dataesr/scientific-paragraphs-categorization huggingface dataset:
We also ran a Dunn's test with Benjamini-Hochberg correction on a third sub-dataset containing only the controls of both experiments, to compare basal refuge use between the four infection categories. We compared RUs of gammarids that received the 10 × 15 V treatment only once 24 hs before refuge use tests and their associated controls to test for a remanence effect in the HI chronic treatment, using a Wilcoxon test. Verifications for confounding factors are reported in Sup. Mat. Ethics. The study complies with the rules of ethics as prescribed by the French legislation and the University of Bourgogne Franche-Comté.
Data collection Data extraction was performed in Excel. The data extraction form relative to systematic analyses on mental health risk of marginalised groups included the following information: (1) authors; (2) journal; (3) date of publication; (4) title; (5) period covered; (6) number of articles included in the review; (7) countries; (8) study design; (9) populations studied; (10) mental health outcomes and (11) main results. Whenever possible, we also extracted p-values. The data extraction form relative to systematic reviews of interventions aiming to reduce the impact of the COVID-19 pandemic on mental health included the following information: (1) authors, (2) journal, (3) setting, (4) characteristics of the studied population, (5) type of intervention and (6) mental health outcomes.
Characteristics of studies describing mental health risks among marginalised populations Overall, we identified 12 systematic reviews examining mental health difficulties in one or several marginalised groups during the COVID-19 pandemic and 5 systematic reviews on interventions that can mitigate the mental health impact of the COVID-19 pandemic. Four systematic reviews also included a meta-analysis. The number of included studies per article ranged from 15 to 294 (Table  1 ). The systematic reviews we identified mainly included crosssectional studies and were mostly based in China or Western industrialised countries (USA, UK, France, Italy and Spain). The majority of systematic reviews presented data on the targeted marginalised groups as a subgroup of the population studied, except  Hintermeier et al.
Data analysis For salivary cortisol, the area under the curve with respect to increment (AUCi)  (Pruessner et al. , 2003) , which represents the cortisol response to the stressor, was calculated. For the emotional response (psychological pain, anxiety, positive and negative mood), the ΔReactivity (difference between the -10 and the +20 scores: Task score -Baseline score), and the ΔRecovery (difference between the +20 and +60 scores: Recovery score -Task score) values were calculated. The absence of outliers was checked and confirmed using the ±3 standard deviation criterion. Then, preliminary analyses were performed using logistic regression models to identify the sociodemographic and clinical variables related to SA. To analyze changes in the emotional scores and salivary cortisol response in the whole sample during the procedure, a growth model was used in which all variables measured several times (psychological pain, suicidal ideation, state anxiety, positive mood, negative mood and salivary cortisol) were considered as dependent variables.
Data collection Dental topographic and enamel thickness analyses The fossil M 1 and the comparative sample of M 2 s were scanned using EasyTom XL duo mCT (Plateforme PLATINA, PALEVOPRIM, University of Poitiers, France). Computed tomographic scans were processed in Avizo v. 7. 0 (Visualization Sciences Group, 2011). Each enamel cap was isolated from the dentine tissue using automatic segmentation tools and then was smoothed using the 'smoothing labels' command (size ¼ 3, 3D volume). Each resulting enamel cap surface was extracted using the 'generate surface' module with the unconstrained smoothing type (smoothing extent ¼ 3; Fig. 1B ). The resulting enamel caps then were separated into two components, the outer enamel surface (OES) and the enameldentine junction surface (EDJ), using Geomagic Studio 2013 (3D Systems Inc.
A phylogeny for the 20 cercopithecid species included in this study was generated using a consensus tree (100 iterations) downloaded from the 10k Trees Project website v. 3  (Arnold et al. , 2010). We also included the OES 2D in our PGLS regression analysis to investigate the effect of size on the distribution of data. Instead of using OES 3D as a size indicator, we chose OES 2D as the latter is not influenced by dental relief. To perform the PGLS, we used the 'caper' package v. 1. 0. 1 in R v. 3. 6 (R Core Team, 2013). To explore the dental traits of the fossil molar of D. ruscinensis with respect to diet, we first examined the morphological differences among the proposed dietary categories. This was achieved in SPSS v. 22. 0 (IBM Corp, 2013) by comparing the variables among the dietary categories (i.
, 2014; Thiery et al. , 2017a). Computations and visualizations for the discriminant analyses were performed using PAST v. 3. 22  (Hammer et al. , 2001). Dental microwear texture analysis To identify microwear texture differences associated with diet, nonparametric Kruskal-Wallis tests were performed on modern species and Dolichopithecus to determine which variables differ significantly among species for each dental facet type (i. e. , phase II and phase I). Post hoc pairwise comparisons among extant species and Dolichopithecus for both dental facet types were performed using Dunn's post hoc tests with Bonferroni adjustment, with a significance level set to 0. 05. All computations were performed using SPSS v. 22. 0  (IBM Corp, 2013). Lastly, to assess the variation in dental microwear textures across dental facet types, we explored the values of Asfc and epLsar between the facet type of one species and another.
a (e) and (þ) indicate values that are either significantly lower or higher, respectively, for species in column compared to the one in the row. Setting these discussions aside, the present datasets provide several pieces of evidence that dental microwear reflects dietary habits rather than amounts of exogenous particles or their geometrical properties. For instance, among colobine monkeys, the most distinct microwear texture differences are not between arboreal and semiterrestrial species, such as Co. guereza and S. entellus. Instead, they are between the two arboreal species, Co. guereza and N. larvatus (Fig. 6 ), the latter including significantly higher amounts of seeds and fruits compared to the former  (Kool, 1993; Bennett, 1994; Yeager and Kool, 1994). This same trend can be seen among cercopithecine monkeys, with L.
albigena (which feeds mostly on fruits and seeds) having similar dental microwear textures on both dental wear facet types, and the semiterrestrial and more opportunistic Ch. aethiops and E. patas showing higher microwear texture variation between dental wear facet types (Fig. 6 ). There is no doubt that the processes behind dental microwear formation are complex and need further investigation to be fully understood  (Teaford et al. , 2021). However, most datasets issued from wild populations as well as in vitro and in vivo experiments support the relationship between texture and proportion of hard items in the diet  (Calandra et al. , 2012; Daegling et al. , 2016; Teaford et al. , 2017; Percher et al. , 2018; Merceron et al. , 2021). When evaluating the two dental facet types, the microwear texture of Dolichopithecus differs from both specialized folivores such as Co.
Acknowledgements We would like to thank  A. Mille  and  M. Didier  from the  Mus eum d'Histoire Naturelle de Perpignan ;  E. Robert  from the  Universit e de Lyon ;  D. Berthet  from the  Mus ee des Confluences (Lyon) ;  G. D. Koufos  from the  Laboratory of Geology and Paleontology of Aristotle University of Thessaloniki (Greece) ;  N. Spassov  from the  Natural History Museum of Sofia (Bulgaria) ;  B. Haiduc ,  B. Ratoi , and  P. Tibuleac  from the  Department of Geology of Alexandru Ioan Cuza University of Ias ¸i (Romania );  T. D. Nishimura  and  M. Takai  from  Primate Research Institute of Kyoto University, Inuyama (Japan ); and the  Royal Museum of Central Africa, Tervuren (Belgium) , the  Seckenberg Museum of Frankfurt (Germany) , and  Laboratory Paleontology Evolution Paleoecosystems Paleoprimatology (PALE-VOPRIM) , e  CNRS University of Poitiers (France).
31 We identified studies for inclusion from the Cohort Studies of Memory in an International Consortium (COSMIC) for the Social Health and Reserve in the Dementia Patient Journey (SHARED) consortium. ELSA was identified through a literature search of the longitudinal studies with multiple social connection variables and was not part of COSMIC. Studies were eligible if they had baseline data for social connection markers and at least two waves of cognitive scores (sample characteristics of cohorts are provided in the appendix p 4). We did not apply any date restrictions. Within each study, we included participants who had social data for at least one wave and cognitive data for at least two waves. We excluded participants with dementia at baseline. We conducted an individual participant data meta-analysis to examine the associations between baseline social connections (ie, relationship status, living situation, frequency of interactions with family and friends, engagement in community groups, social support, having a confidante, relationship satis faction, and loneliness) and change in cognition over time (ie, global cognition, memory, language, and executive function).
We used the STROBE reporting checklist to describe our study (appendix pp 1-2). The project was approved by the University of New South Wales Sydney Human Research Ethics Committee (HC200268). The contributing cohorts had previous ethics approval (appendix p 3). Written informed consent was exclusively or predominantly obtained from participants in all studies, including consent to share da other scientific research (appendix p 3). For EPIDEMCA, consent was obtained from family when the participant was unable to express their consent. For both EPIDEMCA and LRGS TUA, verbal consent and thumbprint mark was obtained from people who were illiterate. Further participant consent for this study was not deemed necessary as only fully de-identified data were shared with the analysis team.
Procedures We completed a COSMIC studies data request to collect data from twelve cohorts, and applied for access to ELSA data separately. We requested data for participants with social data for at least one wave and cognitive data for at least two waves. We collected data for cognitive scores and diagnoses, physical health and lifestyle risk factors for cognitive decline or dementia, and any social variables. The data were shared and stored in a secure OneDrive folder with multifactor authentication. The data were queried and confirmed by SSa and GM via descriptive statistics and visual analyses and crosschecked with original studies where discrepancies, errors, or outliers were noticed. Global cognition, memory, language, and executive function measures, including descriptive statistics and proportion of missing data, are shown in the appendix (pp 5-6).
Total scores for cognitive screening tests (ie, the Mini-Mental State Examination in most cohorts) were used to assess global cognition, rather than creating a composite of domain scores. The standardisation process for global cognition and cognitive domain scores is described in the appendix (p 8). Covariates that were included in the models were harmonised in line with previous research by COSMIC. 32 he measures and harmonisation protocols used for the covariates are described in the appendix (pp 9-13). We controlled for age, sex, and education in the main (partially adjusted) models using data from all 13 cohorts. In fully adjusted models, we also controlled for previous history of diabetes, hypertension, smoking, cardiovascular disease risk (ie, previous history of angina, myocardial infarction, and any other heart disease), and depression.
Fully adjusted models included ten cohorts with data available for the aforementioned covariates (ie, Bambuí,  19  CLAS,  20  ELSA,  21  the H70 study,  23  HELIAD,  24  KLOSCAD,  25  LEILA75+,  26  LRGS TUA,  27  MAS,  28  and MYHAT  29  ). We harmonised social connection markers (ie, relationship status, living situation, engagement in community groups, interactions with family and friends, having a confidante, relationship satisfaction, and loneliness) by consensus among multiple authors (SSa, GM, DML, Y-HJ, MV-D, and HB) using as many potential questionnaire responses as possible. The measures and harmonisation protocols that were used for the social connection markers are described in the appendix (pp 14-21). Data for relationship status and living situation were derived mostly from demographic questions, and data for social interactions with family and friends, having a confidante, and relationship satisfaction were derived from single questions.
We conducted sensitivity analyses with complete cases to determine whether the results obtained from multiple imputation for missing data were robust. Missing data ranged from 0•00% to 43•36% for covariates and 0•00% to 35•08% for social connection markers. We visually inspected data to examine whether our observed variables were related to the pattern of missingness and used auxiliary variables (ie, data from predictors and covariates) in the multiple imputation to reduce the effect of non-random missing data on the pattern of results. Multiple imputation with the Markov Chain Monte Carlo method was used to impute missing data for the demographics, covariates, and social connection markers with less than 50% missing data, incorporating information from auxiliary variables and generating    20 imputed datasets in each study.
38 Estimates computed from each imputed dataset were then pooled by use of Rubin's rules. 39 Although multiple imputation is effective with up to 50% missingness,  39  we conducted additional sensitivity analyses to compare the major findings when only complete cases (ie, 0% missing) were used. -0•20 -0•10 0 0•20 0•10 0•30 Effect size 0•079 (-0•065 to 0•222) -0•028 (-0•069 to 0•014) 0•002 (-0•021 to 0•024) -0•013 (-0•070 to 0•044) 0•103 (-0•003 to 0•209) 0•048 (0•008 to 0•088) 0•028 (-0•057 to 0•113) 0•008 (-0•194 to 0•210) 0•003 (-0•043 to 0•048) 0•011 (-0•001 to 0•023) 0•010 (0• 0•44% 5•23% 18•53% 2•78% 0•81% 5•72% 1•26% 0•22% 4•34% 60•65% 100•00% Overall SLAS PREHCO MYHAT MAS LRGS TUA LEILA75+ KLOSCAD HELIAD H70 study CLAS Bambuí study 0•083 (-0•037 to 0•202) 0•013 (-0•008 to 0•035) 0•002 (-0•008 to 0•012) -0•144 (-0•475 to 0•186) 0•023 (-0•018 to 0•065) 0•011 (-0•013 to 0•035) 0•018 (0•004 to 0•032) 0•021 (-0•040 to 0•081) 0•013 (-0•023 to 0•050) 0•001 (-0•011 to 0•012) 0•006 (-0•003 to 0•016) 0•007 (0•002 to 0•012) -0•60 -0•40 -0•20 0 0 •20 0 •40 Effect size 0•18% 5•53% 26•28% 0•02% 1•49% 4•52% 12•1% 0•69% 1•92% 19•77% 27•43% 100•00% Overall SLAS PREHCO MYHAT MAS Bambuí study -0•020 (-0•102 to 0•062) 0•019 (-0•011 to 0•049) -0•040 (-0•073 to -0•006) -0•014 (-0•065 to 0•037) -0•014 (-0•026 to -0•002) -0•012 (-0•033 to 0•009) -0•15 0 0•10 Effect size 5•69% 23•17% 20•57% 12•18% 38•38% 100•00% Overall SLAS MYHAT LEILA75+ KLOSCAD HELIAD H70 study Bambuí study 2•14% 25•47% 2•19% 36•37% 9•06% 0•93% 23•84% 0•083 (-0•098 to 0•264) -0•018 (-0•050 to 0•014) -0•051 (-0•230 to 0•127) 0•027 (0•015 to 0•039) 0•018 (-0•061 to 0•098) 0•289 (0•010 to 0•568) -0•019 (-0•053 to 0•016) 0•006 (-0•021 to 0•033) Effect size -0•40 -0•20 0 0 •20 0 •40 0 •60 100•00% Overall SLAS MYHAT CLAS 0•25% 15•45% 84•29% -0•023 (-0•152 to 0•105) -0•006 (-0•022 to 0•011) -0•004 (-0•011 to 0•003) -0•005 (-0•011 to 0•002) -0•20 -0•10 0 0•15 Effect size 100•00% Overall SLAS PREHCO MYHAT KLOSCAD H70 study 2•24% 25•51% 19•07% 29•35% 23•83% -0•214 (-0•366 to -0•063) -0•014 (-0•036 to 0•007) 0•043 (0•008 to 0•077) 0•005 (-0•009 to 0•018) -0•005 (-0•030 to 0•019) -0•000 (-0•024 to 0•023) -0•40 -0•20 -0•30 -0•10 0•10 0 Effect size 100•00% Overall MYHAT H70 study Bambuí study 31•97% 1•23% 66•80% -0•010 (-0•046 to 0•026) 0•062 (-0•121 to 0•246) -0•001 (-0•026 to 0•024) -0•003 (-0•023 to 0•017) -0•2 0•2 0•3 0 Effect size 100•00% Overall LRGS TUA LEILA75+ H70 study 7•74% 72•24% 20•02% -0•013 (-0•116 to 0•091) 0•047 (0•013 to 0•081) 0•069 (0•005 to 0•133) 0•047 (0•018 to 0•075) -0•15 0•15 0 Effect size 100•00% I 2 =0•00%); τ 2 =0•00 Random-effects model (Q=8•07, df=10, p=0•62; I 2 =0•00%); τ 2 =0•00 Random-effects model (Q=6•82, df=4, p=0•15; I 2 =50•98%); τ 2 =0•00 Random-effects model (Q=16•16, df=6, p=0•013; I 2 =52•40%); τ 2 =0•00 Random-effects model (Q=0•11, df=2, p=0•95; I 2 =0•00%); τ 2 =0•00 Random-effects model (Q=15•97, df=4, p=0•0031; I 2 =73•28%); τ 2 =0•00 Random-effects model (Q=0•67, df=2, p=0•71; I 2 =0•00%); τ 2 =0•00 Random-effects model (Q=1•73, df=2, p=0•42; I 2 =0•02%); τ 2 =0•00 We used R studio software version 4.
To summarise, harmonised individual participant data from 13 longitudinal cohort studies of ageing were metaanalysed at the participant level to examine the association between social connections and risk of cognitive decline. Better social connection structure (eg, relationship status, living situation, and interaction frequency) and quality (eg, never lonely), but not function (eg, social support and having a confidante), were associated with slower rates of cognitive decline. Additionally, being in a relationship or married was associated with slower decline in memory, but only for women.
NS declares that they are the chair of the data safety monitoring board for a study funded by the NIH at Albert Einstein College of Medicine. DS declares funding under the aegis of the JPND (National Center for Research and Development in Poland, project number JPND/06/2020). JM declares funding as part of the SHARED consortium, a JPND that is supported by the Alzheimer's Society (reference 469) in the UK. All other authors declare no competing interests.
Acknowledgments The head of COSMIC is PSS, and the study coordinator is DML. www. thelancet. com/healthy-longevityPublished online October 20, 2022 https://doi. org/10. 1016/S2666-7568(22 ) 00199-4 (number  03/1/21/17/214 ) from the  Biomedical Research Council, Agency for Science, Technology and Research, Singapore.
To this aim, the objective of the ESRS is to reach out to all professionals, first by strongly developing online activities, including activities on social media. Furthermore, the relationship with European societies hosting those specialists will be developed and strengthened. The partnership with industry should not only be developed at the physical meetings, but all year round. Scientific publications are strongly supported by means of our Society journal, the Journal of Sleep Research and this should continue with new publishing strategies, in particular open access, and academic databases. Education is also a priority for the ESRS. Sonologist examinations will be ongoing and future editions of the textbook are envisioned. Finally, it is crucial to develop a strong relationship with patient organisations and the lay public.
The main task of the Research Network Committee (RNC) is to connect the different research networks within the ESRS, or networking between networks. It also manages the different ESRS travel and visiting grants (by setting out the calls, guiding the reviewing process and deciding on grant assignments). In that context, it takes care of the sleep laboratory database. The RNC increases exchange of information between the ESRS and other related research societies in Europe. Furthermore, the RNC optimises the functioning of the committee itself, as the members of the RNC are representatives of the different ESRS Research Networks, and representatives of all the.
The main task of the Research Network Committee (RNC) is to connect the different research networks within the ESRS, or networking between networks. It also manages the different ESRS travel and visiting grants (by setting out the calls, guiding the reviewing process and deciding on grant assignments). In that context, it takes care of the sleep laboratory database. The RNC increases exchange of information between the ESRS and other related research societies in Europe. Furthermore, the RNC optimises the functioning of the committee itself, as the members of the RNC are representatives of the different ESRS Research Networks, and representatives of all the ESRS Committees. The Early Career Researcher Network (ECRN) is dedicated to collaboration and participation of early career sleep professionals within the ESRS and its activities and events.
Material and Methods The experiment presented here tried to reproduce the results obtained by  Andrews (1990)  on pellets and on isolated small mammal skeletal elements using compression under controlled conditions. Uniaxial compression experiment was conducted using a Zwick/Roell Z5. 0TN machine with testXpert II software, held at the Natural Sciences Museum of Madrid (MNCN-CSIC) at the Laboratory of Environmental Analyses and Experimental Taphonomy (https://www. mncn. csic. es/en/investigacion/servicios-cientificotecnicos/laboratory-environmental-analyses (accessed on 27 January 2022)). This uniaxial compression machine has a test load standard of 5 kN (~500 Kg), although for the present experimental study we applied a special load cell to restrict the compression forces to 500 N (~51 Kg), this being an intermediate force between previous experimental and observational samples described by  Andrews (1990).
This restriction was needed to limit any extra-force exerted by mistake when programming the experiment that could completely destroy these specimens. The equipment to compress the small mammal bones used the same force in the identical direction to prevent the influence of other external parameters which could influence the results. Results of damage were obtained from the compression experiments in the form of curves through the testXpert ® II software of the equipment (Figure  1 ). Small mammal materials used in the experiment derive from a modern collection of pellets of barn owls kept in captivity fed upon laboratory mice (small sized: House mouse, Mus musculus; large sized: Brown rat, Rattus norvegicus; see Table  1 ). Barn owl pellets are characterised by a high relative abundance of skeletal elements and low bone and tooth breakage together with a low proportion and degree of digestion on diagnostic elements (e.
We have consequently named fracture angles as: Right (R), oblique (O), mixed (M); fracture outline as transverse (T), curved (C), intermediate (I) and fracture edge as smooth (S) or jagged (J). Completeness of the shaft circumference was also evaluated considering the classification proposed by Villa and Mahieu  [17]. Circumference completeness includes three categories: Bone circumference less than half of the original 1 ; circumference more than half of the original 2 and complete circumference in at least a portion of the bone length 3 (Figure  3 ). All specimens were photographed before and at the end of each step of the experiment using an automated stereomicroscope (Leica M 205A) provided with a digital camera (Leica DFC450) and image software LAS V4. 4. This procedure and the control of individual bones allowed us to discard the effects of sifting and sorting of the resulting trampled assemblage or pellet, that potentially could add to detachment of bone fragments or increase breakage.
During these excavations, all sediment was collected and processed using a mechanized floatation machine. The recent sampling from Strata 6/7, whose data are reported here (Table  2 ), combined due to the absence of any significant differences between them, has substantially reduced breakage and loss of small mammal skeletal remains  [30] , but the main traits observed by Avery  [44]  are still present in the sample despite processing by floatation.