dataesr/scientific-paragraphs-categorization huggingface dataset:
Subsequently, sections were blocked in BSA solution (2% w/v), incubated with rabbit anti-phospho-p44/42 MAPK (Cell Signaling Technology Cat# 4370, RRID: AB_2315112) primary antibodies for 60 min at room temperature then incubated for 30 min with the anti-rabbit HRP linked (Cell Signaling Technology Cat# 7074, RRID: AB_2099233) secondary antibody and revealed with DAB (3,3'-Diaminobenzidine). After counterstaining with Hematoxylin, the slides are mounted and digitized using a light microscope (DMD108 Leica). The staining area percentages of Alcian blue and IHC markers were determined using image analysis software ImageJ as previously described  [44].
Cycling parameters were 30 s at 95 °C; 40 cycles of 15 s at 95 °C and 1 min à 60 °C. Gene expression was determined in triplicate in three separate experiments and normalized using the housekeeping genes ribosomal protein S29. Analyses and fold differences between the wildtype and XylT-I/cKO mice were determined with the formula 2 -ΔΔCT. The data are relative to the control values. The expression change was calculated from the values of ΔΔCT.
High-Resolution Micro Computer Tomography assessment (µCT) Femurs from 8 weeks old XylT-I/cKO and control mice (n = 6) were scanned using SKYSCAN 1272 High-Resolution X-Ray Microtomograph (Bruker, Germany). Data were acquired at 70 keV, with 7 µm cubic resolution. Three-dimensional reconstructions were generated with Microscan 1272 Brucker.
The tremendous majority of RNA genomes from pathogenic viruses analyzed and deposited in databases are consensus or "democratic" genomes. They represent the genomes most frequently found in the clinical samples of patients but do not account for the huge genetic diversity of coexisting genomes, which is better described as quasispecies. A viral quasispecies is defined as the dynamic distribution of nonidentical but closely related mutants, variants, recombinant, or reassortant viral genomes. Viral quasispecies have collective behavior and dynamics and are the subject of internal interactions that comprise interference, complementation, or cooperation. In the setting of SARS-CoV-2 infection, intrahost SARS-CoV-2 genetic diversity was recently notably reported for immunocompromised, chronically infected patients, for patients treated with monoclonal antibodies targeting the viral spike protein, and for different body compartments of a single patient.
| THE HIV PARADIGM: FROM SANGER TO NEXT-GENERATION SEQUENCING Sequencing of HIV-1 and HIV-2 RNA and DNA has been a major tool to study and manage HIV infection. To date, more than 1. 2 million sequences, including approximately 15 089 genomes, have been deposited in GenBank (https://www. ncbi. nlm. nih. gov/genbank/), and HIV genotypic resistance testing is an unavoidable tool for managing HIV treatment,  64  with several algorithms available worldwide to interpret the mutation patterns of genes encoding targets of antiretroviral therapies (e. g. , https://hivdb. stanford. edu/, https:// rega. kuleuven. be/cev/avd/publications/european_guidelines_page https://www. iasusa. org/resources/hiv-drug-resistance-mutations/, https://hivfrenchresistance. org/). Sequencing of HIV genes developed in the 1990s with the advent of molecular biology techniques applied to diagnosis in clinical virology, particularly that of plasma viral load.
| FINDING BLACKLISTED MUTATIONS Overall, the analysis of the mutations produced in circulating SARS-CoV-2 genomes shows a very large variety of mutations. In a recent work, we highlighted 22 225 nucleotide mutations observed in a library of 61 397 genomes produced in our institute and available in databases. 133  These observed mutations cannot, by definition, be lethal because lethal mutations are not followed by reproduction. We have separated these types of mutations into "hyperfertile," weakly "fertile," neutral or weakly deleterious, or absent. Other authors have classified mutations as "whitelist" mutations or "blacklist" mutations, which are never found and assumed to be lethal. 136  The analysis of lethal mutations is interesting; at the moment, such analysis can probably only be conducted in the real world through the study of quasispecies.
A database containing 494 photomicrographs of 24 Aedes spp. from which those documented with more than ten pictures have undergone a deep learning methodology to train a convolutional neural network and test its accuracy to classify samples at the genus, subgenus, and species taxonomic levels. We recorded an accuracy of 95% at the genus level and >85% for two (Ochlerotatus and Stegomyia) out of three subgenera tested. Lastly, eight were accurately classi ed among the 10 Aedes sp. that have undergone a training process with an overall accuracy of>70%. Altogether these results demonstrate the potential of this methodology for Aedes species identi cation and will represent a tool for the future implementation of large-scale entomological surveys.
Aedes selection and storage The rst WIPs reference collection of Culicidae gathers samples belonging to the Aedes genus using well-established Aedes albopictus laboratory breeds. Specimens were also selected in the ARIM collection (https://arim. ird. fr/) of IRD (Institut de Recherche pour le Développement). In addition, samples collected in natura whose identi cation was performed by trained entomologists at the time of their catch with available regional identi cation keys were also included in the database. The description of the samples used in this study is given in Table  1 , and the worldwide distribution of samples included in the study is illustrated in Figure  1. & ISO 3166-1 country code available at (https://www. atlas-monde. net/codes-iso/) 0r: the sample's origin, W: wild, C: colony, N: number of samples processed.
Image acquisition and database construction The procedures applied to capture WIPs were the same as those described for Glossina sp. WIPs acquisition  7. Wings were dissected and deposited on a glass slide, adding a small cover slide. The picture was taken with a Keyence TM VHX 1000 microscope, using the VH-Z20r camera and a Keyence VH K20 adapter allowing an illumination incidence of 10°. The High Dynamic Range (HDR) function was used for all photos. All pictures were enlarged to a maximal occupancy, making the size of the wing not a discriminating parameter for insect species identi cation. The numerical parameters settled are the same as previously described and detailed in the publication on Glossina spp  7. Collected dataset, image pre-processing, and dataset splitting for training/learning validation.
A image including 494 pictures of 24 Aedes species, is available ( gshare: https:// gshare. com/s/597f4e2f2d8206e09603). Undersampled Aedes species (less than ten samples/pictures) were discarded for training to prevent over tting. All processed images were resized to 256 and 116 pixels for width and height, respectively. Pixel values were normalized within the (0,1) range. The dataset was then prepared for k-fold cross-validation, with k=5 (Figure  1 ). K-fold cross-validation is a classic approach to evaluate the robustness of a machine learning method, including Deep Learning ones. Mainly, for this study, the dataset was randomly shu ed and partitioned into k equal-size subsets with similar class distributions. A separately learned classi er was evaluated for each subgroup using k th of the whole dataset for validation and the remaining k-1 as training data (see Figure  2  for illustration).
Training of the neural network (CNN) The data to develop the Deep Learning model includes Culicidae and non-Culicidae specimens. The whole data set is available at gshare: https:// gshare. com/s/597f4e2f2d8206e09603. The methodology used was initially developed and tested on Tetse ies  7. Brie y, the original CNN architecture MobileNet 9 , ResNet 10 , and YOLOv2 11 architecture were deemed for automatically classifying Aedes sp. with the abovementioned dataset. Compared to classic Deep Learning, ours is more compact to cope with the speci city of our dataset in terms of size; therefore, thinner image recognition and classi cation architecture were developed to consider its reduced size. The rst one is inspired by MobileNet, which takes advantage of depth-wise convolution  9. We propose to work with only one depth-wise convolution per layer of the CNN architecture to reduce the complexity and the number of extracted features.
Results Training and classi cation explored the classi er accuracy on the Aedes dataset and on datasets of Culicidae that do not belong to the Aedes genus (non-Aedes) and from mosquitoes that do not belong to the Culicidae family (non-Culicidae) as negative samples. We trained the CNN on such a combination to improve the model's accuracy. The database initially lled with a total of 494 pictures of Aedes spp. WIPs pictures. Since we cannot ll the database with all Aedes species acting as primary vectors of viruses, parasites, or bacteria with a medical interest, we rst focused on a restricted set of species (Table  1 ). Using this pictures-set, we ascertained the accuracy of the process to discriminate the Aedes genus (Meigen, 1818) from other Culicidae (Meigen, 1818) and including Anophelinae (Grassi, 1900), Culicinae (Meigen, 1818), and non-Culicidae samples members (e.
g. , Psychodidae, Glossinidae…). From our dataset and process, the automatic classi cation process accuracy is rather good and higher than 95% (Table  2 ). We also evaluated the DL process's accuracy at the subgenus level (Table  3 ). The Aedes genus encompasses 79 subgenera, with only 14 gathering species documented to act as primary vectors of viruses, parasites, and bacteria of medical interest. As mentioned in the introductory section, the subgenus Ochlerotatus was "raised" to the level of the genus in 2000  1. Considering that a large number of the scienti c literature refer to the subgenus Ochlerotatus, we refer to this taxonomic nomenclature to carry out our analysis  15. Our Aedes dataset does not represent the overall diversity at the subgenera level. Nevertheless, the computed accuracy for the identi cation process is high (>85%) for the Stegomyia and Ochlerotatus subgenera, with a classi cation accuracy of 97.
0 and 86. 7%, respectively. Still, it fails to address the Finlaya subgenus accurately. Such low accuracy might be due to the small sample size available to train and test the process for Finlaya. Therefore, efforts in implementing the database with enough specimens representative of the Finlaya and other subgenera must be made to address our methodology's accuracy in addressing the classi cation process at the subgenus taxonomic level. Overall, our methods are the rst based on deep learning with the versatility of automatically handling samples at the subgenus level, which might be of interest to speed and facilitate the identi cation of specimens of Aedes species of medical interest. Table  3 : test for accuracy (%) of the DL classi cation process at the subgenus taxonomic level Finally, the reliability of the DL model to accurately classify WIPs pictures of 10 Aedes species was calculated, and the results are presented in Table  4.
Variable level of accuracy is recorded, ranging from not accurate (0. 00%) to perfect classi cation (100. 00%). A perfect accuracy (100. 00%) is achieved for 50% of Aedes species whose WIPs pictures are lled in the dataset. More than 75% of accuracy in classi cation is recorded for 7 Aedes species, but the DL methods failed to assign 2 Aedes species correctly (Ae. echinus and Ae. mariae) (Table  4 ). Even if populations of Ae. albopictus bear a high variability independently of whether the populations are native or invasive  16, 17  , we record a high classi cation accuracy (Table  4 ). The DL approach based on WIPs can manage the diversity of specimens sampled in two distant geographic areas, France and La Réunion. Aedes aegypti exists in two subspecies or forms; the domestic ecotype, Ae.
aegypti aegypti found outside Africa and the presumed ancestral Ae. aegypti formosus occurring in sub-Saharan Africa in sympatry with Ae. aegypti aegypti in some ecologies  18, 19. Our samples of Ae. aegypti originating from sub-Saharan Africa were not identi ed at the subspecies level. They may be a mix of the two ecotypes with subtle variations in their WIPs. This might be the underlying factor triggering the lower accuracy (72. 7%) of the identi cation process of Ae. aegypti in our dataset, as compared to the accuracy recorded for Ae. albopictus (95. 8%) classi cation. This deserves to be investigated. Nevertheless, the overall calculated speci c recognition of samples belonging to Aedes remains satisfactory. Misclassi ed pictures. Inspection of the machine learning model weaknesses helps identify underlying problems.
albopictus and Ae. aegypti  33  and on the fourth instar larvae  34  but with higher accuracy for late larval instar  35. Its e ciency for eld-trapped or colony-reared insect adults is limited. It includes species having (Ae. albopictus, Ae. vigilax, Ae. vexans, Ae. notoscriptus, Ae. polynesiensis) or not (Ae. fowleri, and Ae. dufouri) a medical importance  [36] [37] [38] [39] [40]. However, the interoperability of the MALDI-TOF methodology requires a standardized procedure in the conservation of samples and the choice of the mosquito body part of adult specimens  41. For MS identi cation, standardization of procedures for preparation and reproducibility between instruments and homemade databases will be desirable  [42] [43] [44] [45]. Contrary to MALDI-TOF or DNAbarcoding methodologies, our method is not designed to identify Aedes at larval stages.
Nevertheless, a survey on adult mosquitoes is an alternative to larval surveys because it addresses the vector life stage responsible for transmission and thus adds information on vector density 46. Infrared spectroscopy (NIR and MIR) can detect changes in the mosquito cuticle by quantifying light absorbed  47. The discriminative capability of such methodology at the species level has been investigated on a restricted number of Aedes species having a medical interest (Aedes aegypti, Ae. albopictus, Ae. japonicus, and Ae. triseriatus)  48. The main interest of this methodology relay on its capacity to grade the age of natural Anopheles spp. populations when coupled with a Deep learning approach  49. DNA barcoding is also an alternative to morphological identi cation methods. A quest for "Aedes" as a keyword in the barcode of Life system (BOLD) database indicates 27,458 records with sequences representative of 328 species (http://v4.
The competency to accurately classify Aedes samples at the subgenus level is of interest to medical entomologists and public health services since a restricted number of Aedes subgenus encompass species of medical interest. In this study, among Aedes species accurately classi ed (Accuracy >75%), four are of medical interest (Ae. albopictus, Ae. aegypti, Ae. luteocephalus, and Ae. polynesiensis). Therefore, we can envision that a database of WIPs encompassing proven Aedes vectors at a global or regional scale would allow remote territories to address the vector status of a specimen. Increasing the area of vector surveillance is of high interest, even at a continental scale. Community science relying on the participation of non-expert people was therefore applied for mosquitoes  52, 53. Unfortunately, the quality of entomological data can be poor in terms of identi cation accuracy.
This is of interest in areas where large-scale and/or proactive surveys must be performed, like the Paci c area, where more than 20,000 islands must be surveyed  41, 55. This situation would make exogenous species introduction unnoticed. Nevertheless, the methods need now to be further investigated by evaluating, even qualitatively, whether the proposed approach could be usable in real-life scenarios regarding several important criteria: cost (including infrastructure, material, training, and cost associated with technically skilled personnel), computational resources, analyzing time, sample destructiveness, diversity, and taxonomic level (species, sibling species, cryptic species, sub-species) of the classi cation. All these will increase vector Database availability and code availability.
Acknowledgments. We thank  Pr. P. Marty  and  P. Delaunay  ( CHU Nice ) for gaining access to the microscopic facility of the  CHU. Dr. D. Fontenille  ( UMR MIVEGEC, Montpellier, France ) for his support and fruitfully scienti c discussions on medical entomology aspects. Mr JP Commes , former  CEO  of  2CSI , for his enthusiasm and advice on the digital aspect of the project.
surveillance's programmatic capability and capacity with minimal training and cost. Declarations Authors contribution statement. Conceptualisation DeS, AC, MA, AH, CSC, OR, MS. Data acquisition DeS, AC, MS, AH, DaS. Database construction DeS, DaS, AH, PJ, MS, OR. Sample collection and arthropod management PB, FMD, LCG. Project management DeS, AH, CSC. Writing rst draft AH, DeS, AC. Writing and editing DeS AC, MA, CSC, PB, AH, FMD, LCG.
of medical interest not in the database Aedes (Aedimorphus) cumminsii Aedes (Aedimorphus) dalzieli Aedes (Aedimorphus) dentatus Aedes (Aedimorphus) hirsutus Aedes (Aedimorphus) ochraceus Aedes (Aedimorphus) vexans Aedes (Catageionyia) minutus Aedes (Catageiomyia) argenteopunctatus Aedes (Catageiomyia) tarsalis Aedes (Downsiomyia) harinasutai *Medical interest according to the WRBU database (https://wrbu. si. edu/vectorspecies? eld_family_target_id=1194&title=& eld_mt_products_tags_target_id=& eld_pathogens_target_id=& eld_geographic_locations_target_id=&items_per_page=30) and Wilkerson et al. , 2015 3.
Data analysis All analyses and results in this present work were based on eight years of data. Data were analysed using Selegen Reml/Blup (Federal University of Viçosa, Minas Gerais, Brazil) and the SPSS computer programme. i. Analysis of variance (ANOVA) was performed on DBH, height and survival at both sites for both provenances and families. If the ANOVA was signi cant, Tukey's test was subsequently performed to identify different groups of families in the provenances. Clustering dendrogram analysis and representation were performed to illustrate the above results of the ANOVA between different sources and progeny and thus identify similar groups regarding the growth characteristics (height, DBH and volume). The method used was the agglomeration Ward's and the Minkowski distance since all variables were continuous.
To select the variable with the highest in uence on the productivity of the progeny, the Pearson correlation coe cient (PCC) between the volume and the height or DBH was used, depending on the variable with the highest PCC. iii. The estimates of the variance components and genetic parameters were based on the restricted maximum likelihood method (REML) of the individual progenies from statistical model 5 (Test half-sib progeny, with a randomized block design. This was done with the aid of the computer program SELEGEN-REM / BLUP (Resende 2014). The calculation of variance components was obtained by the formulas proposed for  (Resende 2007 ).
Acknowledgments The authors are grateful to the company  Florestas de Niassa, Lda ; the engineer  José Bernardo Manteiga , engineer  Custódio da Conceição  and  Misters John Mkumbira ,  Hélio Raul ,  Abudo Issufo ,  Candawele Abudo  and  Agostinho Adriano  for their assistance and collaboration during data collection.
Following incubation with the appropriate secondary antibody (anti-rabbit or -mouse IgG linked to horseradish peroxidase; 1:5000; Cell Signaling; cat #7074 and 7076, respectively), the immunoreactivity of selected proteins was detected by ECL reagents (Amersham, Buckinghamshire, UK) and signal of bound antibody was visualized with autoradiographic films (Amersham ECL Hyperfilm). Each band of interest was quantified by densitometric scanning (GS-800 Imaging Calibrated Densitometer, Bio-Rad), and percent changes in immunoreactivity for each marker were calculated for each rat with respect to controlmale samples (100%) in various gels (each sample was run at least 2-3 times in different gels), and the mean value was used as a final estimate. Data were not normalized to any protein, since β-actin analysis served as a loading control (i.
Data statistical analyses GraphPad Prism, Version 9. 5 (GraphPad Software, USA) was used to analyze and plot all graphs following the guidelines in experimental pharmacology for displaying data and statistical methods  [40]. Results are reported as mean values ± standard error of the mean (SEM), and individual symbols for each rat are shown within bar graphs. Parametric tests were used for statistical comparisons, since assumptions for normality of data distribution and homogeneity of variance (in case of analysis of variance) were met (i. e. , D'Agostino-Pearson normality test). Three-way repeatedmeasure (RM) ANOVAs were performed when analyzing potential changes in body weight (g), total fluid (sum of liquid consumed from both bottles independently of its contents and expressed in ml) and water (volume consumed from a single water bottle through the course of the experimental procedures and expressed in ml).
The independent variables of study were Sex, Bottle Choice (Ethanol vs. Water) and Time of Analysis (Day or Week). When measuring ethanol preference (%) and ethanol dose consumed (g/ kg) data were analyzed with two-way RM ANOVAs (independent variables: Sex and Time of Analysis), since all rats were exposed to the choice of one ethanol bottle. Cumulative ethanol load was compared between male and female rats with an unpaired two-tailed t-test. Finally, the regulation of hippocampal markers of induced neurotoxicity (i. e. , neural progenitors and/or cell fate markers) was performed with a two-way ANOVA (independent variables: Sex and Experimental Group). Individual values were normalized to control male rats, to estimate the % magnitude of change. Tukey's or Sidak's multiple comparisons tests were performed for post hoc pair-wise statistical comparisons when appropriate.
, 2021; Yakimov et al. , 2022). In this context, and as if to confirm this assumption, a direct BLASTn search  (Altschul et al. , 1997)  was carried out against the entire collection of nr/nt nucleotides in the NCBI database (August 10, 2022) using the more dissimilar sequences (discontiguous megablast) option. Only one 91. 11% identity match, namely the spacer 39 against a Haloplanus sp. GDY1 plasmid (CP098517), was found. In addition to CRISPR/Cas system, Ca. Nanohalococcus SVXNc encodes another defense mechanism courtesy of the type II restriction-modification (RM) system, consisting of a Dcm-like DNA methyltransferase (SVXNc_0752) and a restriction endonuclease Mrr (SVXNc_0757). These genes do not form an operon, and both are expressed in the SVXNc transcriptome (Supplementary Table  S1 ).
We reanalyzed the SPEARE protein of Ca. Nanohaloarchaeum antarcticus and found no endonuclease domain; instead, as in the cases of SVXNc_0300 and BNXNv_0298, the protein contains a well-conserved VirB4/TraG-like ATPase domain. Thus, the giant proteins in our nanohaloarchaea, along with the SPEARE proteins of Ca. Nanohaloarchaeum antarcticus and Ca. Halopetraeus SG9  (Oren and DiRuggiero, 2015) , appear to be a part of the type IV secretion system, and further supports that it is a secretion system comes from the fact that the gene immediately downstream of the SVXNc_0300 giant protein gene (as well as BNXNv_0298) encodes another AAA+ ATPase (SVXNc_0299 and BNXNv_0297) of the VirB4/TraG-family. In addition to the AAA+ ATPase domain, the latter protein contains an N-terminal domain (PF03135.
FISABIO also performed the quality assessment and the sequence joining (forward R1 and reverse R2). Quality assessment was performed with the PRINSEQ-lite program using the following parameters: Min_length:50 bp trim_qual_right:30 bp trim_qual_typ:mean, trim_qual_window:20 bp. Forward and reverse reads were joined by the FLASH program applying default parameters. All two genomes were assembled as single circular chromosomes and analyzed as described previously  (La Cono et al. , 2020; Sorokin et al. , 2021).