dataesr/scientific-paragraphs-categorization huggingface dataset:
The unidirectional wireless communications from the Sensing Nodes (wirelessly powered, battery-free, and low resources) to the Communicating Node(s) are mainly considered, even if other wireless communications are implemented in the proposed Cyber-Physical System (especially in a mesh network of Communicating Nodes, with the Internet, or with local wireless devices such as smartphones), and attacks based on direct access to the Sensing Nodes are not considered as these are assumed to be physically and wirelessly inaccessible (encapsulated in the reinforced concrete and without data downlink). The solutions to secure both the data and the network are studied, in particular, those provided by the considered standards but also those that are in the stateof-the-art, and considered in regards to the available resources (energy, processing, storage).
Acknowledgments: The authors would like to thank Clarisse ERPELDINGER for its valuable help during the security analysis and Philippe HERAIL for its power characterization of the Bluetooth Low Energy module, and also all their French partners in the McBIM project: CRAN (Nancy, France), LIB (Dijon, France), and FINAO SAS/360SmartConnect (Trans-en-Provence, France) for the relevant discussions about security, placed in the core of the project definition.
Rivers transports coarse sediment (gravel, cobbles, or boulder) as bedload. During a flood, when the discharge is high enough, the sediment grains move by rolling and bouncing on the river bed. Measuring bedload transport in the field is notoriously difficult. Here, we propose a new method to characterize bedload transport by floods. Using a drone equipped with a high resolution camera, we recorded yearly images of a bar of the Vieux-Habitants river, a gravel-bed river located on Basse-Terre Island (Guadeloupe, French West Indies). These images, combined with high frequency measurements of the river discharge, allow us to monitor the evolution of the population of boulders on the river bed. Based on this dataset, we estimate the smallest discharge that can move the boulders, and calculate the effective transport time of the river.
Finally, the rest of the boulders (blue contours on Fig. 3 ) remained in place. The comparison between two consecutive GIS therefore allows us to identify the fate of each block. Based on this method, we attribute to each boulder contour, in each GIS, a label which specifies whether it was already in place during the previous campaign, or if it has been deposited recently. Some cases turn out to be ambiguous: a few boulders disappeared and then reappeared on more recent images, as floods covered them with sediment, before exposing them again. Those ambiguous cases were duly labelled and the corresponding boulders were considered immobile. Following this procedure, we end up with a dataset that contains the position and the size of all boulders larger than 50cm. We also know whether the boulder stayed in place or if, and when, it was deposited and/or entrained away.
4a , green boxes) from those that were already in place during the preceding campaign (Fig. 4a , yellow boxes), and calculate their surface density. The result varies within boulder size (Fig. 4a ). Each year, about half of the population of D1 boulders is made up of freshly deposited sediment (Fig. 4a ). In this class, the boulders are highly mobile, but the number of boulders entrained by floods balances, on average, the number of fresh boulders deposited on the bar, thus maintaining their surface density (Fig. 4b ). This is not true for larger boulders. Indeed, the proportion of freshly deposited boulders rapidly decreases with the grain size, and eventually vanishes for boulders larger than 1. 75 meters (Fig. 4a ). The mobility of a boulder thus decreases with its size. It is because large boulders are seldom mobilized that their surface density remains constant in our dataset.
3) Third, to simultaneously estimate the impact of multiple linguistic features on comprehension capacities, we developed an original stimulus selection and orthogonalization procedure. We generated two speech corpora derived from large databases of natural stimuli and characterized them at the previously described seven linguistic levels, ranging from acoustic to supra-lexical. Thanks to a careful selection, all these features were orthogonalized across stimuli, enabling a fine-grained characterization of their respective influence on speech comprehension. The combination of these three methodological advances provides optimal conditions to investigate the linguistic features governing speech processing ability and limits. Results from three behavioral experiments converge to show that multilevel linguistic features independently constrain speech comprehension, with the syllabic rate having the strongest impact.
Linguistic features were chosen based on a large body of literature identifying them as influential constraints on speech comprehension (see Introduction). Our corpus selection procedure guaranteed that feature distributions selected in the final experimental material were representative of generic stimuli statistics as derived from large databases (Fig. Supp. 1a and 1d). In experiment 1, the limitations in terms of existing monosyllabic words prevented us from reaching a stimulus set in which the syllabic information rate was representative of the original database. Specifically, both the mean and variance of the distribution across stimuli differed between the original and selected stimulus sets (Fig. Sup. 1b and 1e). We thus excluded this feature from the data analyses of experiment 1. We also ensured that within the subset of selected stimuli, correlations between linguistic features were low (all r < 0.
One question we can not answer is whether this is the result of a serial chain of processes or of competing parallel processes, or both. Further work using timeresolved measurements of comprehension could adjudicate between these concurrent hypotheses. Finally, while we used meaningful sentences and words derived from large databases, due to experimental conditions, we artificially accelerated the spoken material to carefully control for speed variations. This controlled experimental task may seem somewhat unnatural but we show that the compressed speech gating paradigm is sensitive to linguistic features that have been shown to influence language processing in more classical experimental settings. Importantly this paradigm allows comparing in a generic framework different linguistic features from previously distinct subfields in the language domain.
Stimuli Speech stimuli. The stimuli in experiment 1 consisted of 251 monosyllabic French words drawn from a set of 1,100 monosyllabic words listed in the Lexique database  (New et al. , 2004). The stimuli in experiment 2 consisted in 100 seven-word-long French sentences drawn from a set of 14,000 seven-word sentences listed in the Web Inventory of Transcribed and Translated Talks database  (WTI3, Cettolo et al. , 2012). For both experiments, the text stimuli were then synthesized in auditory stimuli using Google Cloud Text-to-Speech (Google, Mountain View, CA, 2020, the female voice, "fr-FR-Wavenet-C"). Using text-to-speech technology as opposed to naturally-produced speech has the critical advantage of controlling for the relevant linguistic features. Indeed, naturally produced speech displays variability across utterances in multiple linguistic characteristics (i.
The resulting envelope time-course was downsampled to 1000 Hz. Then, we used Welch's method  (Virtanen et al. , 2020)  to estimate the power spectral density of the envelope, resulting in a modulation spectrum between 1 and 215 Hz with a 0. 1 Hz resolution. This was done for each stimulus. Finally, the center frequency of each spectrum was extracted by taking the global maximum value of each modulation spectrum. The acoustic modulation rate was expressed in Hz. Phonemic rate: it corresponds to the number of phonemes presented per second. It was computed by dividing the number of phonemes (retrieved from the canonical pronunciation provided in the Lexique database  (New, Pallier, Brysbaert, & Ferrand, 2004) ) by the duration of the stimulus. The phonemic rate was expressed in Hz. Syllabic rate: same as the phonemic rate but for syllables.
This feature was estimated from written data derived from the Lexique database. The phonemic information rate was expressed in bits per second. Syllabic information rate: same as phonemic information rate but for syllables (n=3660). It was also expressed in bits per second. Static lexical surprise rate: Derived from the lexical frequency, it measures the unexpectedness of a word without reference to the surrounding context. It was computed as the negative base 2 logarithm of the unconditional probability of a word -log2P(word), where P(word) is the lexical frequency of the word. The lexical frequency was the frequency of occurrence in the Lexique database. In experiment 1, the static lexical surprise was divided by the stimulus duration. In experiment 2, as stimuli were seven-word sentences, the static lexical surprise of each individual word composing the sentences was summed before dividing by the duration of the stimulus.
Procedure and Paradigm Orthogonalisation procedure to select the stimulus sets. In order to avoid collinearity issues due to correlations between features across stimuli, we developed a custom-made leave-one out iterative algorithm to select stimuli with low correlation between features. The algorithm starts with the complete original database (1,100 words in experiment 1 and 14,000 sentences in experiment 2) and computes the correlation between each pair of features (5-7 features, 10-21 correlations in total in experiment 1 and 2 respectively). Then, the algorithm performs a leave-one-out procedure: it removes one stimulus, recomputes the correlation matrix on this reduced set and estimates the specific contribution of the one stimulus on the original correlation matrix, by comparing the correlation matrices of the full and reduced stimuli sets.
This processing step is repeated until all items have been removed once. The 10 percent stimuli that led to the most significant increase in correlation across features are discarded. The algorithm then iterates on this newly selected reduced stimuli set. The algorithm stops when the number of stimuli is equal to 251 (words) in experiment 1 and 100 (sentences) in experiment 2. A last check ensured that the correlations between features were all below 0. 15. Representativeness of the selected stimulus sets. The representativeness of the final selected stimulus sets in comparison to the original datasets was assessed for each feature. This was performed to ensure that any theoretical conclusions derived from the results obtained from a limited subset of stimuli could generalize to a larger corpus-based dataset.
To do so, we computed the value of the features for the complete datasets, hence providing a relatively good estimate of the ecological distribution of each feature. Two indexes were computed to control that each feature's distribution in the selected stimulus sets was similar to its distribution of the original datasets: i) the ratio between the means, ii) the ratio between the variances. A value close to one for both indexes indicates a good match between the distributions in the original dataset and in the selected stimulus sets. Finally, the correlation matrices between the features in the selected stimulus sets and the features in the original datasets were compared. Time compression. Time compressed versions of each stimulus were created. The audio waveforms were linearly compressed at rates  1, 2, 2.
Data analyses Data scoring. Speech comprehension was scored 1 if the response was correct (grammatical errors were allowed) and 0 if the response was incorrect or if no answer was given. In experiment 2 and 3, participants' audio responses were first transcribed using Google Cloud Speech-to-Text (Google, Mountain View, CA, 2018) and checked manually for mistakes or inconsistencies. General linear mixed model (GLMM) analysis. Participant's responses (0: incorrect, 1: correct) were analyzed using Generalized Linear Mixed Models (GLMM;  (Quené & van den Bergh, 2008)  with a logistic link function using the lme4 package  (Bates, Mächler, Bolker, & Walker, 2015)  in R (version 3. 5. 1, Team, n. d. ). The datasets were composed of 52,710 responses in experiment 1 (21 participants x 251 words x 10 compression rates) and 102,900 responses in experiment 2 (21 participants x 100 sentences x 7 words x 7 compression rates).
We thank all participants;  Johanna Nicolle ,  François-Xavier Alario  and all the colleagues from the  DCP team at the Yannick Jadoul  for help with the Parselmouth python package and  Ting Qian from FindingFive  for extensive assistance and advice. Funding sources:  ANR-20-CE28-0007-01  (to B. M),  ANR-CONV-0002  ( ILCB ),  ANR-11-LABX-0036  ( BLRI ),  ANR-17-EURE-0029  (NeuroMarseille), the  French government  under the  Programme «Investissements d'Avenir» , the  Initiative d'Excellence d'Aix-Marseille Université  ( A*MIDEX ,  AMX-19-IET-004 ), la  Ligue Française Contre l'Épilepsie  (LFCE, to J. G).
Introduction Recent European and British guidelines recommend gene-based colorectal cancer (CRC) screening in patients with Lynch syndrome (LS)  1, 2. More precisely, carriers of pathogenic variants (PV) in MSH6 and PMS2 are advised to start colonoscopy screening at age 35, as opposed to age 25 for MLH1 and MSH2 PV carriers. In addition, European guidelines propose increased screening intervals for PMS2 PV carriers, i. e. , colonoscopy every 5 years versus every 2-3 years. A simulation model-based analysis supports such gene-speci c surveillance, as it seems to have favorable impact regarding quality of life and cost, with no deleterious consequences regarding cancer incidence  3. The later CRC onset associated with MSH6 and PMS2 in the Prospective Lynch Syndrome Database (PLSD) justi es these gene-adjusted guidelines  4.
If no early-onset CRC were associated with MSH6 and PMS2 in the PLSD cohort, there are isolated reports in the literature. For example, two Austrian brothers carrying a PMS2 PV developed colorectal cancer in their teenage years  5. A possible explanation was the presence of a modifying variant in the POLD1 exonuclease domain. In addition, we knew of local LS patients who had developed cancer before the recommended screening age. A continuum between LS and constitutional mismatch repair de ciency (CMMRD) has been suggested, with the description of LS patients presenting a CMMRD-like phenotype, and carrying candidate variants, in addition to the LS causing variant 6. More generally, cancer phenotypes associated with multiple variants in cancer susceptibility genes (CSG) have been explored through the Multilocus Inherited Neoplasia Allele Syndrome (MINAS) database  7.
Panel testing and variant analysis Germline testing was performed in the Pitié Salpêtrière and Cochin cancer genetics laboratories. A panel of 34 CSG was analyzed (Table  S1 ). Coding sequences were sequenced after enrichment by capture (Roche NimbleGen kit). PV were con rmed by Sanger sequencing, and copy number variations by MLPA. Large PMS2 rearrangements were con rmed by long-range PCR. Variants were classi ed according to the ACMG criteria  9  and/or national or international variant databases.
It involves a highly conserved amino acid across many species (13) and is only observed at a frequency of 7. 9x10 - 6 in the gnomAD v2. 1. 1 database. The second case was diagnosed with glioblastoma, IDH-wild type at the age of 22. Expression was lost for MSH2 and MSH6. A paternal uncle had developed pancreatic cancer in his fties. A deletion of exons 9 and 10 of MSH2 and the POLD1(NM_001256849. 1): c. 961G > A, p. (Gly321Ser) VUS were identi ed. The POLD1 VUS is located within the exonuclease domain and only reported at 3. 5x10 - 4 frequency in gnomAD. It involves a highly conserved amino acid across many species (12). Again, a pathogenic effect is predicted in silico. Importantly, no POLD1/POLE exonuclease domain variants were identi ed among UO cases. Another patient was diagnosed with CRC aged 21, four years earlier than the recommended screening age.
We identify senescent cells in patient and mouse GBMs. Partial removal of senescent cells expressing high levels of p16 Ink4a using a ganciclovir-inducible p16-3MR transgenic line  17  improves the survival of GBM-bearing mice. To identify the cells expressing high levels of p16  Ink4a  , and to characterize the action of these cells on the tumor ecosystem, we combined single-cell and bulk RNA sequencing (RNAseq) analysis at early and late time points after the senolytic treatment. This approach leads to the identification of the NRF2 transcription factor and its selected targets as a signal triggering the pro-tumoral activity of p16 Ink4a expressing senescent cells. Using these data, we define an unbiased senescence signature that we successfully used to interrogate GBM patient data sets, revealing that higher senescence scores correlate with shorter survival times.
enrichment score running enrichment score. Raw data are provided as a Source Data file. consensus TFs from ChIP-X database using Enrichr  46  on three gene sets enriched in p16 Ink4a Hi senescent cells: (i) differentially downregulated genes in the p16-3MR+GCV vs. the WT+GCV astrocyte cluster from the scRNAseq data at the early timepoint (Early; Fig. 5a  and b ; Supplementary Data 3); (ii) differentially upregulated genes in p16 Ink4a positive vs. p16 Ink4a negative malignant cells from scRNAseq analysis at the late timepoint (Late (1); Fig. 5c  and d , Supplementary Fig. 5a-e ; Supplementary Data 3); (iii) differentially downregulated genes in the p16-3MR+GCV vs. the WT+GCV GBMs from the bulk RNAseq data at the late timepoint (Late (2); Fig. 5e  and f ; Supplementary Data 3). Remarkably, the Nuclear Factor Erythroid 2 Like 2 (Nfe2l2) signaling pathway was enriched in the three gene sets.
7c ). We then computed a single-sample GSEA (ssGSEA) senescent Z-score corresponding to the enrichment Z-score of the 31 genes of the senescence signature in all malignant cells of WT+GCV and p16-3MR+GCV GBM transcriptomes. As expected, the astrocyte cluster in WT+GCV GBMs contained the largest high senescent Z-score distribution rate (Supplementary Fig. 7c ). For unbiased analysis, we defined the high distribution rate as the highest decile. This percentage dropped in all clusters in p16-3MR+GCV GBMs compared with WT +GCV GBMs, as predicted by the partial removal of p16 Ink4a Hi senescent cells (Supplementary Fig. 7c ). We then applied the ssGSEA senescence Z-score to three singlecell data sets of patient GBMs  23, 26, 54. We analyzed separately the Neftel dataset according to the sequencing technology (Smartseq2 (SS2) and 10X).
Administration of a combination of dasatinib and quercitin, a multi-tyrosine kinase inhibitor and a natural flavonoid respectively, eliminates efficiently senescent cells in different pathologies in the mouse including neurodegenerative diseases  83, 84. Results of ongoing clinical trials using these drugs on cohorts of patients with mild cognitive impairment and Alzheimer's disease (NCT04785300i, NCT04685590ii) should encourage or refute this approach for targeting senescence in the central nervous system. Heterotopic and orthotopic mouse models of cancers showed the efficacy of the one-two punch sequential therapy defined by a prosenescence therapy, followed by senolytic therapy clearing the induced-senescent cells and preventing the accumulation of detrimental persistent senescent cells  18, 77, [85] [86] [87].
Surface area quantification Quantifications were performed using Fiji software (v. 2. 1. 0/1. 53c)  98. Region of interest (ROI) corresponding to the tumor, was selected using the ellipse tool. IHC images were then color deconvoluted according to the "Giemsa" or "Hematoxylin and DAB (H DAB)" vector to assess a threshold of the SA-β-gal or DAB signal, respectively. The signal threshold was adjusted in order to remove the unspecific background signal without clearing the specific one. Number of pixels was measured and the values were normalized on the GFP+ tumor surface area. For mice brain tissue, four slides with three sections on each (n = 12) for SA-β-gal quantification and three slides with three sections on each (n = 9) for IHC quantification were analyzed per sample. For the patient sample, four sections were analyzed for SA-βgal quantification.
Western-blotting Total proteins were extracted from tumor samples following the TRIreagent protocol (Molecular Center Research, #TR 118). Protein pellets were solubilized in 1% SDS, 10 M urea, and stored at -80 °C. Protein concentration was assayed using the Pierce BCA protein assay kit (Thermo Fisher, #23225). Proteins were separated on 4-20% stain-free polyacrylamide gels (Mini-PROTEAN TGX Protein Gels, Bio-Rad, #4568096) and transferred on a nitrocellulose membrane 0. 45 µm (Thermo Fisher, #88018). Membranes were probed with primary antibodies (Supplementary Data 5) diluted in Super Block Blocking buffer in TBS (Thermo Fisher, #37535) and incubated O/N at 4 °C under gentle agitation. The secondary antibodies were incubated for 1 h at RT. Fluorescence was detected using the Odyssey CLx (Li-cor), and specific bands were quantified using Fiji software (v.
Bulk RNA-seq and analysis The quantity and quality of the total RNAs extracted were assessed by the Tapestation 2200 (Agilent) and sequenced with the Illumina NextSeq 500 Sequencing system using NextSeq 500/550 High Output Kit v2 (150 cycles, # 20024907), 400 millions of reads, 50Gbases. Quality of raw data was evaluated with FastQC (v. 0. 11. 5). Poor quality sequences were trimmed or removed with Fastp software to retain only good-quality paired reads. Star (v2. 5. 3a) was used to align reads on mm10 reference genome using default parameters except for the maximum number of multiple alignments allowed for a read which was set to 1. Quantification of gene and isoform abundances was done with rsem (v. 1. 2. 28) on RefSeq catalog, prior to normalization with edgeR Bioconductor package (v. 3.
28. 0). Finally, differential analysis was conducted with the glm framework likelihood ratio test from edgeR. For malignant samples, a batch effect was detected in PCA representation. To correct it, we performed the analysis by using an additive model which includes this batch variable. Multiple hypothesisadjusted p-values were calculated with the Benjamini-Hochberg procedure to control FDR. Functional enrichment analysis was performed with clusterProfiler (v3. 14. 3) Bioconductor package on the differentially deregulated genes with over-representation analysis (enricher function) and on all the genes with Gene Set Enrichment Analysis (GSEA function 100 ). Hallmark, Transcription factor targets (TFT), and Canonical pathways (CP) gene sets from MSigDB collections have been used, complete with some custom gene sets (Supplementary Data 1).
Single-cell RNA sequencing and analysis-ddSeq data Cell suspension of one dissociated GBM was loaded in 4 wells (3 wells with GFP+ cells or malignant cells and 1 well with GFP-cells or nonmalignant cells) on the ddSEQ Single-Cell Isolator (Biorad). A library was generated using SureCell™ Whole Transcriptome Analysis 3′ Library Prep Kit for the ddSEQ System (Illumina, #20014280) and was sequenced on a Nextseq 500 Illumina sequencing system, using a High Output Kit (150 cycles), with the following parameters: 400 million reads depth, 50 Gbases, and 70 million reads per sample. Cutadapt 1. 18 was used to trim nextera adapters in 3' on reads, then a quality control of sequences was done with FastQC. Cellular and UMIs barcodes were extracted with the ddSeeker (v 0. 9. 0) tool with default parameters.