dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This study was funded by grant  ANR-10-IAIHU-06  from the program " Investissements d'avenir" , by grant  FRM DEQ20150331725  from the foundation " Fondation pour la recherche médicale ", and by the  ENEDIS company. Valérie Godefroy  is supported by  "Fédération pour la Recherche sur le Cerveau". Arabella Bouzigues  is supported by " Fondation Vaincre Alzheimer ". Raffaella Migliaccio  is supported by  France Alzheimer  and  Philippe Chatrier Foundations , and by  Rosita Gomez  association. We sincerely acknowledge the participants and caregivers for their involvement in this study. Authors' disclosures available online (https:// www. j-alz. com/manuscript-disclosures/ 22-0370r2 ).
Donors and ethical compliance Our study is in agreement with applicable French regulations and with the ethical principles of the Declaration of Helsinki. PBMCs of healthy blood from anonymous donors at the Toulouse blood transfusion center (Etablissement Français du Sang) were from a biobank authorized under agreement number 2-15-36 by the competent ethics board Comit é de Protection des Personnes Sud-Ouest et Outre-Mer II, Toulouse. The cohort ANRS EP_53 X-LIBRIS (coordinator: Pr. P. Delobel, CHU Purpan) allows the study of HIV-1 donors has been previously described  (Azar et al, 2020). The X-LIBRIS cohort was previously registered with ClinicalTrials. gov under identifier NCT01952587.
Flow cytometry cell sorting Cells were incubated with anti-human CD32 (Fc gamma RII) (STEMCELL) for 5 min at 4°C. For cell surface markers, cells were stained in MACS buffer (PBS with 1% FBS, 2 mM EDTA) for 30 min at 4°C with the following antibodies: CD14-VioBlue (clone REA599; Miltenyi Biotec), CD19-PEVio615 (clone LT19; Miltenyi Biotec), BDCA4-APC (clone REA774; Miltenyi Biotec), and CD123-PEVio770 (clone AC145; Miltenyi Biotec). Then cells were stained for viability with DRAQ7 (Abcam) for 5 min at 4°C. pDCs were sorted with a FACSAria II or FACS Aria-Fusion Cell Sorter (BD Biosciences) at one cell by well. Data were analyzed with the FlowJo software V10 (Tree Star).
The qPCR and KASP fluorescence-based assays were performed using the automated pipetting system epMotion 5070 (Eppendorf) and a Light Cycler 480 instrument (Roche). Relative allelic expression was calculated from the ratio of the FAM (allele C or A) and HEX (allele G or T) fluorescence signals from the respective KASP probes; a four-parameter standard curve was generated regularly for each Light Cycler 480 unit, using an R script based on package drc as described  (Souyris et al, 2018). Standard curves were generated by mixing DNA samples from men of rs3853839 G/0 and C/0 genotypes or rs179008 A/0 and T/0 genotypes. Biallelic TLR7 expression in a cell was inferred when the relative expression values of TLR7 transcripts bearing the rs3853839 G allele or rs179008 T allele was comprised between 10% and 90%.
Statistical analysis Statistical analyses and data presentation were performed using GraphPad Prism 9 software (La Jolla). Statistically significant differences between two groups were determined using two-tailed. Mann-Whitney or unpaired t test was performed when indicated. Multiple comparisons were performed using the one-way ANOVA test followed by Tukey's posttest corrected for multiple comparisons as indicated. Values are reported as individual values and plotted as mean ± SEM or as median and IQ range. All statistical tests were two-tailed, and P-values <0. 05 were considered to be statistically significant.
Acknowledgements We gratefully acknowledge support from  F. L'Faqihi ,  L De La Fuente ,  AL Iscache ,  V Duplan , and  Hugo Garnier  at the flow cytometry facility (INSERM U1291, INFINITY);  PE Paulet at the Immunomonitoring core facility  ( INSERM U1291 , INFINITY). The technical assistance of  M Requena ,  M Cazabat , and  R Carc énac  ( Toulouse University Hospital, INSERM U1291, INFINITY ) is also greatly acknowledged. This work was supported by grants from the  French National Agency for Research on AIDS and Viral Hepatitis (ANRS ,  EP-53  study) and SIDACTION (Grant  2018-1-AEQ-12035 ). F Abbas ,  P Azar , and  A Youness  were supported by fellowships from  SIDACTION. A Youness  was also supported by PhD fellowships from the  CSL Behring Research Funds ,  Fondation des Treilles , and from the  "Association de la Charit é des jeunes de Kafarsir" (Libanon).
Propidium iodide integration analysis by flow cytometry and fluorimetry. After either 12 or 24 h of transfection with LPS, RAW-ASC cells were stained with 1 mg/mL of propidium iodide (PI, Sigma). For flow cytometry analyses at 12 h, cells were then washed once in PBS with 2 mM EDTA and 0. 5% v/v HI-FCS, scrapped and analyzed directly by flow cytometry on CytoFLEX (Beckman Coulter). Between 15 000 and 30 000 events were acquired for each condition and the data was analyzed with FlowJo V10 software. For fluorimetry analyses at 24 h, the kinetics of PI integration was monitored immediately at 535 nm (ex) / 617 nm (em) on a TECAN Spark fluorimeter (Life Sciences).
Internal loading control was performed by stain free visualization after 5 min activation on a ChemiDoc imaging system (BioRad). Protein transfer was done either on nitrocellulose membrane or polyvinylidene fluoride (PVDF) membrane (Table  1 ). Membranes were then saturated in TBS with 0. 01% v/v Tween (TBST) with 5% w/v BSA for 1 h at room temperature. Incubation with primary antibodies was performed in TBST with 1% w/v BSA overnight at 4°C (Table  1 ). After three washes in TBST, secondary antibody incubation was done in TBST with 5% w/v BSA for 1h at room temperature under mild agitation (Table  1 ). After three washes in TBST, HRP activity was revealed using the SuperSignal West Femto Commercial TMB Substrate (ThermoFisher) on ChemiDoc imaging system (BioRad). WB quantification was performed on raw data in ImageLab software.
Transfers and Western blots were performed as previously described after denaturing SDS-PAGE. mRNA analyses by RT-qPCR. mRNA was extracted either from the frozen kidneys recovered 3 days post-infection, or from frozen mouse BMDMs using RNeasy Mini kit (Qiagen). Reverse transcription (RT) was performed using Superscript II reverse transcriptase (Invitrogen) according to the manufacturer's recommendations. Generated cDNAs were used for quantitative PCR (qPCR) on a StepOne Plus real-time PCR machine (Applied Biosystems), with primers and probes targeting murine HPRT, caspase 11, IL1, IL6 and RANTES (Table  2 ). The following settings were used (relative quantification program): 50°C for 2 min, followed by 95°C for 10 min and by 40 cycles of 95°C for 15 s and 60°C for 1 min. Data were analyzed using the comparative 2 -Ct method, using a first normalization by internal control HPRT and a second normalization by non-infected controls.
Acknowledgments We acknowledge  Damien Arnoult ,  Stephen Girardin  and  Dana Philpott  for fruitful discussions and for their kind suggestions. We also acknowledge  Sonia Lacroix-Lamandé  and  Sylvain Bourgeais  ( UEPAO, INRAE CVL, France ) as well as  Nadia Benaroudj  ( Institut Pasteur Paris, France ) for kindly providing the bovine and hamster bones, respectively. Finally, we acknowledge  Frédérique Vernel-Pauillac  for initial training of students.
However, and surprisingly, to our knowledge, no study has compared telephone interview and internet-based auto-questionnaire methods for collecting SF-36 data to investigate whether they can be used as alternative methods in mixed mode data collection procedures according to participant preferences and minimize the possible selection bias. This study investigated such questions in detail, owing to the availability of SF-36 data that had been collected in a multicenter randomized trial. The SENTIPAT trial  [27]  explored the concept of sentinel patients who would voluntarily report their health evolution on a dedicated website. Participants enrolled in this trial were randomized to either the internet or the telephone group; patients in the internet group were invited to self-complete questionnaires on their health evolution after their hospital discharge via a dedicated website, whereas patients in the telephone group were invited to complete the same questionnaires through telephone interviews.
However, 2 previous studies issued from the SENTIPAT trial have been reported: the first introduced an original questionnaire developed in the SENTIPAT study to investigate the opinion of patients about the organization of their hospital discharge  [28] , and the second introduced the I-Satis questionnaire, a questionnaire that was distributed in hospitals at a national level in France to investigate patient satisfaction at the time of the SENTIPAT trial  [29]. As the SF-36 questionnaire was also included in the SENTIPAT trial, the corresponding collected data were a perfect opportunity to precisely investigate the influence of the mode of administration of the questionnaire on SF-36 scores. This investigation is the aim of the ancillary study of the SENTIPAT trial reported here.
Survey Administration Patients in the internet group had access to the French version of the SF-36 questionnaire 40 days after discharge on a website dedicated to SENTIPAT. Oral and written instructions had been delivered to these patients for a personal connection to the SENTIPAT website, and they received 1 reminder email per week for 3 weeks in case of nonresponse. Patients in the telephone group were interviewed by telephone approximately 42 days after discharge, and the data were simultaneously entered into the system by the interviewer using a website interface identical to that used in the internet group. The appointments for the telephone interviews of the patients in the telephone group were scheduled at the moment of patient inclusion, and up to 3 calls were tried whenever the first call did not reach the patient.
The raw score of each scale was computed by the algebraic sum of the corresponding item values (the values assigned to each precoded answer were calibrated) and then normalized to a score value ranging from 0 (lowest possible) to 100 (highest possible). According to the recommendations, scale score calculations were performed only if at least half of the items involved were answered, and in such a case, missing item data were treated with a person-specific approach that uses the average score of the completed items on the same scale. Finally, the two remaining scores, the physical and mental component summary scores, were obtained by assigning predefined specific weights to each of the 8 scale scores.
Assessing the respective impact of these 2 features on the observed differences between the SF-36 scores observed in internet and telephone responders is of primary importance, and to get more insight into this issue, we developed a procedure in which responders of the internet group were matched to similar responders of the telephone group according to their baseline characteristics, and we further examined how the score differences between the 2 groups changed in this matched sample, as compared with the score differences observed in the initial unmatched populations. Internet responders were matched to telephone responders according to a propensity score-based procedure, and the R package MatchIt  [35]  was used to match each internet responder to the nearest telephone responder in a 1:1 ratio.
Cryo-electron microscopy (cryo-EM) represents a powerful technology for determining atomic models of biological macromolecules 1. Despite this promise, human-guided cryo-EM data collection practices limit the impact of cryo-EM because of a path planning problem: cryo-EM datasets typically represent 2-5% of the total sample area. Here, we address this fundamental problem by formalizing cryo-EM data collection as a path planning optimization from low signal data. Within this framework, we incorporate reinforcement learning (RL) and deep regression to design an algorithm that uses distributed surveying of cryo-EM samples at low magnification to learn optimal cryo-EM data collection policies. Our algorithm -cryoRLsolves the problem of path planning on cryo-EM grids, allowing the algorithm to maximize data quality in a limited time without human intervention.
Main Cryo-EM is one of the fastest-growing areas of structural biology, enabling 3D structure determination of important macromolecular complexes and membrane proteins  2. The impact of cryo-EM ranges from plant biology to human health and disease. Indeed, the widespread growth of cryo-EM has advanced our understanding of pathogens, such as SARS-CoV-2  3  , and has had a direct impact on drug development  4. Cryo-EM is an expensive and highly involved technique. For example, high-end microscopes cost millions of dollars to purchase and require yearly service contracts costing hundreds of thousands of dollars and full-time staff to maintain the instrument. To collect cryo-EM data, users require microscope time for multiple days to collect sufficient high-quality data in addition to waiting weeks or months to gain access to high-end cryo-EM instruments.
Thus, increasing the throughput of cryo-EM data collection is critical for advancing biomedical research efforts and reducing the cost of cryo-EM. Major advances in microscope hardware (e. g. , detectors  5  and aberration-free image shift  6, 7  ) have led to rapid data acquisition strategies. Despite collecting hundreds of exposures per hour, much of the collected data is discarded during processing. For example, from 2019 to mid-2021, only 50% of exposures collected in the Cianfrocco laboratory had resolution estimates of < 6 Å. This is because cryo-EM users do not know a priori which regions of a cryo-EM grid will produce the highest resolution data. Analysis of micrograph quality across different regions of a grid highlights the complex data landscape users must navigate (Extended Data Fig.
1 ). Cryo-EM samples are challenging samples for data collection, indicating that path planning optimization in addition to rapid data acquisition stands to improve microscope performance and throughput. Artificial intelligence provides a powerful framework to recognize patterns in complex datasets. Recently, incorporating deep learning models into a reinforcement learning (RL) algorithm enabled RL to surpass professional human performance in challenging games such as AlphaGo  8  and Dota 2  9. These approaches demonstrate superior capabilities in learning strategic Li*, Fan* et al. 2022 (preprint) 1 moves from simulation data. Given that many problems in the real world involve perception, planning, and decision-making, RL may impact many areas of human life, such as self-driving vehicles 10 , robotics  11  , healthcare  12  , and even trading and finance  13.
However, the application of RL in life sciences remains underexplored due to the lack of domain knowledge needed to formulate RL into scientific processes. As a step toward developing fully automated intelligent decision-making for cryo-EM data collection, we present an RL-based framework, cryoRL. The goal of cryoRL is to identify the optimal path across cryo-EM samples to collect as much high-quality data as possible in a given time period (see Methods, Problem Formulation). Our algorithm combines a low-magnification survey with pretrained classification models that feed into a deep-Q network for learning data collection policies. We benchmarked the performance of cryoRL against human subjects using a cryo-EM data collection simulator, allowing both cryoRL and humans to simulate data collection on the same dataset.
Our results show that cryoRL outperforms 9 out of 10 human subjects, suggesting that cryoRL will serve as a framework to automate cryo-EM data collection from all cryo-EM instruments. Our data suggest that optimized path planning with cryoRL will enable an approximately 40% improvement in microscope throughput, thus improving access and throughput on these instruments, thereby reducing the overall cost of cryo-EM.
Optimization of path planning on cryo-EM samples with cryoRL Given an unexplored cryo-EM grid, the goal of a data collection session is to obtain the maximal number of micrographs with high-resolution information in a limited time. Importantly, the data collection path needs to be optimized by reducing the time cost associated with stage movement and Z-height adjustment of the microscope. Therefore, two critical problems in cryo-EM data acquisition are 1) quality prediction at the low or medium magnification and 2) trajectory planning during the data collection session. In traditional cryo-EM data collection, users utilize a sequential approach to interrogate, assess, and steer data collection (Fig. 1A ). First, a user will generate an atlas of low magnification "grid-level" images to obtain an overview of the sample (Fig.
2A, see Methods). For this sample, we trained the deep regressor and deep Q-network on a subset of the cryo-EM grid. The deep regressor successfully captured the correlation between hole images and CTFMaxRes values (Extended Data Fig. 2A ). Using these trained models, cryoRL collected micrographs from "good" holes on the grid (Fig. 2B  & 2C ). Given that the overall candidate holes in this dataset are low quality (less than 40% of candidates had a CTFMaxRes < 6 Å (Fig. 2B ) -cryoRL collected ~80% of images with a CTFMaxRes < 6 Å (Fig. 2C ). To visualize the behavior of cryoRL, we overlaid a cryoRL trajectory on the cryo-EM sample (Fig. 2D-2G ). cryoRL visited six squares during data collection. Zooming in on a selected square, we see that this specific square contained holes with different ice thicknesses and quality (Fig.
2D ). CryoRL successfully avoided empty holes or holes with very thick ice (Fig. 2E ). Compared to the ground truth CTFMaxRes of all the candidate holes, we found that cryoRL collected micrographs with low CTFMaxRes only in the patches with many high-quality holes (Fig. 2F  & 2G ). Therefore, cryoRL learned an effective policy that minimizes the time cost increase resulting from moving to a different patch. We conclude from these results that cryoRL successfully collected high-quality micrographs when trained on a small subset of the dataset.
Transferred models enable effective path planning on different cryo-EM samples and grid types Our result on Aldolase showed that cryoRL learns a policy to collect high-quality micrographs when both the regressor and RL network are trained from a subset of the same grid. However, RL models need to be trained on a subset of the dataset where all the possible holes are collected with a ground truth CTFMaxRes. A dataset like this with a sufficient sample size for training is usually not available and not practical to obtain. Therefore, RL models must have good transferability so they do not need to be trained on the same dataset. To test the transferability of RL models, we collected a dataset of a different cryo-EM sample, Apoferritin. We utilized a general deep regressor that was trained on 100,578 hole images from a variety of sample types with associated CTFMaxRes values (see Methods, Extended Data Fig.
2B ) in combination with the deep-Q network from Aldolase (Fig. 3A ). The resulting images collected by cryoRL showed a strong enrichment for images < 6 Å (Fig. 3B ), indicating that the transferred models' allowed high-quality data collection. The overall performance of cryoRL is 1. 5 times above the baseline with ~80% of collected images < 6 Å (Fig. 3C ). Given the performance of cryoRL on Apoferritin, we switched to a different sample on a different grid: RNA Polymerase II (RNAPII) on Au-Flat 1. 2/1. 3. CryoRL utilized a deep regressor that was fine-tuned from the pretrained general regressor with a subset of holes from the RNAPII grid (see Methods, Extended Data Fig. 2C ). For the deep-Q network, instead of training on the same grid, we used the deep-Q model from the Aldolase dataset. Using the fine-tuned deep regressor and transferred deep-Q network (Fig.
CryoRL outperforms humans To test human performance against cryoRL on the same dataset, we designed and implemented a cryo-EM simulator (see Methods, Supplemental Movie 1). We compared the performance of cryoRL versus human subjects on a new systematic dataset of rabbit muscle Aldolase on a different grid type: UltrAufoil 1. 2/1. 3 ("Aldolase Au ", Fig. 4A ). This dataset consisted of 5822 hole images and their ground truth CTFMaxRes from 30 squares. We asked ten users ranging from 3 months to 10 years of cryo-EM experience (see Methods) to collect data with the simulator as if they were in a real data collection session. We compared the performance of human subjects with cryoRL that used the pretrained general deep regressor (Extended Data Fig. 2D ) and the DQN trained with Aldolase Au (Fig.
4A ). From the trajectories of the number of micrographs with CTFMaxRes < 6 Å over time, we found that cryoRL outperformed 9 out of 10 human test subjects (Fig. 4B ). Interestingly, human subjects showed a large variance in performance. The slopes of the lines indicated the "good data rate". CryoRL showed a consistent "good data rate", whereas the rate of most test subjects varied over time (Fig. 4B ). We quantified the averaged performance of human test subjects and compared them with that of the baseline and cryoRL (Fig. 4C ). The baseline showed that this dataset contained only 12% of targets with a CTFMaxRes < 6 Å (Fig. 4C ). The average performance of humans exceeded the baseline, but the variance was significant (Fig. 4C , Extended Data Fig. 3 ). CryoRL outperformed humans by about 43% on average, with a much lower variance, suggesting higher consistency (Fig.
4E ). Surprisingly, we found that human subjects had an average of 0. 13 similarity coefficient, further underscoring the variability of data collection (Fig. 4E ). However, the three users who were able to collect over 200 micrographs < 6 Å (Human-2, -7, and -9) had the highest similarity to cryoRL (Extended Data Fig. 3 , Fig. 4E ). Not surprisingly, the similarity among the best-performing users was relatively high (Fig. 4E ). On the other hand, the three users who collected less than 100 good micrographs (Human-1, -3, and -10) had weak concordance with cryoRL and among themselves (Extended Data Fig. 3 , Fig. 4E ). This comparative analysis showed that good micrograph selections were alike within this dataset, but every bad micrograph selection was bad in its own way.
Discussion In this work, we show that cryoRL optimizes the path of cryo-EM image collection to obtain the most high-quality images in a limited time across four independent datasets. Among our four experiments, three were tested with transferred models, showing the generalizability of cryoRL. Our results showed that the RL networks were able to provide robustness and place cryoRL in the top 10% of human study subjects. Importantly, the deep-Q network is highly transferable as all four experiments shared the same deep-Q model. Our design of low-magnification prediction with RL policy training enables cryoRL to identify optimal paths across cryo-EM samples. The deep regressor provides a consistent prediction of the data quality at the hole level, allowing cryoRL to map thousands of holes, a task that is nearly impossible for human users.
Semi-supervised learning  17  or active learning  18  may be useful techniques that can be applied to cryoRL in the future. cryoRL outperforms average cryo-EM users due to consistent data collection rates on local regions of high-quality images. Analysis of cryoRL behavior versus human subjects showed that cryoRL visited 4 squares, whereas human subjects visited an average of 12 squares (Fig. 4D ). Moreover, from these four squares, cryoRL obtained a near-constant "good data rate" (Fig. 4B ), whereas human subjects showed large variability in the rate of collection (Fig. 4B ). From these data, we conclude that cryoRL is able to consistently collect high-quality data. The reward table within cryoRL will enable tuning cryoRL for different data collection behaviors. For example, by increasing the reward for images from different squares, we can encourage cryoRL to explore larger regions of a sample, as in data screening.