The Relation of Adolescent Narcissism and Prosocial Behavior with Cooperative and Competitive Attitudes

Narcissism has been correlated with prosocial behavior in adolescents (Kauten & Barry, 2014, 2016). However, whether adolescents with high levels of narcissism use more egoistic (i.e., self-serving) or altruistic (i.e., helping others with no direct personal benefit) prosocial behaviors has not been investigated. This issue was addressed in the present study, and attitudes of cooperation and competition were also examined as moderators in these relations. The current study involved 147 at-risk adolescents, ages 16 to 18 (113 males, 34 females) who were enrolled in a residential program. The data were collected through self-report questionnaires which assessed narcissism (i.e., non-pathological, grandiose, vulnerable, and communal), prosocial tendencies, and cooperative and competitive attitudes. Non-pathological and grandiose narcissism were positively correlated with egoistic prosocial behaviors, whereas non-pathological and communal narcissism were positively correlated with altruistic prosocial behaviors. Adolescents reporting both high levels of vulnerable narcissism and high levels of competitive attitudes reported engaging in more altruistic prosocial behaviors than those with low levels of competitive attitudes. The same trend was evident for grandiose narcissism. The implications of these findings and the study’s limitations are discussed

Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii

The marine nitrogen fixing microorganisms (diazotrophs) are a major source of nitrogen to open ocean ecosystems and are predicted to be limited by iron in most marine environments. Here we use global and targeted proteomic analyses on a key unicellular marine diazotroph Crocosphaera watsonii to reveal large scale diel changes in its proteome, including substantial variations in concentrations of iron metalloproteins involved in nitrogen fixation and photosynthesis, as well as nocturnal flavodoxin production. The daily synthesis and degradation of enzymes in coordination with their utilization results in a lowered cellular metalloenzyme inventory that requires ~40% less iron than if these enzymes were maintained throughout the diel cycle. This strategy is energetically expensive, but appears to serve as an important adaptation for confronting the iron scarcity of the open oceans. A global numerical model of ocean circulation, biogeochemistry and ecosystems suggests that Crocosphaera’s ability to reduce its iron-metalloenzyme inventory provides two advantages: It allows Crocosphaera to inhabit regions lower in iron and allows the same iron supply to support higher Crocosphaera biomass and nitrogen fixation than if they did not have this reduced iron requirement.National Science Foundation (U.S.). Chemical and Biological Oceanography Program (OCE-0452883)National Science Foundation (U.S.). Chemical and Biological Oceanography Program (OCE-0752291)National Science Foundation (U.S.). Chemical and Biological Oceanography Program (OCE-0723667)National Science Foundation (U.S.). Chemical and Biological Oceanography Program (OCE-0928414)National Science Foundation (U.S.). Polar Program (ANT-0732665)United States. Environmental Protection Agency (Star Fellowship)Woods Hole Oceanographic Institution. Ocean Life InstituteCenter for Microbial Oceanography: Research and EducationCenter for Environmental Bioinorganic Chemistr

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Early Release Science of the Exoplanet WASP-39b with JWST NIRSpec G395H

Measuring the abundances of carbon and oxygen in exoplanet atmospheres is considered a crucial avenue for unlocking the formation and evolution of exoplanetary systems. Access to an exoplanet's chemical inventory requires high-precision observations, often inferred from individual molecular detections with low-resolution space-based and high-resolution ground-based facilities. Here we report the medium-resolution (R$\sim$600) transmission spectrum of an exoplanet atmosphere between 3-5 $\mu$m covering multiple absorption features for the Saturn-mass exoplanet WASP-39b, obtained with JWST NIRSpec G395H. Our observations achieve 1.46x photon precision, providing an average transit depth uncertainty of 221 ppm per spectroscopic bin, and present minimal impacts from systematic effects. We detect significant absorption from CO$_2$ (28.5$\sigma$) and H$_2$O (21.5$\sigma$), and identify SO$_2$ as the source of absorption at 4.1 $\mu$m (4.8$\sigma$). Best-fit atmospheric models range between 3 and 10x solar metallicity, with sub-solar to solar C/O ratios. These results, including the detection of SO$_2$, underscore the importance of characterising the chemistry in exoplanet atmospheres, and showcase NIRSpec G395H as an excellent mode for time series observations over this critical wavelength range.Comment: 44 pages, 11 figures, 3 tables. Resubmitted after revision to Natur

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships