dataesr/scientific-paragraphs-categorization huggingface dataset:
This result was consistent for other records that we analysed. In Figure  3 , we depict raw data for time series of GE, pneumonia, malaria and asthma admissions, together with their wavelet power spectra. The WTSs of these different diseases or conditions are presented in comparison with the WTS of all admissions data, displaying different slopes of WTS functions (which are given on log-log graphs in Figure  3 ) with different cyclical consistency. This is especially prominent for pneumonia, malaria and asthma WTS functions that WTS of all admissions data. In (E) vertical grey lines serve as visual guides for significant cycles. We further investigated pneumonia and malaria admissions data with respect to their potential meteorological or environmental drivers. In Figure  4 , we show LWTS of pneumonia and malaria admissions.
Discussion We investigated the time series of daily hospital admissions for GE, asthma, pneumonia and malaria from two large public hospitals in Limpopo Province, in relation to time series of temperature, rainfall and air quality ground-based records or satellite data from the same geographical area. We were interested to disentangle physical, specifically, meteorological or environmental variables that might be associated with underlying temporal variations of disease prevalence measured through visits to hospitals. We used wavelet transform analysis and specifically local wavelet crosscorrelation analysis for this purpose; to the best of our knowledge this is the first time these methods have been applied in weather/climate-health analyses. All our admission records have global wavelet power spectra (WTS) of the power-law type, indicating that they are outputs of complex sets of causes acting on different time scales.
The 'all hospital admissions' dataset that we analysed showed the existence of cycles, or peaks in WTS that result from societal organization of daily life, on admission time scales of one to several working weeks or weeks. We investigated WTS cycles that appeared on higher time scales in global admissions data in more detail for prevalence of pneumonia and malaria in our records. We used local wavelet cross-correlation spectra (LCWTs) to monitor coincidences in changes of meteorological (i. e. , temperature and rainfall) and air quality (specifically concentrations of PM2. 5 and NO2) variables with admissions to hospitals for these diseases. It is important to note here that, while in general no direct causal attribution is possible based on the power spectral or cross-power spectral analysis, the existence of auto-or cross-correlations between variables and established connections with triggered variable's (admissions, in this paper) significant WTS peaks are used to suggests a possible causal relationship.
By using such an approach, we were able to provide new (other than already known seasonal variations) statistical estimates of time delays between the change of weather or air pollution and hospital admissions for pneumonia and malaria. In order to assess uncertainties related to provided associations between the hospital admissions and meteorological or air quality variables time series on a case-to-case basis, it is necessary to extend in the future our work in time series analysis to event-based event coincidence analysis  (Donges et al. , 2016). The LCWTs for pneumonia data showed the existence of seasonality in the prevalence of pneumonia that could be 'connected' to changes in temperature. Even if admissions for pneumonia in our dataset occurred throughout all seasons, this result is in agreement with several other recent studies that investigated links between seasonal variations in pneumonia with changes in meteorological variables  (Adegboye et al.
, 2020; Cilloniz et al. , 2017; Tian et al. , 2017). Those point to increased association of pneumonia hospitalization with low temperature; low temperature effects are probably causing the annual seasonality of pneumonia in our dataset too. This pattern could be explained by findings that colder temperatures are associated with increased viral activity in regions of warmer climates  (Lin, Lin et al. 2009 ). The influenza virus has been identified as a common cause of pneumonia and cold temperatures favour the spread of the virus  (Lowen, Mubareka et al. 2007 ). Thus, the increased presence of circulating pathogens in the winter/colder months could result in the increased hospital admissions for pneumonia that were observed in our study. Furthermore, it has been suggested that exposure to cold air increases the incidence of respiratory infections by cooling the nasal epithelium thereby inhibiting respiratory defences against infection  (Eccles, 2002).
In this way a large part of the collected dataset was unusable for the statistical analysis. This in turn constrained our analysis to time scales of up to approximately 250 days and prevented our insight into annual and multiannual cycles of hospital admissions data. It specifically prompted us to exclude asthma records from detailed statistical analysis. In our dataset, admissions for asthma records showed prominent seasonal variations for which we did not have enough data points to conduct statistically significant analysis. Additionally, we decided to exclude GE records from detailed WT analysis due to inability to differentiate between various gastrointestinal ailments. Therefore, we were not able to investigate climate or environmental drivers of, for example, diarrhoeal disease, which is a major contributor to the burden of infectious disease among young children in sub-Saharan Africa  (WHO, 2018).
This limitation could be mitigated in the future by digitalization of hospital records that may offer possibilities to flag diarrhoeal diseases and thus include it in hospital statistics. With respect to weather data, due to the lack of ground-based observations of air quality in the Giyani region, reanalysis data were used. Although the current CAMS model has smaller biases O3, CO and NO2 compared with observations than previous versions there are still uncertainties due to the inherent properties of numerical models, relating to observational datasets, parameterizations, and spatial and temporal resolution. It is possible that the data pre-processing averages out details of statistical behaviour of air quality variables in time  (Bunde and Lennartz, 2012)  and over space  (Blesić et al. , 2019).
, ) = | ( , )| '. (Eq. 3) A plot of. ( , ) is also called a scalogram and is usually presented as a colour map of intensities of WT coefficients (that is, | ( , )| ' ) in real time (x-axis) and over the time scale (y-axis) plots. Such presentations enable detection of locations in time of data recording when some property developed, which manifest as changes in | ( , )| ' values at the very onset of y-axis, and investigation of how those events contribute to dominant modes, by following their vertical (along the y-axis) variations. Each vertical line in LWTS represents a local wavelet power spectrum for a particular time (position at the x-axis). Finally, LWTS can be integrated (or summed, in the case of discrete datasets) over time to produce distribution of data series energy over scale , the global wavelet power spectrum (WTS):.
Acknowledgements The authors would like to acknowledge the  Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS). CYW, TK, ZK and AM receive research funding from the  SAMRC. This research was carried out for the iDEWS (infectious  Diseases Early-Warning System)  project supported by  SATREPS (Science and Technology Research Partnership for Sustainable Development) Program of JICA (JAPAN International Cooperation Agency)/AMED (Japan Agency for Medical Research and Development) in Japan  and the  ACCESS (Alliance for Collaboration on Climate and Earth Systems Science) program  of  NRF (National Research Foundation)  and  DST (Department of Science and Technology in South Africa ). SB received funding from the  Serbian Scientific Research Fund  grant no. 451-03-68/2020-14/200015.
To examine what influences males and females' participation propensity in intergroup encounters we used detailed all occurrence data on intergroup encounters collected between the years 1997-2018. If an intergroup encounter was recorded, observers interrupted the focal follow data collection to document all occurrence data on participation, vocalizations and aggressions observed. In our analyses, we only included intergroup encounters in which at least one withingroup member actively approached and engaged in the conflict and marked those that did so as participants. Thus, cases of intergroup encounters in which all party members showed no reaction to vocalizations by neighbours and/or retreated into their core area after detecting neighbours auditory and/or visually (e. g. ambush) were excluded, as no voluntary participation occurred  22.
Non-participation also meant that we could not evaluate how relationships with participants may contribute to participation, or what causes some community members to participate in the encounter but not others. By limiting our data to intergroup encounters involving an active approach or vocal response towards a neighbouring group, we can reliably investigate influences of inter-individual variation on participation. For a subset of the intergroup encounter data with an active response (collected between the years 2013-2018 and comprising almost half of the entire dataset) we had accompanying information on whether or not intergroup encounters involved a border patrol. Border patrols are often initiated while in core areas and include cohesive movements of in-group members toward and beyond the borders of their territory  23  , during which participation is considered voluntary  22.
Overall, more than 92% of active intergroup encounters followed a border patrol behaviour, thus, this approach sufficiently accounts for participation decisions. We differentiated between two types of active intergroup encounters both involving an active approach towards the out-group but that differ in the degree of contact, either (a) only vocal (vocal encounters) or (b) also visual and/or physical (contact encounters). Relatedness. Maternal kinship and paternity for individuals of all ages of the different groups was established by means of pedigree and genetic data extracted from faecal samples  65  in the genetic lab of Linda Vigilant at the Max Planck Institute for Evolutionary Anthropology. The current Taï genetic dataset includes 259 individuals with an average of 83% complete genotypes at 19 autosomal loci.
Social bonds. We used focal follow data collection of grooming behaviour (grooming bouts: North-14,599, South-18,948, East-9,040) to assess dyadic relationship quality following Samuni et al. 16. In brief, we implemented the dynamic dyadic sociality index method  16, 69, 70  , which provided continuous directed daily measures for grooming for each combination of within-group adult individuals. Using this method, dyads achieve high values by investing in grooming with one another more than others but maintain stable high values only if the investment provided was regular and consistent. We then used the directed grooming scores to evaluate preferences of grooming partners separately for same and opposite sex dyads. If both individuals of a dyad were each other's top preferred grooming partners, this dyad received a score of 1.
Conversely, when the top grooming preference was one sided or entirely absent the dyad received a score of 0. As such dyads who scored 1 were those with mutual and stable grooming relationships. Using this method in the same population, we have previously shown that social bond partners are more likely to engage in the cooperative behaviour of food sharing than non-social bond partners  16. Dyadic association. Chimpanzees live in a fission-fusion social system with transient and dynamic associations  61, 64. Association tendencies of males and females in Taï show intraindividual repeatability across days and years  71  , indicating temporally stable phenotypes of gregariousness in this population which may influence participation likelihood. Thus, to evaluate spatial proximity, we used party association data collected during the year prior to the intergroup encounter date to construct matrices of dyadic association values of all adult individuals in a community.
The time frame of one year prior to the encounter provides sufficient data to construct reliable association matrices, but as well likely the best representation of current social conditions (e. g. , demography, group size, or female reproductive status) as the encounter which might influence association patterns. The dyadic association values were based on the duration two individuals were observed in the same party as a function of the total duration each of them was observed in total. For each intergroup encounter event we calculated the average dyadic association values with all other adult individuals that participated in the encounter. Higher average association values represent individuals that more frequently associated with encounter participants in comparison to lower association values.
Thus, we could account for association degree with encounter participants in shaping participation decisions (see "Statistical analysis"). Fruit availability index. Chimpanzees use their home range according to the distribution of food sources. Thus, to account for the potential effect of food availability on participation likelihood, we calculated a monthly fruit availability index following a standard index for Taï chimpanzees  72. We compiled the index based on the absence/presence of mature fruits, and the density and mean basal area of tree species. Phenology data was collected within the home-range of each chimpanzee group, thus, reflecting local variation in fruit productivity. Statistical analysis. We fitted generalized linear mixed models (GLMM  73  ) with binomial error structure and logit link function, to investigate variables affecting the likelihood to participate in intergroup encounters for adults.
To keep type I error rate at the nominal 5%, we included the maximal random slope structure  79, 80. We thus included random slopes for age (linear and squared), dominance rank, number of maternal kin, number of offspring for males, and average dyadic associations within both random effects. In addition, we included the number of male and female participants, fruit availability and number of days into gestation within the random effect of potential participant. See Supplementary Code 1 for the complete specification of the full and null models. Our datasets included 343 intergroup encounter events with 75 potential female and 36 potential male participants, of three communities, resulting in 3,799 participation decisions for females and 1,377 participation decisions for males. We processed the data and fitted all models in R (version 4.
0. 2  81 ), using the function lmer of the R package 'lme4'  82. Before fitting the models, we ztransformed all of the covariates to a mean of zero and a standard deviation of one  83  and presented the original distribution of covariates in the table legends. We applied the function vif of the R package 'car'  84  , to a standard linear model (lacking the random effects and slopes) to derive variance inflation factors (VIF), which revealed no collinearity issues (largest VIF: 'male participation model' = 2. 11; 'female participation model' = 1. 93  85  ). Using a likelihood ratio test we evaluated model significance by comparing the fit of the full models with those of a respective null model lacking only the four test predictors  86. To obtain individual p-values for all fixed effects we compared the full model with a series of models in which each fixed effect was systematically dropped one at a time, using the drop1 function in R  79.
Model stability, assessed by comparing the estimates of the full model with those derived from a series of models excluding the different levels of the two random effects (identities of potential participants and encounters) one after the other, revealed no influential identities. We report the confidence intervals which were derived by means of parametric bootstraps with the function bootMer of the package 'lme4'. We calculated models' effect sizes (R²) using the function r. squaredGLMM from the R package'MuMIn', and report the variance explained by the fixed (marginal-R²) and fixed and random (conditional-R²) effects  87. Table  3  Second 'Male participation' GLMM testing the effect of strength in numbers and predictability of coalitionary support on participation likelihood (Here, the number of living maternal kin is included instead of adult maternal kin presence in the encounter.
Acknowledgements We are grateful to the  Ministère de l'Enseignement Supérieur et de la Recherche Scientifique  and the  Ministère de Eaux et Fôrests in Côte d'Ivoire , and the  Office Ivoirien des Parcs et Réserves  for permitting the study. We are grateful to  Christophe Boesch  for his dedicated effort over 40 years of research with the Taï Chimpanzee Project. We also thank the  Centre Suisse de Recherches Scientifiques en Côte d'Ivoire  and the  Taï Chimpanzee Project  staff members for their support,  Linda Vigilant  for conducting the paternity analysis, and to  Erin Wessling  and  Roger Mundry  for helpful discussions. Core funding for the Taï Chimpanzee Project was provided by the  Max Planck Society since 1997. This work was also funded by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement number:  679787 ) awarded to C.
Patient cohort All samples (n = 305, two cases from one patient with bilateral BC) in this retrospective analysis were collected at the Department of Obstetrics and Gynecology of the Ludwig-Maximilians-University Munich, Germany between 2000 and 2002. This study was approved by the Ethical Committee of the Medical Faculty, Ludwig-Maximilian-University, Munich, Germany (approval number 048-08; 18th of March 2008) and informed consent was obtained from all patients. Patient data obtained from the Munich Cancer Registry were 1 3 pseudonymized and samples were encoded during experiments and statistical analysis. All clinical diagnostic and therapeutic procedures were completed before this study. All tumors were evaluated according to UICC TNM classification, including tumor size (pT), lymph-node involvement (pN), and distant metastasis (cM).
In total, 1,303 sequences were retrieved (Supplementary Figure  1 ). Sequences were initially resampled, excluding all identical sequences (https://biopython. org/wiki/ Sequence_Cleaner), and after separation by Brazilian states, the outliers were resampled considering the average of the low sampled Brazilian states + 2 x SD (n = 53 sequences; https:// web. expasy. org/decrease_redundancy/). In the end, we worked with a final dataset containing 56 sequences, 53 from São Paulo state and 2 sequences from the atypical patient, and we included the reference genome as an outgroup (NC_045512. 2). The robustness of the groupings observed was assessed using 1,000 non-parametric bootstrap replicates. ML tree was visualized and plotted using FigTree v. 1. 4. 3  (27). All taxon labels for sequences used in this work are presented in the format: hCoV-19/local of isolation/strain name/date of isolation.
FUNDING This work was supported by the  São Paulo Research Foundation  (FAPESP-process # 2017/27131-9 ), the  Institut Pasteur  (# DI2020-16/18 ), and the  Cooperation and Cultural Action Services of the São Paulo French Consulate  (# 185BRA1184--2020/SPPU ). MC, AV, CM, SA, and VB received  FAPESP  Grants Nos. 2019/24518-5 ,  2019/20708-4 ,  2020/01487-4 ,  2017/23519-2 , and  2018/11612-0 , respectively. LO and YA received a  Coordination for the Improvement of Higher Education Personnel (CAPES)  Grant No. 88887. 423542/2019-00and 88887. 474625/2020-00 ,respectively. JP is PI of a G4-grant from  Institut Pasteur  (# FUSP-3303-01 ). LG received a  Brazilian National Council of Scientific and Technological Development (CNPq)  Grant No. 152365/2019-2. AC was funded by  Institut Pasteur/FUSP -Project # 3303.
Particle size analysis The morphometry analysis was done thanks to a dynamic image analyser called QICPIC (SympaTec GmbH, Germany). The image analysis was done thanks to the PAQXOS software (SympaTec GmbH, Germany), with two main indicators as the LEFI (Length of the Fiber) and the DIFI (Diameter of the Fiber) for the particle length and diameter respectively. Several morphological parameters of interested were selected as the (a) the particle length, which is defined as the shortest direct path, along the skeleton of the fibre between their farthest endpoints (without loops or deviations), (b) the particle diameter which is the quotient of the total projection area and the sum of all lengths of the branches of the main skeleton, (c) the particle aspect ratio, which is defined as the length over the diameter.
Conclusion During this study, we compared the values obtained experimentally and numerically from injected compounds reinforced with ground wood particles. These materials were compared to the pure PP matrix and also to reference materials reinforced with wood and flax fibres. Firstly, a fine morphological and mechanical description of the particles and fibres used allowed us to take into account the strengths and weaknesses of each of the reinforcements, and also to build a database that could be used to feed our numerical model. In particular, we were able to highlight significant differences in terms of the aspect ratio and fibre stiffness, in favour of flax fibres. Secondly, the mechanical performances of the composites were experimentally measured and modelled. The aspect ratios of the fibres used in the models were obtained by morphological measurements after the extraction of the fibres from the injected specimens.
A basic DNA model consisting of semi-spheres was used to wrap the plasmid skeleton as shown in Figure  1b. The 2686 base pairs of pUC19 were accommodated in a deformed polygon of 179 straight sides of the same length. Each side contained 14 to 15 base pairs accommodated to avoid overlapping. The number of straights sides and base pairs could be changed by the user. However, we found that between 10 and 20 base pairs (3. 4 to 6. 8 nm) produced a reasonably well behaved figure. The spatial and angular separations between two base pairs were 0. 34 nm and 36 o , respectively. Each base pair was modeled using six cut spheres, two for the nucleobases, two for the deoxyriboses and two for the phosphates. The radii were 2. 45 Å, 2. 93 Å, and 2. 7 Å, respectively. These values were obtained to achieve the same geometric volume as that provided by the DNAfabric software previously reported in  (Meylan et.
Acknowledgements This work is part of D-Kondo N. doctoral studies from the  "Programa de Doctorado en Ciencias Fisica Aplicada, Benemerita Universidad Autonoma de Puebla" Mexico. The first author received financial support for his studies from fellowship  2019-000002-01NACF-05024  from  CONACYT ,  México. Ramos-Méndez  J. ,Schuemann J. and Faddegon B. were supported by the  NIH/NCI   R01 CA187003  ( TOPAS-nBio ) grant. To  Wook-Geun Shin  from  Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Republic of Korea  for his contribution in the development of Geant4-DNA IRT. All the simulation work was made with the support of the computer resources provided by the  Laboratorio Nacional de Supercómputo del Sureste de México  trough grant number  202001012C.