dataesr/scientific-paragraphs-categorization huggingface dataset:
The time costs and the rewards associated with them can also be tuned to reflect the specifications of different instruments. The generality of the cryoRL framework allows for any micrograph metric to serve as a target for reward. In some cases where CTFMaxRes is not a good indicator for data quality, the deep regressor can also be trained to predict other metrics, like data quality measurement output by MicAssess  19  or picked particle numbers, as long as the model is able to converge. We believe that cryoRL has great flexibility and potential to automate both data collection and data screening in various scenarios. Overall, the cryoRL approach will allow efficient microscope usage through optimized path planning and expert-level performance without the need for human intervention. From our results presented, we estimate that microscopes will be 140% more productive than current usage, thus expanding the pool of available microscope time for users worldwide on high-end cryo-EM instruments.
Features to DQN A good RL policy, if possible, should always reward small microscope movements. In other words, the hole path planning strategy should prioritize areas with rich low CTFMaxRes holes first. The number of low CTFMaxRes holes on a patch (square or grid) image is closely related to the quality (or value) of the image in cryo-EM data collection. Based this, we design hierarchical features for the DQN that represent the quality of images at different levels, including histograms of CTFMaxRes on unvisited holes and the number of visited and unvisited low CTFMaxRes holes. The histograms are smoothed by a Gaussian kernel to improve robustness against the imperfect regression results. We listed the details of these features, which are computed separately for each hole and linearly normalized between 0 and 1 in Extended Data Table  2.
Time cost and RL Rewards In real cryo-EM data collection, the time needed to collect a micrograph varies depending on how far the stage needs to be moved from its previous position. In this study, we defined the time cost as in Extended Data Table  1. 𝑡(ℎ 𝑖 ) When the CTFMaxRes of the micrograph collected from is lower than a given threshold , the reward for each hole ℎ Thus, a partial reward can be gained if the obtained CTFMaxRes is only slightly higher than the threshold. In this  study, we presented data with. = 6 Å.
Snowball baseline policy We designed a snowball baseline to compare with the results of cryoRL. The snowball baseline will start from a random hole target and aims to maximize the total number of micrographs in a given time duration regardless of the quality. To train this policy, we used in the reward function so that the reward only depends on the time cost. = 30 Å The baseline RL model was trained on the aldolase dataset and the same model was applied throughout this study.
Cryo-EM simulator and human study We conducted a human study to directly compare the performance of cryoRL with humans. The dataset used was Aldolase Au. We developed a simulator that mimics real data collection (Supplemental Movie 1) without the waiting time to acquire each image. The hierarchical layers in the dataset were preserved and well reflected in the simulator. The tested users were asked to collect 450-500 micrographs, and the resulting trajectories were recorded. We asked the users to collect as many as good micrographs (with low CTFMaxRes), while trying to minimize the time cost of moving the stage to distance positions, as in a real data collection. The users were given the freedom to look through the low magnified images at different hierarchies without penalty for a fair competition to cryoRL.
However, note that this is not possible with the traditional "sequential" data collection strategy. The tested users were told the same time cost table (Extended Data Table  1 ). We then translated the trajectories with the time costs used in this paper and truncated the trajectories at 480 min to compare with the performance by cryoRL on the same dataset. The simulator has two modes, "inspection mode" and "queueing mode". In the inspection mode, the ground truth CTFMaxRes will be given once the hole is "collected" immediately. In the queueing mode, the user may queue up a number of hole targets without knowing their ground truth CTFMaxRes values until switching to the inspection mode. The tested users include experts who are scientists with over 8 years of cryo-EM data collection experience and are still active in collecting data, and novices who have less than 1 year of experience in cryo-EM data collection but have prior knowledge of cryo-EM data collection.
Acknowledgments We thank members of the  Vos and Cianfrocco laboratories , especially  Hye Jee Hahn  and  Nicholas Vangos  for preparing cryo-EM samples of aldolase and apoferritin;  Christopher Lilienthal  for help with Leginon/Appion database interrogation. We thank the  Protex facility  at the  University of Leicester ,  Louise Fairall , and  Christos Savva  for mouse apoferritin plasmid and protocol for purification. We thank  Lucas Farnung  for preparing the RNAPII samples. We thank  Liz Kellogg ,  Joey Davis , and  Bolei Zhou  for giving us comments on this manuscript. Research reported in this publication was supported by the  University of Michigan Cryo-EM Facility (U-M Cryo-EM). U-M Cryo-EM  is grateful for support from the  U-M Life Sciences Institute  and the  U-M Biosciences Initiative.
Study Design and Overall Procedure We conducted a prospective cohort study to collect data before and during the 24th European Athletics Championships in Berlin in 2018 (EOC2018, 7-12 August 2018; https: //www. europeanchampionships. com/2018-berlin-glasgow (accessed on 29 August 2022)) on injury and illness complaints during the month before the Championships, HMI history during the entire career, hamstring specific training, and in-championship injury and illness including in-championship HMI. There was no patient and public involvement. The study protocol was reviewed and approved by the Saint-Etienne University Hospital Ethics Committee (Institutional Review Board: IORG0007394; IRBN742020/CHUSTE) and all included athletes provided their informed consent.
Data Collection Before the start of the championships, athletes registered for the EOC2018 were asked by EA through their national federation and/or national medical team to fill in a PPHQ. The PPHQ was developed by three sports medicine physicians with extensive experience in athletics medicine (PE, PB and FD). It was available in paper and electronic format in English. Athletes were asked to complete the questionnaire themselves and to return it to designated desks at their hotel or the warm-up area, or to their medical team who gave it to the main investigator (PE). The PPHQ included two parts similar to that from previous published studies:  [17, 18]  (1) athletes' characteristics: country, sex, date of birth, discipline, height, weight and mean training time per week during the four preceding weeks; (2) pre-participation health problems (analysed on a binary basis: yes/no) during the four weeks preceding the championship; and a third part (3) on history of HMI and hamstring specific training (Supplementary File S1).
Confidentiality The athletes' sex, date of birth and nationality were used to match data from the PPHQ and the in-championship registration of injury. Information about the purpose of the study and the procedure was provided to the athletes in writing and at information desks at the athlete hotels. All athletes were free to refuse the inclusion of their in-championship injury and illness data in the interpretation. All PPHQ and injury and illness reports were stored in a locked filing cabinet and were made anonymous after the championships. The confidentiality of all information was ensured so that no individual athlete or national team could be identified.
We then analysed the potential differences according to sex × discipline (i) in the distribution of number of athletes with history of HMI during their career and during the 2017-18 season using Chi2 tests, and (ii) for hamstring specific training using Chi2 tests for hamstring strengthening, hamstring stretching, core stability conditioning and/or sprinting (i. e. , categorical variables) and using an ANOVA for the HST score. The significance level was initially set at p < 0. 05. Statistical analyses were performed using JASP (JASP Team software, Version 0. 14. 1, University of Amsterdam, Amsterdam, The Netherlands, https://jasp-stats. org/download/ (accessed on 29 August 2022)). To analyse the potential factors associated with HMI occurrence during the championships, we performed binomial logistic regression with in-championships HMI (yes/no) as the dependent variable and sex x discipline, age, country, history of HMI during the career (yes/no), history of HMI during the 2017-18 season (yes/no), injury complaint (yes/no), illness complaint (yes/no), strengthening, stretching, core stability, sprinting, HST score (0-16) as independent variables.
Population Among the 51 national federations registered at the EOC2018, 24 (47. 1%) agreed to participate in the study, including a total of 794 eligible athletes (50. 6% of the 1570 athletes registered at the EOC2018). Among them, 357 (45. 0%) athletes (22. 7% of eligible athletes) agreed to participate, returned their questionnaires, and were included in the present study. Analysis of the non-responders did not show significant differences between eligible and included athletes for the distribution of discipline but did so for sex (higher proportion of female athletes in the included athletes (53. 8%) compared to the eligible population (45. 3%), p = 0. 005) and age (slightly higher proportion of athletes under 20 and older than 35 among included athletes (7. 0% vs. 4. 0%), p = 0. 01). None of the included athletes refused to allow their data to be used for scientific research.
The characteristics of the 357 included athletes are reported in Table  1. Table  1. Characteristics of the 357 included athletes who participated at the 24th European Athletics Championships in Berlin in 2018 and who were included in the present study, as well as their history of hamstring muscle injuries (HMI), preparticipation health problems and in-championships injuries, illnesses and hamstring muscle injuries. Data are presented using mean with standard deviations (±SD) for continuous variables and using number and frequency with percentages for categorical variables. Note: Data on sex, age, discipline, height and weight were available for all responders (n = 357; 100%). Data on mean time training was available for 93. 0% (n = 332), on health problem during the month before the championship for 94.
In-Championship Health Problems Among the 24 national federations who accepted to participate in this study, 16 medical teams (24%) covering 87. 1% of included athletes participated in the injury and illness in-championships data collection and returned 100% of the expected report forms. Forty-one (11. 5%) of the included athletes sustained a health problem during the championships; 26 (7. 3%) athletes sustained at least one in-championship injury, 17 athletes (4. 7%) reported at least one in-championship illness, and 2 athletes (0. 6%) sustained both. Five in-championship HMIs were reported among the 357 included athletes during the championships (Table  1 ): three in male athletes (60%) and two in female athletes (40%), all in sprints (100%), three during competition (60%), and all occurring suddenly (100%).
Patients Medical records of patients from ten hospitals in Mexico with a con rmed genetic diagnosis of MSMD due to recurrent and severe infections by intracellular bacteria and fungi from 2006 to 2021 were retrospectively reviewed. This study was approved by the ethics committee of the National Institute of Pediatrics, Mexico. All centers were contacted via e-mail and requested to provide patient details using a questionnaire. Demographic characteristics, family history, clinical manifestations, radiological imaging information, treatment, and follow-up data were obtained. Other information collected included routine immunological and microbiological records, laboratory functional evaluation of IFN-γ immunity, and genetic results. Clinical criteria for localized or regional (BCG-itis) and disseminated (BCG-osis) infections have been de ned previously  [17, 18].
Genetic analyses Genetic diagnosis was made by Sanger sequencing (P1-P2, P5-P7, P12-P14, P17-22), next-generation sequencing panel (P10-P11, P16), or whole-exome sequencing (WES; P8-P9, P15)  [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]. Deleterious variants identi ed by WES were con rmed using Sanger sequencing. Familial segregation was performed when genomic DNA was obtained from relatives. PolyPhen-2, sorting intolerant from tolerant, and combined annotation-dependent depletion scores were used to predict the pathogenic effects of unreported variants. The Statistical Package for Social Science version 25. 0 (SPSS Inc. , Chicago, IL, USA) was used for data analyses. The results are presented as medians for continuous variables and percentages for nominal variables. Survival was analyzed using the Kaplan-Meier method.
A principal component analysis (PCA) clearly separated FASS samples from small particle controls along PC1, but DAT:EGFP FASS samples were not separated from striatal or cortical VGlut1 VENUS FASS samples  (Figures S2H and S2I). As above, we used DESeq2 to identify differentially expressed genes (DEGs) across FASS and control samples (see STAR Methods). We found a significant effect of FASS for more than 2,000 genes, but no significant effect of region:FASS or genotype:FASS interactions for any gene (Figure  3F ). These data demonstrate that the FASS transcriptome is distinct from all small particles, but that the VGlut1 VENUS and DAT:EGFP sorted samples are largely indistinguishable. Given the paucity of DA axons in the cortex compared to the striatum, this result argues against any detectable contribution of DA synaptosome-specific mRNAs.
0, 488 nm laser on, area scaling 1. 18, window extension 0. 5, sort precision 0-16-0, FSC (340 V), SSC (488/10 nm, 365V), fluorescein isothiocyanta (enhanced green fluorescent protein) (530/30 nm, 700 V), PerCP (FM4-64) (675/20 nm, 700 V). Thresholding on FM4-64 was set with a detection threshold at 800. Samples were analyzed and sorted at rates of 15,000-20,000 events/s and flow rate of 3. Data were acquired using BD FACS DIVA 6. Cytometry plots were generated using FCS Express 7 (De Novo Software).
8S, 18S, and 28S rRNA using bowtie2  (Langmead and Salzberg, 2012). rRNA-depleted paired-end reads were then aligned to the mouse genome (GENCODE M25, GRCm38. p6) using STAR 2. 6. 7a (Dobin et al. , 2013). Uniquely mapped reads were then quantified at the exon level using feature-Counts version 1. 6  (Liao et al. , 2014). Low input RNA-seq with 96-well plate, pooled library construction The protocol for plate-based, 3 0 end unique molecular indicator (UMI)-based RNA-seq of single cells has been described previously  (Snyder et al. , 2019)  and was further modified to accommodate ultra-low input RiboTag IP samples. See Data S1 for sequences of all custom primers and oligonucleotides used in this protocol. Briefly, an estimated 20-500 pg of total RNA (based on qPCR, see above) for each sample was loaded into the wells of a 96-well plate in a volume of 6 mL of nuclease-free water containing 1 U/mL SUPERaseIN (ThermoFisher).
For most LRTs, the log2 Fold Changes specify the contrast between the two levels of the factor (e. g. , IP versus Input for fraction, Cre -versus Cre + for genotype). For interaction terms, the contrast specifies the difference in log2 fold change for the effect of one factor between the levels of the other factor (i. e. , for genotype:fraction, the difference in IP versus Input between Cre -and Cre + samples). Note that the age factor in Figures 3A and 3B has multiple levels, and so the p values do not relate specifically to any single contrast. The log2 fold changes specified for the age LRT in Data S5 are for the contrast P90 versus P0. A complete DESeq2 summary for analyses related to the following figures is found in the corresponding supplemental data: An analysis of the midbrain RiboTag IP and Input samples in Figures 5 and S5 (from full-length total RNA-seq) was conducted in a GLM in DESeq2.
QUANTIFICATION AND STATISTICAL ANALYSIS Unless otherwise noted, all statistical analysis and data visualization was conducted in Python 3. 7. 3 using SciPy, scikit-learn, Statsmodels, Matplotlib, and Seaborn packages. Statistical comparisons were conducted using Welch's unequal variance t tests, Mann-Whitney U tests, or two-way analysis of variance (ANOVA) with Tukey's HSD post hoc comparisons. The number of replicates and other statistical testing information indicated in the figure captions. Box and whiskers plots display the median, first, and third quartiles, with whiskers at the minimum or maximum values. Error bars on bar charts are standard error of the mean.
ACKNOWLEDGMENTS This work was conducted in collaboration with the  JP Sulzberger Columbia Genome Center. This research was funded in part by  Aligning Science Across Parkinson's [ ASAP-000375 ] (DS and PAS) through the  Michael J. Fox Foundation for Parkinson's Research. For the purpose of open access, the author has applied a CC-BY public copyright license to all Author Accepted Manuscripts arising from this submission. This work was supported by the  JPB Foundation  (DS). This work was supported by  NIH  grants  F30 DA047775-03  (BDH),  R01 NS095435  (DS),  R01 DA07418  (DS), and  R01 MH122470  (DS). This study benefited from the  Agence Nationale de la Recherche consortium fundings (IDEX Bordeaux   ANR-10-IDEX-03-02 ;  Labex BRAIN   ANR-10-LABX-43   BRAIN; France Bio Imaging   ANR-10-INBS-04 ).
Data were retrieved using ACSM local v. 1. 6. 0. 3, implemented within Igor Pro (v. 6. 37, Wavemetrics Inc. , USA). The ACSM is designed and built around similar technology as the aerosol mass spectrometer  (Jayne et al. , 2000) , where an aerodynamic particle focusing lens is combined with particle flash vaporization in high vacuum at 600 °C, electron impact ionization, separation and final detection of the resulting ions using a quadrupole mass spectrometer. Mass concentrations are calculated using a chemical composition-dependent collection efficiency  (Middlebrook et al. , 2012). Black carbon (BC) measurements were conducted using an AE-33 7-wavelength aethalometer (Magee Scientific, US) at a 1-min time resolution. The aethalometer sampled ambient aerosol through a PM2. 5 aerosol inlet (SCC 1.
for component-specific non-parametric wind regression analysis (NWR) performed using the ZeFir toolbox  (Petit et al. , 2017)  developed within the Igor Pro software (Wavemetrics Inc. ). A co-located automatic CIMEL CE370 micro-LIDAR was operated continuously to retrieve the Planetary Boundary Layer Height (PBLH) and better assess the influence of atmospheric dynamic on in-situ ground-based observations.
Proton Air masses backtrajectory analysis: Five-day air mass backtrajectories arriving at 1000m altitude above the sampling site were computed every 6 hours, using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYPLIT4;  Stein et al. , 2015)  using the Global Data Assimilation System (GDAS 1) meteorological data fields (with 1 o spatial resolution). Backtrajectories were coupled to measured concentrations, assessing origins and source contributions to specific chemical components, by applying the Potential Source Contribution Function (PSCF) technique as implemented in the ZeFir toolbox described above. https://doi. org/10. 5194/egusphere-2022-1081 Preprint. Discussion started: 4 November 2022 c Author(s) 2022. CC BY 4. 0 License.
Source Apportoinment analysis Positive Matrix Factorization (PMF) is an advanced multivariate factor analysis tool that attempts to identify the contributing factors, or sources, of atmospheric pollutants at a sampling site. For this study, source apportionment was performed on the organic mass spectra dataset collected by the ACSM. The (PMF) method  (Paatero and Tapper, 1994)  using the multilinear engine (ME-2) model developed by  Paatero (1999)  was implemented using the SoFi (Source Finder) toolkit (SoFi 6D;  Canonaco et al. , 2013). PMF allows the decomposition of the OA mass spectra matrix X into two matrices, G and F and a remaining residual matrix E: 𝑋 = 𝐺 * 𝐹 + 𝐸 (1) Where X is the input dataset matrix (measured quantity), F is the resulting source profile matrix, G is the source contribution matrix (temporal variability of each source), and E represents the model residual matrix.
Based on a number of criteria the optimal solution is selected, aiming at being physically meaningful that can be supported by external indicators (ancillary measurements), and trying to minimize values in the residual matrix E. Model input data and error matrices (in μg m -3 ), were exported using the ACSM software. Data points with a signal-to-noise (S/N) ratio smaller than 0. 2 were removed and points with S/N between 0. 2 and 2 were downweighted by increasing their estimated error values  (Ulbrich et al. , 2009; Paatero and Hopke, 2003). m/z values ranging from 10 to 120 were used in the analysis. CO2 related variables were excluded from the PMF and finally reinserted in the solution. Source apportionment of OA was performed following the general steps described by  Crippa et al. (2014)  and the recently updated harmonised standard operating procedures for seasonal OA PMF  (Chen et al.
The BBOAcy spectrum was calculated as an average of 20 PMF runs from the initial unconstrained PMF for the cold period, validated by it's timeseries correlation to BCwb. Since aged OA (i. e. Oxygenlike OA, OOA) factors show more variability between measurement sites in terms of their mass spectra, no constrain was introduced for these factors  (Canonaco et al. , 2015). In this study, the BBOA factor -a major contributor of OA during winter -could not be properly resolved when performing the PMF analysis on the entire period dataset. A seasonal approach was followed instead, separating the OA dataset into two periods that were then used to describe both the two periods (cold and warm respectively). The criteria used to delineate those two periods are presented and discussed in the below section 3.
On-line aerosol data quality check A chemical mass closure exercise for PM1 was performed at a temporal resolution of 1h to check the quality of the on-line aerosol measurements. Chemically reconstructed PM1 was calculated as the sum of the mass concentration of all non-refractory species measured by the ACSM (OA, NO3 -, SO4 2-, NH4 + , Cl -) plus the BC concentrations measured by the Aethalometer  AE-33 (Putaud et al. , 2004). The contribution of other chemical constituents to submicron aerosol, such as sea salt and dust (measured by colocated filter sampling), was found to be low and therefore neglected here. A scatter plot of the ACSM + AE-33 measurements vs the SMPS-derived PM1 concentrations is shown in Figure  S4b. The results obtained indicate a very good correlation (r 2 = 0. 88; N=1823) and a slope of 1.
15°C in the beginning of April up to to 30°C at the end of May. These two features dictated the division of the dataset into two periods: a cold period of four months (07/12/2018-08/04/2019), with an average temperature of 12 ± 4ºC, and a warm period of two months (09/04/2019 -31/05/2019), with an average temperature of 20 ± 7ºC. Air mass origin: A cluster analysis was performed (Fig. S6 ) for both periods in order to better assess the main upwind regions responsible for long-range transported air pollution over Cyprus and their change relative to the period of the year. Most of the air masses arriving in Cyprus were found to originate from Europe; many of them passing over Turkey before reaching Cyprus. Interestingly, this analysis showed one cluster arriving from Middle East for the cold period, whereas there were not enough trajectories passing over the Middle East to calculate a cluster for this area during the warm period.