dataesr/scientific-paragraphs-categorization huggingface dataset:
The BIC is used for model selection and helps to prevent overfitting by introducing penalty terms for the complexity of the GMM (i. e. , the number of parameters). Hence, minimizing the BIC achieves a good compromise between keeping the model simple and providing a good representation of the data. The BIC is given by: 𝐵𝐵𝐵𝐵𝐵𝐵 ( 𝐾𝐾 ) = 𝑝𝑝 log ( 𝑛𝑛 ) -2 log ( 𝐿𝐿 ), ( 7 ) where K is the number of clusters, L the likelihood of the parametrized mixture model, p the number of parameters of the GMM to estimate, and n the size of the sample (i. e. , total number of months from January 1920 to December 2014, which is 1140 months). The clustering described above is performed using the R package "Mclust"  (Scrucca & Raftery, 2015). A different approach is used to assign each month in the model data to a specific regime.
Era Retrospective analysis for Research and Applications, version 2 and Copernicus Atmosphere Monitoring Service (CAMS)], but the spatial resolution is much coarser (> 0. 25 o ), which usually differ considerably from ground observations partially for using much fewer observational data (46, 47). An attempt has been made to correct CAMS PM 2. 5 simulations by using more ground observations through deep learning, with a small improvement (CV-R = 0. 58-0. 61) (46). A high-quality PM 2. 5 dataset at the annual scale (CV-R = 0. 95) was generated by combining the Bayesian hierarchical model and the GEOS-Chem chemical transport model (  48 ). Many studies have demonstrated that satellite remote sensing products of AOD provide more pertinent information to improve the estimation of PM 2. 5 concentrations retrievals  (19-21, 23, 49).
Using satellite observations, combined with GEOS-Chem (  21 ) and ensemble machine-learning (20) models, the estimation accuracy (CV-R = 0. 51-0. 77) and spatial resolution (10 km) of daily PM 2. 5 estimates were improved. Later, 1 km ne-scale PM 2. 5 maps with improved correlations (R = 0. 90-0. 96) were generated using the GWR model but only for annual  (23)  and monthly (25) mean concentrations. In comparison with these estimates made with the chemical transport model, statistical regression, and machine-learning models, our product is unique for having the highest resolution of 1 km on a daily basis, gap-free, and highest accuracy to date (CV-R = 0. 93-0. 99). Additionally, we compared our data set with the only publicly available monthly Global PM 2. 5 Estimates (V5. GL. 02) data set.
This arduous task was realized on a cloud-computing platform (Google Earth Engine). The generated dataset enables us to monitor global daily PM 2. 5 distributions and variations, and investigate the effects of short-term PM 2. 5 pollution on public health  (50) (51) (52) , especially at the ner city level  (53). Independent cross-validated results demonstrated that our global PM 2. 5 estimates and predictions agree well with ground measurements at varying spatiotemporal scales. Using this reliable and ne-resolution dataset, we investigated the global PM 2. 5 change, exposure risk, and associated mortality burden on a 1 km grid from both short-term and long-term exposure perspectives, also its unique value in seeing the dynamics of the impact of the COVID-19 pandemic on PM 2. 5 pollution in terms of the spatial and temporal variations as it is spreading around the world.
AOD data The MAIAC AOD product (MCD19A2)  (22)  has the highest spatial resolution of 1 km among all MODIS operational aerosol products over land with high accuracy (56, 57). The data were selected as the main predictor for estimating PM 2. 5 concentrations. Terra (~ 10:30 AM overpass time) and Aqua (~ 13:30 PM overpass time) MAIAC daily 1-km AOD retrievals at 550 nm with recommended quality assurances (58) from 2019 to 2020 were used. Spatially complete AOD assimilations (550 nm) from the GEOS Forward Processing (FP) system every 3 hours at a horizontal resolution of 0. 25° × 0. 3125° were employed to ll satellite AOD gaps. The global AERONET network provides instantaneous AOD measurements made at ~ 500 stations over land every 15 min (Figure  S18 ). Only the recommended high-quality (Level 2.
Model validation Evaluation of global AOD gap-lling was performed independently against the AERONET AOD measurements, while PM 2. 5 surveillance was done at three independently sample-based (out-of-bag), space-based (out-of-station), and time-based (out-of-day) levels using the widely used 10-fold crossvalidation method (71), i. e. , all data samples, stations, and days were randomly divided into ten folds, of which nine (90%) were used for modeling and one (10%) for validation, respectively. This process was run ten times, in turn, to get the nal accuracy after averaging. These methods ensure that the training and test data are independent at overall, spatial, and temporal scales, which are used to evaluate the overall accuracy of the model and its predictive ability in areas and on days without any available observations, respectively (58).
Acknowledgements This work was in part supported by the  NASA   Applied Science Program  ( 80NSSC21K1980 ) and  NASA   MODIS  algorithm maintenance funding. The authors greatly thank  Huanbi Yue  from the  Ocean University of China  for helping calculate the mortality burden,  Lorena Castro  from the  University of Iowa  for collecting and processing the OpenAQ data, and  Maureen Cribb  from the  University of Maryland  for editing and polishing the paper.
precipitation, evaporation, and boundary-layer height. Therefore, these meteorological elds were collected from hourly ERA5-Land (~ 0. 1° × 0. 1°) and ERA5 global (~ 0. 25° × 0. 25°) reanalysis datasets (63, 64). In addition, population density, economic level, land cover, and terrain changes impacting air pollution were also considered, which are directly or indirectly represented by the highly relevant and available satellite remote sensing products, i. e. , global high-resolution annual WorldPOP population (1 km resolution) (65), monthly Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights (500 m resolution) (66), monthly MOD13A3 normalized difference vegetation index (NDVI; 1 km resolution), and Shuttle Radar Topography Mission digital elevation model (DEM; 90 m resolution) products, respectively.
In veins, clotting initiation displays a threshold response to flow intensity and injury size. Mathematical models can provide insights into the conditions leading to clot growth initiation under flow for specific subjects. However, it is hard to determine the thrombin generation curves that favor coagulation initiation in a fast manner, especially when considering a wide range of conditions related to flow and injury size. In this work, we propose to address this challenge by using a neural network model trained with the numerical simulations of a validated 2D model for clot formation. Our surrogate model approximates the results of the 2D simulations, reaching an accuracy of 94% on the test dataset. We used the trained artificial neural network to determine the threshold for thrombin generation parameters that alter the coagulation initiation response under varying flow speed and injury size conditions.
We utilized a deep learning model that we trained using numerical simulations of a computational fluid dynamics (CFD) model for coagulation initiation under flow. After validating this CFD model against microfluidics experiments, we took advantage of our high-performance computing resources to conduct 7675 simulations, describing the clotting initiation and growth. In each of these simulations, the model parameters related to coagulability, injury size, and flow pressure difference were randomly sampled, and the thrombin generation parameters were calculated. The obtained dataset was used to train an artificial neural network, which is able to predict the initiation of coagulation based on the thrombin generation parameters in addition to the injury size and flow intensity. We evaluated the accuracy and performance of the surrogate model.
One important feature of this model is that it can reproduce the thrombin generation curves of real patients  [36]. The mean and standard deviation of the ranges from which the parameters were sampled are shown in Table  1. The mean of these ranges corresponds to the parameter values of a healthy subject  [36, 37]. These data were collected in the clinical studies NCT02540187 and NCT02300519, which were approved by the French Ethical Review Board South East I. The subjects were included after having signed an informed consent form in accordance with the ethical principles of the Helsinki declaration. Since we are assuming the same tissue factor concentrations for all simulations, we also used the same K 1 for all of our calculations of thrombin generation curves. For each simulation, we only kept the four parameters of coagulability (lag time, ETP, thrombin peak concentration, and time to peak), in addition to the injury size (δ) and the pressure difference (∆p).
We conducted a total of 7675 simulations and used 70% of the obtained dataset for the training of the neural network and 30% for testing its performance. The mean and standard deviation of the four coagulability parameters in the generated virtual patients population is given in Table  2. We used a neural network architecture that consists of three hidden layers of 500 × 250 × 100 nodes (Figure  1 ). Initial values of weights were set with the uniform (Xavier) initialization  [38]. The Adam learning rate optimization algorithm was used to fit the weights of the ANN  [39]. Table  1. The mean and standard deviation of the parameters used in the simulations to generate the training dataset. The values of parameters related to coagulability were generated by considering the parameters of a real patient and adding a perturbation sampled from a uniform distribution  [36] , while we determined the average values of injury size and pressure difference according to the values used in in vitro experiments  [1].
Parameter We trained the neural network for 200 periods and evaluated its performance by computing metrics such as the multi-cross entropy in log scale and the confusion matrix (Figure  2 ). Further, accuracy was evaluated at 94% on the test dataset. The performances of other NN architectures and classification algorithms are provided in Appendix A. 2. To implement these algorithms, we used the Python libraries Scikit-learn  [40]  and tensorflow  [41]. The database and the algorithm code are publicly available through the following github repository: https://github. com/MPS7/ML_coag (accessed on 8 August 2023).
Discussion This paper presents a new approach to explore the complex interplay between coagulation initiation thresholds in venous flow conditions. We utilized a previously developed CFD model to simulate the initiation of coagulation  [13, 14]. We calibrated our model to reproduce experimental observations and ran a large number of simulations to investigate the effects of blood coagulability, injury size, and flow intensity on coagulation initiation. We then post-processed the obtained dataset to calculate the thrombin generation parameters for each simulation, including the lag time, the ETP, the peak concentration, and the time to peak, which are crucial in quantifying plasma coagulability. By using an artificial neural network to analyze the post-processed dataset, we were able to predict the initiation of coagulation under varying coagulability, injury size, and flow intensity conditions.
After training the model, we achieved a high accuracy of 94% on the test dataset, suggesting that the surrogate model can be used to effectively predict the outcome of simulations without resorting to the computationally intensive CFD model. We took advantage of the accuracy of the surrogate model and its capacity to predict the outcome of coagulation initiation under a large number of conditions in a few seconds to study thresholds of thrombin generation parameters that alter the coagulation initiation dynamics. In particular, we were able to determine these thresholds for varying conditions of flow intensity and injury size. Our model predictions were in good agreement with the reported experimental findings in the literature. Indeed, it was shown experimentally that elevated ETP and peak concentrations values were associated with hypercoagulable states  [42] , while an increased time to peak was associated with poor coagulation  [43, 44].
Methods The study population included working-age women with BC who were enrolled in the French CON-STANCES cohort from 2012 to 2018. BC treatments, antidepressant/anxiolytic and antalgic drug deliveries (used as proxies of depression and pain, respectively) and statutory sick pay (used to estimate RTW and time to RTW) were assessed monthly using data from the French national healthcare system database. BC care pathways were identified with the sequence analysis method. Cox models with time-dependent covariates were used to investigate the impact of BC care pathways and related symptoms on RTW and time to RTW, after adjusting for age and socioeconomic characteristics. Results 73. 2% (231/303) of women returned to work within 2 years after BC diagnosis. Five BC care pathway patterns were identified: (i) BC surgery only, (ii) BC surgery and radiotherapy, (iii) BC surgery and chemotherapy, (iv) BC surgery and chemotherapy and radiotherapy, and (v) BC surgery and long-term alternative chemotherapy/radiotherapy.
Study population The study sample was from the CONSTANCES cohort, a population-based prospective cohort study that included ~ 220,000 volunteers aged from 18 to 69 years at 21 health examination centers throughout France between 2012 and 2020  [23]. At inclusion, a health examination was performed, and self-report questionnaires were given to participants to collect sociodemographic, lifestyle, socio-professional data and medical history. The follow-up included self-report questionnaires (filled in at home) once per year and a health examination every 4 years  [23]. The CONSTANCES cohort was linked to the national health insurance system database  [24]  (SNDS) to obtain additional data on BC diagnosis, BC treatments, other treatments reimbursed to the patients due to BCrelated problems, such as anxiolytic/antidepressant and antalgic drugs, and statutory sick pay from January 2012 to December 2019.
Women with breast cancer were identified in the SNDS from hospitalization records (principal or related diagnoses with two ICD-10 codes = C50-Malignant neoplasm of breast, D05-Carcinoma in situ of breast) and/or long-term chronic disease benefits (ICD-10 = C50, D05). The quality of the BC's identification in the SNDS was previously tested using medical records for a subsample of 265 cases, resulting in a high positive predictive value (PPV = 92% 95% CI [88-95%]). Analyses were restricted to women from the CON-STANCES cohort who were working at the time of BC diagnosis (from 2012 to 2018) and who gave their informed consent for SNDS data collection (Fig. 1 ). BC cases were predominantly diagnosed the year of their inclusion in the CONSTANCES cohort or the year before (n = 391/626, 62. 5%).
Identification of RTW and time to RTW RTW and time to RTW were estimated using data on sick leaves (SL) identified from the daily sick pay in the SNDS. A woman with BC was considered as having gone back to work when her daily sick pay was stopped. No distinction between partial-and full-time RTW was made in the statistical analysis. Women on therapeutic partial sick leave were considered to have gone back to work. Patients were considered to have a BC-linked SL if it lasted more than 21 days to avoid SL due to other occasional health issues. The time to RTW was calculated from the first day of the > 21-day-long SL to the last day of the sick pay in the 2 years after the BC diagnosis. Civil servants and self-employed women, for whom sick pay data are not available in the SNDS, were excluded from the statistical analysis on RTW and time to RTW (Fig.
Indeed, axillary LN dissection is usually performed during breast surgery, and hormone therapy is mainly used to prevent BC recurrence and is prescribed Fig. 1  Flowchart of the study population selection SNDS: French national healthcare system database over a long period of time (5 and up to 10 years after diagnosis), usually when women are already back at work. Then, the optimal matching algorithm  [27]  was used to assess the dissimilarity among the women's sequence of treatments. Treatment sequences were clustered into homogeneous patterns by hierarchical clustering using the Ward criterion. The appropriate number of clusters was chosen on the basis of the dendrogram and the histogram of the gaps in inertia, and also according to the clinical recommendations for BC care and statistical constraints (i.
For each outcome, two multivariate models were performed. In the first multivariate model (model A), each BC treatment was treated as an independent variable, whereas in the second model (model B), the BC care pathway patterns identified by sequence analysis were used instead of the individual BC treatments. The proportional hazards assumption was tested for the fixed variables included in the Cox models using the Schoenfeld residuals analysis and the Hosmer-Lemeshow test. Statistical significance was defined by a p value < 0. 05. All statistical analyses were performed with the R statistical software, version 4. 0. 2, using the packages 'survival, ' 'survminer' and TraMineR'  [25].
Patient selection Data for 89,522 of the 106,932 women included in the CONSTANCES cohort between 2012 and 2019 could be linked to SNDS database. In the group with matched data, 1,213 women received a diagnosis of BC between 2012 and 2018 among whom 626 were working at diagnosis time. SL data were available in the SNDS for 349/626 active women, and a SL > 21 days within the two years after BC diagnosis was found for 303 of these women (Fig. 1 ).
Strengths and limitations This study presents several strengths explained by the quality of the data collected, particularly their temporal and sequential features, using two complementary statistical methods. First, the nationwide CONSTANCES cohort was linked to the SNDS database to obtain exhaustive and dated data on BC treatments, hospital stays, drug deliveries and daily sick pay in the two years after BC diagnosis, thus avoiding self-reported times, memorization bias and attrition. Two different and complementary statistical methods were used to take into account the temporal and sequential features of BC treatments and their related symptoms over time. They gave similar results underlining their robustness. In the literature, the most frequently used models to study RTW are logistic regressions  [9, 10, 14, 20, 22, 34]  and classical Cox models that do not take into account this temporality and treatment sequences  [5, 31].
Third, agricultural and self-employed workers were not included in the CONSTANCES cohort as data on daily sick pay for civil servants are not available in the SNDS. This could have affected the non-RTW rate and the mean duration of sickness absence. However, the percentage of women who did not return to work in the two years after BC diagnosis was similar to the rate reported by a previous French national population-based survey (26. 8% versus 25%)  [35]. The mean SL duration after BC diagnosis (11. 6 months, Additional file 1: Table  S1 ) also was similar to what reported by Fantoni et al. [10]  in France and by Balack et al. [5]  in the Netherlands (11. 5 months). Furthermore, only women with BC and SL > 21 days were included to avoid SL not due to BC. The SL length threshold used in previous studies varied from 14 to 30 days  [20, 22].
Acknowledgements The authors thank the " Caisse nationale d'assurance maladie" (CNAM)  and the  "Centres d'examens de santé" of the French Social Security  which are collecting a large part of the data. The authors would like to thank  Julie Gourmelen  and  Betul Demir  from the  UMS 011-Unité  cohorts épidémiologiques en population- Université de Versailles Saint-Quentin en Yveline- for their advices and help in data management of the French national healthcare system database (SNDS).
Funding This manuscript was prepared in context of the  SIRIC ILIAD program  supported by a grant from the  French National Cancer Institute (INCa) , the  French Ministry of Health , and the  Institute of Health and Medical Research (Inserm) ; contract  INCa-DGOS-Inserm_12558. The Constances cohort is supported and funded by the  Caisse nationale d'assurance maladie (CNAM). The  Constances Cohort Study is an "Infrastructure nationale en Biologie et Santé " and benefits from a grant from  ANR  ( ANR-11-INBS-0002 ) and from the  Ministry of Research. Constances is also partly funded by  MSD  and  L'Oréal.
2] [3]  Therefore, it is plausible that novel SVs and deep-intronic variants that cause PE inclusion will be identified when genetically unexplained cases are screened with WGS. The USH2A gene is one of the most frequently mutated IRD-associated genes, implicated in both USH2 and arRP, as pathogenic variants are identified in 57% to 79% and 12% to 25% of affected individuals, respectively. 10 USH2A-associated IRD is still considered untreatable. However, several gene-specific treatment options are currently in different stages of (pre-)clinical development, including splicemodulation using antisense oligonucleotides (AONs),  11  CRISPR-Cas9-based gene correction,  12  and exon-excision. 13 ltevursen, an AON that induces the skipping of USH2A exon 13, is currently being evaluated in a phase II/III clinical trial (ClinicalTrials.
gov Identifier: NCT05158296). These developments further stress the importance for subjects to obtain a conclusive genetic diagnosis in order to be eligible for future personalized therapies. In this study, we aimed to identify missing variants in USH2A in a cohort of 100 largely mono-allelic arRP and USH2 subjects through WGS, assess an effect on splicing of all detected single nucleotide variants (SNVs), and to develop an AON-based splice correction strategy for specific deep-intronic variants. In addition, our approach could serve as a future framework for detection and treatment of variants in genes associated with other hereditary disorders.
7; or (3) a putative splice-modulating variant with two out of four SpliceAI D scores R0. 1 or one SpliceAI D score R0. 15 (default settings, range 0-1). 20 Copy number variants (CNVs) were called with control-FREEC and SVs were called with Manta Structural Variant Caller V. 1. 1. 0. 21, 22 CNVs and SVs were prioritized if they passed the internal quality filter as previously described  8  ; had a high quality score (R100 of 1,000); had an AF % 1% in databases, such as gnomAD, Decipher, and GoNL; and affected at least one exon of USH2A or the aforementioned 62 arRP-associated or 17 USH-associated genes. Segregation analysis was performed if DNA samples of relatives were available.
Nineteen of these SNVs were not reported in ClinVar or the Leiden Open (source) Variation Database (LOVD) and were therefore considered novel variants in our study. Twelve USH2A SVs on 13 alleles adhered to our criteria, including two duplications, two inversions,  16  and eight deletions of which one (c. 4627þ25436_4987þ659del) was identified in two probands. The largest SV with both breakpoints within USH2A was a duplication from exon 15 to 65 (c. 2994-3030_14343þ488dup), the shortest SV was a deletion of exon 43 (c. 8655_8681þ1681del). All SVs were confirmed with PCR and subsequent Sanger sequencing. After assessment of all individual variants from the total cohort, 41 cases were genetically explained with (likely) biallelic USH2A variants. An overview of all variants that were classified as (likely) pathogenic or were VUS that were deemed potentially causative is presented in Table  1.