Evidence for fire in the pliocene arctic in response to amplified temperature

The mid-Pliocene is a valuable time interval for investigating equilibrium climate at current atmospheric CO2 concentrations because atmospheric CO2 concentrations are thought to have been compara

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field

Late Pleistocene to Holocene slip rates for the Gurvan Bulag thrust fault (Gobi-Altay, Mongolia) estimated with 10^{10}Be dates

We surveyed morphotectonic markers along the central part of the Gurvan Bulag thrust, a fault that ruptured with the Bogd fault during the Gobi-Altay earthquake (1957, M 8.3), to document climatic and tectonic processes along the fault for the late Pleistocene-Holocene period. The markers were dated using 10 Be produced in situ. Two major periods of alluviation ended at 131 ± 20 and 16 ± 4.8 ka. These appear to be contemporaneous with global climatic changes at the terminations of marine isotope stages (MIS) 6 and 2. The vertical slip rates, determined from offset measurements and surfaces ages, are 0.14 ± 0.03 mm/yr over the late Pleistocene-Holocene and between 0.44 ± 0.11 and 1.05 ± 0.25 mm/yr since the end of the late Pleistocene. The higher of these slip rates for the last $16 kyr is consistent with paleoseismic investigations along the fault [Prentice et al., 2002], and suggests that, at the end of late Pleistocene, the fault evolved from quiescence to having recurrence intervals of 4.0 ± 1.2 kyr for surface ruptures with$4 m vertical offset (similar to that of 1957). The inferred recurrence interval is comparable to that of the Bogd fault (3.7 ± 1.3 kyr) suggesting that the two faults may have ruptured together also earlier during the last $16 kyr

The chromatin landscape of healthy and injured cell types in the human kidney

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney’s active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks. © 2024, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Situational factors shape moral judgements in the trolley dilemma in Eastern, Southern and Western countries in a culturally diverse sample

The study of moral judgements often centres on moral dilemmas in which options consistent with deontological perspectives (that is, emphasizing rules, individual rights and duties) are in conflict with options consistent with utilitarian judgements (that is, following the greater good based on consequences). Greene et al. (2009) showed that psychological and situational factors (for example, the intent of the agent or the presence of physical contact between the agent and the victim) can play an important role in moral dilemma judgements (for example, the trolley problem). Our knowledge is limited concerning both the universality of these effects outside the United States and the impact of culture on the situational and psychological factors affecting moral judgements. Thus, we empirically tested the universality of the effects of intent and personal force on moral dilemma judgements by replicating the experiments of Greene et al. in 45 countries from all inhabited continents. We found that personal force and its interaction with intention exert influence on moral judgements in the US and Western cultural clusters, replicating and expanding the original findings. Moreover, the personal force effect was present in all cultural clusters, suggesting it is culturally universal. The evidence for the cultural universality of the interaction effect was inconclusive in the Eastern and Southern cultural clusters (depending on exclusion criteria). We found no strong association between collectivism/individualism and moral dilemma judgements. © 2022, The Author(s), under exclusive licence to Springer Nature Limited

The Immune Landscape of Cancer

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes\u2014wound healing, IFN-\u3b3 dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-\u3b2 dominant\u2014characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field. Thorsson et al. present immunogenomics analyses of more than 10,000 tumors, identifying six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis. This work provides a resource for understanding tumor-immune interactions, with implications for identifying ways to advance research on immunotherapy

The Immune Landscape of Cancer (Immunity (2018) 48 (812–832), (S1074-7613(18)30121-3), (10.1016/j.immuni.2018.03.023))

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.