dataesr/scientific-paragraphs-categorization huggingface dataset:
For all experiments, 10 5 cells were subcutaneous injected into the right dorsal flank of 6-8 weeks-old NOD. Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice. Tumours were measured twice a week using a calliper and volume tumour was calculated using the formula: V = length × width 2 /2. At the end of the experiment, mice were sacrificed by cervical dislocation. Tumours were then weighed and divided in three parts for cell culture, formalin fixation and nitrogen freezing. Growth rate was calculated with the segmental linear regression of GraphPad Prism (GraphPad Software, San Diego, California, USA). Statistical analyses were done using the Kruskal-Wallis test.
Acknowledgements This work was supported by the  Fondation ARC pour la Recherche Contre le Cancer  (T. L. ) and the  Fondation Recherche Médicale  (L. L. , L. D. ),  Association Pour Corentin  and  Phil' Anthrope  (C. M. ), supported this work. We acknowledge the personnel of  Animal Facility A2 Bordeaux  for their technical assistance. We thank  Dr Ray Cooke  for the English editing service.
Models for Diffusion in Cellular System Three models of diffusion in yeast were tested on the collected data: a two-compartmental (Model 0 when fitted in the full range of b-values, Model 0B when fitted in a given interval of b-values), a two-compartmental with an intercept (Model 1) and a three-compartmental model (Model 2). All of the models were fitted to the data in OriginPro2018b software. The overall formula for the echo attenuation in the sample for a particular t m is: E E 0 = e -2τ T 2 •e -tm T 1 • n i=1 p i •e -(γ 2 G 2 τ 2 (t m + 2 3 τ))D i + y 0 , (2) where T 1 and T 2 relaxation times were taken from the distributions shown in Figure  1 , i is i-th compartment in the sample, n is the number of compartments, E is the echo amplitude for the given τ and t m and E 0 is the echo amplitude for the minimal τ and t m  [13] , p i is the molar fraction of i-th population and n i=1 p i = 1, D i (m 2 s -1 ) is the apparent diffusion coefficient in the i-th compartment, and: (γGτ ) 2 t m + 2 3 τ = b, (3) which is a diffusion weighting factor in units (sm -2 ).
Simulations In order to determine the contribution of each compartment to the signal attenuation during t m , Monte Carlo Random Walk (RW) simulations were conducted in the cell, in which random walkers reflected diffusing particles. Simulations were conducted in MATLAB (R2019b) (Natick, MA, USA, The MathWorks Inc. ). The modeled two-dimensional (2D) geometry consisted of a cell wall, nucleus, mitochondrion, vacuole and intracellular space, in which we assumed completely reflecting boundaries. A non-exchangeable system was assumed based on the lifetimes reported in the literature for yeast (see Section 4. 5). Particle jump duration was set to be t s = 5 µs (step length was dependent on the self-diffusion coefficient, D 0i , σ = √ 4D 0i t s ). Number of RW steps depended on t m based on the relation N = t m /t s and was in the range of 40-8000, while the number of particles was equal to 5000.
Characterization of the Compartments Based on TDDCs Based on the TDDCs, compartment characteristics were obtained in Section 3. 4. Additional information can be inferred from Figures  3  and 4. The first compartment visible in Figure  5  is associated with the extracellular water. For t m = 0. 2-1 ms, the mean diffusion coefficient D 01 = 1. 64 ± 0. 15•10 -9 m 2 s -1 (Figure  5A )), which is similar to the self-diffusion coefficient 1. 6 × 10 -9 m 2 s -1 reported for extracellular fluid  [5]. Extracellular space size cannot be accurately determined from (4) (V 1 /S 1 R 1 = 2. 84 ± 0. 36 µm), because there are very few data points for which the extracellular signal is still detectable, which means that the first exponent contains averaged information about the first and the second compartment and the obtained D 1 s are underestimated.
Patients Between January 1, 2008, and December 31, 2018, 53 consecutive patients were treated or referred to our institution for allo-SCT for transformed MPN, MDS with blast excess-2 (MDS-BE2) and AML. Diagnosis and classification of secondary MDS and AML were done according to the World Health Organization classification. Patients with transformed atypical chronic myelogenous leukemia, chronic myelomonocytic leukemia, and myeloid/ lymphoid neoplasms with eosinophilia and rearrangement of PDGFRA, PDGFRB, or FGFR1 or with PCM1-JAK2 were excluded from our analysis  [5]. The list of patients was obtained from the database of the multidisciplinary consultation meeting, where every patient's situation is shared before the beginning of a treatment and using the Promise database, which includes all patients who underwent allo-HSCT in Saint-Louis Hospital.
Statistical Analysis Data are described as median and range for quantitative variables and as frequency and percentage for qualitative variables. Percentages were compared using Fisher's exact test. All statistical tests were 2-tailed, with a significance threshold of. 05. Analyses were performed with R version 3. 6. 0. Median follow-up was estimated using Korn's method  [34]. Survival analyses were performed using Kaplan-Meier methods. The Mantel-Byar test with a Simon-Makuch plot  [35]  was used to compare the survival of transplant recipients with non-transplanted patients. For the Mantel-Byar test, we used R version 3. 6. 0 using the SAS function  [36]. We performed subgroup analysis according to first-line treatment and according to HSCT. We also analyzed characteristics of long-term survivors in patients who were alive at more than 1 year after transformation.
, 2018 (Chen et al. , , 2017)). In this paper, we introduce our solution. We design, fabricate, and test a differential oxide-based resistive memory array, including all peripheral and sensing circuitry. This array, based on two-transistors / two-resistors (2T2R) bit cell inherently reduces bit errors without the use of ECC, and we show that it is particularly adapted for in-memory computing. Then, we design and simulate a fully binarized neural network based on this memory array. We show that the XNOR operations can be integrated directly within the sense operation of the memory array, and that the resulting system can be highly energy efficient. Based on neural networks on multiple datatsets (MNIST, CIFAR-10, ImageNet and ECG data analysis), we evaluate the amount of bit errors in the memory that can be tolerated by the system.
Based on this information, we show that the memory nanodevices can be used in an unconventional programming regime, where they feature low programming energy (less than five picoJoules per bit) and outstanding endurance (billions of cycles). Partial and preliminary results of this work have been presented at a conference  (Bocquet et al. , 2018). This paper adds additional measurements of OxRAMs with shorter programming pulses, an analysis of the impact of bit errors on more datasets (ImageNet and ECG data analysis), and a detailed comparison and benckmarking of our approach with processors, non binarized ASIC neural networks and analog RRAM-based neural networks.
The estimates for system-level energy consumption were obtained using the Cadence Encounter tool. We used Value Change Dumps (VCD) files extracted from simulations of practical tasks so that the obtained energy values reflect the operation of the system realistically. This section first presents the electrical characterization results of the differential OxRAM arrays. We program the array with checkerboard-type data, alternating zero and one bits, using programming times of one microsecond. For programming devices in HRS (RESET operation), the access transistor is fully opened and a reset voltage of 2. 5V is used. For programming devices in LRS (SET operation), the gate voltage of the access transistor is chosen to ensure a compliance current of 55µA. Fig. 4 (a) shows the statistical distribution of the LRS and HRS of the cells, based on 100 programmings of the full array.
Do All Errors Need to Be Corrected? Figure  9. Recognition rate on the validation dataset of the fully connected neural network for MNIST, the convolutional neural network for CIFAR10, and AlexNet for ImageNet (Top-5 and Top-1) accuracies and the ECG analysis task, as a function of the bit error rate over the weights during inference. Each experiment was repeated five times, the mean recognition rate is presented. Error bars represent one standard deviation. Based on the results of the electrical measurements, and before discussing the whole system, it is important to know the OxRAM bit error rate levels that can be tolerated for applications. To answer this question, we performed simulations of binarized neural networks on four different tasks: • MNIST handwritten digit classification  (LeCun et al.
This binary classification challenge takes as input the ECG signals of twelve electrodes. The experimental trial data are sampled at 250Hz and have a duration of three seconds each. To solve this task, we employ a convolutional neural network composed of five convolutional layers and two fully-connected layers. The convolutional kernel (sliding window) sizes are decreasing from 13 to 5 in each subsequent layer. Each convolutional layer produce 64 filters detecting different features of the signal. Fully binarized neural networks were trained on these tasks on Nvidia Tesla GPUs using Python and the PyTorch deep learning framework. Once the neural networks were trained, we ran them on the datasets validation sets, artificially introducing errors in the neural networks weights (meaning some +1 weights are replaced by -1 weights, and reciprocally).
Indeed, these two populations of circulating cells are both very rare in the bloodstream and require the same technical enrichment steps to distinguish them from the surrounding normal blood cells (e. g. , leukocytes). The objective of this study was to develop an approach for NIPD of monogenic diseases caused by point mutations or triplet repeat expansions based on the isolation of single CFTCs from maternal peripheral blood. We evaluated the suitability of enrichment technologies currently used for CTCs using Huntington's disease (HD) as a clinical application model. Then, we analyzed the CFTC characteristics by compiling data obtained with different molecular analysis methods: microsatellite multiplex PCR genotyping for HD and Whole Genome Amplification (WGA) followed by mini-exome sequencing for point mutation detection.
The study participants were couples at risk of transmitting a monogenic disease to their fetus. They were recruited through two clinical trials: DIACCIMEX (ClinicalTrials. gov, NCT 03087526) and DIAFEXOME (ClinicalTrials. gov, NCT 03743948). Sample collection for research was approved by the Sud Est V Research Ethics Committee (Personal Protection Committee, CPP), on April 18, 2017, for couples at risk of transmitting HD (DIACCIMEX), and by the Ile de France VI CPP, on October 9, 2018, for couples at risk of transmitting monogenic diseases covered by the TruSight One expanded mini-exome sequencing kit 11 (DIAFEXOME). Informed consent was obtained from all participants. All methods of the study were carried out in accordance with relevant guidelines and regulations. In total, 16 couples were included (n = 7 at risk of transmitting HD; n = 9 at risk of transmitting a monogenic disease caused by point mutations).
Acknowledgements We acknowledge  Dr. Jean-Baptiste Gaillard  from the  "Unité de Génétique chromosomique CHU Montpellier"  for his help with primary trophoblastic cell culture. We acknowledge  Prof. Michel Koenig  for his enthusiastic support and useful remarks. We thank  Dr. Elisabetta Andermarcher  for assistance with her comments and proofreading that greatly improved the manuscript. This research was funded by  French Ministry of Health -DGOS , grant number  PHRCI-16-068  (DIACCIMEX) and by  Agence de la biomédecine , grant number  18AMP009  (DIAFEXOME)". The university regional hospital of Montpellier was the project promoter of this research.
At the Regional Scale At the regional scale, the tree cover variations between 2000 and 2019 over the 16 basins were extracted from the Global Forest Change (GFC2019) datasets produced by the University of Maryland, based on Landsat 4, 5, 7, and 8 imagery. These provide 30 m spatial resolution data of tree gains and tree losses compared to land use cover of the year 2000. Cloud-free observations for each pixel, based on quality assessment layers, are used to create composite imagery in bands 3, 4, 5, and 7 during the growing season. Image interpretation relies on a decision tree supervised learning approach and training data, including very high resolution Quickbird imagery as described in  Hansen et al. (2013)   [29]. Google Earth Engine© (Google, Silicon Valley, CA, USA) geoprocessing capabilities  [30]  were used here to reduce the time to extract and combine pixel data for the 16 basins.
At the Basin Scale At the basin scale, a special analysis was conducted in the upper Bafing basin. At the Upper Bafing basin scale, changes in vegetation were searched using a time series analysis (TSA) of the normalized difference vegetation index (NDVI) based on a MODIS (Moderate-Resolution Imaging Spectroradiometer; EOS Earth Observing System, NASA, Washington, D. C. , USA) (MODQ13 dataset (250 m resolution; 16 days temporal resolution; from February 2000 to February 2020). Mann-Kendall's tau test is an efficient trend detection method  [31]  and has been used for this data type for other regions of western Africa  [32] [33] [34]. Mann-Kendall's tau test can be submitted to null hypothesis (P value) testing as the data come from a population with independent realizations and are identically distributed.
Any Explanation from the LULCC? The analysis of the relationship between, on the one hand, runoff and depletion coefficients and, on the other hand, land use/land cover changes (LULCC) during the period of 2000-2019 is made with the data provided by the 30 m Global Forest Change datasets  [29]  (Figures  7  and 8 ). Considering no local field validation has been done and the inconsistencies in some results, the forest evolution shown here must be considered as relative and not as absolute. Water 2020, 12, x FOR PEER REVIEW 19 of 32.
Any Explanation from the LULCC? The analysis of the relationship between, on the one hand, runoff and depletion coefficients and, on the other hand, land use/land cover changes (LULCC) during the period of 2000-2019 is made with the data provided by the 30 m Global Forest Change datasets  [29]  (Figures  7  and 8 ). Considering no local field validation has been done and the inconsistencies in some results, the forest evolution shown here must be considered as relative and not as absolute. One of the most important trends is the higher deforestation rate measured in the Upper Niger basin (Kouroussa stream gauge) and the Konkouré basin (mostly in the basins of Pont de Linsan and Pont de la Route de Télimélé stations, secondarily Kaba and Nianso stations). These basins are the ones where: -RC paradoxically decreases or remains low during the last period, which is characterized by rainfall recovery, possibly because natural reservoirs continue to be filled.
Acknowledgments: The authors warmly acknowledge the  Rivers Basins Agency (NBA, OMVS and OMVG ) as well as the  Direction of Hydraulics of Guinea  for providing the daily discharge data, and the  SIEREM  and  AMMA Catch  databases as well as the  ANACIM (Senegalese Met Office ) for the daily rainfall data. We kindly acknowledge the reviewers who allowed us to propose a deeply improved version of the manuscript.
The cytotoxic effect of USBA 371 was related to the action of a diverse mixture of DKPs namely cyclo (Pro-Phe), cyclo (Leu-Phe), cyclo (Val-Phe), cyclo (Phe-Phe), cyclo (Pro-Leu), cyclo (His-Pro), cyclo (Pro-Val), cyclo (Pro-Ala) and cyclo (Pro-Tyr). The extract containing these DKPs presented a high cytotoxic effect against the adherent murine mammary cell carcinoma 4T1 and human mammary adenocarcinoma MCF-7 cell lines (IC 50 < 5. 5 µg/mL)  [15]. In addition to DKPs, this mix contained other compounds that did not match any of the databases used  [15] , indicating the need to further explore the metabolic diversity of the strain and the cytotoxic activity of these new compounds. In the present study, we performed a metabolic profiling study of crude extracts from L. aggregata USBA 371 with high cytotoxic activity followed by the isolation and identification of the compounds responsible for the biological activity previously detected.
Secondary Metabolite Profiling, Genome Mining and Proteomics The draft genome of L. aggregrata USBA 371 is composed of 6,417,675 bp with a calculated G + C content of 59. 14%. It encodes 5916 protein coding genes, of which 4800 have a known function based on sequence similarity. We identified the 16S rRNA gene using the RNAmmer (v1. 2) software and confirmed strain USBA 371 classification as L. aggregata with 99% similarity with the reference strain. The properties and statistics of the genome are summarized in Tables  S1  and S2. The distribution of genes into clusters of orthologous groups (COGs) functional categories shows that most of them are involved in amino acid transport and metabolism (599, 10. 2%), transcription (559, 9. 5%) and carbohydrate transport and metabolism (527, 9. 0%) categories (Figure  S12 ).
4. 3 annotation, while antiSMASH predicts the formation of the peptide nrp-valine-tyrosine-glycine. The BGC has thiolation, condensation, epimerization and thioesterase domains (Figure  1 ). Although the NRPSpredictor2 in the antiSMASH tool predicts with 100% certainty that two of the four adenylation domains have specificity for tyr and gly (Stachelhaus code match) amino acids, the substrates for the first two adenylation domains were predicted with a lower certainty (80%), suggesting that other amino acids than those predicted could be incorporated. Although the NRPS-.
Labrenzia Alba CECT 5094  Although the NRPSpredictor2 in the antiSMASH tool predicts with 100% certainty that two of the four adenylation domains have specificity for tyr and gly (Stachelhaus code match) amino acids, the substrates for the first two adenylation domains were predicted with a lower certainty (80%), suggesting that other amino acids than those predicted could be incorporated. Although the NRPS-T1PKS polypeptide chain cannot be associated with the diversity of DKPs found, it is possible that in case of iterative NRPS, multiple residues of the same amino acids are incorporated iteratively or in non-linear NRPS, which do not follow the domain organization, yielding unexpected products  [46]. Betalactone 1 1 1 2 2 1 1 1 1 1 Bacteriocin 1 1 1 1 2 1 1 1 1 1 Ectoine, Hserlactone 0 0 0 0 0 0 0 0 0 0 NAGGN 1 1 1 1 1 0 0 1 0 0 T1PKS 1 1 1 1 1 0 1 1 0 0 NRPS 2 4 6 1 1 0 0 1 0 0 TransAT-PKS, T3PKS-NRPS 0 0 0 0 1 1 0 0 1 1 Thiopeptide 0 0 0 0 0 1 0 0 1 1 NRPS-T1PKS 1 The second NRPS identified has three condensation domains, two adenylation domains specific for threonine and 2,3-dihydroxybenzoic acid (2,3DHB) activation, one isochorismatase and one thiolation domain (Figure  2 ).
Accession Numbers The draft genome of Labrenzia aggregata strain USBA 371 was generated at the DNA Sequencing Core Facility of Los Andes University (GenCore) following standard procedures. The Labrenzia Since we found no evidence for biosynthetic pathways associated with the CDPSs or NRPS, we suggest that they are produced by means of proteolytic enzymes such as dipeptidyl peptidases. They cleave the terminal ends of proteins to generate dipeptides which can then cyclize to form cyclodipeptides (CDPs)  [47]. Our analysis identified five genes associated with the production of Xaa-Pro aminopeptidase and one gene associated with the production of Xaa-Pro dipeptidase. Proteomics analysis confirmed the bacterium's production of endopeptidases and exopeptidases with 10 to 153 MS/MS spectral counts which suggests that they are very abundant in the four biological replicates analyzed (Table  S3 ).
Accession Numbers The draft genome of Labrenzia aggregata strain USBA 371 was generated at the DNA Sequencing Core Facility of Los Andes University (GenCore) following standard procedures. The Labrenzia aggregata USBA 371 draft Genome was deposited in the European Nucleotide Archive (ENA) under accession number ERS4291993 and the 16S rRNA gene sequence under accession number MF197928.
Mass spectrometric detection was performed using electrospray ionization in positive mode in full scan, applying a mass range from 100 to 1200 m/z. The mass spectrometer source conditions consisted of a capillary voltage of 3500 V in positive mode, a scan rate of 1. 02 scans per second. During all analysis, two reference masses were continuously injected for mass correction: m/z 121. 0509 (C 5 H 4 N 4 ) and m/z 922. 0098 (C 18 H 18 O 6 N 3 P 3 F 24 ). Features were putatively identified using the CEU Mass Mediator tool (http://ceumass. eps. uspceu. es/mediator/) [48]  by matching the observed accurate mass of each compound with the m/z values available online using the four following databases: METLIN (http://metlin. scripps. edu), KEGG (http://genome. jp/kegg), lipid MAPS (http://lipidMAPS.
Isolation of Compounds from Crude Extract of L. aggregata USBA 371 The crude extract was fractionated by Sephadex LH20 using MeOH as solvent to obtain 8 fractions. Fractions 3 to 5 were pooled and then separated by preparative thin layer chromatography (p-TLC) using CH 2 Cl 2 -MeOH (9:1) to yield compounds 1 (15 mg) and 2 (2 mg). Fractions 6 to 8 were also pooled and separated by pTLC using the same discontinuous gradient of CH 2 Cl 2 -MeOH to yield the mixture of compounds 3a and 3b (4 mg). The identification of compounds 1, 2, 3a and 3b was done by NMR using a Bruker Fourier 300MHz. The 1D and 2D NMR data were collected, analyzed and the data were compared with the above-mentioned published in literature.
Cell viability was estimated in triplicate using the trypan blue assay. The percentage of viability was calculated as absorbance of the vehicle/absorbance of the treatment per 100. Inhibitory concentration 50 (IC 50 ) values were defined as the concentration of the extracts that generated 50% inhibition of tumor cell growth. IC 50 values were calculated with three or four parameter nonlinear regression curves using GraphPad Prism software (GraphPad Software, Inc. , San Diego, CA, USA). IC 50 values are presented as the mean ± standard error of mean (SEM). The selectivity index was calculated using the formula: IC 50 Fibroblasts/IC 50 tumor cell. A potential against tumor cell lines was considered when the selectivity was greater than 1. The solvent used in the reconstitution of the extract (DMSO) was evaluated as a negative control for each test in the same volume as the extracts.
After staining with Coomassie BlueSafe stain (Invitrogen), the corresponding polyacrylamide band containing the high molecular weight proteome (>200 kDa) of each sample was sliced, destained, treated with iodoacetamide, and proteolyzed in the presence of detergents to increase peptide recovery with trypsin as previously described  [50]. The resulting peptides were resolved on a nanoLC reverse phase column connected to a high-resolution tandem mass spectrometer (Q-exactive HF instrument from Thermo Scientific, MA, USA) operated with a 60 min gradient in data-dependent acquisition mode. The parameters for MS and MS/MS acquisition were the same as previously described  [51]. MS/MS spectra were interpreted against the protein sequence database specific of strain USBA 371 with the MASCOT software (Matrix Science) as described  [51].