dataesr/scientific-paragraphs-categorization huggingface dataset:
Data processing From 1993 to 2020, participating laboratories reported 12, 177 cases of Enterococcus infections to the network. Given that Enterococcus can be associated with several diseases in the same flock, 12, 667 EAD cases were collected by the network. Using R software (ggplot 2 and sp packages), data processing involved the analysis of the evolution of the species involved in enterococcal cases reported in the network, the types of diseases associated with Enterococcus and the poultry production sector affected, considering the evolution of the annual relative frequency of enterococcal diseases in a given poultry production sector (percentage of the number of diseases with Enterococcus out of the total number of diseases reported in the year).
Methods: Data used in this study were extracted from 2 waves of the Shandong Rural Elderly Health Cohort (SREHC), and the final analytic sample included 2785 respondents who participated in both baseline and follow-up surveys. Multilevel linear mixed effects models were used to quantify the strength of the longitudinal association between frailty and multimorbidity combinations and psychological distress using 2 waves of data for each participant, and then, cross-level interactions between CSS and combined frailty and multimorbidity were included to test whether CSS would buffer the adverse impact of coexisting frailty and multimorbidity on psychological distress.
Study Design and Data Sources Data used in this study were extracted from 2 waves of the Shandong Rural Elderly Health Cohort (SREHC), an ongoing longitudinal study of community-dwelling older people in rural Shandong, China. The baseline survey was completed in June 2019, which was considered prior to the COVID-19 pandemic on the basis of the declaration from the World Health Organization on  March 11, 2020 , that COVID-19 can be characterized as a pandemic  [32]. The detailed study design, sampling, and data collection have been described elsewhere  [2, 33, 34]. Briefly, we used the multistage stratified random sampling method to select participants, excluding those who had a clinical diagnosis of dementia or psychiatric diseases, could not complete the interview due to severe physical conditions, or were unwilling to cooperate with the interviewers.
There were 3243 respondents aged 60 years and above who participated in the baseline survey. Next, a follow-up survey was conducted during the COVID-19 pandemic from August to September 2020. The final analytic sample included 2785 (85. 88%) respondents who participated in both baseline and follow-up surveys. We restricted the analysis to these 2 data points for the following reasons: First, we were specifically interested in examining the short-term consequences of combined frailty and multimorbidity on psychological distress so as to identify the most vulnerable group with the greatest risk of worse psychological outcomes and inform early interventions during the pandemic. Second, previous research has shown that social support would have an immediate effect on a scaled measure of psychological health  [27] ; therefore, we assessed the moderating role of CSS between combined frailty and multimorbidity and psychological distress with just a 1-year lag based on the assumption that there may be a limited time lag for CSS to mitigate one's psychological distress.
Statistical Analysis First, we compared the baseline characteristics between respondents and nonrespondents using cross-tabulation with t tests for continuous variables and chi-square tests for categorical variables (see Table  1 ). To examine the combined effects of frailty and multimorbidity, we created a categorical indicator with the following 6 groups: (1) nonfrail without a chronic condition, (2) nonfrail with 1 chronic condition, (3) nonfrail with multimorbidity, (4) frail without a chronic condition, (5) frail with 1 chronic condition, and (6) frail with multimorbidity. Multilevel linear mixed effects models were used to quantify the strength of the longitudinal association between frailty and multimorbidity combinations and psychological distress using 2 waves of data for each participant (see model 1 in Table  2 ), and then, cross-level interactions between CSS and combined frailty and multimorbidity (see model 2 in Table  2 ) were included.
Strengths and Limitations This study has several strengths and limitations. The novelties and strengths of this study include the study design and the measures we used. First, we used a large sample of community-dwelling older adults and followed up the psychological health outcomes with little missing data. Moreover, we used repeated measures of the key predictors and outcomes, including frailty, chronic conditions, social support, and psychological distress, and adjudicated K10 and CSS using standardized criteria. Furthermore, we not only used 2 waves of the data to show the longitudinal association between coexisting frailty and multimorbidity and psychological distress but also used baseline values to predicted psychological distress during the COVID-19 pandemic, which showed the robustness of the results.
Acknowledgments This work was supported by the  National Science Foundation of China  (grant nos. 72274109 ,  72204147 ,  71774104 ,  71974117 ); the  China Medical Board  (grant no. 16-257 ) the  Cheeloo Youth Scholar Grant, Shandong University  (grant nos. IFYT1810 ,  2012DX006 ); and the  China Population Welfare Foundation  (grant no. GJB-6-20211028-556 ). We thank the officials of health agencies, all participants, and staff at the study sites for their cooperation.
Surgical microscope was used for case 9. Electrical stimulation was used as previously reported  [25] [26] [27]. Monitoring was performed by a speech therapist (MB, IP, SL, CPT) and, on surgeon's request, assessed motor functions (continuous repetitive movement of left superior limb), counting, picture object naming, nonverbal semantic association, and the test "read the mind in the eyes". Resection was stopped when a functional boundary was encountered. Imaging. All patients underwent the same imaging protocol, as previously described  25, 27, 28. In this study, the extent of resection was estimated on FLAIR sequences and computed as 100 × (1 -residual volume/initial volume). Surgical cavities were segmented with MI-Brain 2020. 04. 09 software  29  (Sherbrooke, Canada, https:// github. com/ imeka/ mi-brain) on 3D-T1 images and resized to a resolution of 1 × 1 × 1 mm 3.
Images were then registered to the MNI template using the Antsregistration algorithm and displayed with MRIcro-GL 1. 2. 20201102 software  30  (https:// www. nitrc. org/ proje cts/ mricr ogl/). In cases 8, 11, 13-20, language fMRI was performed to confirm the left lateralization of language networks, following the same methodology as previously reported  12. Neuropsychological testing. Patients were thoroughly evaluated neuropsychologically by a speech therapist (MB, IP, SL, CPT) just before, immediately after, and four months after the surgery. After a short non-structured interview with the patient, aiming to record spontaneous complaints, the evaluation assessed language, memory, executive and visuospatial functions, and social cognition. The most common tests were administered to all patients, whereas some tests were added in a patient-specific approach, as expected for evaluations performed in a clinical rather than research context.
Furthermore, patients were evaluated only at 4 months, so we cannot rule out that a different pattern of deficits would have been seen one year later. However, there are some data in the literature demonstrating that, in general, the cognitive recovery curve reaches a plateau after 4 months (see, for example  66  , for spatial attention and awareness). Hence, although this is not proven, we made the reasonable assumption that the 4-month measure is a good proxy of the 1-year measure. Last but not least, the small size of our series did not allow us to perform a multivariate analysis that would have included all regressors known to influence cognitive recovery, including age, preoperative cognitive status, somatic gene polymorphisms  67  , location and extent of resection, and growth rate of residual tumor.
In addition, because the resistance rate could vary according to healthcare settings, it should be investigated and continuously updated. In line with the national action plan on antimicrobial resistance, we investigated the antimicrobial susceptibility of S. pneumoniae isolates collected from CAP children at three hospitals in Quang Nam province and Da Nang city from September 2017 to September 2018. This study aims to provide clinicians with critical and updated data on antibiotic resistance patterns and support them to select appropriately and safely antibiotics for empirical treatment of CAP.
Materials, samples, and study process Sample size calculation for estimating the population proportion  [14]  n = (Z 1-α/2 ) 2 x P (1-P)/d 2 = 323 Where: -n: Minimum sample size -Z 1-α/2 : The number of standard errors away from the mean (Z The minimum sample size was calculated at 323 children and rounded to 360. The number of patents of each hospital was 120. The association between qualitative variables was analyzed with Pearson's chi-square test using SPSS statistics software 20. 0.
Statistical method Data were processed by using excel version 16. 49 and analyzed by using SPSS statistics software 20. 0. Variables specific to the research population used such as hospitals (DN, QN1 and QN2); age groups (2-23 months and 24-60 months); gender (Male, female); culture (positive, negative); isolation (positive, negative). The association between qualitative variables was analyzed using Pearson's chi-square test with a level of confidence of 95%.
ACKNOWLEDGEMENTS The authors would like to thank  Professor William Couet -Director  of the unit of  Pharmacology of Antimicrobial  agents,  Dr. Julien M. Buyck -INSERM  U1070 for research orientation and conceptualization. We thank  Professor Stephen Baker  for supporting materials and facilities to conduct the study at  Oxford University Clinical Research Unit, the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. Besides , we thank  Danang Hospital for Women and Children  and two hospitals in  Quang Nam  for bacterial samples used in this study and  Danang University of Medical Technology and Pharmacy  to access their research facilities. All other contributions to this study should be acknowledged.
Data Source In France, all health insurance reimbursements for out-of-hospital care (visits, procedures, and drugs) are recorded in a medical-administrative database called SNIIRAM (Système National d'Information Interrégime de l'Assurance Maladie = French National Health Insurance Administrative Database). Moreover, administrative and medical data on every hospital stay (either in a private or public healthcare facility) are gathered in the national administrative health insurance database (Programme de Médicalisation des Systèmes d'Information [PMSI] = French Medical-based Information System). The PMSI is then included in the SNIIRAM. Diagnoses are encoded according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), and the procedures performed during the hospital stay are encoded following the French Common Classification of Medical Procedures (CCAM).
Data Extraction The National Commission for Data Protection (Commission Nationale de l'Informatique et des Libertés no. DR-2019-099) approved the use of the SNIIRAM database, and this study adhered to the tenets of the Declaration of Helsinki. Institutional Review Board approval and patient consent were not required, as we used anonymized data from a medicoadministrative database. We included all women with hospital discharge records involving a code for pregnancy (ICD-10 and CCAM codes) from 1 January 2009 to 31 December 2018 that was associated with the codes for anti-VEGF or corticosteroid IVTs during pregnancy and in the month preceding pregnancy, which were performed on an inpatient or outpatient basis. The use of anti-VEGF or corticosteroid IVTs was identified in the SNIIRAM using the CCAM code BGLB001 for IVT ("injection of a pharmacological agent in the vitreous") and a corresponding dispensation for anti-VEGF or corticosteroid IVTs using French Presentation Identifying Codes (CIP).
Main Outcomes Obstetric and neonatal complications were identified using ICD-10 and CCAM codes from the records of maternal obstetric stays. The following complications were studied: pregnancy loss (early miscarriage or intrauterine fetal death), medical termination of pregnancy, hypertensive disorders of pregnancy (gestational hypertension, preeclampsia), gestational diabetes, intrauterine growth restriction, macrosomia, amniotic fluid loss or excess, fetal lesions or fetal distress, prematurity, threatened preterm labor or preterm rupture of membranes, abnormal fetal heart rate, emergency cesarean section, and neonatal distress. Data on voluntary abortions and on patient age were also collected. We searched for the indications of IVTs: diabetes, uveitis, or high myopia. We also searched for a history of hypertension prior to pregnancy, a history of diabetes prior to pregnancy, and a diagnosis of gestational diabetes, as these are well-known risk factors for obstetric complications  [36] [37] [38].
Statistical Analysis Continuous variables with a normal distribution are expressed as means (standard deviation, SD), and categorical variables are expressed as numbers and percentages. We used a logistic regression model to assess associations between obstetric or neonatal complications and anti-VEGF IVTs, corticosteroid IVTs, age, preexisting diabetes, and preexisting hypertension. A multivariable logistic regression analysis was then performed to obtain adjusted odds ratios (ORs). The tests were two-tailed, and statistical significance was set at p < 0. 05. The analyses were carried out with SAS software (V. 9. 4. ; SAS Institute).
1% vs. 3. 1%, respectively, p = 0. 09). This is probably related to the lack of statistical power due to the low number of patients (n = 62 and n = 32 for patients treated with anti-VEGF agents or corticosteroids, respectively). The pregnancy losses we identified in the national database were probably late miscarriages and intrauterine fetal deaths, as patients suffering from early miscarriages are rarely hospitalized. The incidence rates of pregnancy loss reported in our study are much higher than those observed in the general population of women in France, where the incidence of late miscarriage and intrauterine fetal death is less than 1% and 0. 5%, respectively. Chronic hypertension and uncontrolled diabetes before pregnancy multiply the risk of intrauterine fetal death by 2. 6 and 2. 9, respectively  [42, 43].
First, despite a 10-year study period, the number of patients studied was quite small (n = 139 in the descriptive analysis, n = 94 in the comparative analysis), limiting the power to reach statistical significance for most of our comparisons. However, it must be emphasized that corticosteroids and anti-VEGF IVTs in pregnant women are very rare events, as they represented 0. 004% of the overall IVTs over the 10-year study period. Second, we lacked clinical data on the pregnant women included in our study. Some clinical factors that are well-known risk factors for obstetric complications could not be confidently identified in the database and included in our analysis-for example, smoking and obesity. Indeed, even if smoking, for instance, can be registered, the accuracy of the recording is quite low.
Interestingly, bevacizumab and ranibizumab only bind to VEGF, whereas aflibercept also binds to the placental growth factor (PlGF). PlGF may play a role in embryo development and implantation and in fetoplacental circulation  [49]. Finally, corticosteroids were used as a reference group since they are widely used in pregnant women, especially systemic corticosteroids. However, there is controversy regarding the potential adverse events-notably, teratogenicity, a reduction in birth size, and cerebral palsy-and IVT corticosteroids should not be considered a totally harmless treatment  [14, 50]. Our study also has several strengths. It was a national study over a 10-year period using the medico-administrative database. IVTs in pregnant women are extremely rare, as we have demonstrated. Other studies have shown that the national medico-administrative database is suitable for studying rare events such as postoperative endophthalmitis  [35, 51].
Conclusions The use of the medico-administrative database allowed us to establish an exhaustive collection of data on pregnant women treated with IVTs of anti-VEGF agents or corticosteroids during a 10-year study period at a national level. We reported on all the cases (139) of pregnant women treated with anti-VEGF agents or corticosteroid IVTs during pregnancy or in the month before pregnancy in France between 2009 and 2018; our findings confirmed that IVTs in pregnant women are rare events. Even though we found a high incidence of obstetric complications in our series, we could not demonstrate a statistically significant association between the intravitreal agents and these complications after adjusting for age and preexisting comorbidities. Indeed, this high rate of obstetric complications is likely supported by the underlying condition responsible for the IVT treatment.
Institutional Review Board Statement: The study was conducted in accordance with the  Declaration of Helsinki. The  National Commission for Data Protection (Commission Nationale de l'Informatique et des Libertés  no. DR-2019-099 ) approved the use of the SNIIRAM database. Institutional Review Board approval was not required, as we used anonymized data from a medico-administrative database. Informed Consent Statement: Not applicable. Patient consent was not required, as we used anonymized data from a medico-administrative database.
Data Availability Statement: The SNIIRAM and PMSI databases were made available by the French National Agency for the Management of Hospitalization Data (Agence technique de l'information sur l'hospitalisation, ATIH). The use of these data by our department was approved by the CNIL. We are not permitted to share these data. SNIIRAM and PMSI data are available from ATIH to researchers who meet the criteria for access (requests for access are evaluated by the CNIL).
Organic geochemistry characterization The organic geochemistry characterization of the Chela Formation samples was carried out at the TOTAL CSTJF facilities in Pau, France. Maceral identification of the Chela Formation carbonates and the Grey and Red Cuvo formations was undertaken on sidewall core samples (Table  1 ; Fig. 2 ) using a LEICA DM6000M microscope. Bitumen and vitrinite reflectance were determined using a LEICA CTR photometry system and Diskus-Fossil software in the random mode under reflected white incident light in oil immersion. The sidewall core samples were also measured for their Tmax and TOC contents with Rock-Eval 6 after removing the soluble oil fractions and possible drilling mud with pentane (Table  1 ).
Redundancy in enrichment results is often due to the hierarchical structure of certain annotation databases, such as Gene Ontology (GO)  [1, 2]  or Reactome  [3]. In these databases, the annotation terms are linked by parent-child relationship where ancestor terms correspond to more general terms and descendant terms correspond to more specific terms. Importantly, ancestor terms annotate all the genes annotated by their descendants. The overlap of annotated genes causes the risk of both parent and child term to be significant in the enrichment analyses. A first solution to overcome redundancy consists in using down-sized, non-redundant collections of annotation terms. Such collections include for instance "GO subsets", also known as "GO slims". GO subsets contain only a portion of the GO terms, providing a coarse-grained ontology.
Generic and organism-specific GO subsets are maintained by the GO consortium and different communities  [4, 5]. The hallmark collection of The Molecular Signatures Database (MSigDB)  [6]  is another down-sized collection of annotation terms. It contains 50 non-redundant terms obtained by clustering and filtering over 4000 terms from different databases. The enrichment analysis tool FunMappOne  [7]  has its own down-sized versions of the Kyoto Encyclopedia of Genes and Genomes (KEGG)  [8] , GO and Reactome databases. The annotation terms are stored in three hierarchical levels, ranging from the full list of terms to the summary list. Down-sized annotation collections are built a priori; they hence allow directly obtaining non-redundant enrichment results. In addition, using a lower number of annotation terms implies less statistical tests.
The multiple testing correction will hence be more gentle. The main drawback of down-sized annotation collections is that the terms may be too general or too specific for the user's study. Additionally, these collections are not available in all the enrichment analysis tools. Finally, the user is dependent on the curators of these collections for updates; for instance, some GO subsets may become outdated while GO is updated. The second solution to overcome redundancy in enrichment results is the clustering of the enriched annotation terms. The clustering methods calculate the similarity between two annotation terms either with statistical measures on the corresponding sets of annotated genes or with semantic similarity measures. Various semantic similarity measures exist. The most common ones depend on the frequencies of the assessed terms and of their closest common ancestor term in the annotation database  [9, 10].
One widely used and highly cited tool in this category is REVIGO  [19]. REVIGO is available as a web tool. It selects pairs of terms that are more similar than a threshold based on semantic similarity measures and compares them. In the comparison, REVIGO checks step by step whether one of the terms is very general, or less significant than the other, or a child term of the other. It then discards the term that satisfies the condition. The Cytoscape  [20]  app of STRING database  [21] , stringApp  [22] , provides functionality for enrichment analysis and removal of redundant terms. From two terms with high number of overlapping genes, the less significant one is discarded. A popular enrichment tool, g:Profiler  [23, 24] , also provided filtering options that considered term significance and parent-child relations on its web interface (before version e94_eg41_p11_5fca2e9) and in its previous R package, gProfileR.
GOsummaries  [25] , which is an R package for visualizing enrichment results, uses gProfileR package for enrichment analysis and filtering. Clustering and filtering approaches process the user's enrichment results. They hence provide summarized results tailored to the input enrichment analyses. The tools developed for clustering and filtering can work with any enrichment analysis result; the user is thus free to choose any enrichment analysis tool. However, available clustering and filtering approaches are based on similarity measures that can be complicated to handle. It can indeed be complex for the users to set thresholds for similarity and anticipate the consequences on the obtained summarized results. Finally, many popular tools work with built-in annotation data. In this case, the user is dependent on the developers for updates.