dataesr/scientific-paragraphs-categorization huggingface dataset:
17 Major complications were defined as Clavien-Dindo grades C3a. 9] [20]  Only complications graded as B and C were noted. Data on surgical site infections or radiological interventions were not collected. Postoperative outcomes were recorded up to 90 days postoperatively. Resection margins, including transection and posterior margins, were by all centers similarly categorized into: R0 (distance margin to tumor C1 mm), R1 (distance margin to tumor \1 mm), and R2 (macroscopically positive margin) according to the Royal College of Pathologists definition. 21 The tumors were classified according to the American Joint Committee on Cancer (AJCC) 8th edition staging system. 22.
Statistical Analysis Data were analyzed by using IBM SPSS Statistics for Windows version 26. 0 (IBM Corp. , Orchard Road Armonk, NY). Analyses were performed according to the intention-to-treat principle. Normally distributed continuous data were presented as mean with standard deviations (SD) and were compared by using the two-tailed Student ttest. Nonnormally distributed continuous data were presented as median with interquartile range (IQR) and were compared using the Mann-Whitney U test or the Kruskal-Wallis test, as appropriate. Categorical data were presented as frequencies with percentages and were compared by using the chi-square or Fisher's exact test, as appropriate. The overall survival and disease-free interval were calculated by using Kaplan-Meier estimates and reported until 36 months of follow-up.
demonstrated an increase in  lymph node yield from 7 to 18 by standardizing the pathology examination without changing the surgical technique. 33 In addition, the clinical relevance of the difference of only two lymph nodes could be questioned here, as no difference in positive lymph nodes or survival were observed between both groups. The comparable overall survival rates and disease-free intervals between RDP and LDP align with the results of a prior study that investigated the long-term outcomes between RDP and LDP in patients with pancreatic cancer in the National Cancer Database. 6 hese results indicate that the choice of approach does not impact patients' survival. The results of this study should be interpreted in light of several limitations. First, the retrospective design may have impacted the results as selection bias might be present and some important data were not available, such as on resection of Gerota's fascia.
ACKNOWLEDGMENT For the European Consortium on Minimally Invasive Pancreatic Surgery (E-MIPS) Collaborators: Beatrice Aussilhou, MD, PhD 12 , Sivesh K. Kamarajah, MD, PhD 31 , Stijn van Laarhoven, MD, PhD 20 , Thomas Malinka, MD, PhD 30 , Ravi Marudanayagam, MD, PhD 29 , Claudio Ricci, MD, PhD 9 , Patricia Sanchez-Velazquez, MD, PhD 7. DISCLOSURES Prof. Marc Besselink and Prof. Mohammad Abu Hilal received a grant from Medtronic for investigator-initiated DIPLOMA randomized trial minimally invasive vs open distal pancreatectomy, a grant from Intuitive for investigator-initiated DIPLOMA-2 randomized trial minimally invasive pancreatoduodenectomy, as well as a grant from Ethicon for investigator-initiated for PANDORINA randomized trial drain placement distal pancreatectomy.
Karyotypying and STR analysis iPSCs were blocked in metaphase with colchicine (Eurobio) for 90 min, warmed with a hypotonic solution and fixed with a Carnoy fixative. A m-FISH-24Xcite probe (MetaSystems) and ProLong-Gold-Antifade-Mountant with DAPI (ThermoFisher) were used for m-FISH staining. Seventy metaphases were acquired and analyzed with Isis software (MetaSystems). iPSCs and fibroblasts were sent to ATCC Cell Authentification Service for STR analysis.
Flow cytometry A single-cell suspension of iPSCs was collected and centrifuged at 900 rpm for 5 min and resuspended in 2 % FBS in cold PBS. Cells were stained with fluorescent dye-conjugated antibodies (listed in Table  2 ) for 30 min on ice. Cells were assessed by a MACSquant analyzer (Miltenyi Biotec). Data were analyzed with FlowJo Software (BD Biosciences).
To estimate the GPS velocity (on-ice displacement rate) on the surface of Glacier d'Argentière, we develop a gradient boosted tree regression model using the features extracted from the data. Specifically, we use the XGBoost package and routines form scikit-learn  (Chen & Guestrin, 2016; Pedregosa et al. , 2011). When optimizing model hyperparameters the choice of data split and feature preprocessing is performed iteratively by solving the model using fivefold cross validation on the training data set to minimize the average mean-squared-error for each validation fold. A Bayesian optimizer is implemented for a search space to select the best hyperparameters  (Head et al. , 2018). The procedure randomly selects hyperparameters for 100 iterations, then gradient descent is applied to converge on the best selection for an additional 100 iterations.
Splitting the data to best represent the data proved critical to assess the model improvements due to non-stationary signals in the time series and avoid a testing data set that is out-of-distribution. Different techniques applied to the feature preprocessing included standard scaling (S), quantile transformation (Q; n_quantiles = 50), principal component analysis (P; n_components = 50, whiten = True), and a random forest regressor (R; n_estimators = 200, max _depth = 3, n_features_to_select = 20, step = 1). All routines are available in the scikit-learn package  (Buitinck et al. , 2013; Pedregosa et al. , 2011). We optimized the hyperparameters for each type of split on the training fraction using the original data and each possible combination of S, Q, P, and R. The results show that the best-fit model hyperparameters with the lowest loss, hence, the best model, strongly depends on the choice and combination of data split and feature preprocessing.
Data Availability Statement The MFP source codes are described and available via https://lecoinal. gricad-pages. univ-grenoble-alpes. fr/resolve/ (last access: 11/11/2021) under a creative commons attribution 4. 0 international license. The data derived from the MFP analysis (i. e. , 29 sources localizations per second over 34 days and for 20 frequency bands) together with 1 day of raw seismic signal recorded over the 98 seismic stations are available via https://doi. org/10. 5281/zenodo. 5645545 under a creative commons attribution 4. 0 international license  (Nanni et al. , 2021). The complete set of raw seismic data can be found at https://doi. org/10. 15778/resif. zo2018 under a creative commons attribution 4. 0 international license. The processed GPS (GNSS) velocity time series data is stored at https://zenodo.
Reported symptoms. At the time of the follow-up in June 2022, all participants were queried regarding the existence of a battery of 31 symptoms. The mean number of symptoms reported was 2. 2 ± 3. 22, with a notable significant difference in the number of symptoms reported between no long COVID and long COVID subjects, 1. 6 ± 2. 7 versus 3. 8 ± 3. 9, SMD -0. 6550, p=0. 0001 (Table  2 ; p-value not shown). There was no observable variation in the number of symptoms reported in those with a singular COVID-19 infection compared to those with a reinfection (mean ± standard deviation) 2. 1 ± 3. 1 symptoms versus 2. 7 ± 3. 9, SMD -0. 1747, p= 0. 1043 (data not shown). As illustrated in Figure  3  and Table  3 , there was a noteworthy differentiation in the prevalence of 15 out of the 31 symptoms between individuals with long COVID and those without.
Acknowledgments: We thank the participants in this study and organization team of each falla of Borriana for their support and help. In addition, we wish to thank  Belen Cervera-Ferrer ,  Ursula Clerig-Arnau ,  Claudia Dols-Bernad ,  Paula Vidal-Utrillas ,  Sonia Valls-López ,  María Dolores Llopico-Vilanova ,  Irene Suarez-Linares ,  María Soledad Carregui-Alagarda , and  Belen Martinez-Martinez  for your assistance and help.
Alignment Simulations For our study, we separately considered DNA and protein data. We simulated 15 MSA sets, seven sets containing DNA MSAs and eight containing protein MSAs, respectively. In the following, we refer to an MSA set as a data collection. To simulate the MSAs for each data collection, as well as for data discrimination, we used two empirical data collections as reference, one per data type. The empirical DNA data collection contains MSAs obtained from TreeBASE  [37]. The empirical protein data collection consists of MSAs obtained from the HOGENOM database  [36]. We removed outliers based on MSA length (i. e. , number of sites), number of sequences, as well as MSAs with less than four sequences to ensure a reliable and efficient analysis. Very long sequences would inflate the memory footprint of the CNN, while very short MSAs are uncommon and are more difficult to accurately classify as empirical or simulated.
Here, to simulate a site, a profile is drawn from a set of profiles. To this end, we used the following two models: the C60 model with 60 profiles (LG+C60)  [45]  and the more recent UDM model with 256 profiles (LG+S256)  [41]. The advantage of the latter model is that each profile is assigned a probability (i. e. , weight) of generating a site, while under the C60 model profiles are drawn with equal probabilities. In addition, the UDM model is based on a subset of MSAs from the HOGENOM database, and should therefore generate alignments that are similar to empirical HOGENOM MSAs. To increase model complexity, we performed further simulations accounting for among site heterogeneity using the Γ model  [52]  again. Here, we simulated two data collections, one using four discrete Γ rate categories (LG+S256+G4) and the second applying a continuous Γ distribution (LG+S256+GC).
We set the α shape parameters of the Γ distributions based on the values inferred during tree reconstruction when building the HOGENOM database (see Supplementary Material Section 1. 1. 1). In the following, we will refer to these parameters as empirical α parameters. For the simulations, we drew α parameters from the probability density function (PDF) estimate of the empirical α parameters (see Supplementary Material Section 1. 1. 1). We compared the ECDF of the empirical α parameters with the ECDF of 7000 samples from the PDF estimation to guarantee a reasonable approximation of the distribution (see Figure  S1 ). We sampled the MSA length we used for MSA simulations from the approximated empirical distribution of HOGENOM MSA lengths, using the same approach as for the α parameters outlined in Section 1.
We report the accuracy as average accuracy on the test set across the 10 cross-validation iterations. We report the balanced accuracy (BACC)  [11]  as this metric allows for varying proportions of simulated/empirical MSAs in the data collection and better represents classification accuracy for imbalanced datasets. The best BACC value is 1 and the worst value is 0. We used the hyperparameter optimization framework Optuna  [4]  to determine the optimal set of hyperparameters for each classifier. For each GBT classifier, we performed 100 Optuna iterations using a Tree-structured Parzen Estimator algorithm  [7]  to sample the hyperparameter space. To prevent the classifiers from overfitting the data, based on preliminary experiments, we limited the depth of the individual decision trees to a maximum of 10, the maximum number of leaves to 20, and the minimal number of samples per leaf to 30.
Furthermore, for the classification we used diverse and representative empirical protein and DNA databases: TreeBASE comprises representative data sets that are commonly analyzed in the field because it only contains MSAs of published studies, whereas HOGENOM offers a diverse sample of existing data, drawing from 499 nuclear Bacterial genomes, 46 from Archaea, and 121 from Eukaryotes. Finally, we used phylogenetic trees reconstructed from these empirical data collections to simulate the data as realistically as possible. Thereby, we circumvented having to simulate realistic trees and can invoke simulations that are as similar as possible to the empirical MSAs. However, it is important to note that the realism of the simulations depends on the quality of the inferred phylogenetic trees when deploying this procedure.
Acknowledgements We thank  Philippe Veber , research engineer at the  Laboratoire de Biométrie et Biologie Évolutive , for providing us with the source code of his simulator. We gratefully acknowledge support from the  CNRS/IN2P3 Computing Center (Lyon -France)  for providing computing and data-processing resources needed for this work. This work was granted access to the HPC resources of IDRIS under the allocation 2022- AD011011137R2  made by  GENCI. This work was financially supported by the  Klaus Tschira Foundation , the  ANR  grant  PIECES ANR-20-CE45-0017 , and by the  European Union (EU)  under Grant Agreement No  101087081  ( Comp-Biodiv-GR ).
Acknowledgements The reported XRD residual stress measurements were performed at the  IMDEA Materials Institute (Madrid, Spain ). The authors are indebted for excellent technical assistance. Work partly developed under support of the  Institut Laue-Langevin campaign   ILL-4/2019-1-02-274  and the granted internship of WWP. The authors are indebted to  Dr. Th. Pirling  for his fundamental scientific support. Work partly supported by  MINECO  (Spain; Grants  MAT2012-37782 ,  MAT2015-63974-C4-2-R  and  PID2019-104351GB-C21 ).
Acquisition system To get the proton tracks, the signals collected by the micromegas pads are treated by three successive cards: Zap, SAM and ZedBoard. The details of these cards are given in [14 (II. B) or 15]. Once treated by these cards, the signals are sent to the acquisition system composed of the Cobo software, developed by the CEA Saclay. Once saved, the events can be read by the CoboFrameViewer software, also developed by the CEA Saclay. As observed in the example of Figure  3 , a frame is composed of 64 curves, each one corresponding to one pad response.
Background: Accurate estimation of incidence and prevalence is vital for preventing and controlling diabetes. Administrative data (including insurance data) could be a good source to estimate the incidence of diabetes. However, how to determine the look-back period (LP) to remove cases with preceding records remains a problem for administrative data. A short LP will cause overestimation of incidence, whereas a long LP will limit the usefulness of a database. Therefore, it is necessary to determine the optimal LP length for identifying incident cases in administrative data. Objective: This study aims to offer different methods to identify the optimal LP for diabetes by using medical insurance data from the Chinese population with reference to other diseases in the administrative data. Methods: Data from the insurance database of the city of Weifang, China from between January 2016 and December 2020 were used.
Since medical insurance reforms in 2012  [12] , China has vigorously promoted medical policies and expanded coverage of the population  [13]. Effective data have gradually accumulated during the past few years, making it possible to conduct research in this population. However, multiple records of the same patient make it a general challenge to identify true incident cases through medical insurance data, as it is difficult to define the starting point of certain diseases for a patient. Before the index year, a look-back (LP) period is used to deal with this problem. Patients with a diabetes diagnosis in the index year and without a diabetes diagnosis in the LP would be identified as incident cases. Until now, there has been no consensus on the length of the LP. An insufficient LP might cause overestimation of incidence  [14] , whereas an overlong LP would limit the number of reporting years and the usefulness of the database.
Researchers have found that different diseases have different LPs due to specific trajectories  [11]. For diabetes, Brameld et al  [15]  identified an LP of 13 years using data from the Western Australian Data Linkage System, while Asghari et al identified an LP of 5 years using data from the Régie de l'assurance maladie du Québec  [7]. We believe that different patterns of medical contact cycles among countries may cause these different findings on LPs. However, there is no study on the optimal LP in the Chinese population for diabetes. In this study, we will use 2 methods to identify the optimal LP for diabetes in the Chinese population, using data from an insurance database for 2016 to 2020 obtained from the Medical Security Bureau (MSB) of the city of Weifang, China, as an example. Weifang, a prefecture-level city in Shandong province with 9.
Data Sources The database used in this study included all in-or outpatient claim records of patients in Weifang for 2016 to 2020 provided by the local MSB. The social health insurance system covered approximately all residents in Weifang (close to 9 million individuals) during the period from 2016 to 2020. Each claim record contains (1) basic information about the patient, including personal identification number, gender, date of birth, and type of medical insurance; (2) medical information, including hospital name and code, admission and discharge time, and diagnosis of disease; and (3) details of medical service expenses, including total expenses, out-of-pocket expenses, and reimbursement expenses.
Data Processing Firstly, due to the changes in the Weifang disease coding system and the special disease codes in outpatient chronic-disease records, the initial disease codes in the database were partly based on the International Classification of Diseases, 10th Revision (ICD-10). Therefore, the diagnoses were harmonized with the ICD-10 according to the code-disease mapping table provided by the MSB of Weifang. Later, records with critical missing information, such as gender (418/21,855,530, <0. 001%) or disease diagnosis (1,068,910/21,855,530, 4. 89%), were deleted. The data processing is shown in Figure  1.
Definition of Incident Diabetes for the Application of Different LPs First, we defined diabetes (ICD-10 codes E10-E14) cases as those with at least 1 inpatient claim record or 3 outpatient claim records with a diagnosis of diabetes, considering that inpatient records are more reliable than outpatient records  [16]. In this database, we found that all patients defined as having diabetes had more than 3 outpatient records, reflecting the regularity of medical contact. Second, we defined incident diabetes cases. The procedure to identify incident cases was to exclude cases with any in-or outpatient records of diabetes in the given LP, before the index year. Different LPs may classify patients as having different statuses. Two scenarios for assumed different LPs are shown in Figure  2. Scenario 1 (LP1) shows that patient B will be identified as an incident case but patient A will not.
Results After removing records according to the criteria above, 16,132,023 records from medical insurance claims data from 2016 to 2020 were used in this study; these records came from 3,185,337 individuals. There were 3,600,089 records and 247,254 cases of diabetes in total. The insurance record history of 109,476 diabetes prevalent cases in 2020 was screened with the different LPs to identify incident cases. Inclusion and exclusion details are shown in Figure  1 , and basic information on the data is shown in Table  1. Applying the longest LP identified 19,086 incident cases of diabetes. Only 17% (19,087/109,476) of the prevalent patients in 2020 were identified as incident cases as they did not have preceding records for diabetes in the longest LP before 2020, revealing that around 80% (90,389/109,476) of patients in 2020 were not truly incident cases.
Principal Results In our study, a 2-year LP had good CEIs with excellent κ agreement (0. 950>0. 9), a high PPV (92. 2%) and a lower than 10% overestimation rate. As for the retrograde survival function, it showed that a 2-year LP can reliably distinguish new cases in a prevalent pool. According to the results of the methods above, we confirmed that the optimal LP for identifying incident cases of diabetes using the Chinese insurance database provided by the MSB of Weifang is around 2 years, and all the methods are effective and stable.
Optimal Length of LP Compared to Other Work A 2-year LP for diabetes incident case identification using administrative data is the same length as used by a few previous studies  [20, 21]. A study in Manitoba, Canada, also showed a probability of 0. 96 for patients with diabetes to have subsequent medical contact for diabetes within 2 years  [22]. However, our results differ from those of Asghari et al  [7] , who reported an LP of 5 years for health insurance data from Quebec, Canada. A possible reason might be different patterns and medical contact cycles among Chinese and Canadian populations caused by the different medical insurance policies. The hazard function figure (Figure  3B ) reflects this difference. In our study, the hazard function was not monotonic, indicating that a few diabetes patients had medical contact irregularly in this database, unlike the Canadian insurance data.
Limitations Our study has some limitations. In our database, without more detailed records for disease classification, it was not possible to differentiate between type 1 and type 2 diabetes. However, there are far fewer patients with type 1 diabetes than with type 2 diabetes in the overall population. Thus, we considered the study population to be generally homogeneous and that these results may be representative and stable. Furthermore, we only applied up to a 4-year LP. Although the results of the retrograde survival function analysis showed stabilization of risk of being a preceding case after a 2-year LP, we expect to use longer LPs in future studies with more accumulated data.
7a. The key of the mechanism is the special design of the cams (see Fig. 7b ) according to the specific formulae in Fig. 7c. The pistons are pushed with springs towards the cams to avoid detachment under peak accelerations. The setup resembles the mechanical transmission system of a music box, as shown in Fig. 1a , and allows to deform the bottom of the channel with a programmed spatial distribution. For the experiment, the basin was filled up to 20 mm with a mixture of water and white ink for image detection purposes. For data acquisition, we used a Phantom VEO 440L camera controlled by PCC 3. 5 software. The resolution of the images was 1280 × 252 pixels, and the acquisition time was approximately 94 s at 400 frames per second for every amplitude. Images were saved in greyscale to simplify the contour detection process.
Acknowledgements The authors thank  Milena Páez-Silva  for improvements of the experimental setup. J. F. M. thanks  Laurette Tuckerman  for fruitful discussions. J. F. M. was funded by  ANID  through the  FONDECYT POSTDOCTORADO  grant No. 3200499. R. R. -A. was funded by  PUCV  and  UTFSM scholarship   N ∘ 065/2022. R. R. A. and M. A. G. -Ñ. acknowledge the support of  PROGRAMA DE COOPERACIÓN CIENTÍFICA   ECOS-ANID ECOS200006 , and S. C. , the  Program ECOS -Sud C20E07. M. A. G-Ñ. and L. G. were funded by  ANID FONDECYT  grants No. 1201434  and  1221103. J. F. M. also acknowledges  ECOS-Sud  project No. C15E06  for supporting a collaboration visit at  Université de Lille, France , where part of this work was conceived. Finally, the authors are grateful to  Dicyt- USACH  for providing the funds required for publication.