dataesr/scientific-paragraphs-categorization huggingface dataset:
In what follows, we discuss the implications and limitations of our findings, and point to avenues for future research. We expected that children and adolescents in harsher environments would allocate less time to food production and more time to childcare and domestic work, reflecting a stronger age-graded division of labour. However, we found no strong effect of environmental factors on participation in these work activities. There are several potential reasons for this. First, sampled societies were heavily skewed towards tropical and subtropical regions. Though there is still considerable variation in sampled environments, only Dukha inhabit a northern latitude, reflecting a broader dearth of time allocation data from foraging societies inhabiting colder climates. Table  2. Model summaries and comparison statistics.
All models include random effects for Individual and Society. Age Early Childhood (between 3 and 6 years of age, early juveniles for the Mikea dataset-reference category), Middle Childhood (between 7 and 12 years of age, late juveniles for the Mikea dataset), Adolescence (between 13 and 18 years of age, young adults for the Mikea dataset). Gender: Girls (reference category) and Boys. Prop Non-Foraged Food proportion of diet which was domesticated foods and foods purchased and/ or traded. Gendered Division of Food Production Labour Standardized to between -2, women do all the food production labour and 2, men do all the food production labour. NPP Net Primary Productivity in gC/m  2   Our findings may thus be explained by the limited variation in climates in our sample. Alternatively, parents and alloparents may buffer children from the direct effects of the environment through provisioning and care  64, 86.
Limitations. There are several limitations to the present study. We pooled behavioural observations of foraging children and adolescents that were collected using different methods and across different temporal scales (see "Methods" and SI for details). The variability inherent to our data has limited our ability to make strong inferences. Further, our models most strongly predicted children's participation in childcare, an activity that was variably defined across datasets, and relatively rare across societies. We also used broad behavioural categories to facilitate comparison across datasets. As a result, we were unable to investigate cross-cultural correlates for more nuanced activities (e. g. , hunting vs. collecting). Environment may shape variation in children's daily and seasonal activities rather than overall time allocation.
Thus, our null findings should be interpreted cautiously. To facilitate comparisons of children's direct and indirect provisioning, we focused on time allocation 9. Children's foraging returns, an alternative currency, may be differently influenced by environment and local ecological risk. Our estimates for dangerous mammal density came from a global dataset  107. Local-level densities would yield more precise results. Further, dangerous mammal density was split between sites in Africa (high) and all other sites (low) due to the risk posed by African elephants and hippopotami. Thus, it's possible that these animals alone constrain children's activities, or that other unmeasured geographical factors act as confounds within the model. Our measures for the gendered division of food production labour and the proportion of non-foraging foods consumed in each community were estimated by ethnographers.
The effects of additional environmental and ecological variables are reported in the SI. Further, the small sample of societies hindered our ability to account for potential interaction effects. For example, environment and ecological risk may predominantly constrain the time allocation of younger children, while adolescent activity patterns may more closely resemble those of adults. We note, however, that results from Models 3 and 4 remain consistent when excluding observations of adolescents (see SI for discussion). Time allocation data often exhibits temporal autocorrelation, in the sense that observations taken closer together in time are more likely to evidence the same behaviour than those taken further apart  83, 110. In this study, the cross-cultural data were collected with heterogeneous sampling methods, and for some behaviors the temporal autocorrelation within study sites may be relatively high.
In principle, modeling this autocorrelation is possible, though the cross-cultural data and multinomial outcomes in this study would require strong assumptions to be made at the outset. Therefore, we have elected not to impose autocorrelation structures on our models. We acknowledge that the parameter estimates may be biased as a result. We did not account for children's time allocation to school. This was because schooling was not available in all communities, with only four datasets having any observations for time allocation to school. The SI documents that, in half of the surveyed societies, there were no on-site schools. Even when school was available, ethnographers mostly report only sporadic attendance (see SI for details). Only among the Mayangna was school normatively compulsory and regularly attended.
Methods This project was approved by the Simon Fraser University Office of Research Ethics (2020s0075). All datacontributing authors obtained ethical approval and/or in-country permission for data collection (see SI). All methods were performed in accordance with the relevant guidelines and regulations. All participants, parents, and/or legal guardians provided free, prior, and informed consent. Procedure. Behavioural observations. We pooled behavioural observational data collected in twelve societies that rely fully or partially on foraged resources for subsistence (Fig. 1 ; Table  1 ). Two behavioural observation techniques were used to collect the data: focal follow and scan sampling. Both methods are designed to systematically capture a representative sample of a population's everyday life, allowing for quantitative analyses of time allocation.
Focal follow sampling involves an observer recording a single individual's activities during a specified period using predetermined behavioural coding schemes. During scan sampling, the observer records the activities of all individuals in a group at regular intervals  113. Studies examining the validity and commensurability of these two methods have found that they provide comparable time budget estimates for non-human primates, though certain categories of behaviour (e. g. , feeding time, social interactions) may diverge  [114] [115] [116] [117]. In humans, focal and scan sample data produced equivalent time budget estimates among Ache foragers  118. Thus, the present paper considers data collected using focal and scan sampling methods as comparable. Of the datasets included in the present study, four employed the focal follow sampling technique, with observations recorded continually or at one-to five-minute intervals.
Three datasets scanned behaviour every 15-60 min within randomly allocated time blocks ranging from approximately 3-4 h. The remaining five datasets scanned behaviour daily, with observations ranging from every 2-to 3-min to 1-2 times a day. Some coding schemes included in the present analysis allowed for up to two categories to be coded simultaneously (e. g. , activity coded as play and food production). Only 2. 6% of observations from datasets which allowed for concurrent coding included activities which fell into two categories. We counted each of these observations as unique. Detailed descriptions regarding the setting and methods for data collection for each society can be found in the SI. Variables. Dependent variables. This paper focused on children's participation in childcare, food production, domestic work, and play.
Childcare was broadly defined as children tending to the physical or emotional needs of a young child or infant. Food production was defined as children's tending to, or collecting, wild or domestic foods (e. g. , gardening, hunting, etc. ). Activities considered domestic work involved participation in household maintenance, such as food processing, cooking, and cleaning. Play was considered as consisting of intrinsically-motivated activities pursued for enjoyment  119. To understand overall time allocation to each of these activities, we also accounted for other activities (e. g. , socializing, eating, resting), which collectively acted as the reference category in the statistical models (see below). Table  S1  describes the activities coded as childcare, food production, domestic work, play, and other activities for each dataset.
Age. Because many foraging societies do not record age in years  120  , exact ages could not be determined in all cases. Children were thus grouped into three developmental age categories: early childhood (between 3 and 6 years of age, early juveniles for the Mikea dataset), middle childhood (between 7 and 12 years of age, late juveniles for the Mikea dataset), and adolescence (between 13 and 18 years of age, young adults for the Mikea dataset). Proportion of non-foraged foods. Each ethnographer estimated how many daily calories, on average, came from the following food categories for their field site: wild plant food, large game, small game, fish/seafood, insects, honey, domestic plants and animals, and traded/purchased foods. Where possible, ethnographers reported estimates based on published or unpublished food return data.
Where contemporaneous data were not available, ethnographers estimated daily caloric returns based on their field experience. Because observations for Matsigenka were from published datasets  121, 122  , we relied on values in Tables  14  and 16  in Johnson  123. Values were reported as mean estimates, or as a minimum and maximum percentage. In the latter case, the dietary proportion produced for each food source in each society was calculated by taking the mean of the minimum and maximum reported dietary intakes per food category. All values were normalized so that the total intake from all sources for each society summed to 100%. The proportion of non-foraged foods was calculated by summing the proportion of domesticated foods and foods purchased and/or traded in each society. Gendered division of food production labour.
Environmental factors. NPP (gC/m 2 /year) was calculated using data from the Net Primary Production dataset version-55 from the Numerical Terradynamic Simulation Group at the University of Montana 81 (MOD17A3). This dataset is part of the NASA Earth Observation program, using Moderate Resolution Imaging Spectroradiometer satellite data. The dataset provides estimates of NPP at 1 km resolution for the earth's entire land surface. We averaged the NPP values in the 9 square kilometers surrounding the location of each field site. We used the data layer from 2010 as it was consistently available for all field locations. Annual Mean Temperature and Annual Precipitation were calculated using the University of East Anglia Climate Research Unit gridded Time Series dataset  124. This dataset, on a 0.
5° latitude by 0. 5° longitudinal grid, provides monthly climatic data spanning 1901-2018 interpolated using angular-distance weighting. Using the raster 125 package in R  126  , we extracted maximum temperature (°C), minimum temperature (°C), and total monthly precipitation (mm) for each field site. An average of 3. 48 and 6. 42 weather stations contributed to the temperature and precipitation monthly values respectively. Monthly values were averaged over the 30 years preceding and including the most recent year of behavioural data collection for each field site. We then used these monthly values to calculate Annual Mean Temperature and Annual Precipitation using the biovars function in the dismo 127 package. The specific formulas used for each variable can be found in O'Donnell and Ignizio  128.
Local ecological risk. Dangerous Mammal Density was extracted from the PanTHERIA Database 107 as follows: using maximum and minimum latitude and longitude values for all species in the database, we extracted a list of dangerous mammals (elephants, hippopotami, and carnivores over 50 kg) for each field site. We focused on mammals that could predate on children or that could attack children upon accidental encounter rather than mammals which may injure in self-defense, such as during hunting. Each ethnographer then reviewed the list for accuracy. Animals which were known to pose a risk at each site, but which were not reflected on the site-specific list, were added, including mammals under 50 kg (e. g. , wolves). Mammals which appeared on the site-specific lists which were not known at the site were removed.
Posterior means of fixed effects for models 1-5. Standard deviations are in parentheses. Parameters in bold represent estimates whose 95% credible intervals do not cross zero. Boys 1 = boys, 0 = girls. Middle 1 = Middle Childhood (between 7 and 12 years of age, late juveniles for the Mikea dataset), 0 = Early Childhood (between 3 and 6 years of age, early juveniles for the Mikea dataset). Ado 1 = Adolescence (between 13 and 18 years of age, young adults for the Mikea dataset). Propnon-foraged Proportion of domesticated foods and foods purchased and/or traded. NPP Net Primary Productivity in gC/m 2 /year. Annual mean temp Annual Mean Temperature, in °C. Annual prec Annual Precipitation, in mm. Mammal density Density of Dangerous Mammals, Low/High. Water qual/quant Water Quality/Quantity, four-point scale.
Acknowledgements Our deepest gratitude to all participants in the study. Thanks to  Allen Johnson  for answering questions our about Matsigenka;  Jeffrey Winking ,  Robert TSIAZONERA ,  Barry Hewlett ,  Tessa Minter ,  Russell Greaves , and  Ayana Tanaka  for acting as inter-coders;  Tyler Moulton  for answering questions about ecological measures;  Lee T. Gettler  for suggesting relevant literature;  Erik J. Ringen  for his feedback on modelling and interpretation;  Abigail E. Page  for her editorial expertise; and two anonymous reviewers for their feedback. Site-specific acknowledgements can be found in the Supplemental Information.
Funding S. L. L. was supported by the  Cambridge International Trust , the  Social Sciences and Humanities Research Council of Canada Doctoral Scholarship  ( 752-2016-0555 ), the  Social Sciences and Humanities Research Council of Canada   Postdoctoral Fellowship  ( 756-2019-0102 ), and the Alexander von Humboldt postdoctoral fellowship during the writing of this manuscript. R. R. and S. M. K. were supported by the  Gates Cambridge Trust  during the writing of this manuscript. J. S. acknowledges  Institute for Advanced Study  in Toulouse funding from the  French National Research Agency (ANR)  under Grant  ANR-17-EURE-0010  ( Investissements d' Avenir program ). Open Access funding enabled and organized by Projekt DEAL.
In line with the applicable legislation in each country, the protocols were reviewed and authorized by national investigational review boards (reference for France: CPP Est I 2017-A02847-46; reference for Switzerland: LRH RS 810. 30). During the project, we collected microcosting data that reflected actual expenditure. These included the cost of drafting the protocol by human factors engineers and statisticians and the costs of usability testing (the fee for the actress simulating an anaphylactic shock, the rental of the premises, the cost of the device, etc). The needle-free adrenaline autoinjector was tested by 140 participants (80 in Switzerland and 60 in France). This testing enabled the detection of 39 acceptable errors and 0 unacceptable errors. The sample size of 140 was unusually high for usability testing but was chosen because we included groups that differed with regard to the fidelity of the testing environment (high vs low fidelity), end user experience (inexperienced vs highly experienced), and country of residence (France vs Switzerland).
In line with the current regulatory process and to ensure reliability, n ¼ 20 participants were included before the value-of-information was first estimated. We considered that all the errors initially observed were acceptable; otherwise, the medical device would have had to return to the redesign stage (ie, formative testing). Next, participants were added in blocks of 10, which corresponds approximately to a full day of usability testing. All the statistical routines in this article have been implemented in an R package called useval, which is available on GitHub (https://github. com/alexandre-caron/useval) (Box 2).
Given the value of ò U and the large number of coefficients, A i sol , the wind is strongly constrained to the observed cloud-level profile, thus ensuring that deviations from the observed values are only permitted if they result in a significant lowering of the cost function. Given an initial guess for Y, a minimal value of L is derived using the Matlab function fmincon 19  while taking advantage of the cost function gradient that is derived from the adjoint of the dynamical model.
Interestingly, these proteins were described to promote resistance to anoikis in several cancers. Nevertheless this is the first study showing that mechanical stress increases IAPs expression, especially cIAP1 and XIAP, likely by modifying their protein turnover  (Toruner et al. , 2006; Liu et al. , 2006; Berezovskaya et al. , 2005). In an effort to revert resistance to anoikis and restore caspase-dependent cell death, we used SMAC mimetics developed to efficiently deplete IAPs. Currently, several SMAC mimetics such as birinapant and LCL161 are in phase two clinical trials for ovarian cancer and myeloma (source: https://clinicaltrials. gov/). Importantly, we found that BV6 treatment re-sensitized cancer cells subjected to CM to anoikis, reinforcing the relevance of SMAC mimetics for clinical use.
Bioinformatics analysis All genomic data were analysed with R/Bioconductor packages, R version 4. 0. 3 (2020-10-10) [https:// cran. r-project. org/https://cran. r-project. org/; http://www. bioconductor. org/] on a linux platform (x86_64-pc-linux-gnu [64-bit]). Illumina sequencing was performed on RNA extracted from triplicates of each condition. Standard Illumina bioinformatics analyses were used to generate fastq files, followed by quality assessment [MultiQC v1. 7, https://multiqc. info/], trimming and demultiplexing. 'Rsubread' v2. 4. 3 was used for mapping to the hg38 genome and creating a matrix of RNA-Seq counts. Rsamtools v2. 6. 0 * was used to merge two bam files for each sample (run in two different lanes). Next, a DGElist object was created with the 'edgeR' package v3. 32. 1 [https://doi.
Quantitative RT-PCR Total RNA extraction was performed using the Nucleospin RNA Macherey-Nagel kit (740955) and quantified by NanoDrop. The conversion of messenger RNA into cDNA was performed using the Sensifast cDNA synthesis kit (Bioline, BIO-65053). cDNA was then amplified by PCR using specific primers for each gene designed with Primer-blast software (https://www. ncbi. nlm. nih. gov/tools/ primer-blast/) and listed in Table  2. GAPDH, ACTB, and HPRT were used as housekeeping genes. The thermal cycling steps included an initial polymerase activation step at 95°C for 2 min, followed by 40 cycles at 95°C, 5 s, and 60°C, 30 s. The qRT-PCR experiments were performed using SYBR Green and a Lightcycler96 (Roche, Indianapolis, USA). In vivo lung metastasis model and lung imaging MDA-MB-231 control and constricted cells (2.
5 × 10 4 ) were suspended in 100 µl PBS and injected into the tail vein of NMRI nude female mice. To attain statistical significance of a p value between 0. 05 and 0. 02, we grafted 15 mice for each group. Sample-size calculation was performed as previously described  (Fitts, 2011). The absence of mycoplasma in injected cells was controlled before injection in animals. Burden of lung metastasis was evaluated over time by X-ray microCT-Scan (Quantum FX, Perkin Elmer). Mice were anesthetized with a continuous flow of 2-4% isoflurane/air (1. 5 l/min). The lungs were imaged in a longitudinal manner for 2 min with an exposure of 0. 746 Gy and the obtained raw data were reconstructed with the following acquisition settings: a 24-mm FOV diameter, 512 slices, and 50 µm voxel. The resulting images were viewed and analysed using 'Analyze of Caliper' software (AnalyzeDirect) and the remaining healthy lung volume was quantified and 3D represented.
Histological analyses If limiting points were not observed, mice were euthanized 8 weeks post-engraftment. Lungs were fixed in 4% buffered formalin, paraffin embedded, and three 3-µm sections separated by 300 µm were stained with hematoxylin-eosin. The slides were scanned using the panoramic scan II (3D Histech). These were then analysed with CaseViewer 2. 2. 0. 85100 software (3DHISTECH Ltd) for the detection of metastasis.
NK-mediated immune surveillance Control and constricted MDA-MB-231 cells were cocultured with human NK cells sorted from peripheral blood using the NK cell isolation Kit (Miltenyi Biotec 130-092-657) at the ratio of 1:20, in triplicate for each condition. Before co-culturing, tumour cells were pretreated with 10 µg/ml mitomycin for 1 hr to stop proliferation, and were incubated and tagged with CFSE (Invitrogen CellTrace, C34570) at 1 µl/ ml for 20 min. Twenty-four hours later, cells in each condition were recovered by trypsin and stained intracellularly with Granzyme B (Biolegend, AF647, clone GB11). Flow cytometry was performed by BD LSR Fortessa HTS and data were analysed using GraphPad Prism V9.
List of qRT-PCR primers. -adjusted p value <0. 05 and a minimum absolute fold change of 2. All raw and processed RNA-Seq data have been deposited at the Gene Expression Omnibus (GEO) repository, under accession number GSE176081. Rsamtools: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import. R package version 2. 6. 0. https://bioconductor. org/packages/Rsamtools.
(E) Heatmap representing fitness scores for the top 50 GOF individual amino acid substitutions in D-Met and M9 media. n = 3. DMS in D-Met media was repeated three independent times, each time with at least three technical replicates. The majority of the GOF substitutions in D-Met are condition specific. (F) Lysis assays with wild-type Tae1, empty vector and catalytically inactive Tae1 C30A as negative controls, and three GOF Tae1 variants (A116T, D134N, S147I). Data are means ± standard deviation, n = 3, *p<0. 05. Induced with 0. 0025% arabinose. Lysis experiment was repeated three independent times, each time with at least three technical replicates. Western blots below measure Tae1 protein expression with RNAP as a control. All three GOFs from DMS show increased lysis phenotype in individual lysis assays.
Hyperactive variant Tae1 C110S evades binding and inhibition by cognate immunity We reasoned that if Tae1 proteins are too structurally divergent, the toxin may evade binding by the cognate Tai1 partner in P. aeruginosa (Tai1 PA ) and consequently antagonize kin cells or other closely related species. To assess whether the Tae1 C110S variant affects Tae1-Tai1 interactions, we first assessed whether Tae1 C110S adopted any major structural changes by solving its 3D crystal structure (PDB ID: 7TVH) (Table  1 ). We did not observe any major structural rearrangements that would immediately implicate distinct modes of Tai1 PA -binding (RMSD = 0. 746, Figure  6A ). However, we did observe some key side chain alterations near the Tae1 C110S active site, which is the enzyme region most intimately engaged with Tai1 PA upon binding (Figure  6 -figure supplement 1).