Should we measure professionalism with an index? A note on theory and practice in state legislative professionalism research

Legislative professionalism has played a prominent role in state politics research for decades. Despite the attention paid to its causes and consequences, recent research has largely set aside questions about professionalism’s conceptualization and operationalization. Usually measuring it as an aggregate index, scholars theoretically and empirically treat professionalism as a unidimensional concept. In this article, we argue that exclusive use of aggregate indices can limit state politics research. Using a new dataset with almost 40 years of data on state legislative resources, salary, and session length, we reconsider the validity of using an index to study professionalism across the states. We evaluate the internal consistency of professionalism components over time, the relationship between components and the Squire Index, and the degree to which professionalism components are unidimensional using classical multidimensional scaling. We find enough commonality and enough variation between professionalism components to support a range of measurement strategies like the use of unidimensional indices (such as the Squire Index), disaggregating the components and analyzing their effects individually, or formulating multidimensional measures. Scholars should take care to choose the appropriate measure of the concept that best fits the causal relationships under examination

Ecosystem processes, land cover, climate, and human settlement shape dynamic distributions for golden eagle across the western US

Species–environment relationships for highly mobile species outside of the breeding season are often highly dynamic in response to the collective effects of everchanging climatic conditions, food resources, and anthropogenic disturbance. Capturing dynamic space-use patterns in a model-based framework is critical as model inference often drives place-based conservation planning. We applied dynamic occupancy models to broad-scale golden eagle Aquila chrysaetos survey data collected annually from 2006 to 2012 during the late summer post-fledging period in the western US. We defined survey sites as 10 km transect segments with a 1 km buffer on either transect side (n = 3540). Derived estimates of occupancy were low (4.4–7.9%) and turnover rates – the probability that occupied sites were newly occupied – were high (88–94%), demonstrating that annual transiency in occupancy dominates late summer behavior for golden eagles. Despite low philopatry during late summer, variation in golden eagle occupancy could be explained by a suite of land cover and annual-varying covariates including gross primary productivity, drought severity, and human disturbance. Our summary of 13 years of predicted occupancy by golden eagles across the western United States identified areas that are consistently used and that may contribute significantly to golden eagle conservation. Restricting development and targeting mitigation efforts in these areas offers practitioners a framework for conservation prioritization

Pulsating Stellar Atmospheres

We review the basic concepts, the present state of theoretical models, and the future prospects for theory and observations of pulsating stellar atmospheres. Our emphasis is on radially pulsating cool stars, which dynamic atmospheres provide a general example for the differences with standard static model atmospheres.Comment: 9 pages, 2 figs, LaTex, in Proc. of IAU Symp 189, "Fundamental Stellar Properties...", eds. T. R. Bedding, A. J. Booth and J. Davis, Kluwer, p.253, 199

Landscape characteristics influencing the genetic structure of greater sage-grouse within the stronghold of their range: a holistic modeling approach

Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high- and low-quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage-grouse (Centrocercus urophasianus) across Wyoming. We found that pairwise resistance derived from the distribution of low-quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33-km-diameter moving windows were preferred, suggesting small-scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.U.S. Bureau of Land ManagementU.S. Geological SurveyWyoming Game and Fish Departmen

High-Entropy 2D Carbide MXenes: TiVNbMoC3 and TiVCrMoC3

Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, are a fast-growing family of 2D materials. MXenes 2D flakes have n + 1 (n = 1–4) atomic layers of transition metals interleaved by carbon/nitrogen layers, but to-date remain limited in composition to one or two transition metals. In this study, by implementing four transition metals, we report the synthesis of multi-principal-element high-entropy M4C3Tx MXenes. Specifically, we introduce two high-entropy MXenes, TiVNbMoC3Tx and TiVCrMoC3Tx, as well as their precursor TiVNbMoAlC3 and TiVCrMoAlC3 high-entropy MAX phases. We used a combination of real and reciprocal space characterization (X-ray diffraction, X-ray photoelectron spectroscopy, energy dispersive X-ray spectroscopy, and scanning transmission electron microscopy) to establish the structure, phase purity, and equimolar distribution of the four transition metals in high-entropy MAX and MXene phases. We use first-principles calculations to compute the formation energies and explore synthesizability of these high-entropy MAX phases. We also show that when three transition metals are used instead of four, under similar synthesis conditions to those of the four-transition-metal MAX phase, two different MAX phases can be formed (i.e., no pure single-phase forms). This finding indicates the importance of configurational entropy in stabilizing the desired single-phase high-entropy MAX over multiphases of MAX, which is essential for the synthesis of phase-pure high-entropy MXenes. The synthesis of high-entropy MXenes significantly expands the compositional variety of the MXene family to further tune their properties, including electronic, magnetic, electrochemical, catalytic, high temperature stability, and mechanical behavior

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