dataesr/scientific-paragraphs-categorization huggingface dataset:
If the experimental intervention affects health parameters (e. g. increasing sensitivity to infections), the test group size might decrease with time. As a result, larger groups need to be provided at the beginning of the study, in anticipation of these losses. Growing evidence indicates that independent validation of the reported main effects is crucial when analysing longitudinal datasets  (Herting et al. , 2018). This can be done by multiple statistical approaches, such as independent tests on two independent subsamples of the data or leave one out or k-fold statistical cross validation procedures. The ability to perform such analyses is fully tied to cohort size. For developmental studies, a related challenge is the ability to scan all individuals within the same age windows. This is especially critical in the very early months following birth in which brain morphology (in terms of myelination, overall brain size and gyrification) can change dramatically on a weekly basis.
A potential risk is that experimenters become more proficient over time at positioning the heads of animals relative to the coils and the magnet, which can have an impact on image quality, especially when one uses non-standardized arrangements of surface coils. For awake NHP imaging, an additional problem is the impact of animals' movements on image quality: if awake animals are not already well accustomed to the scanning environment and radio-frequency noise, the amplitude and frequency of their movements can decrease over time, resulting in a progressive improvement in data quality. It is therefore important to assess data quality a posteriori, and to identify outliers or check that data quality is not correlated with the longitudinal design (i. e. data quality increasing with time) (fig.
While there is general agreement that using a subject-specific template is best for longitudinal studies during adulthood  (Iglesias et al. , 2016; Reuter et al. , 2011 Reuter et al. , , 2012;; Ashburner et al. , 2013) , no such consensus has been reached for developmental studies where both subject-specific and age-specific templates have been used in combination with different normalization procedures  (Scott et al. , 2016; Ball et al. , 2019)  to characterize the drastic changes during these more dynamic phases of the lifespan (fig. 3 ). Here, we demonstrate using a simulation experiment of VBM data that the template choice is important when the pipeline contains both linear and non-linear registration steps to a final common space (see online Supplementary text). Specifically, the grey matter density (GMD) estimate after non-linear registration to the age-specific template is closer to the ground truth compared to the GMD estimate after non-linear registration to a subject-specific template (fig.
, 2012 , Chen et al. 2013 , and Bates et al. , 2015). Briefly, the response (for subject i, and response vector length N i ) can be described as the sum of fixed effect and random effect , i. Practically for neuroimaging studies, 3dLME  (Chen et al. , 2013) and lme4 (Bates et al. , 2015) , can be used to model a variety of complex experimental designs and directly test the factor of interest without requiring implementation detail. For example, 3dLME allows the user just to specify the component of interest without building any design/contrast matrix. 3dLME can be applied in situations with a few response variables up to tens of thousands of voxels in voxel-wise fMRI datasets. However, such great modelling flexibility usually requires prior knowledge to choose certain models, which is not trivial.
Conclusion Longitudinal primate neuroimaging offers unique opportunities and promises to open exciting new research ventures. However, it is also associated with specific challenges. Some of these challenges can be circumvented but need to be anticipated. Considering the human and financial investment necessary to acquire longitudinal datasets, and in line with the '3Rs', sharing such datasets should be encouraged. However, considering the associated dilution of primacy of data origination, which is important to investigators' career progression, it should also be properly  an interventional study with one control group and one treatment group (with equal number of subjects in each group). In the interventional scenario, the cross-sectional design with two groups is compared to a longitudinal design with two groups scanned once before treatment and once or several times after treatment (to study the sustainability of the treatment effect).
The increased power for longitudinal experiments is illustrated using asymptotic statistical methods for simulated data with a fixed effect size and a fixed detection threshold, using the longpower package (https://cran. r-project. org/web/packages/longpower/index. html). It should be noted that practically, signal variability is not homogenous across brain regions and that different power value estimates would likely be obtained for different brain regions  (Suckling et al 2014).
Acknowledgements Colline Poirier was supported by a  Newcastle University   Research Fellowship. Nathan Kindred  was supported by a  DiMeN MRC studentship. Suliann Ben Hamed  was supported by funding from the  Centre National pour la Recherche Scientifique (CNRS)  and  LABEX CORTEX funding  ( ANR-11-LABX-0042 ) from the  Université de Lyon , within the  program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). Adrien Meguerditchian  was supported by funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  grant agreement No  716931 -GESTIMAGE -ERC-2016-STG. Hugo Merchant was supported by  Consejo Nacional de Ciencia  y tecnología  A1-S-8430. Pamela García Saldivar  was supported by  Consejo Nacional de Ciencia  y tecnología studentship number  280464.
Sweden (Stefan Jacobson, Stockholm) The Stockholm region has been most affected by COVID-19. When the pandemic first struck, there was rapid coordinated reorganization among the hospital systems, with the large acute university hospitals focusing on COVID-19 and doubling intensive care unit (ICU) capacity within days. New teams and collaborations were established swiftly, with close to 100% of ICU beds occupied by COVID-19-infected patients and most using ventilators. 43 The Swedish Renal Registry is collecting COVID-19 data from all dialysis centers, not yet available. During the first wave, only 10% to 15% of HD patients at our center with RT-PCR-positive SARS-CoV-2 required hospitalization. Most had no or mild symptoms and continued to receive outpatient dialysis, even among older and frail persons.
Peter Blake, Director of Ontario Renal Network, Aug 10, 2020, and unpublished data Quebec Renal Network Study -currently under review). Early on, the Canadian kidney community rapidly mobilized, developing, implementing, and sharing best practices (Fig  4 ). 51, 52 Perhaps the limited spread of COVID-19 infection among patients with kidney disease, particularly among in-center HD patients, may be attributed in part to hypervigilant monitoring, diligence, and information sharing. Since October 2020, as infection has become more widespread across the country, cases and deaths among dialysis patients have occurred in most provinces, though at the time of the writing, updated counts are not yet available.
Sequence generation {16a} Patient will be randomized to group 1 (deep NMB) or group 2 (standard NMB) prior to surgery by an independent researcher. The randomization sequence is generated by a dedicated computer randomization software, Castor (Castor EDC, CIWIT B. V. , www. castoredc. com), and is stratified by center and by BUPA category (MAJOR, MAJOR PLUS or COMPLEX MAJOR, a list of examples is provided in Table  3 )  [18, 19]. To ensure a balanced distribution among randomization arms, randomization will be performed using randomly varying block sizes of two and four.
Plans for assessment and collection of outcomes {18a} After informed consent and prior to randomization, the following patient baseline data will be acquired: ▪ Age, sex, length (m), and weight (kg) ▪ Heart rate (/min) and blood pressure (mmHg) ▪ Planned procedure and center of admission ▪ ASA Physical Status Class and concurrent comorbidities ▪ Baseline NRS (Numeric Rating Scale; 0 no pain to 10 worst pain imaginable) ▪ QoR-40  [13, 14]  and SF-36  [12]  questionnaires.
Data management {19} A local subject pseudonymization key file will be stored at each individual site; data confidentiality will be ensured by measures described in paragraph {27}. For trial data capturing, we will use electronic data capture (EDC) via eCRFs (Castor EDC, CIWIT B. V. , www. castoredc. com) specifically designed to capture trial information with an audit log trail and enabling data entry by both the researcher and patient through the eCRF system. To minimize data entry errors, data will be range checked during data entry; a stop error will occur when entered data is outside the expected range. Intraoperative data at the operation theater, recovery room, and ward will be generated by direct data entry by the local researcher. Source data will be stored at the specific trial site where it originated and will be safeguarded by the local researcher.
Only authorized investigators and personnel can correct or complete eCRFs, and all corrections will be documented in an audit log trail. All data will be handled confidentially and in a pseudonymized fashion. After the database has been declared complete and accurate, the EDC database will be locked. The sponsor, local researchers, and project leader are responsible for data processing. If a subject withdraws consent, data collected until that moment will be used. All data will be stored for the length of the trial and for 15 years afterwards, for further analyses and publication. All handling of personal data will comply with the Dutch Personal Data Protection Act. A data management plan is available on file with additional details regarding data storage and handling.
Secondary study parameters Continuous normally distributed variables will be expressed as means and standard deviations or when not normally distributed as medians and interquartile ranges. Categorical variables will be expressed as absolute and relative frequencies. To statistically compare groups, Student's t tests will be used, if continuous data are not normally distributed, the Mann-Whitney U test will be used. Categorical variables will be compared using a Chi-squared test or Fisher's exact tests. P-values < 0. 01 will be considered significant for secondary outcome variables. Data analyses will be performed using R Studio, Boston, MA, USA. Longitudinal data (heart rate, blood pressure, L-SRS, BIS, per depth of NMB at the given moment) will be analyzed using linear mixed models using NONMEM (ICON Development Solutions, Ellicott, MD, USA).
Methods for additional analyses (e. g. , subgroup analyses) {20b} In subgroup analyses, the incidence ClassIntra® grade ≥ 2 will be analyzed per BUPA category and type of anesthetic (total intravenous versus volatile anesthesia). Demographic data, intraoperative administered cumulative drugs dosages, postoperative NRS scores, and length of admission (at PACU, ICU, and/or ward) will be reported. Data analyses will be performed as described in the paragraph secondary trial parameters {20a}.
Oversight and monitoring Composition of the coordinating center and trial steering committee {5d} LUMC and Radboud UMC are the coordinating centers. The trial steering committee (TSC) is composed of Monique van Velzen, Maarten Honing, Albert Dahan, Chris Martini, Martijn Boon, Michiel Warlé, and Gabby Reijnders-Boerboom. The TSC will oversee whether data collection and intraoperative procedures are in accordance with the trial protocol. In addition, an adjudication committee (BAC), consisting of members of the STC and local investigators of actively recruiting centers, will discuss on a regular basis progress of the trial and review every adverse event of classic grade 2 or higher, or any uncertainties in scoring of the primary outcome. This is to ensure uniform and consequent scoring of the primary outcome among centers.
The BAC will be blinded to the treatment allocation. Minutes of the BAC will be made and stored by members of the TSC. Composition of the data monitoring committee, its role, and reporting structure {21a} The on-site monitoring will consist of the data monitoring committee (DMC), which will control presence and completeness of the research files and informed consent forms. Source data checks will be performed as described in the monitoring plan. Every participating center will be visited and monitored at least once every year. All trial sites will be monitored by a trained, unblinded, independent monitor. The DMC will report directly to the principal investigator and the monitored local center. Additional monitoring details are specified in a dedicated monitoring plan.
This includes SAEs that influence postoperative recovery or clinical outcome. The investigator will report all SAEs to the sponsor without undue delay after obtaining knowledge of the events. Subjects will be followed up for AEs and SAEs until the final trial procedures or 7 days after discontinuation of the trial. All reports will be digitally filed in the electronic clinical data capture form. The sponsor will report the SAEs to the accredited Medical Research and Ethics Committee (MREC) Leiden-Den-Haag-Delft, within 7 days of first knowledge for SAEs that result in death or are life threatening followed by a period of maximum of 8 days to complete the initial preliminary report. All other SAEs will be reported within a period of maximum 15 days after the sponsor has first knowledge of the serious adverse events.
Dissemination plans {31a} The trial protocol and analysis plan are registered at ClinicalTrials. gov (NCT04124757 (EURO-RELAX)). The results of the trial will be published in (inter-)national scientific journals and at international congresses in the field of anesthesiology and/or surgery. The results of this trial will be disclosed unreservedly, including to trial participants, according to the Central Committee on Research Involving Human Subjects (CCMO) statement on publication policy. Material for public dissemination will be submitted to the MREC for review prior to submission for publication.
Climate variables In ecological studies, drought is better characterised by soil moisture anomalies i. e. agricultural drought  (Sherriff et al. , 2011; Seneviratne et al. , 2012; Samaniego et al. , 2018) , than atmospheric drought indices. We therefore base our drought assessment on two complementary soil moisture datasets. The first is the observation-based soil moisture dataset obtained from SoMo. ml  (Sungmin and Orth, 2021) , used as reference in this study, and the second, for comparison with SoMo. ml, is given by ERA5 volumetric soil water content  (Hersbach et al. , 2020). The SoMo. ml data are generated using a long short-term memory neural network model trained with meteorological forcing from ERA5 and land surface characteristics as inputs and global in situ soil moisture measurements  (Dorigo et al.
Vegetation and soil data We used the 16 d enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor from the MOD13C1 Climate Modeling Grid (CMG) product. The MOD13C1 CMG provides continuous cloudfree spatial composites from 1 km data projected on a 0. 05 • lat/long grid  (Didan et al. , 2015)  and was selected for the period 2001-2019. Standardised EVI anomalies (EVI anom ) were calculated following the same approach as for climate variables. The standardisation allows comparing the relative magnitude of anomalies for pixels with distinct temporal variability patterns and with vegetation productivity simulated by LSMs, which have different physical units. We used land cover distribution in 2018 from the ESA Climate Change Initiative land cover  (Kirches et al.
Drought characterisation We use the observation-based SoMo. ml as a reference dataset to define agricultural drought conditions. Regions with average SM anom below -1σ  (Seneviratne et al. , 2012)  during summer (June, July, and August, JJA) are considered drought-affected areas during the DH events. Then, a regional domain affected by both DH18 and DH19 events is selected to evaluate the impacts of two consecutive DH events. Within this region most pixels had negative SM anom and the majority registered SM anom < -1. 5σ , but they differ in the magnitude of agricultural drought in DH19. This allows for comparing responses across pixels for different combinations of stress between DH18 and DH19. Since we are interested in evaluating how recovery from DH18 affected impacts of DH19, we limit our analysis to pixels with negative EVI anom in DH18.
Interactions between compounding disturbances can further contribute to forest C losses  (Seidl et al. , 2017; Kleinman et al. , 2019). To anticipate such impacts, process-based modelling of ecosystem response to such events is needed. The LSMs perform well in simulating the magnitude and evolution of productivity anomalies in 2018 but not in 2019. The recovery simulated by LSMs in DH19 can be partly explained by a strong recovery of modelled soil moisture (Fig. B7 ) but may also result from limited ability of LSMs to simulate changes in ecosystem vulnerability during the two DH events. The latter is supported by the fact that simulated SM anom shows good agreement in the temporal evolution of soil moisture anomalies with both observation-based datasets but not of GPP anom (Table  1 ). The comparison of the reference and factorial simulations allows showing that the poor performance in 2019 may be related with interannual legacy effects.
We showed that inter-annual legacies from DH18 played an important preconditioning role in amplifying the impacts of DH19. While LSMs simulated the impacts of the first event (DH18) well, they showed limited skill in simulating the impacts of the subsequent compound event (DH19). Our results show that compounding effects of multiple and repeated stressors and ecological dynamics can result in non-linear and unexpected impacts  (Schuldt et al. , 2020)  that LSMs still cannot realistically simulate. Attribution of interannual legacy effects from DH18 and of LSM errors to internal processes (e. g. drought-induced damage and mortality) or others such as insect outbreaks remains challenging because up-to-date datasets on tree mortality and tree carbon reserves or spatially explicit information on biotic disturbances are very limited.
The allergy community engagement: the allergy & COVID-19 international survey We developed a web-based survey in English, to reach out to the allergy community worldwide. An online questionnaire was constructed using GoogleDocsÒ, enabling responses to be recorded in a unique database (Annex 1). We launched an introduction letter containing a link to the questionnaire unique to each participant. We received help from a number of relevant international and national societies, including World Allergy Organization (WAO), Italian Society of Allergology and Clinical Immunology (SIAIC), and French Association for Continuing Education in Allergology (ANAFORCAL) in distributing the survey among their members. The link directed respondents to a page explaining the purpose of the survey. The survey was disseminated by email and social media.
The online questionnaire was beta-tested and launched via Internet on 20 April 2020 and closed 8 weeks later (on 22 June 2020). A reminder was sent out after 4 and 7 weeks. The survey had anonymous and voluntary standard. Of the 24 questions (Annex 1), 7 covered demographic data from the participants, 4 were related to clinical practice during this period, 12 covered data related to the management of allergic patients, including specific treatments such as allergen immunotherapy (AIT) and biological agents, and 1 related to the new international classification and coding tools addressed to COVID-19 morbidity and mortality data recording.
23 Three types of RIHP are defined according to their risks for research participants, 24 from the more risky or invasive research (RIHP1) to the less risky or less-invasive ones (RIHP3). Activities related to the procurement, collection, preservation and use of biological samples and attached data can occur in the three types if justified and detailed within the research protocol. 25 very RIHP project needs to be registered 26 prior to the submission to the competent authorities. Drug clinical trials covered by the EU Clinical Trial Regulation 27 will need a EudraCT number. RIHP1 ones (e. g. interventional research on medical devices) will need to obtain an ID-RCB number with registration at the National Agency for the Safety of Medicines and Health Products (ANSM). The protocol must be reviewed and approved by a competent Research Ethics Committee (Comité de Protection des Personnes-CPP).
Like the GDPR, the research activities covered are scientific, historical research, statistics and archiving in the public interest,  43  and includes technological research (e. g. on medical devices) and innovation. The LIL, following its amendment in 2018, did not fundamentally change the existing framework but incorporated some of the GDPR provisions, notably those updating the right to information, and provisions regarding Data Protection Officers 44 (DPO), Data Protection Impact Assessment 45 (DPIA), data transfers 46 and CNIL remits. The LIL directly refers to the GDPR in several articles. New rules were inserted into Chapter III on accessing the National Health Data System (SNDS) databases for research purposes. Definitions of 'personal data' and 'processing' 47 are identical to the GDPR ones.
Sensitive personal data 48 are a special category of personal data whose processing is forbidden in principle, with limited exemptions including processing that is necessary for scientific research. 49 Sensitive data include data concerning health, genetics or biometrics, as defined in the GDPR. The CNIL developed a flexible approach to  42  Articles 57-79, Section 4 fixing specific rules for health research, study or evaluation purposes. 43 Articles 44(3) and (  6 ) LIL. 44 CNIL (2018) Referentials for the certification of DPOs' skills. https://www. cnil. fr/fr/ certification-des-competences-du-dpo-la-cnil-adopte-deux-referentiels. 45 CNIL (2018) List of activities requiring DPIA: https://www. cnil. fr/sites/default/files/atoms/files/ liste-traitements-avec-aipd-requise-v2. pdf; DPIA guidelines and tools: https://www.
57 Biobanks' duty to ensure database security applies to facilities and ICT systems used to store, process and make available the data, including measures for external data users. 58 French quality norm NFS-96-900 on BRCs and the ISO norms, in particular ISO 20387:2018 on Biotechnology and Biobanking, together with potential new labels 59 on personal data protection, allow a certain alignment of management practices. Also, biobanks can apply to the Ministerial ASIP for specific certification of health databases hosting. 60 50 CNIL. Qu'est-ce qu'une donnée de santé? See: https://www. cnil. fr/fr/ quest-ce-ce-quune-donnee-de-sante. 51 Article4 LIL. 52 Article 4(2) LIL. 53 Under Title I, Chapter IV, V and Title II Chapter III LIL. 54 Article 4(5) LIL. 55 This presupposes the existence of a right of biobanks to refuse deposit or access requests based on legal or ethical non-compliance or uncertainties and of attached responsibilities they could endorse.