dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012; Kennedy et al. , 2014; Pound and Salzmann, 2017; Willard et al. , 2019) , providing a certain level of confidence  (Klages et al. , 2020; Pross et al. , 2012). The PDF method is used to statistically constrain the most likely climate co-occurrence window for an assemblage  (Harbert and Nixon, 2015; Willard et al. , 2019; Klages et al. , 2020). The bioclimatic envelope for each NLR was identified by cross-plotting the modern distribution from the Global Biodiversity Information Facility (GBIF; GBIF, 2021) with the gridded WorldCLIM  (Fick and Hijmans, 2017)  climate surface data using the dismo package  (Hijmans et al. , 2021)  in R. Some taxa were grouped at the family level because of their potentially ambiguous climatic affinity. This includes (1) pollen taxa affiliated with the modern-day genus Micro-Clim.
Acknowledgements. This work used Deep Sea Drilling Project archived samples provided by the  International Ocean Discovery Program (IODP). We thank the staff at the  Gulf Coast core repository (GCR)  for assistance in ODP Leg 113 core handling and shipping. We thank  CNRS  for the salary support of  MAS. Financial support. Nick Thompson  received funding from the  Natural Environment Research Council  from a  NERC-funded Doctoral Training Partnership ONE Planet  (grant no. NE/S007512/1 ). Funding for this research was also provided by the  Spanish Ministry of Science and Innovation  (grant nos. CTM2014-60451-C2-1/2-P  and  CTM2017-89711-C2-1/2-P ) cofunded by the  European Union  through  FEDER funds. Peter K. Bijl  received funding from the  European Research Council (OceaNice  (grant no.
For instance, Lopez et al. [LCMP19b]  enable parameter tweaking resulting in varied adaptation times and freedom as to detour exploration. Data-driven. More recently, data-driven and learning-based methods have received increasing interest from numerous domains. For the purposes of collision avoidance, literature has taken advantage of such methods to alleviate the need for "handcrafting" rules or functions. The most intuitive way to use data for learning purposes is via searching datasets. In particular, Charalambous and Additionally, real-life data can be utilised by deep learning networks. Most work focuses on character animation, but research like [AGR * 16] and [GJF * 18] use deep learning for trajectory prediction in the context of multiple characters (LSTM and GAN respectively). Most deep learning models aim to fully automate the learned behaviours, with user intervention being at the selection of the criteria based on which the network learns, which are usually fixed and defined before training.
In practical applications, several game engines offer motion or character templates to allow authoring at the animation level. Golaem's commercial software revolves around composing assets representing categories of characters with similar characteristics. Since 3D animation is a visual criterion determining the believability and naturalness of the crowds, being able to author its aspects is of particular importance. For the scope of this survey, we present two aspects of 3D animation which can be edited by users: the animation clips; and the motion variations. This section provides an explanation of the most popular methods used to author each aspect and discusses how such manipulations affect the animation.
Animation clips Data-centric approaches include methods utilising motion capture or synthetic data. The main result is, in both cases, some type of database of animation clips. There has been an overwhelming amount of work on animation, but for the scope of this survey we focus on authoring. Users can edit the animation clips by blending or interpolating between them, as well as perform other operations such as rearranging and cutting them. It is, however, important to note that finding the best blending method does not benefit authoring, since it affects the animation quality rather than the content. Additionally, the animation clips can be recomputed by changing the input data or editing the motion capture data e. g. , via layering or splicing. Animation clips can also be used to construct some sort of data structure (e.
For instance, Rose et al. [RISC01]  address the inverse kinematics problem by employing and interpolating RBFs according to artist-generated examples, while Huang et al. [HK10]  present an optimisation-based method to find blending weights, subject to user-defined spatial constraints. Additionally, Grochow et al. [GMHP04]  synthesise new motion using a GPLVM (Gaussian Process Latent Variable Model) approach. Authoring-wise, they achieve different motion styles, by training on different input datasets (Figure  25 ), as well as developing a method for interpolating between styles, according to some interpolation parameter.
This indirectly affects motion diversity; when only considering feet joints, the system searches a wider space of possible clips. As with other methods in this section, changing the database also influences the associated animation. The field of animation is very large, with successful work on authoring animations being done by research labs like Disney (e. g. , with  [Res] ), allowing for drawings and gestures to exaggerate movement or several refinements and corrections. However, these mostly focus on one character at a time and have not been easily extended to crowds so far. Some work has been done for small groups of two-three characters (e. g. ,  [SKY07, KCPS08] ). Recent work Starke et al. [SZZK21]  develops a learning-based method for a two-person martial arts simulation, achieving high-quality results and allowing control over the affected body parts.
Industry software There are many tools dedicated to character animation; these typically integrate some motion databases and allow for authoring and editing character animations. However, an extensive discussion of these tools is out of the scope of this survey. Some of the most popular ones include Autodesk's Maya (  [Autb] ), 3D Studio Max (  [Auta] ) and MotionBuilder (  [Autc] ), Reallusion's iClone (  [Reab] ), Mixamo ( [Ado]), etc. Several game engines also allow for the manual design of motion graphs between motion clips (e. g. , Unity's Mecanim system  [Unib] ), the creation of Blend Trees and more recently Motion Matching. In general, commercial software for editing animation clips usually allow a mixture of drag-anddrop interfaces with coding, enabling authors to access the scripting editor.
Niswar et al. [NKF12]  present a customisation algorithm based on 12 human body measurements to modify the avatar's shape by scaling its mesh's vertices globally and locally. In addition, Shi et al. [SOWO17]  implement a data-driven approach to generate a variety of body shapes using the body measurements and demographic data available in the CAESAR database. They found, by conducting perceptual studies, that the height and girth (waist circumference) measurements were the most salient features that differentiate people from each other. Then, they used these attributes to distribute and select the set of data entries to be modeled as a 3D body shape. Moreover, Silva et al. [SdSdSH19]  generate variants of body meshes with different levels of obesity, using the body mass index (BMI) as parameter.
Industry Software The practices for authoring the visualisation aspect of virtual crowds are pretty consistent throughout most of the current systems. More specifically, for systems like MakeHuman, Mixamo, and Character Creator ( [Hum,Ado,Reaa]), even for setting up your own avatar in game platforms, the process is typical: go through body parts and properties; for each of them go through drag-anddrop interfaces with some kind of GUI/sliders for specification and fine-tuning. Software used in the industry enables authoring the distributions of certain characteristics like accessories, giving control to the artist as to what proportions of the population should have them. Also, there are tools (e. g. , from Golaem) which facilitate the specification of body shapes, easily and quickly.
Acknowledgements This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska Curie  grant agreement No  860768  ( CLIPE  project). This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No 739578 and the  Government of the Republic of Cyprus  through the  Deputy Ministry of Research, Innovation and Digital Policy.
Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an auto-immune neurological disorder characterized by the presence in the cerebrospinal fluid (CSF) of antibodies against the GluN1 subunit of NMDA receptors in the brain. The etiology of the disease remains largely unknown. In this study, we aimed to investigate the possible existence of pharmacovigilance signals relating to a link between vaccination and the occurrence of anti-NMDAR encephalitis. We performed a case/non-case study using data from the World Health Organization pharmacovigilance database (VigiBase) up to 31 December 2021. All individual case study reports (ICSRs) linked to a vaccine and coded with the MedDRA Lower Level Term (LLT) "anti-NMDA receptor encephalitis" were analysed. We calculated the Reporting Odds Ratio (ROR) and 95% Confidence Interval (CI) for each type of vaccine.
Data source VigiBase is the World Health Organization (WHO) global Individual Case Safety Report (ICSR) database. VigiBase has been collecting pharmacovigilance data since 1978, and now includes more than 30 million reports of ICSRs sent to the WHO Uppsala Monitoring Centre by the national pharmacovigilance systems of more than 150 countries. ICSRs are anonymized and coded according to the Medical Dictionary for Regulatory Activities (MedDRA ® )  (Brown et al. , 1999). Each ICSR includes the patient's demographic characteristics, the drug(s), the notifier, the characteristics of the ADR and the reporting country.
Statistical analysis Using the data obtained from VigiBase, we calculated the ROR with 95% confidence intervals (CI) for each type of vaccine, classed according to the coded active ingredient. Vaccines with fewer than three ICSRs were excluded from statistical analysis. A p-value < 0. 05 was considered statistically significant. Analyses were performed using SAS version 9. 4 (SAS Institute Inc. , Cary, NC, United States).
We found no publications mentioning the occurrence of anti-NMDAR encephalitis after HPV vaccination, although the largest number of notifications in VigiBase implicates this vaccine. Since the vaccines against COVID-19 are new, especially those using messenger RNA (mRNA) technology, there is currently no firm data available about their possible implication in the onset of auto-immune encephalitis. However, several publications have described cases of autoimmune encephalitis occurring after COVID-19 vaccination, regardless of the type of vaccine used. One such case involved a young women vaccinated with the Bnt162b2 vaccine (mRNA), who developed anti-NMDAR encephalitis with acute psychosis  (Flannery et al. , 2021). Another report described three patients who received the ChAdOx1 nCov-19 (non replicating viral vector) and who developed encephalitis  (Zuhorn et al.
, 2021). A further publication reported the case of a 50-year old woman with a history of multiple sclerosis who presented a deterioration of neurological symptoms, with detection of anti-NMDAR antibodies in the serum. Two weeks previously, the patient had received the second dose of BBVIP-CorV vaccine (inactivated)  (Etemadifar et al. , 2022). Four cases of anti-NMDAR encephalitis were identified in VigiBase involving COVID-19 vaccines, all mRNA vaccines. This type of vaccine was the most widely used in the worldwide fight against SARS-CoV-2, as compared to the other vaccine types (WHO data -dedicated COVID-19 vaccination dashboard, available at https://covid19. who. int/). Therefore, their higher representation among the ICSRs is not unexpected. This hypothesis is strengthened by a descriptive study of the frequency of adverse events reported after COVID-19 vaccination, where the rate of ADRs reported was lower with mRNA vaccines than with live non-replicating vaccines (rate of adverse effects reported after the first injection: 92.
To the best of our knowledge, this is the first study to investigate the existence of potential pharmacovigilance signals concerning vaccine administration and the potential occurrence of anti-NMDAR encephalitis. The wide use of the VigiBase reporting system in over 150 countries makes it possible to avail of universal and representative data from around the world. The case/non-case design is a validated method used in research and surveillance of drug safety  (Pierfitte et al. , 1999; Montastruc et al. , 2011). This study has some limitations. The main limit is that reporting is spontaneous, although serious ADRs are more often reported than mild ADRs. This type of encephalitis represents approximately 80% of all auto-immune encephalitis  (Guan et al. , 2016) , and accordingly, of the 674 ICSRs recorded in VigiBase under the term "auto-immune encephalitis," one might have expected a higher proportion to be anti-NMDAR encephalitis, and in any case, more than the 70 cases identified here.
Feeding behaviour Cow activity patterns (eating, ruminating, resting, and other activities) and posture (standing or lying down) were measured throughout the experiment using a commercial Precision Livestock Service device (Axel Medria Ò sensor; Medria Farm Technologies, Chateaubourg, France) attached to a collar strapped onto the neck of each animal. The device is a 3-axis accelerometer that automatically monitors animal posture and activity at every 5-min interval and sends the data to a radiofrequency receiver in the barn. Cow activity was determined according to neck and muscle movements detected by the accelerometer. The data are made available on a web platform in a table that summarises the different types of activities detected by Medria's algorithms, with a 0 for activity not done or a 1 for activity registered.
Only one activity could be different from 0 at any given time, and posture is added to this activity (as a cow can be at rest or ruminate either standing or lying). The data were averaged to have times of feed intake, rumination, and rest per hour (min/h) per week. The data were also averaged by combining activity and posture (rest-lying, rest-standing, eating-lying, eating-standing, ruminating-lying, ruminating-standing). Eating, ruminating, resting and rest-lying are the only data reported. The average pattern of activity during the day was reported graphically per group.
05. Normality of the data and residuals were checked using the Shapiro-Wilk Test. For all data, in order to compare the AL cows against the two restricted groups (QR and QTR) and compare QR cows against QTR cows, we established the orthogonal contrasts 2; À1; À1 and 0; À1; 1, respectively. Curd features following the lactofermentation test were analysed with a j2 test of independence in R (R Core Team, 2018) using the chisq. test function.
Acknowledgements The authors thank the staff at the  INRAe-Marcenat experimental  farm for the technical support during the experiment. We also thank  Sandrine Faure  and  Aline Le Morvan  ( Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genès-Champanelle, France ) for handling laboratory analysis, and the farmer-members of the association 'Eleveurs Autrement' for sharing their knowledge, which helped set up of this experiment.
Acknowledgments We would like to thank  José Barbón ,  Alice Bernamonti ,  Federico Galli ,  César Gómez ,  Javier Martín-García ,  Juan Pedraza  and  Jorge Rocha  for discussions. We are also sincerely grateful to  Le-Chen Qu  for alerting us of a mistake in an earlier version of the article. Work for this article began while MS was visiting the  Institute of Cosmos Sciences at the University of.
Background: Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. Methods: Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum® De-identified Clinformatics® Data Mart Database using L1regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history.
Materials and methods This study was conducted within the European Health Data & Evidence Network (EHDEN) project and involved a multidisciplinary team of rheumatologists, clinicians, epidemiologists, data custodians, and data scientists. We developed and validated prediction models using the Patient-Level Prediction framework from the Observational Health Data Sciences and Informatics (OHDSI) initiative  [18]. This framework allows for standardized development and extensive validation of prediction models using observational health databases and can be applied to datasets that are mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM)  [19] [20] [21]. The OMOP CDM was developed to transform source data into a common format and enables analytical source code to be shared among researchers.
Data sources We used 15 claims and EHR databases with data mapped to the OMOP CDM from 6 European countries (Spain, Estonia, Netherlands, Germany, France, and the United Kingdom (UK)), the United States of America (USA), Australia, and Japan. The databases are listed in Table  1. Data from the Optum® De-Identified Clinformatics® Data Mart Database, a USA claims database, were used for model development and internal validation. Data from the 14 other databases were used for external validation. Each site obtained institutional review board approval for the study or used de-identified data. Therefore, informed consent was not necessary at any site. Descriptions of the databases are also provided in Table  A. 1 in Appendix A.
Study population Adult RA patients (aged 18 years and over) were included in the study population if they had at least 365 days of observation in the database prior to the first drug utilization record of MTX (the index event) and met all of the following inclusion criteria: (1) a diagnosis of RA within 5 years prior to or on index, (2) no drug utilization record of any DMARD any time prior to index, (3) no drug utilization record of any other DMARD on or within 7 days after index, (4) no record indicating any cancer any time prior to or on index, and (5) no record indicating any other inflammatory arthritis (psoriatic arthritis, ankylosing spondylitis, reactive arthritis, any axial spondyloarthropathy) any time prior to or on index. Detailed definitions of these inclusion criteria, including code lists, are available at http://atlas-demo.
Outcomes We investigated outcomes for which RA patients have increased risks compared to the general population and for which RA patients identified at high risk could be targeted for increased screening or monitoring throughout the course of treatment. The first event (binary) of each of the following adverse health outcomes within a period after initiating first-line MTX monotherapy (the index event) was considered: (1) leukopenia, pancytopenia, and infection (serious, opportunistic, all) recorded from 1 day up to 90 days after index, (2) myocardial infarction (MI) and stroke recorded from 1 day up to 2 years after index, (3) cancer (colorectal, breast, uterine) recorded from 1 year up to 5 years after index. Detailed definitions of these outcomes, including code lists, are available at: https://github.
Candidate predictors Candidate predictors were extracted from data routinely recorded in the database. This included binary indicators of 5-year age groups (i. e. , 20-24, 25-29, etc. ) and sex, as well as a large set of binary indicators of recorded OMOP CDM concepts for health conditions and drug groups  [20]. For health conditions, we considered all data prior to index. For drug groups, we separately considered data from the 30 days prior to index and data from the 365 days prior to index. Finally, three established risk scores (CHA2DS2-VASc, Diabetes Complications Severity Index (DCSI), Charlson Comorbidity Index (CCI) (Romano adaptation)) were calculated using all data prior to index  [23] [24] [25] [26]. No clinical measurements or sporadically recorded variables were included as candidate predictors to maximize transportability (i.
Calibration was assessed graphically by plotting the predicted risks against the observed risks. To examine model performance across multiple databases, we externally validated the models. Only models with an AUC of at least 0. 7 on internal validation were considered good enough to warrant external validation. External validation of each model was limited to those databases within which the corresponding outcome events could be identified. Discrimination and calibration were assessed in the same way as on internal validation. Software packages containing the analytical source code that was used to develop the models and to externally validate the developed models on databases with data mapped to the OMOP CDM are available at https://github. com/ohdsi-studies/EhdenRaPrediction.
Study population In the development database (Optum Claims), 21,547 RA patients met the inclusion criteria. For breast cancer and uterine cancer, we only included female patients, resulting in 15,311 RA patients who met the shows demographics and baseline characteristics of the final study population for each outcome, based on all data prior to or at initiation of MTX monotherapy. Patients with an outcome event on average had more comorbidities at treatment initiation.
Model specification A total of 12,724 candidate predictors were extracted from data routinely recorded in the database, of which 18 were binary indicators of 5-year age groups and sex. The number of predictors in each final model is specified in Table  3. Full lists of candidate predictors and detailed specifications of all final models are available in an interactive R Shiny web application at htt ps://data. ohdsi. org/EhdenRaPrediction/. The candidate predictors can be explored interactively in the 'Model Table' under 'Model'; an overview of the predictors in the final model can also be exported from this tab using 'Download Model'.
Therefore, this outcome was omitted from further analysis. The calibration plots for serious infection, MI, and stroke (Fig. D. 1-D. 6 in Appendix D) indicated adequate calibration besides good discrimination. We externally validated the models for these three outcomes across the 14 other databases. The AUCs are presented in Table  4 , ordered by highest AUC for the data-driven approach. Overall, the models demonstrated good performance across multiple databases. Several 95% CIs were wide due to limited statistical power, making those results difficult to interpret. We therefore focused on the databases within which at least 100 RA patients with an outcome event were identified, which is a recommended minimum for external validation  [28]. In these databases, the data-driven approach consistently outperformed the benchmark, with AUCs ranging from 0.