dataesr/scientific-paragraphs-categorization huggingface dataset:
Disproportionality analysis Disproportionality analyses, also called case -non-case studies are similar to case -control studies but for purpose of pharmacovigilance studies. These studies are nested in a database of spontaneous reports to assess possible disproportionality in reporting. Disproportionality analysis compares the proportion of a specific adverse drug reaction reported in a specific group (e. g. 5] [36]  This statistical approach has shown its value to assess relative risks of adverse drug reaction, that are correlated in most cases with results from meta-analyses of clinical trials. 37 assess an association between inflammatory heart reactions after mRNA vaccines and patient age or sex, we performed a disproportionality analysis among different patient groups.
Statistical analysis Descriptive analysis was performed on the cases retrieved from the two databases. Quantitative variables were expressed as median ± interquartile range (IQR) and compared using nonparametric analysis. Qualitative variables were expressed in number and percentages. Time to reaction onset was analyzed using a survival Mantel-Cox model. Disproportionality in reporting between groups is expressed using reporting odds ratio (ROR) and its 95% confidence interval (95% CI). ROR is a ratio similar in concept to the odds ratio in case -control studies and corresponds to the exposure odds among reported cases of interest over the exposure odds among reported non-cases. ROR (95% CI) were calculated as ௗ ሺ ݁ േଵ. ଽටቀ భ ೌ ା భ ್ ା భ ା భ ቁ ሻ,.
Demographics and characteristics of cases from the worldwide VigiBase As of the end of June 2021, of a total of according to age and sex (p<0. 0001) for both myocarditis and pericarditis (Figure  1 ). Specifically, most of myocarditis cases have been reported in 12-17 and 18-29 years old males. Myocarditis cases originating from the U. S. were mainly reported after May 2021, although reactions occur alongside with vaccination achievements (Figure  2A  and suppl. figure  1 ). Overall, the median time to onset was 3 [2-14] days after vaccine injection. Pericarditis onset was delayed compared to myocarditis with a median time to onset of 8 [3-21] vs. Overall clinical features from these databases are also consistent with the experience reported by the Israeli investigators. They have identified 110 myocarditis cases amongst their 5 million population who received 2 doses of the tozinameran vaccine.
On one hand, myocarditis has suffered from a notoriety bias in relation with media communication which has probably increase its reporting in May and June 2021 compared to other vaccine related events. On the other hand, because of an exceptionally high number of reports with these new COVID-19 mRNA vaccines that are associated to an important reactogenicity, non-cases reporting may be inflated as well, resulting in an underestimation of this risk. Finally, it is not likely that study groups in our disproportionality analysis, based on patient age and sex, may have been imbalanced in relation with these limitations. Sensitivity analyses showing consistent results strengthen our findings. Hence, data from VigiBase and VAERS reports should always be interpreted with caution, and consistency across datasets will be most important for validation of conclusions.
Acknowledgments VigiBase is a fully deidentified database maintained by the  Uppsala Monitoring Center (UMC). The authors are indebted to the National Pharmacovigilance centres that contributed data. Information from VigiBase comes from a variety of sources, and the probability that the suspected adverse effect is drug-related is not the same in all cases. The information does not represent the opinion of the  Uppsala Monitoring Center (UMC)  or the  World Health Organization  and only reflects the authors' opinion. According to VigiBase access rules, no specific ethical approval is needed. VigiBase access is granted to national and regional pharmacovigilance centers such our teams.
This database is maintained, deduplicated and deidentified by Uppsala Monitoring Centre (Uppsala, Sweden). VigiBase is the world's largest pharmacovigilance database, with over 23 million reports of suspected adverse drug reactions gathered from national pharmacovigilance systems and continuously updated. More than 130 countries, over five continents submit spontaneous safety report in this database. Information concerning the reporter, the patient, the suspected and concomitant drugs, the suspected adverse drug reactions and their seriousness, are reported for each case. Reactions are coded using the hierarchical Medical Dictionary for Regulatory Activities (MedDRA) (https://www. meddra. org/). VAERS (www. vaers. hhs. gov) is the national pharmacovigilance database to monitor the safety of U.
As an example, a person who has a good musical knowledge through playing guitar and want to learn to play the piano would learn in a more efficient way, compared to another person who had no musical background, by transferring his knowledge to the task of learning to play the piano  ( Weiss et al. , 2016 ). Thus, transfer learning is possible. Considering the huge data size required during the training phase of deep networks, data augmentation has been extensively used to enrich training dataset. Data augmentation is an artificial expanding of dataset samples by applying classical image transformation to the existing samples including random gray-level transformation of pixels ( < 3%), cropping, rotating, zooming and shifting  ( Hao et al. , 2019 ; Pavinkurve et al. , 2019 ) , applying a low frequent intensity variation  ( Sharma et al.
, 2019 ). Regarding renal images, GAN were used to generate real-like micro-anatomic renal images  ( Murali et al. , 2020 ). Authors have used a cyclic GAN to create an artificial effect of staining without physically tampering the histopathological slide. Their proposed GAN was demonstrated to be able to translate different stain styles of renal pathology (e. g. transforming hematoxylin and eosin stain to periodic acid-Schiff stain). The same team has used the concept of GAN in order to generate realistic looking synthetic images of renal biopsy  ( Lutnick et al. , 2020 ). Most interestingly, GAN was employed in  ( Sivanesan et al. , 2019 ) , to create synthetic US images as seen in Fig. 7  , therefore expanding their limited dataset.
An example of a CED architecture is given in Fig. 8  (left panel). Kidney segmentation using deep learning was tested on MR, US and CT images. An overall segmentation accuracy of 86% on MR images of kidneys with ADPKD was reached by Bevilacqua et al. in their study  ( Bevilacqua et al. , 2018 ). Authors have tested different CNNs-based segmentation approaches on their augmented dataset. The full-image segmentation based on an FCN has exceeded the CED (SegNet with VGG-16 as encoder) segmentation and the introduction of a region-based CNN (R-CNN) prior to the semantic segmentation did not enhance the delineation performance. Moreover, a novel boundary distance deep network served by transfer learning was developed by Yin et al. in order to segment US kidney images of children with congenital abnormalities of the kidney and urinary tract (CAKUT) and controls, achieving an accuracy of 98.
9 for both left and right kidneys  ( Thong et al. , 2016 ). Most interestingly, the Combined Healthy Abdominal Organ Segmentation (CHAOS) benchmark aims to develop deep networks that are able to segment multiple abdominal organ including kidneys, liver and spleen on CT and MRI images (T1-DUAL and T2-SPIR sequences) of a public dataset, provided with a groundtruth organ delineation  ( Conze et al. , n. d. ). Different tasks were involved under the CHAOS challenge, going from liver segmentation on only CT or MRI modality to the most complicated task that tackles abdominal organ segmentation on both CT and MRI modalities using a single network architecture (pre-trained or not)  ( Conze et al. , 2020 ). All participating teams, except for one, have used an extension of the U-Net deep network and all have achieved a high segmentation process.
ACKNOWLEDGMENTS The authors thank all those whose involvement made the development of this emerging model possible:  Nicole Boury-Esnault  and  Jean Vacelet  for their help in launching our Emerging  Marine Model Organisms  studies on this species; our PhD students  Pascal Lapébie ,  Eve Gazave ,  Quentin Schenkelaars ,  Laura Fierro-Constain ,  Amélie Vernale  and  Kassandra de Pao Mendonca ; the numerous internship students who helped in perform ing preliminary experiments; the imaging facilities of the  France Bioimaging infrastructure ; the diving facilities of the  Institute OSU Pytheas  and divers from the  IMBE lab ; and the molecular biology and morphology support services of IMBE. We thank  Haley Flom  for English editing. The authors acknowledge the  Région Sud (Provence Alpes Côte d'Azur) , the  French Research ministry , the  French National Center for Scientific Research (CNRS) ,  Aix-Marseille University  and the  Excellence Initiative of Aix-Marseille Université-A*MIDEX  for providing funds to support our fundamental research, in particular for the funding of  the project for international scientifi c cooperation (PICS) STraS, the A*MIDEX foundation projects  (n°  ANR   11-IDEX-0001-02  and  AMX-18-INT-021 ) and the  LabEx INFORM  ( ANR-11-LABX-0054 ), both funded by the " Investissements d'Avenir" French Government program , managed by the  French National Research Agency (ANR).
The climate in the Panjshir catchment is characterised by warm-dry summers and cold-wet winters with a large spatial and temporal heterogeneity. The annual precipitation increases from the north-east to the south-west. The average annual precipitation (1981-2010) is approximately 565 mm, of which 31% occurred during winter (DJF), 42% in spring (MAM) and 13% in summer (JJA) based on ERA5-Land climate datasets  (Muñoz-Sabater et al. , 2021). The average annual mean air temperature of the study area is -4 °C. However, it varies from -17 °C (January) to 9 °C (August) (Fig. 2 ). The land cover types in the subbasin include rangeland (86%), permanent snow and water bodies (7%), cultivated areas (3%), barren land (2%), and forest and shrub (2%).
Meteorological data Following several studies in the Himalaya region  (Li et al. , 2018; Lutz et al. , 2016; Wijngaard et al. , 2017)  that used ERA based datasets, the latest meteorological data from the ERA5-Land dataset was chosen for the historical period  as the reference dataset. The ERA5 includes global-scale, hourly atmospheric land and oceanic climate variables that are estimated through the assimilation of several observation-based datasets into an advanced modelling data assimilation system  (Hersbach et al. , 2020). ERA5-Land is an improved version of the land component of the ERA5 climate reanalysis with an enhanced resolution of 9 km against the 31 km in ERA5. The temporal scale of the ERA5-Land dataset is hourly for both precipitation and average temperature  (Muñoz-Sabater et al.
Evaluation of input data Due to the limited spatial coverage of meteorological observation stations in the mountainous region of the Panjshir catchment, the global ERA5-Land gridded datasets are used for spatially distributed snow simulations. The ERA5-Land dataset uses the satellite observations and meteorological observations from stations data to interpolate to a larger domain using advanced modelling and data assimilation techniques. The Panjshir catchment has only three meteorological stations (one station per 740 km 2 ). The stations are mostly located in river valleys and there is no coverage for high-altitude areas. Moreover, these stations cover various time periods, with one of them starting in 2009, one in 2012 and one in 2018 (Supplementary Fig. 1 ). For cross-evaluation of ERA5-Land, we compared the selected ERA5-Land grid with the nearest observed station.
(2015)  also indicated that in the upper Indus basin, the observed stations underestimate the precipitation by about 50% by considering the precipitation required to close the water balance. An advantage of ERA5-Land gridded dataset is the availability of data for a longer period and complete spatial coverage which comes particularly handy for spatially distributed modelling studies. Regarding ERA5-Land temperature, the seasonal variability matches well with observed stations, but with a negative bias in the winter season, that may be associated with elevation differences in the ERA5-Land grid and location of the station. In Fig. 3 , a corrected temperature dataset is shown where the ERA5-Land mean temperature is further processed to match the elevation of the station using the global temperature lapse rate of -0.
Snow cover data The snow cover data are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) snow products  (Hall and Riggs, 2007). They have two important advantages: (i) a long duration (18 years), which is necessary for building long-time scenarios; (ii) a robust and wide use everywhere in the world including mountain ranges, which allows comparisons with other studies. An improved daily snow cover data set developed by  Muhammad and Thapa (2021)  for High Mountain Asia was used to validate the model-based, simulated snow cover area. The final product is a Terra Aqua version 6 combined daily snow product namely, M*D10A1GL06 (hereafter known as improved MODIS snow cover). This dataset improves on the original MOD10A1 (Terra) and MYD10A1 (Aqua) derived from the same sensor  (Hall and Riggs, 2007)  using a multi-step approach for reducing uncertainty resulting from both sensor limitation and cloud cover.
Empirical-statistical downscaling For downscaling and bias correction, the quantile mapping method, which is a robust empirical-statistical bias correction method for mountainous regions, was used  (Immerzeel et al. , 2013; Themeßl et al. , 2011). The coarse resolution of current GCMs can lead to biases in climate variables, which need to be corrected through a series of downscaling processing steps. Quantile mapping adjusts the biases in climate variables from GCMs to the reference datasets at a daily resolution. In our study, this mapping was conducted, first, by applying a bilinear interpolation in space to re-grid the coarser GCM spatial resolution to the finer resolution of the reference datasets. Thereafter, the quantile mapping method was used to correct biases in the reference datasets.
The correction factor was developed by applying the empirical cumulative distribution function (ECDF) between the raw GCM run datasets and the reference datasets for each month based on daily data. Correction factors, corresponding to the difference between the raw GCM values and the reference datasets, were then applied to the monthly values of the GCM datasets for the whole period. A similar approach to downscaling and bias correction was applied by  Lutz et al. (2016)  and  Kaini et al. (2020).
The J2000 model was then run for the 1981-2010 period to create a reference simulation for comparison with projected periods (2011-2100). The snowfall, snow storage, and snowmelt data were used to analyse the impact of climate change on future snow dynamics. Simulated river discharge was additionally used as a multi-response outcome of the model for further discussion on the impact of snowmelt water availability on water resources and its implications for various sectoral activities. The J2000 snow module is a component of the J2000 hydrological model, which is implemented in the JAMS Modelling System (http:// jams. uni-jena. de). The J2000 snow module is able to simulate several snow-related processes such as snow accumulation, metamorphosis and snowmelt  (Knauf, 1980). The snow module also considers the prognosis of the snowpack state, paying particular attention to the linkages between snow density and the process of melting and subsidence.
Model set up, calibration, validation and uncertainty analysis The J2000 hydrological model uses hydrological response units (HRUs) as spatial modelling entities. The HRUs were derived from the SRTM digital elevation model at the 90 m resolution, land cover data based on GlobCover  (Defourny et al. , 2006)  and soil properties based on the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012). The DEM provides topographical properties (slope, aspect, elevation), whereas landcover properties include root depth and leaf area index for evapotranspiration. Similarly, soil data includes water holding capacity that influences infiltration, percolation, evapotranspiration and soil moisture flow. An HRU delineation workflow integrated these datasets and resulted in 7699 HRUs with an average size of 0.
GCM selection and downscaling The GCM simulations under both RCPs were selected based on the method described in Section 2. 3. Out of the 105 GCM model runs, about 50% of the model runs with the largest seasonal bias were first removed in Step 1. In Step 2, 16 model runs with the lowest annual bias in precipitation and temperature were selected for the RCP4. 5 and 8. 5 scenarios. The seasonal and annual bias of the selected 16 model runs is provided in Table  2. Supplementary Fig. 2  shows the monthly cycle of both selected and discarded models in the different steps, alongside the ERA5-Land reference period. While the selected GCM models reproduce the precipitation patterns of the reference datasets well, the temperature data shows a warm bias compared to the reference ERA5-Land. Such biases may result from the typical spatial scale currently available for GCMs, which is larger than that of the Panjshir catchment.
Future climate scenarios After downscaling the GCM simulation, high-resolution temperature and precipitation data for 51 grid points of the Panjshir catchment were obtained. Fig. 5  and Table  3  show the future changes in precipitation and mean temperature for cold-wet and warm-dry climatic conditions for RCP4. 5 and RCP8. 5. The figure shows that there is clear agreement among models for increasing temperature scenarios. All models project an increase in annual mean temperature towards the end of the century, with larger changes in RCP8. 5 compared to RCP4. 5. However, regarding precipitation, the models exhibit a much larger spread with both increasing and decreasing patterns (depending on the climatic conditions). Compared to temperature, precipitation projections are not uniform, as the model selection also prioritises models with a decrease in precipitation under a drier climate.
6C ). Overall, the average annual percentage bias of model-derived snow cover is 10% higher than MODIS. These discrepancies can be attributed to the different processing methods used by MODIS compared to the J2000 model and different spatial resolutions. Furthermore, we compared the modelled and MODIS snow cover across 500-m elevation bands in the Panjshir catchment. In Supplementary Fig. 4  the comparison between the fraction of snow-covered area from J2000 (model) and improved MODIS snow cover (MODIS) for each elevation band is shown. Overall, the model outcome compares well with that of MODIS datasets between 2500 and 5000 m, where the r 2 ranges between 0. 70 and 0. 90. However, below 2500 and above 5000 m, the r 2 range is 0. 63 and 0. 18, respectively. Overall, the model reproduces the snow cover variation across all elevation bands except for higher than 5000 m.
Since the snow cover melts when the temperature rises, the lower boundary is very close to the observed MODIS datasets. However, in some years, for e. g. , 2015 and 2016, the MODIS maximum snow cover is outside the uncertainty range (see also Supplementary Fig. 5 ). The parameter uncertainty is also at its highest during the melt phases whereas snow accumulation and maximum extent are much less affected by parameter uncertainty. Nevertheless, the coefficient of determination (r 2 ) between MODIS data and the median value of ensemble runs for the entire period (2003-2018) is 0. 91, suggesting a low degree of uncertainty in the results. These considerations from our GLUE-based uncertainty analysis confirm the robustness of the J2000 model in estimating snow cover in the Panjshir catchment.
Discussions related to runoff should also be interpreted considering the limitations in our study, as the discharge output from the J2000 model could not be validated independently with the measured discharge data. The results of our uncertainty analysis related to the model parameters suggest that the improved MODIS snow cover datasets remain within the parameter uncertainty range, and thus can be considered as low (Fig. 8 ). However, it should be noted that there are inherent factors contributing to uncertainties in the snow-cover products, e. g. , resulting from the large spatial resolution of satellite observation, cloudiness and saturation of signal  (Muhammad and Thapa, 2020). As the assessment of climate change impact on snow cover (and other environmental variables) are subject to uncertainties with the model projections, our study uses two extreme changes, i.
Given the robustness of the method adopted throughout our study and its use of globally and freely available datasets, this approach can be transferred to other snow-dominated mountainous regions of varying catchment scales that are smaller than the typical spatial scale of general circulation models. It should be noted that the long-term simulations presented here are based on changes in climate alone, and they do not consider possible changes in land use, biodiversity, demographics, regulations and technological advances whose feedback on climate may alter the results of the scenarios examined here. The results presented here describe likely scenarios under two opposite spectrums of climate change. Consideration of the land-use and land-cover changes, climate mitigation and adaptation strategies, as well as improvement in the model processes and projections will help reduce the uncertainties in the projected changes in snow dynamics in mountainous regions.
Acknowledgements This study was supported by the  Strengthening Water Resources in Afghanistan (SWaRMA) Initiative of ICIMOD , which is supported by the governments of Afghanistan and Australia. This study was also supported by the  Indus Basin Initiative of ICIMOD  with financial support from the  Sustainable Development Investment Portfolio (SDIP)  of the  Department of Foreign Affairs and Trade (DFAT), Government of Australia , and the  Swiss Agency for Development and Cooperation (SDC). This study was supported by core funds of ICIMOD contributed by the  Governments of Afghanistan, Australia, Austria, Bangladesh, Bhutan, China, India, Myanmar, Nepal, Norway, Pakistan, and Switzerland. We further acknowledge the support of the  German Aerospace Center (DLR)  which co-funded the project (grant number  D/943/67249358 ).
Objectives of the CPSS CPSS is a prospective dual-centre, UK cohort study that at its core aims to characterise deeply the clinicobiological attributes of the non-symptomatic AD stage in individuals at differing levels of risk for development of MCI and AD dementia, based on cognitive test scores at screening. CPSS participants thus could form a readiness cohort to be recruited onto future AD dementia prevention trials. Specifically, using data from participants with evidence of detectable Aβ pathology versus those with belowthreshold levels, the study will: All study investigators, sponsor team and participants are blinded as to Aβ status information, with the exception of an unblinded team member for verification of imaging and CSF Aβ information. Blinding was put in place to avoid bias for conducting, monitoring and interpreting results from the clinical assessments (except for research analysis purposes).