dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , mature transcript, despite a much lower number of SNPs per gene when SNPs are   (The GTEx Consortium, 2020). Abbreviations for the 21-tissue dataset: burs, bursa of Fabricius; cctl, cecal tonsils; crbl, cerebellum; duod, duodenum; fatG adipose tissue around the gizzard; hard, harderial gland; hert, heart; ileu, ileum; kdny, kidney; livr, liver; lung, lung; mscB breast muscle; optc, optical lobe; ovry, ovary; pcrs, pancreas; pvtc, proventriculus; skin, skin; spln, spleen; thym, thymus; thyr, thyroid gland; trch, trachea; and for the 5-tissue dataset: adip, abdominal adipose tissue; blod, blood; embr, 4. 5 day embryos; hypt, hypothalamus; livr, liver; for more details in these 3 datasets and associated samples see  Jehl et al. (2020). Black dashed line: gene expression with TPM ≥ 1 and red dashed line: TPM ≥ 0.
The hierarchical clustering was performed using the "snpgdsHCluster" from the package SNPRelate v1. 8. 0 (see also section "Materials and Methods"). indicating that these SNPs detected by RNA-seq and their associated genotypes allow distinguishing different populations. The classification separated clearly the RJFh (red circle arc with a Red Jungle Fowl population, used here to represent the "ancestral" population), then the broilers (blue circle arc), the brown-egg layers (dark green circle arc), and the creamor white-egg layers (brown circle arc with Fayoumi breed and Fr-Ag population which is an experimental leghorn line). We also observed the expected sub-groups within these 3 types of populations: the commercial lines (Novo1 and Novo2 for the layers, Cobb and HerX for the broilers) separated from the experimental lines (RpRm for the brown-egg layers, FLLL for the broilers).
CONCLUSION We show here that RNA-seq data, which are cheaper to generate and store compared to DNA-seq data, can be a reliable resource for performing different analyses based on polymorphism detection. By comparing DNA-seq and RNA-seq results generated from the same animals in two independent chicken populations, this study provides a workflow to produce reliable SNPs and genotypes from RNA-seq data. We ran through this pipeline 767 RNA-seq of 382 birds from 11 populations and provided a per-population estimation of the average genotyped SNPs count per tissue (more than 550,000) and an overview of the predicted consequences of SNPs located in coding regions. In particular, thanks to this large RNA-seq dataset, we identified 440 genes containing a stop-gained impact, known to be rare because of their potentially severe impact, especially when located in the first third of the coding sequences (133 genes).
In a companion study  (Degalez et al. , submitted) , we checked the possible existence of more than one SNP in a given codon, that could "rescue" a stop-gained situation. We then gave an overview across 11 populations of genes that could be analyzed for ASE, i. e. , having at least one SNP allowing to distinguish expression from both chromosomes. We applied phASER on liver RNA-seq data of two populations and identified around 21 to 30% of cis-regulated genes depending on the analyzed population and the gene biotype (PCG versus lncRNA), these results were consistent with other studies conducted in other species. This study represents a first step to more ambitious projects that could analyze tens of thousands of available RNAseq datasets to build a GTEx-like atlas reporting cis-and trans-genetic associations with gene expression, as previously performed in human (The GTEx Consortium, 2020) and more recently in cattle  (Liu et al.
ACKNOWLEDGMENTS We are grateful to the  GeT-PlaGe  platform (doi:  10. 15454/1. 5572370921303193E12 )for RNA-seq libraries and sequencing and the genotoul bioinformatics platform  Toulouse Occitanie  ( Bioinfo Genotoul , doi:  10. 15454/1. 5572369328961167E12 )for providing help and/or computing and/or storage resources. A warm thanks to  Morgane Boutin  who passed away this year and who did a lot in RNA extraction.
FUNDING The sample and/or data were collected in the frame of projects that received financial support from the  European Union 's  H2020 Program  under Grant Agreement No. 633531  ( Feeda-Gene  project), the  French National Agency of Research  ( FatInteger  project  ANR-11-SVS7 ;  ChickStress  project,  ANR-13 - ADAP ;  EpiBird ANR  project  PCS-09-GENM-010 ), from  French institutions as Institut Agro-AGROCAMPUS OUEST  and  INRAE [Fr-AgENCODE  project ( 2015-2017 ),  ELASETIC  project (2012)]. This work was also supported by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contrat  ANR-10-INBS-09 ). FJ and FD are Ph. D. fellows supported by the  Brittany region (France)  and the  INRAE (Animal Genetics Division).
Therefore, we aimed to quantify the geographic variation of HIV seroprevalence among MSM and PWID by age and sex through using georeferenced HIV testing services data aggregated at the level of local government areas (LGAs). We planned to detect hot spots and compare HIV seroprevalence from our study to that of the 2014 IBBSS. We defined HIV seroprevalence as the overall occurrence of HIV within a defined population at a particular time, as measured by blood or serologic tests. The IBBSS was scheduled to occur every 5 years, with the last one taking place in 2014. The survey results served as the primary data source for the national response to AIDS among KPs in Nigeria. To track changes in HIV infection among MSM and PWID in the selected study and survey states, we compared HIV seroprevalence from our study with HIV prevalence from the 2014 IBBSS.
HIV Testing Algorithm HIV testing delivery services to KPs in the target communities adopted a 2-step HIV rapid testing serial algorithm testing strategy. Fourth generation rapid test kits were used for HIV testing (Alere Determine HIV-1/2 Ag/Ab Combo, Alere Scarborough, Inc). A nonreactive HIV test result after the first test was considered HIV negative. A reactive result was tested with a second test. When the result was reactive again, it was considered HIV positive, and when it was nonreactive, it was considered HIV negative. All HIV test results were documented using the national HIV testing and counseling register. Based on program design and data reporting requirements, the HIV test results of each KP (MSM and PWID) were aggregated up by the program level performance indicator, "Number of individuals who received HIV Testing Services and received their test result," and linked to the location or LGA where the KP was tested.
HIV test results are aggregated by the KP group and the LGA where the test is conducted. To reduce double-counting of individuals and to account for retesters in a reporting period, tracking systems, such as "unique identifiers", are established and used to monitor the frequency of contact or outreach of the KPs over time. A unique ID is generated and assigned to a KP group before HIV testing commences, and the results collected are entered into an electronic database. Data validation is conducted every quarter by running a query of all individual-level KP data on HIV testing using IDs to determine first-time testers and repeat testers. All duplicated KP data within the reporting period are deduplicated before data transmission. This is accomplished by community-level data review and reconciliation exercises between testing counselors and monitoring and evaluation officers to ensure that the first-time testers and repeat testers are adequately tracked.
Study Population All MSM and PWID aged ≥15 years with a documented HIV test result were included. At the time of analysis and in line with the Nigeria KP program, HIV testing data were disaggregated into 4 age categories, 15-19, 20-24, 25-49, and 50+ years. The age classification for the HIV test data was based solely on the nature of the KP program. MSM was defined as self-identification as male and report of oral or anal sex with a man in the previous 12 months  [35]. A PWID was defined as an individual meeting one of the following conditions: self-reporting ever injecting any illicit drug and having a visible injection site on the body and self-reporting injecting illicit drugs in the past month  [36]. All KP groups were screened before enrollment by known MSM or PWID recruited as peer navigators by the national KP program to ensure clients were members of the target population.
Statistical Analyses We analyzed aggregate-level data from index testing, mobile, and voluntary counseling and testing modalities by counting the number of KPs who received HIV testing services via the The President's Emergency Plan for AIDS Relief (PEPFAR) and the Nigerian KP program between October 1, 2016, and September 30, 2017, at the LGA level. We georeferenced all MSM and PWID who accessed HIV testing services at the LGA level and performed 4 analyses. First, we constructed maps of HIV seroprevalence among MSM and PWID. Second, we used spatial autocorrelation (global Moran I) statistics to measure the degree to which HIV seroprevalence was clustered, dispersed, or randomly distributed. Under the null hypothesis, the expected value is that there is no pattern of HIV infection in selected LGAs.
Moran I values range from -1 (indicating perfect dispersion) to 1 (indicating perfect spatial clustering). Third, we used hots pot analysis in the ArcGIS software (Environmental Systems Research Institute) to calculate the Getis-Ord Gi* statistic for each LGA. The resultant z score identified where LGAs with either high or low HIV seroprevalence cluster spatially. Each LGA was analyzed within the context of neighboring LGAs: the larger the z score, the more intense the clustering of high seroprevalences or hot spots. Similarly, smaller z scores indicated clustering of low seroprevalences. To be a statistically significant hot spot, an LGA required a high HIV seroprevalence and to be surrounded by other LGAs with high HIV seroprevalence. For the 2 study groups, the map scale used varied: 1:25,000 was used for MSM and 1:42,000 for PWID.
At the time of analysis, and in line with the Nigeria KP program, HIV testing data was disaggregated into 4 age categories, 15-19, 20-24, 25-49, and 50+ years. The age range for HIV test results was based purely on the nature of the program, and thus the age range of 25-49 years could not be broken down into broader age categories, which might have influenced the variability of HIV seroprevalence in that age group between MSM and PWID. Given the length of the HIV testing data period (between October 2016 and September 2017), the analysis findings provide valid benchmark evidence for potential program reviews evaluating improvements in the adoption of HIV testing services and tracking practices relevant to the study of HIV seroprevalence variability among KPs. Program data used for this analysis were collected at an aggregate level and did not include individual or patient-level socioeconomic and clinical characteristics of the study population that might have provided further insights into the variation of HIV seropositivity.
Nigeria has a mixed epidemic, and HIV prevalence is disproportionality distributed among KPs. As we must wait for every 5-years to conduct an IBBSS, which depends entirely on the availability of funds, it is imperative to ensure frequent measurements to monitor changes in new HIV infections. The use of routine program data from groups with high-risk behaviors like MSM and PWID can help monitor trends in HIV seroprevalence at the community level. Identification of high HIV prevalence regions using routine program data can guide timely program management decisions on potential locations to scale up antiretroviral treatment interventions (the era of test and treat). Across several sub-Saharan Africa countries (including Nigeria), UNAIDS currently uses routine program data in Spectrum Software to estimate the national HIV prevalence and people living with HIV burden.
Acknowledgments Data used for this study were collected from the  KP program  in Nigeria through  PEPFAR/USAID. OK was supported by a professorship grant from the  Swiss National Science Foundation  (grant no. 163878 ) and a  Swiss National Foundation  project grant ( 320030_192452 ). We thank the staff of  Heartland Alliance Nigeria  for their contribution to data collection and data transmission from program implementation states. We thank  Ben Spycher  for comments on an earlier version of this manuscript. We thank  Sara Blough  for proofreading the manuscript and providing English editing services.
Both have reached levels that enable previously impractical applications. AI is capable of tackling complex classification problems, especially in image classification  (Krizhevsky et al. , 2012). Such breakthroughs were made possible by deep convolutional neural networks  (Le-Cun et al. , 2015) ; however, AI encompasses many other techniques that also benefit from larger datasets and increased computational power  (Besse et al. , 2018). In this paper, we explore how the estimation of BLH from backscatter profiles can be formulated as a classification problem and how appropriate algorithms can be applied to solve this problem. Machine-learning techniques are categorized into two broad families: supervised learning (mimicking a reliable reference) and unsupervised learning (learning without a reference;  Hastie et al.
The training set consists of atmospheric boundary layer identifications made by human experts, which is acknowledged as being more reliable than available automatic methods  (Seibert et al. , 2000). Algorithms classifying from a reference dataset (e. g. , ADABL) are called supervised algorithms, while algorithms classifying without a reference dataset (e. g. , KABL) are called unsupervised algorithms. Supervised algorithms make it possible to automatically reproduce human expertise in boundary layer identification. To our knowledge, this is the first time that a supervised algorithm has been applied to this problem. This study is of practical interest because it includes the publication of the source code, which uses only free software. In Sect. 2, we describe the data used in this study, i.
Lidar network Since 2016, Météo-France has deployed a network of six automatic backscatter lidars to help the Toulouse Volcanic Ash Advisory Centre characterize layers of volcanic ash and aerosol in the atmosphere. One of the six sensors can be quickly redeployed at a more suitable geographic location depending on the transport event being tracked. The network, fully operational since April 2017, is continuously functioning and has detected aerosol events at altitudes of up to 17 km. It is part of the wider automatic lidar and ceilometer network of the E-PROFILE program described in  Haefele et al. (2016). Two sampling sites in this network were selected: Brest (48. 444 A comprehensive description of MiniMPL can be found in  Ware et al. (2016).
, 2007 , for more details). These raw signals are processed to obtain the quantity of interest, i. e. , the range-corrected signal (RCS), which is also called the normalized relative backscatter. This processing consists of several procedures including background, overlap, afterpulse, and dead-time corrections. A comprehensive description of the processing is given in  Campbell et al. (2002). The copolarized and crosspolarized range-corrected signals, RCS co and RCS cr , respectively, as delivered by the manufacturer's software, are used as predictors for the machine-learning algorithms described in Sect. 3. The raw data type and format depends on the instrumental device used. To make the algorithms usable on other devices, we converted the files to a harmonized format using the raw2l1 software 1  ; we then used these files as the algorithm input.
Radiosonde data The algorithms were evaluated with respect to estimates derived from radiosonde (RS) profiles. Météo-France operates several RS sites for the World Meteorological Organization Global Observing System. Two RS sites are colocated with the lidars at Brest and Trappes. These sites are equipped with Meteomodem robotsondes and typically launch a Meteomodem M10 sonde at 11:15 and 23:15 UTC every day. Many methods exist to estimate BLH from RS data, several of which have been used in the literature. Some of these methods are listed below. T. Rieutord et al. :  ABL from aerosol lidar with machine learning -Parcel method. BLH is the height at which the profile of the potential temperature θ reaches its ground value. -Humidity gradient method. BLH is the height at which the gradient of the relative humidity is strongly negative.
Ancillary data Ancillary data were used to describe the meteorological situation at the observation sites. These data were not used by the machine-learning algorithms. All the instruments were colocated with the lidar and radiosonde launches. -Rain gauges were used to detect rain events. -Vaisala Ceilometer CL31 instruments were used to detect the cloud base height and distinguish cases with clouds on top of, or inside, the boundary layer. Even though MiniMPL is capable of detecting clouds, we relied on the CL31 cloud detection because the MiniMPL algorithm was found to report non-existent clouds. -Scatterometers were used to estimate the horizontal visibility and detect the occurrence of fog. 3 Machine-learning methods.
In this study, weak learners in AdaBoost are trees with a maximum depth of five (a maximum of five forks between the root and the leaves). AdaBoost is based on decision tree classifiers. It aggregates these classifiers to determine the most accurate border. The concept behind AdaBoost is illustrated in Fig. 3. First, a shallow decision tree is fitted to the entire dataset using the classification and regression tree (CART) algorithm  (Hastie et al. , 2009). All points have the same weight in this first step. Some points in the dataset are misclassified, and the error  in the classifier is the weighted average of the misclassified points. Another shallow decision tree is then fitted on a resampled dataset where the previously misclassified points are over-represented. This new tree has new misclassified points that will be over-represented in the training of the next tree and so on, up to the specified number of trees (M = 200 in our case).
Retained configuration Four predictors were used: the two lidar channels, time (number of seconds since midnight), and altitude (meters above ground level). The ADABL configuration used was weak learner -decision tree of depth five; number of weak learners -200; and predictors -time, altitude, RCS co , and RCS cr. This configuration was chosen because more complex classifiers do not necessarily improve the performance. The computation time of the algorithm was still reasonable: training took 23 s on the full dataset and predicting BLH for a full day took 3. 7 s with a modern laptop. AdaBoost was chosen after a comparison of multiple classification algorithms, i. e. , random forest, nearest neighbor, decision trees, and label spreading (study not shown here). The benchmark score was the accuracy as measured by the percentage of individuals that were correctly classified.
The accuracy was estimated by group K-fold cross-validation, where labeled datasets are grouped into chunks of 3 consecutive hours; one group was used as a testing set and all the rest as a training set. This operation was repeated until each group was used as the testing set. The resulting accuracy was 96 %. However, this figure overestimates the generalization ability of AdaBoost. A more correct estimation would be obtained with an independent validation set (e. g. , a new hand-classified day). An independent validation set was not used here because the crossvalidation accuracy was only used to discriminate between the classification algorithms. It is possible to quantify the relative importance of the predictors  (Breiman et al. , 1984; Hastie et al. , 2009). After training, the relative importance of the time, RCS co , RCS cr , and altitude predictors was 30.
Flowchart and description of KABL parameters A simplified flowchart of KABL and ADABL is shown in Fig. 6. This section focuses on the KABL parameters to introduce the sensitivity analysis made in Sect. 4. 1. The parameters of the KABL software are detailed here. algo is the applied machine-learning algorithm; possible values are -"gmm" for the EM algorithm (Gaussian mixture model) and -"kmeans" for the K-means algorithm. -classif_score is the internal score used to automatically choose the number of clusters (only used    2. when n_clusters is "auto"). See Sect. 3. 4 and Table 1 for a description of the internal scores. init is the initialization strategy for both algorithms. Three choices are available: -"random" -randomly pick an individual as the starting point (both K-means and EM), -"advanced" -use a more sophisticated initialization (kmeans++ for K-means -Arthur and Vassilvitskii, 2007 -and the output a K-means pass for EM), and -"given" -start at explicitly selected point coordinates.
For both time periods, the variables can be chosen from -RCS co , the copolarized range-corrected backscatter signal, and -RCS cr , the cross-polarized range-corrected backscatter signal. The parameters of the KABL software are written in typewriter font in the following explanation of the KABL algorithm. A netCDF file generated by the raw2l1 software needs to be provided as input data to KABL. The data, namely, the altitude vector z (size N z ), the time vector t (size N t ), and the range-corrected signals RCS co and RCS cr (N t × N z matrices), are extracted from this file. Such data are prepared to fulfill the machine-learning algorithm requirements. For each time, the n_profiles last profiles are extracted. Then, the data they contain are normalized (by removing the mean and dividing by the standard deviation); this provides a matrix X (N × p, where N is n_profiles •N z and p = |predictors| is the number of elements in the list).
Other metrics In addition to the internal and external scores, the computation time and the number of invalid values (NaN or Inf) were recorded. BLH estimates of NaN or Inf can occur when all the points of the profile are assigned to the same cluster; this reflects a faulty configuration of the algorithm. Even though these metrics do not measure how well a program is performing, they are useful to the user. All the metrics used to measure the performance of KABL are summarized in Table  1.
Sensitivity analysis of the KABL algorithm A sensitivity analysis was performed on the KABL code to identify the "best" configuration. Various KABL configurations were extensively tested on a single day, 2 August 2018, at the Trappes site, for which we have a hand-classified reference (Fig. 4 , left). The most relevant configurations were retained and tested on the 2-year lidar dataset. There are eight parameters in the KABL code (see Sect. 3. 3 for their descriptions). To assess the sensitivity of KABL to these parameters, the performance metrics (given in Sect. 3. 4) were calculated using the hand-classified BLH as Z ref and with the output of KABL as Ẑ for different combinations of input parameters. The output metrics given in Table  1  were tested using the input values given in Table  2.
8c ) and the computing time (Fig. 8d ). In this case, given appears to be the best choice. To set n_clusters, we examined RMSE (Fig. 8e ) and the silhouette score (Fig. 8f ). They indicate that the best num-bers of clusters are three and auto, respectively. We chose to give priority to RMSE because the silhouette score has very high values for two clusters, which is suspicious given the presence of a cloud and a residual layer on this day. To set predictors, we examined the silhouette score (Fig. 8g ) and the Calinski-Harabasz index (Fig. 8h ); here, co appears to be the best choice. Following this methodology, we can identify a few configurations worth trying. These configurations were tested on the 2-year dataset. The configuration used to generate the results in Sect. 4. 2. 1 is given in Table  3.