File 4).
Number of SNPs and Genotypes Detected by RNA-Seq in 11 Populations As shown in Table  1  which gives an overview of the SNP diversity in 11 chicken populations, we detected between 1. 1 and 3. 8 M SNPs per population using liver RNA-seq datasets. Using all the tissues available (1-5 tissues depending on the population), we detected more SNPs, consistently with our previous result (see Figure  3D ): between 1. 7 and 5. 5 M SNPs with a fold increase of × 1. 18 to × 2. 48 depending on the number and nature of analyzed tissues. Across populations and using all tissues, we found a total of 9. 5 M SNPs having at least one alternative allele in at least one population (SNP union), and 241,960 SNPs that had at least one alternative allele in each of the 11 populations (SNP intersection). The union of our SNPs contains 23% (2,175,528) yet-unreported SNPs in the reference Ensembl v94 dbSNP database [  (Ensembl, 2018) : 21 M SNPs].
Rare Deleterious Variants Detection in the Populations We predicted the impacts of the 9,496,283 SNPs detected in at least one population using the VEP tool  (McLaren et al. , 2016)  which predicts the potential consequences of the SNPs in each of the transcripts carrying them: we found 33,304,412 consequences. As expected, the vast majority of the SNPs affected non-coding regions (Figure  5A ) and among the 472,319 SNPs affecting a coding-region, a majority were synonymous (63%) or non-deleterious missense (28%) as shown in Figure  5B. Among all these predictions, we focused on the predicted consequences with the most severe putative impacts as defined by the gnomAD consortium, which only considers the PCG (Protein Coding Genes) (Genome Aggregation Database Consortium et al. , 2020): variants in the splice regions, start and stop codon loss or stop codon gain even if the severity of the latter depends on its position in the coding sequence.