dataesr/scientific-paragraphs-categorization huggingface dataset:
S2 ), demonstrating brain state stability across physiological conditions. We concluded that homologous brain states exist in all three species examined. Brain states were first matched between species based on spatial homology with previously described networks  [9, 12]. To obtain a data-driven comparison, we generated synthetic human brain states from the mouse maps. Mouse brain states were vectorized and modeled as the weighted sum of 3218 gene expression maps obtained from the in situ hybridization mouse brain database  [15]. The parameter estimate for each mouse gene expression was multiplied with the homology-matched human gene expression derived from the microarray human brain database  [14] , and summed to obtain synthetic brain maps. Matched human brain states and corresponding synthetic maps exhibited greater spatial correlations than mismatched maps (Fig.
central). result suggests a revision to models for monoamine involvement in human networks that have associated the serotoninergic system with the default-mode network exclusively  [42, 43]. To validate this neuroanatomical model, we humanized the three tracer-based structural networks using the conversion method detailed above. We compared gray matter overlap with the projections estimated from probabilistic tractography in a diffusion tensor imaging dataset (N = 178). Both the humanized default-mode and lateral cortical network grey matter loadings were strongly associated with the tractographyestimated projections (Fig. S5b , Synthetic DMN ~Tractography DMN : Coef = 0. 34 [0. 08, 0. 60], Synthetic LCN ~Tractography CEN : Coef = 0. 60 [0. 27, 0. 94]). The humanized mouse default-mode structural network recapitulated the key features of the human network, namely hyperintensity in the ACA, PCA, as well as medial temporal lobes (Fig.
S5c ). We conclude, therefore, that the mouse structural networks recapitulate features expected in humans. Further, we posit that the fine subcortical neuroanatomical details established in the mouse also translate to the human brain. Understanding the functional contribution of these networks is central to our understanding of psychopathologies. Hence, we tested the functional integration of two nodes within the salience network implicated in depressive disorders, namely the DR nucleus and insular area. We modeled the distributed response to neuronal perturbations evoked by channelrhodopsin-2 (ChR2) photostimulation as a function of the neuroanatomical maps for the three networks (Fig. 2 ). First, we examined the changes in cerebral blood volume in an fMRI dataset where optogenetic (ChR2, N = 63 runs) photostimulation was performed on ePetpositive DR neurons relative to yellow fluorescent protein (YFP, N = 18 runs) controls  [44].
Whether these network involvement inconsistencies are due to species differences, or to anesthesia, will need to be determined; however, viral tracers are independent of anesthesia, providing anatomical support for our hypothesis. Finally, it should be noted that the serotoninergic system extends beyond the dorsal and CS nuclei, with at least nine nuclei characterized and each containing heterogeneous neuronal populations  [50]. The regulation of distributed neuronal networks is expected to be mediated by complex mechanisms. This work is facilitated by the availability of large online databases for human and mouse rsfMRI, meta-analysis tools, diffusion imaging, viral tracers, and spatial transcriptomics  [14, 15, 17, 18, 27, 31]. While we used nonhuman primate data as an intermediary for our first comparison, the relative lack of resources, such as spatial transcriptomics, highlights a need in the community for additional nonhuman primate resources to strengthen our comparisons  [51, 52].
ACKNOWLEDGMENTS This work was supported by the  Singapore BioImaging Consortium core funding  and the  Singapore BioImaging Consortium  award # 2016  to JGr and # 2017  to FM. FM was supported by a Ph. D. scholarship funded through the  A*STAR Research Attachment Programme  and the  University of Manchester  (awarded to  JGi ). RMV was supported by the  Dutch Research Council  grant  OCENW. KLEIN. 334 (awarded to JGr). This work was supported by the  A*STAR Investigatorship  (awarded to FY). RBM is supported by the  Biotechnology and Biological Science Research Council (BBSRC)  [ BB/N019814/1 ]. The  Wellcome Centre for Integrative Neuroimaging  is supported by core funding from the  Wellcome Trust  [ 203129/Z/16/Z ]. Data were provided in part by the  Human Connectome Project, WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
Simulation framework We obtain the real imputed genotypes of N=290,273 "unrelated white British" individuals in the UK Biobank by extracting individuals with self-reported British ancestry who are > third-degree relatives (pairs of individuals with kinship coefficient < ½  (9/2)  , as defined in ref. 48 ). Filtering on MAF > 0. 5% leaves 200,235 variants on chromosome 1. A list of 1,083 genes on chromosome 1 and their coordinates were downloaded from https://github. com/bogdanlab/gene_sets (Data Availability). For each variant, genotypes are standardized such that the mean is 0 and variance is 1 across individuals. Phenotypes were simulated under a variety of genetic architectures according to the following steps. First, we randomly select 3%, 8%, or 16% (out of the 1,083 genes) to be causal ( ℎ !"#" $ > 0 ).
Analysis of 25 UK Biobank phenotypes We analyzed 25 quantitative phenotypes in the UK Biobank. Phenotypes and imputed genotypes were filtered according to the same procedures used in the simulation analyses, leaving N=290,273 individuals and M=5,650,812 variants with MAF > 0. 5%. Quantitative phenotypes were quantile-normalized to a Gaussian distribution with mean 0 and variance 1. We then performed a GWAS for each trait using the "assoc" option in PLINK (Web Resources) with age, sex, and the top 10 genetic principal components included as covariates. We computed in-sample LD for each approximately independent LD block  93. We downloaded gene names and coordinates from https://github. com/bogdanlab/gene_sets and, for each gene, we define the estimand of interest to be a function of the variants in the gene body and those located within 10-kb upstream/downstream of the gene start/end positions.
Acknowledgments We thank the  UK Biobank Resource  (application # 33297 ) for making this work possible. We are also grateful to  Alkes Price ,  Harold Pimentel ,  Luke O'Connor ,  Nasa Sinnott-Armstrong , and  Ruth Johnson  for providing helpful comments and discussion. This work was funded in part by the  National Institutes of Health  under awards  R01-HG009120 ,  R01-MH115676.
Here we build and analyse a database on cancer-related mortality using data on adult zoo mammals (110,148 individuals, 191 species) and map age-controlled cancer mortality to the mammalian tree of life. We demonstrate the universality and high frequency of oncogenic phenomena in mammals and reveal substantial differences in cancer mortality across major mammalian orders. We show that the phylogenetic distribution of cancer mortality is associated with diet, with carnivorous mammals (especially mammal-consuming ones) facing the highest cancer-related mortality. Moreover, we provide unequivocal evidence for the body size and longevity components of Peto's paradox by showing that cancer mortality risk is largely independent of both body mass and adult life expectancy across species. These results highlight the key role of life-history evolution in shaping cancer resistance and provide major advancements in the quest for natural anticancer defences.
Moreover, the effect of longevity was generally tested using the much-debated metric of maximum reported lifespan  [15] [16] [17]. Nonetheless, cancer prevalence (the parameter exclusively considered by earlier studies  [15] [16] [17]  ) is expected to correlate with life expectancy (the average time lived by individuals in the population of interest), not maximum lifespan potential (that very few individuals achieve), making these analyses inherently flawed. To characterize cancer incidence in a homogeneous sample and across a wide taxonomic range, here we used the Zoological Information Management System (ZIMS), managed by Species360 (a non-profit organization custodian of zoo and aquarium data)  19. We assembled information on 110,148 adult non-domesticated mammals distributed over 191 species, including data on their age, sex, dead/alive status and postmortem pathological records for 11,840 individuals.
Cancer is registered in this database only for deceased animals and only if the inspecting veterinary pathologist considered it to be a factor that contributed to the individual's death. First, to characterize species longevities, we used survival modelling (n = 110,148) and calculated species-specific adult life expectancies, representing average adult longevity in our sample  20, 21. Second, to estimate cancer mortality risk, we used two metrics, both estimating the proportion of individuals dying of cancer. We first calculated a simple measure of cancer mortality risk (hereafter CMR; that is, the ratio between the number of cancer-related deaths and the total number of individuals whose postmortem pathological records were entered in the database, n = 11,840), a measure adopted by earlier comparative studies  15, 17.
This measure relies solely on dead individuals, ignoring the incomplete records of live animals, potentially introducing bias in cancer mortality estimates. Therefore, we also calculated the cumulative incidence of cancer mortality (hereafter ICM), a metric of cancer mortality risk eliminating potential biases due to disregarding left-truncation (that is, cancer before individuals enter the study) and right-censoring (individuals alive, thus with unknown fate at data extraction). Using these two metrics, we explored the phylogenetic distribution of cancer-related mortality across mammals. We then investigated Peto's paradox and tested whether cancer mortality risk is associated with body size or the mean number of years lived by adults in the explored populations (that is, adult life expectancy).
2  that also control for body mass, life expectancy and phylogeny. This highlights the quasi-universality of oncogenic phenomena across mammals, illustrating that with adequate sampling, cancer is likely to be detected in all mammals. Our results further emphasize the fact that some members of the order Artiodactyla, besides rodents, are particularly cancer-resistant. Rodents have long been subject to scrutiny in the search for natural cancer resistance mechanisms  35  , owing to notoriously low cancer incidence in some species  36. Nonetheless, cancer mortality risk in our dataset was lowest among ruminants, complying with rare cancer case reporting in this taxonomic group  15, 37. This indicates that other mammalian groups, especially Artiodactyla, might serve as informative model organisms in cancer research.
Methods Documenting cancer in wild animals is extremely challenging in most cases owing to the lack of information on the age of individuals, the difficulty retrieving the bodies for necropsy and the likelihood of cancer negatively influencing survival before cancer itself could be detected. Although data on cancer incidence from wild populations would be indispensable to describe natural incidences of malignancies, such data, especially with corresponding ages and demographic histories, are unfortunately still far from our reach. Therefore, to estimate cancer mortality risk, we used data provided by Species360 and the Zoological Information Management System (ZIMS, Data Use Approval Number 73836), an international non-profit organization that maintains a real-time and centralized database of animals under human care (regrouping information from over 1,200 zoos worldwide).
Although we recognize that the interpretation of data gathered on zoo animals requires caution, owing to strong human control on the diet, health, mortality factors, environment or standard biological functions of the animals, zoos provide exceptionally high data resolution on the demography and cause of death for a wide range of species. Here we rely on the high probability of body retrieval of deceased zoo animals and the necropsy routinely performed on most of them (unless found in an advanced stage of decomposition), aiming to identify the most likely pathology causing the death of the animal. These examinations are likely to reveal most solid tumours, but (although possible) benign tumours, liquid tumours (for example, leukaemia) or early-stage cancers are unlikely to be recorded here, either owing to their diagnostic difficulty or their perceived limited role in contributing to the death of the animal.
Specifically, here we use the husbandry module of ZIMS, providing information on birth, death, sex and pre-defined categories of pathological findings, including neoplasia (by definition tumours that contributed to the death, albeit with no option to specify the cancer type or other details). No statistical methods were used to predetermine sample size, but to minimize bias caused by potential temporal heterogeneity in data management practices and necropsy record-keeping  15  , here we focused on individuals alive or born after 1 January 2010 (data extraction: 30 May 2020). This sample was then used to characterize species-specific life expectancies and cancer incidence, but only after the exclusion of data that did not fulfil a series of criteria, to ensure the highest and most homogeneous data quality possible.
First, cancer is an age-related disease that rarely occurs in juveniles, and pediatric cancers are usually medically distinct from adults' cancers. As such, infant mortality differences observed across species can significantly confound cancer incidence estimates. Therefore, we gathered sex-specific or species-specific (wherever the former was not available) ages at sexual maturity and we considered individuals for analyses only if they reached maturity before or during our sampling period. For individuals of unknown sex (about 12% of all individuals in the raw data extract), the maximum of ages at sexual maturity of males and females was used as an inclusion age threshold. Sex-specific age at sexual maturity for each species was obtained from Conde et al. 19  or from published literature resources (see data sources in https://github.
Following these restrictions, data extraction on age and cause of death resulted in information for 110,148 (62,556 live and 47,592 dead) individuals (n = 191 species). For calculation of ICM, we included only species in which survival is correctly estimated until old ages (that is, data allowing the estimation of age-specific survival until the age at which only 10% of individuals are surviving, n = 172 species). While these restrictions removed multiple sources of bias in our cancer mortality risk estimates, we cannot exclude the possibility that some species (for example, more charismatic ones) are subject to more frequent or more detailed necropsies. Nonetheless, our statistical approach, especially the complete case analysis, is largely insensitive to such biases, as individuals not having available postmortem diagnostic records are considered censored (see below).
Estimation of adult life expectancy As we have no reason to believe that censored individuals would not have the same prospect of survival as those who continue to be followed, we estimate adult life expectancy from age-specific survival estimated using the Kaplan-Meier procedure (using the survfit function in the R package survival  40  ). Individuals older than their age at sexual maturity on 1 January 2010 were left-truncated at their age at this date; individuals reaching sexual maturity after this date were left-truncated at their age at sexual maturity. Individuals still alive at the time of data extraction were considered right-censored (samples per species varied from 42 to 5,816 individuals), while known fate individuals were assigned as dead (n = 47,592), irrespective of whether their cause of death was specified or not.
Estimation of ICM ICM was calculated using a competing risk approach, based on the cumulative hazard of cancer-related deaths and survival probability of the species under human care. First, age-specific survival S x , at age x, was estimated from KM analysis as above. However, here we performed a complete-case analysis, using only 11,840 individuals for which the cause of death was specified together with right-censored survivors. Complete-case analysis assumes that missingness in the cause of failure is random, but we had no reason to believe that this was not the case in our dataset. Postmortem examinations are routinely carried out on most recovered bodies in zoos, and once examinations are performed the results are equally likely to be entered in the database irrespective of the pathologies identified.
ICM estimates were thus based on n = 74,396 individuals, n = 179 species. Second, the cancer mortality hazard h c x was estimated using a KMx1 analysis where only deaths by cancer were incorporated as a death event. ICM is then such that ∑ S h ICM = x α x x = ∞ c where α is the age at sexual maturity. The only difference with classic estimation is that we extracted h c x (and S x ) for each time unit with discrete jumps (and falls) at event times at age t and with h c t≤x<t+1 = 0 (S x constant) between these events; instead of estimating discrete hazard h d n = / t t t c c on these time intervals (where d c t is the number of deaths by cancer within the interval and n t is the number of survivors at the beginning of the interval). We chose this method to reflect true variation in the data for the interspecific comparison where species differ greatly in the number of events and time interval between these (sometimes a third of the organism's adult lifespan), a situation rarely met when comparing human groups.
Covariates and statistical analyses For each species, we obtained sex-specific adult body mass data from Species360's ZIMS (see https://github. com/OrsolyaVincze/VinczeEtal 2021Nature/blob/main/SupplementaryData. xls). Species-specific body mass was calculated as the average of all body mass measurements recorded in the ZIMS database in adults, while species-specific values were obtained by averaging the body masses of males and females. These were calculated only in species for which there were at least 100 adult body mass records; otherwise, body mass was taken from the literature and database review by Conde et al. 19. We verified that there was a one-to-one correspondence in the body mass information for species with records in both datasets. Diet information was obtained from a global diet dataset for terrestrial mammals  32  , providing information on diet composition at four hierarchical levels of food items (never consumed, occasionally consumed, secondary food item, primary food item).
We collected information on animal content in diet, as well as subcategories of this diet class, namely invertebrate or vertebrate consumption, as well as specifically fish, reptile, bird and mammal consumption. Given that most food items had few species in the intermediate levels (occasional consumption and secondary food item), we re-categorized the diet variables at two levels: never/rarely consumed or representing the primary/secondary food item of the species. The effect of diet was tested in PGLS regressions using only species with non-zero cancer mortality risks. Models were run separately for each food item that were entered to a base model including body mass and adult life expectancy as covariates. Results are shown in Extended Data Table  2. To account for statistical non-independence due to phylogenetic relationships, we obtained a sample of 1,000, equally plausible phylogenetic trees, from the posterior distribution published by Upham et al.
41  , covering 5,911 species. We then obtained a rooted consensus tree using the sumtrees Python library  42. Two species recently raised to species level were manually added to the tree as sister taxa of the species it was recently separated from (that is, Cervus canadensis to Cervus elaphus and Gazella marica to Gazella subgutturosa). Phylogenetic signal of cancer risk was assessed using the function phylosig from the R package phytools  22. Partial coefficients of determinations were calculated using the function R2. pred from the R package rr2 (ref. 43 ), based on models presented in Extended Data Table  3. Models of cancer risk testing Peto's paradox were performed using zero-inflated logistic models, which allow us to make inferences on the probability of detecting at least one cancer case in the species and, given that cancer was detected, inferences on the CMR or ICM.
Therefore, the first part of this consisted of a phylogenetic binomial regression (using the function binaryPGLMM, in the R package ape  44  ), where the dependent variable explained the presence of zeros and non-zeros in CMR or ICM. This model contained the log number of deceased individuals with available postmortem pathological records as an explanatory variable, due to the higher probability of detecting cancer with a higher number of dead individuals inspected. Additionally, the model contained body mass and adult life expectancy as covariates. The second part of the model consisted of a PGLS regression that investigated variance only in non-zero cancer risks. ICM and CMR were logit-transformed in all PGLS models as recommended when analysing proportions  45. These models were weighted by log number of deceased individuals with available postmortem pathological records, as the precision of cancer mortality risk estimates is expected to increase with the number of dead individuals subject to postmortem examination, but it is not expected to explain bias in the estimation of the dependent variable in any particular direction (as in the case of the binomial models).
Nonetheless, all models were performed using the entire dataset, and the results were highly consistent with and without the exclusions (Supplementary Table  4  and Extended Data Fig. 7 ). Order differences in cancer incidence were tested using standard linear regressions, built using only taxonomic orders in which at least two species had their cancer incidence estimated. The model contained CMR or ICM (non-transformed) as dependent variables and order as the sole explanatory factor. Pairwise order differences were assessed using the R package emmeans  46. All analysis were performed in the R Statistical and Programming Environment, version 4. 0. 4 (ref. 47 ). Cancer mortality risks were transformed to percentages in figures and in the analysis performed on order differences (Extended Data Table  1 ), for easier interpretation.
Random noise was added to cancer occurrence to facilitate the visualization of overlapping points. Predictions and associated 95% confidence intervals were obtained from a binomial GLM without phylogenetic control. Non-zero cancer mortality risks were plotted against c, body mass or d, adult life expectancies. Slopes were obtained from the PGLS model presented in Extended Data Table3b. Points are proportional to the log number of individuals with known cause of death. Extended Data Fig. 6| Predicted CMR at various hypothetical life expectancies for small, medium, and large-bodies species. CMR was predicted based on the logistic model presented in Extended Data Table3afor a series of hypothetical adult life expectancies, ranging from one to 70 years (x-axis). Predictions were obtained for three body masses, corresponding to a small, medium and a large bodied mammal in our dataset.