dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006). Recall that we detected these behavioral patterns and their specific emotional drivers by using data from the 1st 2 weeks of the lockdown. We expect the detected behavioral patterns and the links with negative emotions to have become even stronger during the following weeks of the European lockdown in 2020. This is indirectly confirmed by  Sabater-Grande et al. (2021) , who detected an average lower levels of daily life satisfaction by females in Spain during spring 2020, although females in their study exhibited a stronger tendency to report higher levels of life satisfaction the longer their lockdown forecast. Our findings confirm those of other recent studies (e. g. ,  Zacher and Rudolph, 2020)  that the COVID-19 pandemic represents not only a major medical and economic crisis, but also has a psychological dimension, as it can be associated with declines in key facets of people's subjective wellbeing.
Results Deidentified data and analyses for Experiment 1 are posted at https://osf. io/ank3p/files/. We first removed 4% of the data corresponding to RTs faster than 150 ms and slower than 850 ms (±2 standard deviations from the mean). RTs were analyzed by fitting linear mixedeffect models (LMM) to the data, using the lmer function from the lme4 package in R  [53]. Fig  2  shows, for each of the four sequences, the evolution of response times (RT) to reach the second position (TT1) and the third position (TT2). TTs are reported for each block of the learning phase (10 first blocks) and for the switch phase (last block). Analyses were carried out to test for the effect of first-order transitional probability and type of transition (TT1 vs TT2) for the learning phase (block 1 to 10), but for the switch phase (block 11), only TT2 has been taken into account because the modifications created by the switch were related to the third stimulus.
This work has been supported by the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01 , awarded to Frederic Lavigne, and by a grant from  National Research Agency (ANR)  with the reference number  ANR-17-CE28-0013-01  awarded to  Fabien Mathy. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Participants and procedures Using EMA, we prospectively assessed 36 adult patients, who were being treated at our suicide prevention out-patient clinic because of a high risk of suicide. EMA was delivered using the MEmind smartphone app, which is available for both Android and iOS operating systems. EMA questions were announced as push notifications on users' screens. A detailed description of the MEmind app has been published elsewhere. 3, 4 Participants were recruited from an ongoing multisite study examining longitudinal risk factors for suicide (SmartCrisis  4  ). Patients were included in the study if they had a history of at least one suicide attempt or an emergency department visit because of suicidal ideation. Written informed consent was obtained from all patients. Pseudonymization of the participants' personal data was employed, by using a unique identification code for each participant.
Acknowledgements This study received grant support from  Instituto de Salud Carlos III  ( ISCIII PI13/02200 ;  PI16/ 01852 ;  CM19/00026 ),  Delegación del Gobierno para el Plan Nacional de Drogas  ( 20151073 ), the  American Foundation for Suicide Prevention  ( LSRG-1-005-16 ), the  Ministerio de Ciencia, Innovación y Universidades  ( RTI2018-099655-B-I00 ;  TEC2017-92552-EXP ), and by the  Comunidad de Madrid  ( Y2018/TCS-4705 ,  PRACTICO-CM ).
Database This study used data from the Disease Analyzer database (IQVIA). Full details of the database have been published elsewhere  (Rathmann et al. , 2018). Briefly, the Disease Analyzer database is composed of sociodemographic, diagnosis, and prescription data obtained in general and specialized practices in Germany. Diagnosis data are based on the German adaptation of the International Classification of Diseases, 10 th revision (ICD-10), while prescription data are coded using the European Pharmaceutical Marketing Research Association (EphMRA) Anatomical Therapeutic Chemical (ATC) classification system. The quality of the data is regularly assessed by IQVIA on a number of criteria (e. g. , completeness of documentation and linkage between diagnoses and prescriptions). Finally, it has been previously found that the panel of practices included in the Disease Analyzer database is representative of general and specialized practices in Germany  (Rathmann et al.
Regarding trauma type, 23. 3% experienced physical abuse, 26. 2% sexual abuse, 19. 4% accident or injury of a loved one, 18. 4% traumatic loss and 12. 6% disaster/and other. The present study used all available treatment outcome data (N = 101) from this trial, inclusive the participants that were first randomized into wait-list. Two participants dropped out immediately after randomization (one from EMDR, one from CBWT) and were excluded because they did not receive any treatment. Figure  1  provides the CONSORT flow chart for the trial.
Statistical analyses All analyses were conducted using version 23 of SPSS (IBM Corp, Armonk, NY). For the predictor/moderator analyses, we carried out linear mixed modelling (LMM) using all available measurement occasions for the outcome variable (child-reported PTSD symptoms as assessed by the CRTI-C). For all analyses, we used the baseline (pretreatment) values for the candidate predictor/moderators. Variables lacking an observed value of zero with substantial interpretation were (grand) mean centred. In the first series of analyses, candidate predictors of change in child-reported PTSD symptoms were evaluated for all participants (i. e. across EMDR and CBWT conditions) from pre-treatment to posttreatment, pre-treatment to 3-month follow-up, and pre-treatment to 12-month follow-up. For the moderator analyses (cross-level) interaction terms were added to the model to assess whether the moderator influenced changes in child-reported PTSD symptoms for the three time episodes described above.
As with any study, several strengths and limitations need to be noted. The present study benefits from the data being collected as part of a large RCT comparing two active, evidence-based treatments for paediatric PTSD, and involving a blinded diagnostic interview, a wide range of standardized child-and parent-report symptom measures, low attrition rates (2%), and 3-and 12month follow-ups  (De Roos et al. , 2017). This study shows a high degree of external validity. However, an important limitation is that this trial was not designed to test for predictor and moderator effects. The choice of candidate predictors and moderators for the present study was pragmatic, reflecting the measures that were used to assess clinical outcomes in the earlier RCT. To restrict the risk of obtaining chance findings, we did not test for interactions between predictors or moderators.
They all indicate that the range of the species is expected to expand in the near future, since not all suitable areas have yet been colonized. In the present paper, we focused on the BMSB as a model species and we analyzed the data collected during two programs of citizen science initiated when the species started to spread in Europe and in France. We explored the validity of the data, the diversity of the volunteer profiles and the temporal dynamics of their contributions. The occurrence data were also used, together with additional data available from the Global Biodiversity Information Facility (GBIF) and the literature, to calibrate an SDM. This model is thus based on an up-to-date BMSB dataset and makes use of the newest current and future climate projections. The model predictions were used to identify suitable areas worldwide.
Materials and methods All the data analyses and graphical outputs were done using the R language  34. Occurrence datasets. We collected occurrences from different sources: the international database GBIF (Global Biodiversity Information Facility: https:// www. gbif. org/ fr/), French citizen science campaigns supported by a smartphone application (Agiir by Inrae http:// ephyt ia. inrae. fr/ fr/P/ 128/ Agiir and INPN-Espèces by MNHN: https:// inpn. mnhn. fr/ espece/ jeudo nnees/ 21092), citizen sightings and reports from entomological associations and naturalists, and data from the literature. These heterogeneous data were carefully examined in order to check the validity of the occurrences. For all sources, the diagnosis consisted of evaluating (1) the level of confidence in the taxonomic identification of H.
Environmental datasets. This study is based on climate data available from the worldclim database v2. 1 (https:// www. world clim. org/)  41. Worldclim provides both average climate values for the period ranging from 1970 to 2000 and yearly data from 2001 to 2018. The latter were averaged to obtain a dataset representative of the current conditions i. e. 2001-2018. Future climate projections described 2 periods spanning from 2020 to 2060 (2021-2040, 2041-2060). For each period, we considered 6 GCMs: BCC-CSM2-MR  42  , CNRM-CM6-1  43  , CNRM-ESM2-1  44  , CanESM5  45  , MIROC-ES2L  46  , MIROC6  47  and the shared socio-economic pathways SSP245. Shared Socioeconomic Pathways (SSPs) are scenarios of projected socioeconomic global changes used to infer greenhouse gas emissions according to various climate policies.
We used SSP245 which is considered as a scenario of moderate future greenhouse gas emissions. It stabilizes radiative forcing at 4. 5 W m -2 in the year 2100 corresponding to an increase of 1. 1 °C to 2. 6 °C of global mean surface temperature by the end of the twenty-first century  48. The data used in the study corresponded to a resolution of 2. 5 min (≈21 km 2 at the equator). Determining which climate descriptors are most informative and must be used to calibrate a niche model is both difficult and challenging  49. We identified a set of climate variables that were a priori good candidates as they convey thermal constraints in different ways: maximum temperature of warmest month (bio5), minimum temperature of coldest month (bio6), mean temperature of wettest quarter (bio8), mean temperature of driest quarter (bio9), mean temperature of warmest quarter (bio10) and mean temperature of coldest quarter (bio11).
Variables describing precipitations were not included in our analyses because the observed BMSB distribution may be partly affected by agricultural irrigation which cannot be accounted for in the available climate datasets. Some explanatory variables may be collinear with possible effects upon species distribution models. This point is discussed below. Occurrence data thinning and background points. Occurrences. The model was calibrated using a subset of available occurrence data corresponding to the core distribution of the BMSB. We defined three core areas, i. e. Asia, North America and Europe (Supplementary Figure  S2A ). The rationale is that occurrences located at the margin of these areas, although valid, could correspond to unsettled populations or interceptions. We did not distinguish between occurrences originating from native or invaded ranges because adaptive shifts occurring in the course of the process of the invasion may change the realized niche allowing invasive species to spread in areas where climate conditions change from the ones prevailing in the native range  17, 50.
We generated 10,000 randomly distributed background locations within the areas defined as core distribution areas (see Supplementary Figure  S2B ) excluding seas, lakes as well as locations where H. halys was present. Environmental filtering. Our occurrence input dataset originates from heterogeneous sources and is subject to possible sampling bias which may alter model performances  51. We addressed this issue by adopting an environmental filtering approach known to perform better than geographic filtering  52. We performed a Principal Component Analysis (PCA)  55  on the bioclimatic variables recorded at each occurrence point. We divided the range of the occurrence scores upon the first PCA axis in 50 bins. We used bins of similar amplitude to divide the range of the scores upon the second axis.
The resulting grid was used to thin the occurrences on the basis of the following rule: when a grid cell contained more than one point, one point was randomly selected and retained for further analyses  56. Maxent calibration. The Maxent model was calibrated using the R package MIAmaxent  57. It implements a flexible approach to Maxent calibration that allows to select a subset of transformed explanatory variables using a forward stepwise selection. This results in lower model complexity hence better model generalizability  58  , an interesting feature in the present study where we project model predictions outside of the spatial or temporal context of the data  59. Five types of transformation were used for bioclimatic variables: linear, monotonous, deviation, forward hinge, reverse hinge and threshold  54, 60.
For spline-type transformations (namely forward hinge, reverse hinge and threshold) an infinity of transformations is possible and the package MIAmaxent uses 20 for each and retain the ones that best explain the variation in the presence/background data (details in package documentation). The forward stepwise selection was carried out as a two-steps hierarchical procedure as detailed in  61  : first, a selection is performed amongst transformed variables derived from each explanatory variable. Second, explanatory variables represented by the transformed variables from first step explaining a significant amount of variation in the response variable under the significance threshold α = 0. 001 are selected. At each step of the selection process, the fraction of deviance explained by the model is computed.
Ideally, the model should be evaluated using truly independent observations. In this study, we used the observations that were discarded during the data thinning process. These occurrences are valid and do not contribute to model calibration. A set of 34 datasets was constituted by randomly selecting 100 occurrence points (sampling without replacement) within the 3420 points discarded during the thinning process. We computed the 50% (median), 2. 5% and 97. 5% quantiles of the resulting P/E curves for each dataset. The CBI was computed as the Spearman-rank correlation between the values of the median and the values of the CS classes. The 2. 5% and 97. 5% quantiles were used to estimate CS thresholds following  63. Future geographical distribution. The model was projected using the data representing possible future climates for the period 2021-2040 and 2041-2060 and the SSP245.
Results Occurrence records and citizen science. A total of 9,467 occurrences were obtained from the GBIF database, of which 6,620 were considered to be valid (69. 9%). Because citizen sightings were analyzed year by year, 2020 data were incomplete at the time of data analysis (mid-2020) and were thus discarded. It should be noted that citizen reports could be valid according to one criterion e. g. species identification whilst invalid for another e. g. missing date. As a consequence, the number of occurrences available could be different according to the question at hand. A total of 3560 online forms were gathered by the Agiir application including 887 valid BMSB identifications (24. 9%)  85. The naturalist application INPN-Espèces gathered 147 valid occurrence data. Additionally, FD and JCS respectively gathered 74 and 87 reports from naturalist networks.
Correct identification of H. halys accounted for 73% of the naturalist records gathered by JCS. Furthermore, JCS directly collected 377 reports from citizens off which 186 corresponded to valid identification of the BMSB. An additional 383 occurrences in Asia were gathered from Zhu  31  and 17 supplementary occurrences were mined in the literature  26, 27, [65] [66] [67] [68]. Figure  1A  shows the geographical distribution of both Agiir and INPN-Espèces citizen networks. Overall, sightings are somewhat homogeneously distributed, although valid records are clearly spatially aggregated and indicate the clusters of colonized areas. Naturalist sightings are less numerous, but the proportion of valid records is very high compared to citizen contributions (Fig. 1B ). Figure  1C  shows the dynamics of the BMSB expansion across French departments as depicted by citizen science data and GBIF records.
Environmental thinning resulted in removing 3420 occurrences leading to a final dataset of 724 points (17. 47%). Models calibration and evaluation. The 6 original climate variables (bio5, bio6, bio8, bio9, bio10, bio11) used as explanatory variables were transformed into 52 derived variables among which 5 were finally selected by the forward procedure. These transformed variables included 3 deviation-type transformations with parameters of 2, 0. 5 and 2 for bio5, bio8 and bio10 respectively, the variable bio6 after a reverse hinge transformation with a knot value of 7 and the variable bio8 after a threshold transformation for a knot value of 9. Details can be found in Vollering et al. 2019  57  and Phillips and Dudík 2008  54. The model was evaluated using the 3,420 occurrences data that were excluded from the calibration dataset during the thinning process.
However, the large number of records stemming from non-naturalists compensated for their limited identification skills and led to a large number of valid sightings in various regions of the country. It must be noted that these data come at the expense of the important work of sorting and checking the records stored in our databases, highlighting the fact that citizen science should not be considered as a 'magic bullet'. www. nature. com/scientificreports/ Another interesting outcome of the survey is the possibility of gathering information on the phenology of the species. This has already been illustrated in the seminal paper by Maistrello et al. 70  where the authors reported a peak in sightings in October in the Emilia Romagna region (Italy). We reported a similar peak in our study. This pattern is likely to reflect the well-known behavior of the BMSB in which it tends to enter houses and human installations in the fall to find shelter and then overwinter  24.
Because of its behavior and its rather large size, the BMSB is a good example of how citizen science can contribute to studying agricultural pest invasions. Once refined and checked, the datasets may be used to calibrate expansion models and possibly to monitor population dynamics in various part of the country. Nevertheless, our results show that it takes several years (2012-2015) for the number of valid sightings to reach more than 10 per year. This reflects the fact that (1) at the beginning of the invasion, the insect density was low and (2) that volunteers were not well aware of the BMSB. This is mostly true for non-naturalist contributors, while naturalists, although much less numerous, contributed to the valid sightings in the early stages of the invasion. In the specific context of the BMSB expansion in France, we informed citizens as early as possible through various channels including national publications (in French)  69  and continue to do so  76.
Acknowledgements We are greatly indebted to all the volunteers who participated to this study. The team at  UMS PatriNat  ( MNHN/ OFB/CNRS ) kindly provided INPN-Espèces occurrence data. We are especially grateful to the people responsible for the conception, development and maintenance of INPN-Espèces:  Matthias Laville ,  Sarah Figuet ,  Laurent Poncet  and  Julien Touroult.
g. , fruits, animals, clothes). These cards were laid on the table in such a way that the categories were not highlighted, but they could be moved into category groupings (semantic grouping strategy). Both strategic and recall scores were noted. A second, transfer, sort-recall task was then presented, which was the same as the main task except it used drawings from three new categories (e. g. ,  tools, vegetables, vehicles). The authors considered it near transfer because the tasks are structurally similar (a sort-recall task with a semantic grouping category) and vary only on the specific categories covered. Also, the context is the same. Following  Barnett and Ceci (2002) ' s taxonomy, far transfer would have occurred in this study if several contextual elements had changed between the main and the transfer task, such as the place where the study was conducted (school vs home), the people gathering the data (experimenter vs teacher), or the functional context (academic vs play).
Tasks Serial Recall We used two test lists (A and B) and two familiarization lists (C and D) of 6 black and white line drawings each. Lists A (test list 1) and C (familiarization list 1) included animals, fruits, and cooking utensils; drawings in lists B (test list 2) and D (familiarization list 2) were of plants, vegetables, and gardening tools (see online supplemental materials for details, Tables  S1   to S4 ). The drawings came from  Alario and Ferrand (1999)  and frequency of use of the items' names was controlled through the MANULEX lexical database  (Lété, Sprenger-Charolles, & Colé, 2004). The task was presented as a game of memorizing the drawn objects. Following  Hitch, Woodin, and Baker (1989) , drawings were lined up on the table, face down. The experimenter turned over the leftmost card for two seconds, then turned it face down again.
Preliminary Analyses We wanted to rule out any effects of gender and, especially, difficulty of word lists. Although the counterbalancing of list order eliminated any confounding, ideally the word lists would be of equal difficulty so that a t-UD would occur when moving between lists of equal difficulty. A 2 (gender) × 2 (list E vs list F) × 2 (time of test: pre-vs posttest) MANOVA with repeated measures on the last factor was performed on mean sorting, clustering and recall scores. Due to missing data on the sorting score at posttest in 21 participants (trained: 8 girls, 5 boys, mean age 84 months; untrained: 5 girls, 3 boys, mean age 83 months), the analysis included only 51 participants. Time of test was significant, Wilk's Lambda =. 40, F(3,46) = 22. 59, p <. 001, η 2 p =. 60; but gender and list were not.
People's emotions are rarely put into words, far more often they are expressed through other cues. The key to intuiting another's feelings is in the ability to read nonverbal channels, tone of voice, gesture, facial expression and the like. Facial expressions are used by humans to convey various types of meaning in a variety of contexts. The range of meanings extends from basic, probably innate, social-emotional concepts such as "surprise" to complex, culturespecific concepts such as "neglect". The range of contexts in which humans use facial expressions extends from responses to events in the environment to specific linguistic constructs in sign languages. In this paper, we will use an artificial neural network to classify each image into seven facial emotion classes. The model is trained on a database of FER+ images that we assume is large and diverse enough to indicate which model parameters are generally preferable.
Labels distribution and statistics For a better understanding of the FER+ data, we decided to display the histogram to see the distribution of the emotion classes (Figure  3 ). For statistics detail, we used the command describe of pandas libraries, the results are presented in the Table  2. The images in the database vary in many parameters which can affect directly on recognition accuracy and performance. These are some difficulties such as rotation, brightness and illumination changes even for the same person's images. To address this problem, a normalization of the face image such as detecting, de-noising and some other preprocessing such as correcting the rotation is performed. The image brightness and contrast variations increase the complexity of the problem. In image processing, normalization is a process that changes the range of pixel intensity values.