dataesr/scientific-paragraphs-categorization huggingface dataset:
Transmission electron microscopy PDTO were cultured on microscope coverslip. The PDTO were fixed with 2. 5% glutaraldehyde in cacodylate buffer 0. 1M pH 7. 4 overnight at 4°C and rinsed in cacodylate buffer 0. 1M pH 7. 4. They were then post-fixed 1 h with 1% osmium tetroxyde in cacodylate buffer 0. 1M pH 7. 4 at 4°C and rinsed in cacodylate buffer 0. 1M pH 7. 4. The PDTO were then dehydrated in progressive baths of ethanol (70-100%) and embedded in resin Embed 812. After 20h of polymerization at 60°C the coverslips were then separated from the PDTO's resin bloc and the polymerization was continued for 28 h. Ultrathin sections were done and contrasted with uranyle acetate and lead citrate. The PDTO were observed with transmission electron microscope JEOL 1011 and image were taken with camera Orius 200 (Gatan) and Digital Micrograph software.
Transcriptomic pipeline Total RNA yield and quality were assessed on the Agilent 2100 bioanalyzer (Agilent Technologies, Massy, France). One color whole Human (072363_D_F_20190204 slides) 60-mer oligonucleotides 8 × 60k microarrays (Agilent Technologies) were used to analyze gene expression. cRNA labeling, hybridization and detection were carried out according to supplier's instructions (Agilent Technologies). For each microarray, Cyanine 3-labeled cRNA were synthesized with the low input QuickAmp labeling kit from 50ng of total RNA. RNA Spike-In were added to all tubes and used as positive controls of labelling and amplification steps. The labelled cRNA were purified and 600ng of each cRNA were then hybridized and washed following manufacturer's instructions. Microarrays were scanned on an Agilent G2505C scanner and data extracted using Agilent Feature Extraction Soft-ware© (FE version 10.
Acknowledgements We thank  Lucie Lecouflet  and  Guillaume Desmartin  ( ORGAPRED Core facility, Services Unit PLATON, Université de Caen Normandie ),  Benoît Goudergues  ( Biological Resources Center 'OvaRessources' , Services Unit PLATON, Université de Caen Normandie and Comprehensive Cancer Center François Baclesse ) and  Marie-Hélène Louis  ( Inserm U1086 ANTICIPE unit ) for their helpful technical support. Thorel et al. J Exp Clin Cancer Res (2023) 42:260.
Funding LT was funded by a grant from Normandy Regional Council. PMM was supported by the "Institut Thématique Multi-Organisme Cancer" of the French National Alliance for Life and Health Sciences (AVIESAN) Plan Cancer 2014-2019 (doctoral grant) and from the Comprehensive Cancer Center F. Baclesse. HP was recipient of a doctoral fellowship from the Fench "Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation" granted by Normandy Doctoral School 497 "EDN Bise ". CT was recipient of a doctoral fellowship co-funded by the Normandy Regional Council and the "Ligue Contre le Cancer" (Calvados's commitee). This work is founded by Cancéropôle Nord-Ouest ("ORGRAFT" project), "Ligue contre le Cancer" (Calvados's commitee), Fondation de l' Avenir (#AP-RM-19-020), the "Fondation ARC pour la recherche sur le cancer" (#PJA20191209649).
Then, the acquired spectra were analysed by e-Spinoza software by correcting the baseline. The EPR signal intensity used for the analysis corresponds to the TMN positive derivative peak at 334. 5 mT. The area under the TMN peak at 334. 5 mT (Fig. S2 ) was calculated by Origin 8. 5 software. Vol:. (  1234567890  for which TMN (t = n) is the amount of free TMN calculated from recorded EPR spectrum at each time interval, TMN (t = 0) is the amount of free TMN calculated from recorded EPR spectrum at the starting point (t = 0) of the stability test and TMN (tr) is the amount of free TMN calculated from recorded EPR spectrum after treatment of Lipo-TMN with 1% (v/v) Triton-X100 that corresponds to the 100% TMN released. Long time stability was performed by leaving Lipo-TMN (25 mM TMN) immediately after its preparation at 4 °C and at different time intervals (n = 0, 2, 9, 25, 32 day) it was diluted 20 times in NaCl/Hepes buffer and measured by EPR.
Limit of detection of PLA2 activity. The limit of detection of PLA2 was measured by incubating for 3. 5 h Lipo-TMN at 0. 3 mM TMN concentration in NaCl/Hepes buffer (pH 7. 4) supplemented with 2 mM CaCl 2 as described above, at 37 °C, w/o or with PLA2 (0. 030, 0. 061, 0. 091, 0. 122, 0. 131, 0. 142, 0. 151, 0. 161, 0. 202, 0. 244, 0. 350 U/mL corresponding to 3. 44, 6. 88, 10. 32, 13. 75,14. 96, 15. 97, 17. 08, 18. 19, 22. 73, 27. 51, 39. 44 nM PLA2) under stirring (400 rpm) in a thermomixer (final volume 0. 2 mL). After the incubation, each sample was acquired by EPR. The EPR intensity (a. u. ) of each sample was plotted against Log of PLA2 concentration. A dose-response curve was obtained and EC20 were calculated by fitting the data with an Origin 8. 5 software using the Eq. (  2 ): where A1 and A2 are bottom and top asymptote, respectively; LOGx0 and p are center and hill slope, respectively.
LDhelmet performance Population-scaled recombination rates (⍴) were inferred from the simulated polymorphism datasets using the program LDhelmet  (Chan et al. 2012 ) (referred to as "inferred landscapes"). Three block penalty (BP) values (i. e. the degree of landscape resolution), which determine the number of allowed changes in ⍴ value within windows of 50 consecutive SNPs were tested (BP=5, 10, or 50) for each of the 4 SS-Ne combinations, resulting in 12 tested conditions (Figure  1A ). Underlying and simulated landscapes were converted into population-scale recombination rates (⍴=4Ner), and each underlying, simulated and inferred maps were smoothed in 500bp (i. e. underlying landscape resolution) and 2,500bp windows (i. e. a resolution-level better suited to the SNP density in our low-Ne simulations).
The question is then: how to obtain a good-quality recombination map when dealing with low Ne species? The sampling effort also determines, to a lesser extent, the polymorphism level of the dataset (Supplementary Figure  S3 ), improving the accuracy of the inference  (Figure 3, 4). A sample size of 20 is recommended based on our simulations. Moreover, as previously mentioned  (Chan et al. 2012; Singhal et al. 2015) , the block penalty parameter of LDhelmet, which determines the resolution level of the inferred landscape, also influences the length of the inferred map (i. e. a higher BP tends to mitigate the tendency of LDhelmet to overestimate the map length) and the number of detected hotspots (Supplementary Figure  S2, S6 ). Therefore, a small BP, that allows more fine-scale changes in the inferred ⍴ value, should be used to detect recombination hotspots.
Inferred landscapes Recombination rates were estimated for each of the simulated samples with LDhelmet (v1. 10,  (Chan et al. 2012) , Figure  1 ). Briefly, LDhemet uses phased sequence data to infer the rho parameter locally, using likelihood computation betweens pairs of SNPs and then averaging over 50 consecutive variants to obtain a composite likelihood. The ⍴ parameter is inferred with a reversible-jump Markov Chain Monte Carlo algorithm using a step function applied to every window of 50 consecutive SNPs and determined by three parameters: the number of change-points, the locations of changes, and the recombination rate value of each constant fragment between two changes. We used VCFtools 0. 1. 17 (Danecek et al. 2011 ) and the vcf2fasta function of vcflib (https://github. com/vcflib/vcflib) to convert the SNP data obtained from MSPRIME simulations into the input format to LDhelmet, consisting of FASTA sequences of each individual haplotype.
True/false positive rates (TPR = TP/(TP+FN) ; FPR = FP/(FP+TN)), true/false negative rates (TNR = TN/(TN+FP) ; FNR = FN/(FN/(TP+FN)), and true/false discovery rates (TDR = TP/(TP+FP) ; FDR = FP/(TP+FP)) were calculated by comparing the simulated and inferred landscapes. The mean pairwise linear correlation (R²) and the proportion of shared hotspots was calculated between the 5 underlying landscapes, and for each condition and for the three threshold values tested (i. e. 2. 5, 5 and 10) between the simulated and inferred landscapes from the 5 different underlying landscapes, as well as between the two populations from the same underlying landscape. The statistical analyses were performed using home-made R scripts available upon request.
Acknowledgments We are grateful to  Julien Joseph ,  Nicolas Lartillot ,  Frédéric Baudat ,  Bernard de Massy  and  Laurent Duret  for the discussions and feedback. We also thank  Khalid Belkhir ,  Jimmy Lopez  and  Mathieu Massaviol  for their support to build the Singularity container used to run our simulations. This project was funded by the  ANR HotRec   ANR-19-CE12-0019.
Immunosensor Analytical Performance SWV technique was utilized to evaluate the sensing process performance of the immunosensor. Figure  4A    The value of CPE of the fitted EIS complex data indicates a considerable increase after the deposition of nanomaterials on the bare electrode, indicating a massive increase in surface area and more porous surface morphology. Nevertheless, the CPE value decreases after the immobilization of HopQ-Ab and blocking with BSA, indicating the additional spacing added by immobilizing BSA and HopQ-Ab  [84].
This brief deals with the impact of spin-transfer torque magnetic random access memory (STT-MRAM) cell based on double-barrier magnetic tunnel junction (DMTJ) on the performance of a two-layer multilayer perceptron (MLP) neural network. The DMTJ-based cell is benchmarked against the conventional single-barrier MTJ (SMTJ) counterpart by means of a comprehensive evaluation carried out through a state-of-theart device-to-algorithm simulation framework. The benchmark is based on the MNIST handwritten dataset, Verilog-A MTJ compact models developed by our group, and 0. 8 V FinFET technology. Our results point out that the use of DMTJ-based STT-MRAM cells to implement digital embedded non-volatile memory (eNVM) synaptic core allows write/read energy and latency improvements of about 53%/61% and 66%/17%, respectively, as compared to the SMTJ-based equivalent design.
We evaluate the impact of DMTJ-based STT-MRAM cell on DNN, by using Cadence-Virtuoso environment for circuit-level simulations, along with the multilayer perceptron (MLP) + NeuroSimV3. 0 simulator computing-in-memory (CiM) based neural network accelerator  [7]. More precisely, NeuroSim is used to support a 2-layer MLP neural network to benchmark the DNN architecture, relied on SMTJ-based and DMTJbased digital synapse devices, in online learning and offline classification with MNIST handwritten dataset. Our results point out that the use of DMTJ-based STT-MRAM cell in a digital eNVM synaptic core allows write/read energy and latency improvements of about 53%/61% and 66%/17%, respectively, as compared to the SMTJ-based counterpart. This is also achieved by ensuring a learning accuracy of about 91%, suggesting that the DMTJ-based STT-MRAM cell could be a promising candidate for digital synapse in neuro-inspired computing.
II. SIMULATION FRAMEWORK -MLP + NEUROSIMV3. 0 NeuroSim simulator allows to estimate the algorithm-level performance by emulating the online learning and offline classification scenario with MNIST handwritten dataset in a 2-layer MLP neural network  [7] ,  [14] ,  [15] ,  [16]. As shown in Fig. 1 , the evaluation framework takes into account the whole system from device and bitcell levels to memory architecture and algorithm levels. The input parameters of the simulation tool include memory type, non-ideal device parameters, transistor technology node, network topology and array size, training dataset and traces, etc. For the full list of input parameters/variables, the reader is referred to  [7]. The outputs of the simulator include: (1) the memory architecture-level performance metrics, such as area, latency, dynamic energy, and (2) algorithm-level learning accuracy in run-time.
C. Algorithm Level At the algorithm level, the standard MNIST benchmark data is used for online learning (6k images for training dataset and 10k images for testing dataset) and offline classification  [7]. The considered MLP is a fully connected neural network, where each neuron node in one layer connects to every neuron node in the following layer. Fig. 1(e)  shows the flow of Neural Network, where the MNIST images are cropped and encoded into black and white data for simplification on hardware implementation. The network consists of an input layer, hidden layer and output layer. The connections between input-hidden and hidden-output layers represent the weight matrix W IH and W HO , respectively. As shown in Fig. 1 (e), the network topology contains 400 neurons (20×20 MNIST image) of input layer, 100 neurons of hidden layer, and 10 neurons (10 classes of digits) of output layer.
IV. CONCLUSION In this brief, we have explored the STT-MTJ synaptic pseudo-crossbar array architecture and device/transistor models in NeuroSim. We have used the NeuroSim emulator to evaluate the learning accuracy with 2-layer MPL neural networks at the run-time of online learning in eNVM devices such as MTJ-based STT-MRAM. Our results show that, at parity of TMR and oxide thickness, as compared to the conventional SMTJ-based alternative, the DMTJ-based solution proves to be faster during feed forward and weight update operations of about 18% and 66%, respectively, more energy efficient under read (-60. 7%) and write operation (-53. 7%), and less area hungry (-35%) at an energy-optimal bitcell configuration/size. This occurs while also achieving an accuracy closed to 91% when running the neural network with the MNIST dataset.
EURO-INSPIRED computing systems such as deep neural networks (DNNs) have been successfully Manuscript received 22 December 2022; accepted 23 January 2023. Date of publication 30 January 2023; date of current version 6 March 2023. This work was supported by the Italian Ministry of University and Research (MUR) through Project PRIN under Grant 2020LWPKH7. This brief was recommended by Associate Editor A. L. Zimpeck. (Corresponding author: Tatiana Moposita. ) Tatiana Moposita is with the Department of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, 87036 Rende, Italy, also with the Laboratoire d'Informatique, Signal et Image, Électronique et Télécommunications, Institut Supérieur d'Électronique de Paris, 75006.
N EURO-INSPIRED computing systems such as deep neural networks (DNNs) have been successfully Manuscript received 22 December 2022; accepted 23 January 2023. Date of publication 30 January 2023; date of current version 6 March 2023. This work was supported by the Italian Ministry of University and Research (MUR) through Project PRIN under Grant 2020LWPKH7. This brief was recommended by Associate Editor A. L. Zimpeck. (Corresponding author: Tatiana Moposita. ) Tatiana Moposita is with the Department of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, 87036 Rende, Italy, also with the Laboratoire d'Informatique, Signal et Image, Électronique et Télécommunications, Institut Supérieur d'Électronique de Paris, 75006.
Statistical Analyses of Experimental Data For the statistical comparison of average values from two datasets, an unpaired Student's t-test was applied after the appropriate evaluation of the normal distribution of the data. For the statistical comparison of values from multiple quantitative datasets from experiments where multiple variables applied, a one-way ANOVA test or a two-way repeated measures ANOVA test (i. e. , with the Geisser-Greenhouse correction) was performed and was followed (i. e. , when appropriate) by a posthoc Tukey's multiple comparison test. A p value < 0. 05 was retained as a general base for the statistical significance determination. The statistical calculations and/or data presentation were performed using Microsoft Excel, Microsoft PowerPoint (Microsoft Corporation, Redmond, WA, USA), and GraphPad Prism v.
97 and 0. 96 for unfiltered and filtered samples, respectively. Furthermore, the TEAC measurement results for the γ-irradiated versions of the same LYO-WC samples indicated that the dose-dependent relationship was conserved after γ-irradiation processing at 31 kGy, with R 2 values of 0. 99 and 0. 91 for unfiltered and filtered samples, respectively (Figure  1B ). In order to confirm the characterization data obtained in the TEAC assays, an alternative antioxidant capacity determination kit was used. A linear and dose-dependent relationship was again found for the same LYO-WC samples (i. e. , non-irradiated and unfiltered samples) when using a FRAP assay setup, where the experimental R 2 value was 0. 98 (Figure  S5A ). In addition to the linear and dose-dependent nature of the TEAC values for whole-cell lyophilizates, it was determined that small (i.
e. , potential products of sugar radiolysis, Figure  1B ). However, the maintenance of the linearity between the observed TEAC values and the cellular extract concentration after γ-irradiation and the comparison of absolute TEAC values between datasets indicated that additive properties existed between the observed TEAC of γ-irradiated sugars and γ-irradiated biological components (Figure  1A, B ). A separate experiment aimed to compare the TEAC of freshly harvested progenitor tenocytes (i. e. , in viable form from confluent in vitro culture monolayers) to the TEAC of whole cell lyophilizates (i. e. , LYO-WC) of the same cell type. The results indicated that fresh cells possess significant antioxidant activity, which is not significantly impacted by 0. 22 µm filtration (Figure  S5B ). Furthermore, the TEAC of fresh cells was not found to be statistically significantly lower than that of lyophilized cells (i.
In Pakistan we deployed a pre-post design, whereby 25 city districts received six weeks of the intervention. Weekly COVID-19 vaccination data was sourced through government databases. Using Poisson regression models, the association between the intervention and vaccine uptake was estimated. In Ukraine we conducted a survey among Facebook users at three time points during the RCT, to ascertain vaccination intentions and trust in vaccines. The campaigns reached more than 110 million Facebook users and garnered 2. 9 million clicks. In Ukraine, we found that the intervention did not affect oblast-level vaccination coverage (Relative Risk (RR): 0. 93, 95% Confidence Interval (CI) 0. 86-1. 01). Similarly, in India and Pakistan we found no effect of our intervention (India: RR 0. 85, 95% CI 0. 70-1.
Study designs and randomization For all three countries the feasibility to conduct an RCT was assessed. However, due to the availability and granularity of COVID-19 vaccination data and the varying geographies, three different prospective study designs were employed for Ukraine, India, and Pakistan. Ukraine. In Ukraine, a stepped-wedge RCT was designed, with the 24 'oblasts' (i. e. , provinces, Kyiv region and Kyiv city were combined into one oblast) of the country as clusters. While the number of clusters was relatively small, the stepped-wedge design enhanced the statistical power of the trial. With this design, in Step 0 (the first five weeks) no oblasts received intervention. At Step 1, in the second five-week period, half of the oblasts received intervention, while the other half did not receive intervention.
Due to the large number of districts in these states (174), we were able to use a parallel RCT, whereby 40 of the 174 districts were randomized to receive the intervention messages for six weeks, see S3 Table. We used an augmented synthetic control method  [26, 27] , utilizing the GeoLift tool from Facebook to run simulations to identify a combination of districts that would produce a well-powered study. For this, we used data from the first 53 weeks of the COVID-19 vaccination in the five states, from January 16, 2021 to January 15, 2022. The simulations determined that the number of districts that should be assigned to intervention should be 40, to have 80% power to detect an increase in vaccination of 2%. Districts that were assigned to intervention received the posts on Facebook between February 19, 2022 and March 31, 2022.
Like the campaigns in Ukraine and India, the posts were optimized for reach on Facebook. The intervention took place between February 5, 2022 and March 18, 2022. The studies were funded by Meta and approved by the Yale Institutional Review Board (protocol number: 2000031351). Informed consent was obtained before the start of the surveys in Ukraine. Potential participants were asked to read our information sheet online, containing information about the study as well as contact information for the Yale research team. Participants were then asked for their consent. Those who consented continued to the main survey. The study protocol and analysis plan for each country was registered with Open Science Framework prior to data collection: https://osf. io/27t6r/.
Outcomes & statistical analysis Primary outcomes. The primary outcome of these studies was the regional COVID-19 vaccination coverage across time. This was measured using the weekly updates on the number of first and second doses by oblast (Ukraine), district (India), and city/district (Pakistan). In Ukraine, weekly COVID-19 vaccination data were obtained through a database maintained by the Ukrainian government (https://health-security. rnbo. gove. ua/). In India, weekly vaccination doses were ascertained through the COWIN Dashboard, which was maintained by the Ministry of Health in India. UNICEF Pakistan provided weekly COVID-19 vaccination data, sourced from a database maintained by the Government of Pakistan. To understand whether the intervention had an effect on COVID-19 vaccine uptake, we used Intention-to-Treat (ITT) analyses in Ukraine and India.
We considered a lagged relationship between the outcome and the intervention variable to account for the possibility that it takes time once an intervention begins to see its effect more widely. Therefore, instead of including the intervention indicator on the same week as the coverage outcome, we included the intervention status from the week before (i. e. , one week lag). The model was fitted in R Statistical Software (R Core Team 2022). In India, with the parallel RCT design, we compared the intervention group to the control group, using the same Poisson regression analysis as previously described for Ukraine. We once again used a one-week lag for the intervention variable. However, the intervention variable differed in the India analysis as some regions never received intervention (i. e.
e. , survey wave), study step (no intervention, intervention), age group (18-29, 30-44, 45-59, 60+), sex (male, female) and region (West, Center, East, South). Oblast was added to the model as a fixed effect. Tertiary outcomes. In all countries, data were obtained from Meta on: • Reach: how many individuals were reached with the ads • Impressions: how many times users were reached in total with the ads • Clicks and clickthrough rate: clicking on link to access further information or schedule a vaccination appointment, the clickthrough rate was calculated by dividing the number of clicks by the number of impressions Primary, secondary and tertiary analyses were registered before data were collected at OSF: https://osf. io/27t6r/. Sensitivity analyses/non-registered analyses. We carried out a non-registered sensitivity analysis in Ukraine, whereby we look at actual exposure to the intervention, which was determined by reach and traffic of the ads in each oblast.