dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, a propensity score assessed by stratifying the score in five quintiles in marginal efficacy estimates obtained by regressing the two outcomes, performed and checked as previously described in the perfect speech model, was used to obtain the best balancing of covariates in compared subjects. The predicted average BM change and average  probability of perfect speech were also estimated for every combination of variables: genetics, diagnosis, BMpre, and age (< 7 years vs ! 7 years). Seven years was used as cutoff because this is a cutoff crucial age to achieve a good result, as we demonstrated in another paper. 38 The significance level used in the analysis was 5%. The objectives of the study were strictly exploratory; no formal statistical power analysis was performed. No missing data were observed.
84%, resolved with a further pharyngoplasty with good results. On the other end, pharyngoplasty as a first-line treatment has not registered any failure, but we had a patient with a diagnosis of OSA. To obtain a prediction of outcomes that can be useful in surgical decision-making, many statistical tools can be employed. There are studies in the literature dedicated to predicting features associated with poor surgical outcomes. They have shown that age, severity of VPI, history of cleft palate, and syndromic association may affect outcomes, but without consistent results across studies. 26, 42, 43 Our study aims to predict features associated with a perfect speech result after surgical treatment of VPI. To achieve the probability of good outcome, from logistic regression of our dataset, we calculated the marginal analysis to obtain a generalization of the pattern of features necessary to achieve perfect postoperative speech.
Conclusions Given the limited literature background of speech surgery, we compared two surgical techniques for the management of VPI and built a perfect speech patient profile with the aim to provide a decision-making tool to surgeons for a postoperative forecast of the surgical result. More multicentric studies with larger datasets and the possibility to modulate specific variables are necessary in the future to better understand this surgical variability. The progress of data management could help to build a multidisciplinary VPI community across institutions to share standardized data. This broad collaboration could help to personalize treatment and select the best procedure for the each patient.
39% (57 154) of transcript annotations were retained after computing a filtration step based on a minimum annotation score (electronic supplementary material, table  S1 ). The assembly and annotation metrics are shown in table 1. Raw read sequences coming from RNA-seq were deposited in the NCBI BioProject database (https://www. ncbi. nlm. nih. gov/bioproject/) with links to BioProject accession number PRJNA749800. Individual SRA numbers are listed in electronic supplementary material, table  S2. 2 ). These first results indicate that the transcriptomic profile of MtrBTN2 differs from the transcriptomic profile of healthy Mytilus haemocytes, irrespective of their species or geographical origin. We investigated differentially expressed transcripts in MtrBTN2 cells (CAN1-3) by comparing their abundance with M.
edulis (EDU1-3) and M. trossulus haemocytes (TRO1-3) using DESeq2 v1. 34  [36]. These comparative results were then validated by RT-qPCRs (see § §4. 3 and 4. 5) targeting 17 key-gene transcripts and confirmed in 10 additional haemolymph samples (CAN7-11, EDU7-11) (see electronic supplementary material, table  S3 , figures S1 and S2). A total of 4356/138 071 transcripts were significantly differentially expressed; 2632 were more expressed and 1724 were less expressed in MtrBTN2 cells than in haemocytes (see log2 fold change (Log2FC) and adjusted p-values (FDR) in electronic supplementary material, table  S4 ). We further validated the significance of differential expression for 4254 transcripts by running QuantEval  [37] , which builds connected components from the assembled transcripts based on sequence similarity and evaluates the quantification results for each connected component (electronic supplementary material, tables S4 and S5, figure  S3 ).
De novo transcriptome assembly and functional annotation Raw reads were processed using RCorrector v1. 04 (https:// anaconda. org/bioconda/rcorrector/files?version=1. 0. 4) with default settings and -rf configuration to correct sequencing errors  [122]. Uncorrectable reads were then removed using the FilterUncorrectabledPEfastq tool (https://github. com/harvardinformatics/TranscriptomeAssemblyTools/blob/master/Fil-terUncorrectabledPEfastq. py). The output reads were further processed for adapter removal and trimming with TrimGalore! v0. 6. 4 (https://github. com/FelixKrueger/TrimGalore) with default parameters and -q 28, -length 100. Ribosomal RNAs potentially still present after polyA capture were removed by alignment against the SILVA Ribosomal database with Bowtie2 v2. 4. 1  [123]. Read quality was assessed before and after read trimming using FastQC v0.
0. 1 (https://github. com/TransDecoder/ TransDecoder/wiki) was run on these contigs to identify CDSs with a minimum length of 90 amino acids. Finally, CDS sequence redundancy was reduced using CD-HIT-EST v4. 8. 1 (https://github. com/weizhongli/cdhit/wiki) with the following options: -n 6, -c 0. 86, -G 0, -aL 1, -aS 1. We assessed the quality of the assembly using several tests. The assembly was first analysed using TrinityStats, and the final pantranscriptome completeness was estimated using BUSCO v5. 1. 1 [128]  against the conserved single-copy metazoan gene database (n = 978). Finally, filtered reads were mapped back to the filtered pantranscriptome to assess the individual mapping rate with Bowtie2 v2. 4. 1 [123]. To conclude that our pantranscriptome strategy was reliable, we performed a redundancy analysis (RDA) on the normalized read counts to analyse the impact of explanatory variables ('cell type': haemocyte/MtrBTN2 cells, 'species': trossulus/edulis, 'environment': Barents Sea/English Channel) on response variables (gene expression), followed by an ANOVA-like permutation test (nperm = 999, model = 'full') (https://cran.
r-project. org/web/packages/vegan/vegan. pdf ) (electronic supplementary material, figure  S7 ). Both 'cell type' and 'environment' were retained as significant explanatory variables ( p < 0. 05). This confirms that the inclusion of healthy M. edulis mussels in the analysis is necessary to subtract the environment effect (English Channel versus Barents Sea). For functional annotation, the transcripts were searched against the Uniprot (Swissprot and TrEMBL) protein reference database [129] using PLASTX v2. 3. 3 algorithm with an e-value cutoff of 1. 0 × 10 -3 [130]. Domain prediction against the Inter-Pro database [131] was carried out with InterProScan v5. 48-83. 0. Both results were combined and we used the OmicsBox program v2. 0  [34, 35]  to assign GO terms to the annotated sequences with an e-value hit filter of 1 × 10 -3 , an annotation cutoff of 55, a HSP-Hit coverage of 60%, and an evidence code of 0.
Quantification of transcript expression To investigate differential expression between MtrBTN2 cells and M. edulis-M. trossulus haemocytes, reads were first aligned to the pantranscriptome using SALMON v1. 6 [133]. DETs were identified using DESeq2 v1. 34  [36]  with a FDR < 0. 05. Count normalization was performed using the RLE method implemented in the DESeq2 package  [134]. To confirm the identified DETs, we ran the QuantEval pipeline (https:// github. com/dn070017/QuantEval, 37) with the 'contig mode' and DESeq2 on the quantification outputs. We compared the quantification results by Spearman's correlation analysis and we retained the shared DETs for the subsequent analyses. To validate the quantifications and differential expression profiles obtained from our RNA-seq data, we carried out two alternative analyses on additional samples (see §4.
Enrichment analysis We performed a GO term enrichment analysis focusing on biological processes with the GO_MWU tool (https:// github. com/z0on/GO_MWU) using adaptive clustering and a rank-based statistical test (Mann-Whitney U-test). We used the following parameters for adaptive clustering: largest = 0. 5; smallest = 10; clusterCutHeight = 0. 5. We considered both the level of expression and the significance of the differential expression: we assigned the log2 fold change value to transcripts that were significantly differentially expressed (adjusted p < 0. 05), while assigning zero to the others. We considered as enriched a biological process with a FDR < 1%. To display the results synthetically, we used the Enrichment Map v3. 3. 3 tool [135]  in Cytoscape v3. 9. 1 [136]. The intensity of the enrichment was evidenced in the network and was calculated as follows: (i) for the processes enriched with over-expressed transcripts, 'number of transcripts significantly over-expressed in the process/total number of transcripts in the process'; (ii) for the processes enriched with under-expressed transcripts, '-1 × (number of transcripts significantly downregulated in the process/total number of transcripts in the process)'.
Hub and top expressed gene identification The top 20 genes in terms of connectivity ranking in the PPI network were selected as hub genes. We used the Search Tool for the Retrieval of Interacting Genes (STRING), a biological database designed to predict PPI networks  [137]. The results were visualized in Cytoscape v3. 9. 1 using NetworkAnalyzer v4. 4. 8 visualization software [100], which can construct comprehensive models of biological interactions. Isolated and partially connected nodes were not included. To identify marker genes of the cancerous condition, we considered both differential expression (log2FC) and expression level (RLE) values to build a plot graph. We defined arbitrarily thresholds (log2FC values > |5| and RLE > 500) to highlight the most discriminating genes between the two conditions (cancerous/healthy circulating cells).
Targeted analyses We performed a targeted analysis focusing on genes and pathways that have been identified by Sanchez-Vega et al. [27]  as being altered at high frequency across many different human cancer types. We searched for the presence of a differential expression of these genes in MtrBTN2 cells or of MtrBTN2specific alleles (i. e. present only in MtrBTN2-affected mussels, and present in all MtrBTN2-affected mussels). We also identified M. edulis and M. trossulus-specific alleles. Allele visualization and calculation of variant frequencies were carried out using the IGV v2. 12. 3 [138]  and iVar [139] tools on BAM files obtained after read alignment to the transcript sequences with Bowtie2 v2. 4. 1 [123]. The minimum quality score threshold to count a base and the minimum frequency threshold were set to 20 and 0.
Acknowledgments. We acknowledge the  Animal Health-Clinical Biology unit of the LABÉO laboratory (Caen, France) , especially  Maryline Houssin  and  Ludovic Petit , for their help during cytological screening and  Professor Petr Strelkov  from the  Saint Petersburg State University  for the sampling of M. trossulus individuals. We thank the  Réseau d'Histologie Expérimentale de Montpellier, RHEM facility , supported by  SIRIC Montpellier Cancer  (grant  INCa_ Inserm_DGOS_12553 ), the  European regional development foundation  and the  Occitanian region  ( FEDER-FSE 2014-2020   Languedoc Roussillon ), for processing our animal tissues, histology technics and expertise. This study is set within the framework of the ' Laboratoires d'Excellence (LABEX)' Tulip  ( ANR-10-LABX-41 ). royalsocietypublishing.
These maps are different for the various four-telescope arrays offered at the VLTI on account of their varying baseline lengths. The location of maximum A43, page 12 of 13 Chingaipe, P. M. , et al. : A&A proofs, manuscript no. aa46118-23  collinearity in these maps can be used as a first estimate of the position (α, δ) and contrast c of the actual companion, which can then serve as the starting point in a parametric χ 2 minimisation procedure. While we have been able to infer the position of a single companion with the collinearity map, this investigation could be extended to multiple companions in the FOV. For such an arbitrary high-contrast scene, the image can be reconstructed using a variety of inverse model techniques such as SQUEEZE  (Baron et al. 2010)  or MACIM  (Ireland et al. 2006).
We assessed the characteristics of a kernel-nuller discovery observing program for the direct imaging of exoplanets. By exploiting the synergy between astrometry and direct imaging, we show the target-selection process for this program. The complete analysis of the targets identified is beyond the scope of this paper as we would also need to take into account other indicators of binarity such as the renormalised unit weight error (RUWE;  Lindegren et al. 2021 ) and determine whether or not the potential giant planetary mass companions identified are gravitationally bound to their host star. Additionally, the possible positions of the companion generating the astrometric signature can be further limited by using ∆v T,G3 , as shown by  Bonavita et al. (2022). Furthermore, with all subsequent Gaia data releases, the sensitivity of the PMa technique will continue to improve.
The PCR reactions were carried out in 10 μL of final volume with: 1 μL of template DNA, 5 μL 2X Platinum TM Multiplex PCR Master Mix (ThermoFisher Scientific), 1 μL 10X primer mix and 3 μl H 2 O. The program used was: 2 min 95 °C, followed by 30 cycles of 30 sec 95 °C, 90 sec 60 °C, 60 sec 72 °C), and a final extension for 30 min at 60 °C. Genotypes were scored using an ABI 3130 XL Genetic Analyzer, with GeneScan TM 500 LIZ TM size standard (ThermoFisher Scientific, USA) and GeneMapper TM software v4. 1 (Applied Biosystems TM , USA). For each locus, several alleles with different sizes were sequenced using homozygous specimens. These sequences allowed us to translate allele www. zoores. ac. cn relative sizes obtained with GeneMapper into real allele sizes. For each specimen captured in 2022, two independent samples (swabs) were genotyped.
For full siblings, two unrelated parents were first generated; then, each descendant was generated by randomly sampling one allele from each parent. For half siblings, one mother and two unrelated fathers were first generated; then, one descendant was generated by randomly sampling one allele from the mother and one allele from one father. The other descendant was generated by randomly sampling one allele from the same mother and one allele from the other father. For each kind of relationship, one thousand simulations were performed to estimate the empirical distribution of pairwise distances according to a given kinship. Simulations and data analyses were carried out in the R statistical environment (R Core Team, 2021). The R script "GenerateIndividual. r" can be found in GitHub "jmorode/Genetics".
The R script "GenerateIndividual. r" can be found in GitHub "jmorode/Genetics". Estimation of genealogical relationships: The software ML Relate  (Kalinowski et al. , 2006)  was used to find evidence of relatedness between cavefish. The accuracy of inferred genealogical relationships by the software was evaluated using simulated families of individuals of known genotypes. One thousand families were generated using the R script described above. Each family was composed by a mother, two unrelated fathers, two full siblings and two pairs of half siblings. This family composition allowed us to test the four relationships assessed by ML Relate: parent-offspring, full siblings, half siblings and unrelated. The genotypes of the members of these 1,000 families were written in an input file for ML Relate.
We therefore considered that identical genotypes in different samples corresponded to the same individual that had been recaptured. On the 22nd February 2022, 29 fish were sampled by swabbing in El Pachón Main pool and genotyped, and 29 fish were sampled 3 days later in the same place and genotyped. We identified 3 pairs of identical genotypes that we assumed to be recaptured individuals (Supplementary Table  S5 ). Using equation (  3 ) and (4), the estimated population size for the Main pool in El Pachón cave during the dry season was therefore of 218 cavefish (95% CI 40-395). We also analysed the multi-year data to obtain population size across pools, both accessible and non-accessible to humans that readily exchange individuals during rainy seasons. Among 55 genotypes observed in 2022, we found three genotypes that were identical to genotypes observed in 2019 (Figure  1B ), suggesting that after a minimum of two events when the pools get joined into a single body of water, three individuals have been recaptured three years after their first capture (Supplementary Table  S5 ).
Genealogical Relatedness Individual genotypes from a small population give an opportunity to examine kinship. However, the reliability of kinship inferences depends on the level of polymorphism. We assessed the possibility to infer kinships among surface fish on the one hand and El Pachón cavefish on the other hand using the software ML Relate. First, pairs of individuals with different relatedness (unrelated individuals, offspring-parent, full sibs and half sibs) were simulated taking into account allele frequencies. Then the true kinship was compared to the one inferred by ML Relate. For surface populations, the accuracy was 81% and 92% for Pozo Pachon Praxedis Guerrero and Arroyo Tampemole respectively, suggesting that the polymorphism is sufficient to infer relatedness with a good confidence (Supplementary Table  S6 ).
The aim of this paper was to propose a method to characterize the ability of a GNSS user to detect a spoofing attack from the behavior of the clock bias. Spoofing interference is not a new issue, especially in military GNSS, although it is a new challenge for civil GNSS, since it is currently implemented and used in many everyday applications. For this reason, it is still a topical issue, especially for receivers that only have access to high-level data (PVT,CN0). To address this important issue, after conducting a study of the receiver clock polarization calculation process, this led to the development of a very basic Matlab model that emulates a spoofing attack at the computational level. Using this model, we were able to observe that the clock bias is affected by the attack. However, the amplitude of this disturbance depends on two factors: the distance between the spoofer and the target and the synchronization between the clock that generates the spoofing signal and the reference clock of the constellation.
Simplified Model Approach To show the influence of a spoofing attack on the clock bias, a simple Matlab model was developed to emulate a self-consistent receiver spoofing attack. The model emulates the spoofing attack only on the computation level of the receiver, which means that only the equations used to compute a GPS position are taken into account, not the signal model nor the power level. The chosen spoofing attack for this model is a simple one. It is an attack that will induce a fixed fake position (i. e. , the position that the spoofer wants the receiver to calculate, instead of the real one) on a fixed GPS receiver. The spoofer is supposed to be "perfect", i. e. , the spoofing constellation is identical to the real one (the same SVs, the same positions, and the same time). It is worth noting that, in the results shown hereinafter, the fake position matched the location of the spoofer, being 240 m away from the real position.
Influence of the Different Parameters In order to understand which parameters of the model will likely increase the value of the clock bias jump during a spoofing attack, the variation of four parameters was tested. These parameters were: (i) the number of SVs used during the computation; (ii) the initial drift used to model the clock bias; (iii) the distance between the target and the spoofing antenna; and (iv) the time de-synchronization between the two GPS constellations. To show their influence, the Empirical Cumulative Distribution Function (ECDF) of the value of the clock bias jump and the ECDF of the amplitude of the peak of the drift were calculated for different values of the parameter of interest. An ECDF is plotted with a dataset of measured clock bias jumps and clock drift peaks during 100 iterations of the Matlab model.
The ordinate of the plot F(x) is the percentage of data from the dataset that is less than or equal to the value x. If ECDFs are computed for different values of a parameter, it is interesting to compare them with each other. Figure  5  shows the ECDF of the clock bias jump for different distances between the receiver and the spoofing antenna. The ECDFs for each distance are well separated, and the ECDF of the smallest distances shows a smaller value of clock bias jump than the ECDF in the higher distances. This is the same case with the ECDF of the clock drift peak, as shown in Figure  6. This highlights that the distance between the targeted receiver and the spoofing antenna has a clear influence on the amplitude of the clock bias jump caused by a spoofing attack: the higher the distance, the higher the amplitude of the jump is.
Fixed Spoofing Attack Using Two Different Clocks To investigate the effects of a spoofing attack, it is necessary to carry out a spoofing attack with a GNSS signal generator that will broadcast the spoofing signals. However, due to legal reasons, it is not possible to set up such an experiment. Therefore, all our experiments were implemented using wired hardware, as shown in Figure  9. To carry out the experiments, two GNSS signal simulator were used: the LabSat 2 and the Spirent GSS6560. The LabSat is a GNSS signal simulator that can record and repeat GNSS signals, whereas the Spirent is a GPS signal simulator that can simulate a complete GPS constellation on a maximum of 12 channels and offers many parameters to configure. The GNSS receiver is a receiver manufactured by u-blox that is delivered with software that allows the user to collect a large amount of data from the receiver on a computer.
• Strong attack: starts with high-power signals (-40 dBm on the Spirent). • Jamming attack: starts with a high-power noise to jam the target before broadcasting the spoof signals (-65 dBm on the Spirent). • Acquisition mode: similar to the smooth attack, but the receiver is forced into acquisition mode at each rise in power (5 dB by 5 dB). These attacks can be considered of the first category defined in Section 2. 1. No synchronization was realized between the LabSat (real constellation) and SPIRENT (spoofer constellation), which were launched separately "by hand". This may seem trivial at first glance, but perfect synchronization of the attacker with the real constellation is neither obvious nor easy to achieve. An example of successfully conducted spoofing experiments to trick the Google map software was given  [22]  without any kind of synchronization.
There was only a Software-Defined Receiver, which replayed the spoofing signal. Then, it does not seem so simple to determine what would be the most-realistic scenarios in a real case of a spoofing attack. Probably the three firsts, but the most-important is that each scenario be tested several times with success. Figures  11  and 12  show the clock bias and its drift during a smooth attack. The red dashed lines indicate each power increase on the Spirent. The first arrow shows the moment when the receiver loses the "real" constellation. The second arrow shows the moment when the receiver locks onto the spoofer. We can see that both the clock bias and its drift showed an abnormal leap of value when the receiver switched from the real signals to the spoofing signals. The same behavior was noticed on the other types of spoofing attack on both receivers, but in some cases with the u-blox 6, the clock drift showed no jump.