dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA sequencing RNA sequencing was performed at the Institut du Cerveau (IGenSeq platform -Paris). Samples were randomized and libraries were generated from total RNA using TruSeq Stranded mRNA Kit (Illumina, USA). Paired-end sequencing (2 × 75 bp) was performed on a Nextseq500 instrument (Illumina, USA). Quality of raw reads was assessed with FastQC49, trimming was done with Trimmomatic39 to cut adapters and exclude reads <40 bp. The processed reads were aligned on human reference genome hg19 with Star v 2. 5. 3a (Dobin et al. , 2013). RSEM  (Li and Dewey, 2011)  was used to obtain expected read counts and FPKM (Fragments Per Kilobase of transcript per Million mapped reads), which eliminated the influence of different gene length and discrepancy of the library size. Samples types, RNA isolation and sequencing methods were identical for samples from individuals with BD and HC, therefore these data were used for the following analyses  (Zhao et al.
Genes selection FPKM data for the following circadian genes were selected from the dataset: ARNTL1 and ARNTL2 (Aryl hydrocarbon receptor nuclear translocator-like protein), BHLHE40 and BHLHE 41 (Basic helix-loophelix family, member E), CLOCK (Circadian Locomoter Output Cycles Protein Kaput), CRY1, CRY2 (Cryptochrome Circadian Regulator), CSNK1D, CSNK1E (Casein Kinase 1 Delta and Epsilon), DBP (D-Box Binding PAR BZIP Transcription Factor), GSK3B (Glycogen Synthase Kinase 3 Beta), NR1D1 (Nuclear Receptor Subfamily 1 Group D Member 1), PER1, PER2, PER3 (Period Circadian Regulator), RORA (RAR Related Orphan Receptor A), NPAS1 (Neuronal PAS Domain Protein 1) and TIMELESS (Timeless Circadian Regulator)  (Kavakli et al. , 2022). We added to this list CIART (Circadian-Associated Transcriptional Repressor) because of its recent characterization as a potential contributor to CR alterations  (Yang et al.
Validation using PCR In order to validate the differential expression of genes identified in the RNA-seq analyses, RT-qPCR were performed on the same samples. One μg of total RNA was reverse transcribed using the iScript™ Reverse Transcription Supermix following the manufacturer's protocol (Bio-Rad laboratories, France). One patient and one HC with no RNA left could not be included in the PCR experiments. Commercially available Prime PCR primers targeting identified genes from RNAseq data analyses were purchased from BioRad laboratories (France). Based on previous results, prime PCR primers targeting GUSB and TBP were used as reference genes  (Curis et al. , 2019) , as described in the GeNorm method the geometric mean of the two reference genes was used as a normalization factor. SsoAvanced Universal SYBR Green Supermix (Bio-Rad laboratories) was used for amplification following the manufacturer's instructions.
Statement of ethics This research protocol (Clinical Trials Number NCT02627404) was approved by the French medical ethics committee (Comité de Protection des Personnes (CPP) -IDRCB2008_AO1465_50 VI -Pitié-Salpêtrière 118-08) and carried out according to the approved guidelines. Written informed consent was obtained from all participants. Author contribution BE is the principal investigator of this study and obtained the funding. EM performed the RNA-seq experiments. JG performed the QC and extraction of the RNA-seq data. CC performed the cellular experiments, RNA isolation and qPCR validation. BE and CMC performed the first exploratory statistical analyses and wrote the first draft of the manuscript. BE, CMC and GG reviewed the statistical analyses and wrote the final draft of the manuscript.
The main disadvantage of dubbing is AV asynchronies. In their study,  Llorach et al. (2022)  described the recording set up and the selection process of the shortest asynchronies (ideally, less than 240 ms, which represents the upper acceptable limit before deleterious effects on speech intelligibility  (Grant et al. , 2003). The scripts and guidelines to record, process, and cut the video material automatically can be found in the github repository (https://github. com/gerardllorach/audiovisualdubbedMST/). Here, we reproduced the methodology (creation and validation) of  Llorach et al. (2022) , and compared the results of the two studies in terms of AV training effect, test-retest variability, lipreading ability in the visual only (Vo) condition, and audio-visual gain.
Dubbing the auditory material and recording the videos The French version has been validated in 2012  (Jansen et al. , 2012)  and is largely used in clinical practice. The initial corpus included 280 sentences. We manually discarded the sentences for which the original audio recording was distorted. From the remaining sentences, we created balanced lists of 20 sentences, i. e. , each word appeared no more than twice in a list, and no sentence was repeated. Forty-five lists were created based on a final set of 150 sentences. The MATLAB scripts to create these lists can be found in the github repository (see Statements). The same female speaker who originally recorded the auditory French version participated in the video recordings. The videos were recorded in an anechoic room, transformed into a film studio (Figure  S1 ).
This visual guide sequentially displayed three vertical bars stemming for the pure tones followed by the acoustic waveform of the sentence being played. The waveform changed from blue to red as the signal was being reproduced (Figure  S1 , left side). During the recording procedure, the sentences to play were selected through a user interface in MATLAB. Several takes could be recorded for the same sentence. These scripts to record the material can be found in the github repository. The resulting video files, which contained the visual speech, the original sentences of the French MST and recorded speech, were processed automatically via scripts. During the recording session, the camera recorded continuously i. e. , a video file could contain several sentence takes. Each sentence take contained an identification signal, three pure tones, and four sentence repetitions, which were present in one of the audio channels of the video files.
Acknowledgements: We thank  Marie-Amélie Bizouard  and  Gregory Gerenton  for their technical help,  Jan Wouters  and  Volker Hohmann  for relevant discussions. This work was funded by  Fondation Pour l'Audition   FPA RD-2020-10  (for LLR, TD, LHA, DL) and by the  Deutsche Forschungsgemeinschaft  (for GL,  DFG ,  Cluster of Excellence   EXC 1077/1  " Hearing4all ", and  SFB1330 Projects B1  and C4). Statements: The video recordings for the female French Matrix Sentence Test will be given free of charge upon request to the corresponding author ( Diane Lazard ) if the new users certify they will cite the present article and the following:.
| Hadza data collection Following the same protocol as above, n = 11 Hadza males aged 18-65 (mean age 34. 5) drank DLW and collected urine specimens to measure TEE  (Pontzer et al. , 2015). Hadza urinary testosterone was analyzed at the University of New Mexico with an in-house enzyme immunoassay also based on R156/7 (C. Munro, UC-Davis)  (Muir et al. , 2001). Testosterone in urine samples was first deconjugated by treatment with betaglucuronidase (Helix pomatia, Calbiochem, <2% aryl-sulfatase) and ether extraction. The interassay CVs were 5. 9% for the low and 11. 0% for the high controls; within plate CVs were 6. 2%. Specific gravity corrected urinary testosterone was treated as above to create summary measures.
| Accelerometry data Tsimane DLW study participants also wore an accelerometer (Actigraph wGT3X, Actigraph LLC, Pensacola, FL) to quantify physical activity level during the period of TEE measurement. Participants removed the accelerometer to bathe, but otherwise wore the device at all times while engaging in routine activities. Participants wore the accelerometer for 2. 5 days, on average. Daily step counts were used as a proxy to estimate physical activity (see  Gurven et al. , 2013  for additional details). Height was measured with a portable stadiometer (Seca 213), weight was measured on a Tanita BC1500 Scale, and fat free mass was determined using isotopic dilution; range and median values for anthropometrics are shown in Table  1. Hadza participants did not wear accelerometers.
Due to the logistics, time, and resource-intensive nature of accelerometer data collection, physical activity data were only measured for a subset of the days (mean 2. 5 days) over which the DLW data was collected. While DLW is minimally invasive, wearing an accelerometer 24-h a day for multiple days is difficult, and collecting a full week of accelerometry data would be unacceptable to most participants; it should be noted that physical activity is difficult to measure with no gold standard. While accelerometry is an objective measure that captures daily activity without relying on participant recall, it may not accurately assess all types of physical activity. Additionally, information on cycle day was not collected for Tsimane women (though it should be noted that half of the women in this sample were post-reproductive).
Data Collection and Methodology Data collected for our research has two origins: the first part consists of 59 educational resources produced by pre-service teachers during workshops about the teaching of informatics in France and Switzerland. Those resources, dedicated to activities in educational robotics, were part of a more important corpus composed during the first phase of our research. More details are given about them in  [4]. The second part of the data consists of 61 online resources on the same topic from more experienced teachers. Our resources consist of pedagogical scenarios serving as preparation for a school lesson with one or more activities: teachers describe the learning objectives of the lesson, the roles of teachers and pupils, a schedule and the material needed. Some resources also contain feedback about the difficulties encountered, either anticipated or experienced.
Design and procedure Based on estimates from the National Statistics Institute, participants were recruited according to a four-stage stratified sampling based on: (a) the proportions of girls and boys in affected settings, (b) the demographic weights of the rural and urban areas affected by the outbreak, (c) the ages of participants in affected settings (12-17 years) and (d) the school enrolment rate. Given the lack of data on the population of the Equateur Province, this four-stage stratified sampling was used to ensure adequate gender, urban/ rural and age representation of participants and to consider that not all adolescents are enrolled in school. The baseline survey (T 1 ) was conducted from 11 March to 23 April 2019, 7 months after the end of the 2018 EVD outbreak. The follow-up survey (T 2 ) was conducted about 17 months later, from 12 August to 26 September 2020.
Two psychologists coordinated the research assistants in the recruitment of participants. During T 1 , we collected data to facilitate the conduct of the follow-up survey (e. g. schools, addresses). However, many times addresses were non-existent. So, we noted details about areas and location points (e. g. school, church, small stores closest to the house, community leader) to help the coordination of the recruitment. T 2 was conducted while the province of Equateur was encountering cases of both EVD and COVID-19. All the research assistants wore personal protective equipment, washed their hands after each interview and maintained physical distance. Back-translation methods were used to crossculturally adapt survey questionnaires into Lingala. The entire translation process was carried out by a panel of seven professors from DRC universities in linguistics, psychology, psychiatry and education.
Statistical procedure Data from the longitudinal observations were analysed. Missing observations of the longitudinal data were imputed using multiple imputation procedures. We first computed descriptive statistics to estimate the prevalence of severe symptoms of depression and anxiety and of comorbid depression-anxiety symptoms, according to the sociodemographic characteristics of the sample, using Pearson's chi-squared and Fisher's exact tests at baseline and follow-up. The prevalence of comorbid depression and anxiety was estimated using contingency tables for participants who had severe symptoms of both depression and anxiety. The comorbidity group was compared with a group of participants who had only anxiety, only depression, or no severe symptoms of depression or anxiety. The prevalence rates and mean differences across time were tested using McNemar's test and paired t-tests, respectively.
Standardised scores for continuous variables were used for the GEE analyses. Sociodemographic variables, including gender, disability, residence area, having a disability, parents' education and employment status, were time-invariant predictors; hence, the baseline variables were used for all the analyses. We also used two models to test the moderating effect of social support on the associations of Ebola stigmatisation and exposure to Ebola with anxiety and depression after controlling for sociodemographic factors. We used IBM SPSS Statistics 28 for all statistical analyses. The prevalence rates of anxiety symptoms at baseline (χ 2 = 3. 73, P = 0. 054) and follow-up (χ 2 = 0. 53, P = 0. 465) were not significantly different statistically between boys (T 1 = 32. 10%, T 2 = 26. 34%) and girls (T 1 = 40.
The diversity of edible grasshoppers worldwide remains largely undocumented due to inconsistent reporting of ethno-and scientific species, and paucity of reference DNA sequences. This study uses traditional knowledge and scientific methods for identification of ethnospecies, taxonomic species, and phylogenetic groups that likely represent genetic species. Community members in the Limpopo province of South Africa assisted with specimen collection (n = 176) and identification of 34 ethnospecies (i. e. vernacular names given by the local communities). Scientific taxa were identified by alpha taxonomy and DNA barcode sequence queries on BOLD Systems and GenBank. Genetic groups likely representing distinct species were determined by phylogenetic analysis and intragroup p-distances. Acrididae dominated the dataset with nine subfamilies and 17 genera represented.
Database queries yielded high sequence similarity/identity matches (> 95%) for only 58. 6% of the sequences on BOLD and 37. 1% on GenBank, thus highlighting the low representation of African Orthoptera in public repositories. Phylogenetic analysis and pairwise distances revealed 36 genetic groups likely representing distinct species. Comparison of the results obtained from the different sources of information showed incomplete overlap, and numerous inconsistencies. Despite the difficulties, the overall diversity of edible grasshoppers traditionally utilized in South Africa is higher than previously reported. This work may inform future studies for documenting edible insects in Africa and other world regions.
All African species from these and related genera were then checked to identify specimens using available generic revisions. Finally, specimens were compared to those from a reference collection in Museum National d'Histoire Naturelle (Paris) and Musée Zoologique de la Ville et de l'Université de Strasbourg. When the state of the samples did not make it possible to identify the species with certainty (absence of hind legs, only females available, etc. ) a tentative identification was proposed based on the distribution of possible species. If only one possible species is known from the area, the tentative identification is marked as probable. If several species are possible, but one is more frequent according to our expertise, the identification is indicated as possible. All DNA barcode sequences generated in this study were queried against the National Center for Biotechnology Information (NCBI) database (www.
ncbi. nlm. nih. gov) using BLASTn, and against the Barcode of Life Data (BOLD) Systems database (https://www. boldsystems. org/) using the Identification tool (Species Level Barcode), both accessed on November 5, 2021. The top match for each sequence and its percentage of sequence similarity (BOLD) and percentage of sequence identity (BLASTn) was recorded. For both BOLD and BLASTn searches, the threshold of > 95% was considered a reliable match. A large proportion of the sequences did not yield a match > 95%; in those cases, when at least two of the three identifications (BOLD, BLASTn or alpha taxonomy) were consistent, phylogenetic clustering patterns were used for inferring which taxonomic group was potentially correct (see "Determination of genetic groups likely to represent distinct species" section).
In cases where taxonomic identifications (morphology, BOLD and/or NCBI) resulted in inconsistent subfamily identification, the most likely subfamily was inferred based on phylogenetic clustering patterns (GR10 and OR95 in G1, OR76 in G2, OR24 in G3, OR102 and OR105 in G6, MBO01 and MBO04 in G10, MAS01 in G15, MBO03, MBO05 and MBO06 in G24), or labelled as undetermined (all specimens in G19 to G20 and G27 to G28) (Fig. 4 ). We adopted a conservative approach for appending scientific taxa to DNA sequences for the purpose of deposit on GenBank which aimed at the highest taxonomic level that could be attained considering alpha taxonomy and phylogenetics. For example, G27 and G28 were deposited as Orthoptera, and sequences in G7, G8 and G9 were all deposited as Morphacris fasciata. In other cases, alpha taxonomy was unambiguous and took priority over database searches: for example, sequences in G35 were identified by alpha taxonomy with confidence as Zonocerus elegans elegans although database searches resulted in Z.
BOLD and NCBI sequence queries The new DNA barcode sequences (n = 116) were queried by BLASTn on the NCBI database and on the BOLD Identification Engine (Table  S4 ; Table  S5 ). BOLD queries returned matches with high sequence similarity (> 95%) for 59% (68/116) of the sequences (Table  S4 ). Of these, 83. 8% (57/68) of the sequences were identified to genus level and resulted in 15 genera. Additionally, 51. 5% (35/68) were identified to species level and resulted in 11 species. NCBI queries returned matches with high sequence identity (> 95%) for 37% (43/116) of the sequences (Table  S5 ). Of these, 100% of the sequences (43/43) were identified to genus level and resulted in 11 genera. Additionally, 58. 1% of the sequences (25/43) were identified to species level, resulting in seven species.
Overall, BOLD yielded more matches with high sequence similarity (> 95%) than NCBI. Although the NCBI queries yielded a greater proportion of high sequence similarity matches to genus and species level, BOLD queries resulted in an overall higher number of matches with high similarity to genus and species level, as well as a higher number of genera and species. Therefore, BOLD seems to be a more comprehensive database in the case of these particular Orthoptera. Furthermore, we detected some taxonomic inconsistencies between BOLD and NCBI: for example, Parapleurus alliaceus was classified in Oedipodinae on BOLD but in Acridinae on NCBI, and Coryphistes ruricola was classified in Acridinae on BOLD but in Catantopinae on NCBI. Additionally, there were no records on the NCBI Taxonomy Database for three genera (Plegmapterus, Pycnodictya, and Sudanacris) and six species (Acrotylus trifasciatus, Catantops momboensis, Cryptocatantops haemorrhoidalis, Locusta pardalina, Rhachitopis nigripes and Sphodromerus undulatus) present in our dataset.
Overlapping of specimen identification methods This study aimed to document the diversity of edible grasshoppers in South Africa using four sources of information: (1) identification of ethnospecies by members of local communities, (2) morphological identification based on alpha taxonomy, (3) genetic identification by querying the new DNA barcodes against public sequence databases, and (4) genetic identification by phylogenetic clustering and estimates of genetic divergence. A summary of the results obtained from the different sources of information is displayed in Fig. 5. Of the 116 specimens for which DNA barcodes were generated, 100 specimens were identified to subfamily level based on alpha taxonomy and sequence queries on BOLD and GenBank (Table  S1 ). Of these, 14% (14/100) were inconsistently identified to subfamily.
Overall, the overlap between genetic groups, ethnospecies and taxonomic species was imperfect, as several genetic groups included specimens identified in different ethnospecies and scientific taxa (e. g. G1, G10 and G25). In some cases, taxonomic identification was not concordant within a given genetic group (e. g. G27), with sequence matches on BOLD and/or GenBank (e. g. G1), and the two databases also discorded in some cases (e. g. G14). However, it is remarkable that almost half of the ethnospecies for which DNA barcodes were generated (11/24) overlapped with an exclusive genetic group and others, such as Nwa-Rhuda fell in closely related genetic groups or belonged in the same subfamily (e. g. Tatakgope in Cyrtacanthacridinae). Some cases of inconsistency among ethnospecies could be explained by the fact that ethnospecies names differ based on the language of the community and geographic area, and even households within communities that share the same language may use different names for the same grasshoppers (Z.
Challenges in DNA barcoding Compared to our previous experience in other insect taxonomic groups (e. g. Powell et al. 2019; Langley et al. 2020; Ajene et al. 2020; Egan et al. 2021; Hlaka et al. 2021) , DNA barcoding of Orthoptera was particularly challenging. PCR amplifications and high-quality sequences were only consistently produced when newly designed primers with higher specificity relative to universal primers were used. Even so, several of the genetic groups 1 3 still showed some ambiguous positions in the electropherograms due to overlap of two different nucleotides. For example, the sequences obtained using the new PCR primers for Z. elegans, which were designed based on the complete COI sequence from the mitogenome of a representative of the species, still showed a few ambiguous positions; however, no premature stop codons were detected in any sequence in the whole dataset.
Challenges in NCBI and BOLD queries Although NCBI yielded more matches at genus level than BOLD, sequence identity was generally lower suggesting that even though NCBI returned more matches, these did not necessarily allow for correct taxonomic identification. In a study where the two databases were compared, NCBI was shown to be more accurate for insects; however, this result was not found to be statistically significant, and the study was not conducted specifically in Orthoptera  (Meiklejohn et al. 2019). Despite these limitations, both the previous study and ours evidenced the low percentage (< 70%) in  Meiklejohn et al. (2019)  of genus and species level identification when performing sequence queries on BOLD and NCBI. Moreover, our low rate of identification success by sequence queries was likely impacted by the poor representation of African Orthoptera in both databases.
Misidentification of specimens, taxonomic confusion and clerical mistakes negatively affect DNA barcoding as an endeavour to record and document biodiversity, as the scientific name appended to the deposited sequences forms the foundation of databases. Thus, to minimize potential problems, public databases should require metadata such as photographs and descriptions of morphological characteristics of the uploaded specimen, along with the location where it was found (Friedheim 2016). While BOLD allows for recording metadata, GenBank is purely a sequence repository  (Meiklejohn et al. 2019). Additionally,  Friedheim (2016)  proposed that detailed information about ambiguous base calls that were edited based on interpretation of electropherograms should be disclosed  (Friedheim 2016).