Are women legislators punished for not supporting women’s substantive policy interests? We test these gendered expectations. We marshal an original content analysis of cable news coverage and two survey experiments testing voters’ assessment of hypothetical legislators on the issues of abortion and equal pay. We find that voters rate both women and men legislators positively for supporting women’s issues and negatively evaluate legislators of both genders when they do not support women’s interests. We also find that women voters negatively evaluate women legislators who act against women’s interests at a greater rate than men voters. While we do not find evidence of voters holding women legislators to gendered expectations, we do find that legislators, regardless of their gender, have strategic incentives to promote women’s substantive representation. Our results suggest that voters care more about the substantive representation of women’s political interests than who supports those interests.

Constrained econometric techniques hamper investigations of disease prevalence and income risks in the shrimp industry. We employ an econometric model and machine learning (ML) to reduce model restrictions and improve understanding of the influence of diseases and climate on income and disease risks. An interview of 534 farmers with the models enables the discernment of factors influencing shrimp income and disease risks. ML complemented the Just-Pope production model, and the partial dependency plots show nonlinear relationships between income, disease prevalence, and risk factors. Econometric and ML models generated complementary information to understand income and disease prevalence risk factors.

Objectives/Goals: Methicillin-resistant Staphylococcus aureus (MRSA) is a human bacterial pathogen and is classified as a serious threat. MRSA has become resistant to most B-lactam antibiotics (penicillins and cephalosporins). The goal of this study is to identify an antibiotic adjuvant capable of resurrecting B-lactams for the treatment of MRSA infections. Methods/Study Population: A fluorescence-reporter assay was used to screen a compound library. Minimum-inhibitory concentrations were assessed for the compounds against various MSSA and MRSA strains. A common resistance mechanism to B-lactams by MRSA is by the function of the bla operon. One gene in this operon encodes for a B-lactam sensor/signal transducer protein BlaR, the primary target of this study. Inhibition of BlaR by compound 1 (best potentiator of oxacillin) was studied by nano-differential scanning fluorimetry (nanoDSF), surface plasmon resonance (SPR), scanning electron microscopy (SEM), and time-kill assays. Results/Anticipated Results: We identified 80 compound hits from a 1,974-compound NCI library. Twenty-four compounds showed potentiating ability (2- to 4,096-fold decrease in MIC for oxacillin). Seven compounds exhibited melting temperature shifts by nanoDSF of BlaR, indicating binding. SPR determined compound 1 has a binding affinity of 31 micromolar to BlaR-SD. SEM images showed disruption in the S. aureus cell wall on exposure to compound 1 and oxacillin. S. aureus N315 showed 3-log reduction in bacterial count treated with a mixture of compound 1 and oxacillin. Discussion/Significance of Impact: Compound 1 targets BlaR-SD, which restores S. aureus susceptibility to treatment by oxacillin. There are currently few antibiotics available in the clinic capable of treating MRSA infections. The combination hold promise of a treatment option for MRSA.

Eucalyptus cladocalyx, known for its drought tolerance, has complex wood anatomy influenced by environmental conditions. This study investigated the xylem response of E. cladocalyx seedlings to cyclic drought stress compared to continuous irrigation. Seedlings were subjected to alternating drought and watering cycles, and their growth, xylem traits and cambial activity were monitored. Continuously irrigated seedlings exhibited greater height and stem diameter growth than periodically irrigated ones. Xylem response between the periodic and continuous irrigations showed no significant differences. Vessel and fibre features showed significant temporal variation, with substantial interaction between treatment and time for vessel area, fibre area and fibre thickness and not for vessel frequency. The cambium remained active under drought conditions, indicating resilience. Overall, anatomical properties varied complexly and inconsistently across drought cycles, likely due to differences in drought intensity, strategies and genetic factors.

The recommended first-line treatment for insomnia is cognitive behavioral therapy for insomnia (CBTi), but access is limited. Telehealth- or internet-delivered CBTi are alternative ways to increase access. To date, these intervention modalities have never been compared within a single study. Further, few studies have examined a) predictors of response to the different modalities, b) whether successfully treating insomnia can result in improvement of health-related biomarkers, and c) mechanisms of change in CBTi. This protocol was designed to compare the three CBTi modalities to each other and a waitlist control for adults aged 50-65 years (N = 100). Participants are randomly assigned to one of four study arms: in-person- (n=30), telehealth- (n=30) internet-delivered (n=30) CBTi, or 12-week waitlist control (n=10). Outcomes include self-reported insomnia symptom severity, polysomnography, circadian rhythms of activity and core body temperature, blood- and sweat-based biomarkers, cognitive functioning, and magnetic resonance imaging.

This manuscript introduces a new Bayesian finite mixture methodology for the joint clustering of row and column stimuli/objects associated with two-mode asymmetric proximity, dominance, or profile data. That is, common clusters are derived which partition both the row and column stimuli/objects simultaneously into the same derived set of clusters. In this manner, interrelationships between both sets of entities (rows and columns) are easily ascertained. We describe the technical details of the proposed two-mode clustering methodology including its Bayesian mixture formulation and a Bayes factor heuristic for model selection. We present a modest Monte Carlo analysis to investigate the performance of the proposed Bayesian two-mode clustering procedure with respect to synthetically created data whose structure and parameters are known. Next, a consumer psychology application is provided examining physician pharmaceutical prescription behavior for various brands of prescription drugs in the neuroscience health market. We conclude by discussing several fertile areas for future research.

Existing test statistics for assessing whether incomplete data represent a missing completely at random sample from a single population are based on a normal likelihood rationale and effectively test for homogeneity of means and covariances across missing data patterns. The likelihood approach cannot be implemented adequately if a pattern of missing data contains very few subjects. A generalized least squares rationale is used to develop parallel tests that are expected to be more stable in small samples. Three factors were varied for a simulation: number of variables, percent missing completely at random, and sample size. One thousand data sets were simulated for each condition. The generalized least squares test of homogeneity of means performed close to an ideal Type I error rate for most of the conditions. The generalized least squares test of homogeneity of covariance matrices and a combined test performed quite well also.

The past few years were marked by increased online offensive strategies perpetrated by state and non-state actors to promote their political agenda, sow discord, and question the legitimacy of democratic institutions in the US and Western Europe. In 2016, the US congress identified a list of Russian state-sponsored Twitter accounts that were used to try to divide voters on a wide range of issues. Previous research used latent Dirichlet allocation (LDA) to estimate latent topics in data extracted from these accounts. However, LDA has characteristics that may limit the effectiveness of its use on data from social media: The number of latent topics must be specified by the user, interpretability of the topics can be difficult to achieve, and it does not model short-term temporal dynamics. In the current paper, we propose a new method to estimate latent topics in texts from social media termed Dynamic Exploratory Graph Analysis (DynEGA). In a Monte Carlo simulation, we compared the ability of DynEGA and LDA to estimate the number of simulated latent topics. The results show that DynEGA is substantially more accurate than several different LDA algorithms when estimating the number of simulated topics. In an applied example, we performed DynEGA on a large dataset with Twitter posts from state-sponsored right- and left-wing trolls during the 2016 US presidential election. DynEGA revealed topics that were pertinent to several consequential events in the election cycle, demonstrating the coordinated effort of trolls capitalizing on current events in the USA. This example demonstrates the potential power of our approach for revealing temporally relevant information from qualitative text data.

We consider a multidimensional noncompensatory approach for binary items in passage-based tests. The passage-based noncompensatory model (PB-NM) emphasizes two underlying components in solving passage-based test items: a passage-related component and a passage-independent component. An advantage of the PB-NM model over commonly applied compensatory models (e.g., bifactor model) is that the two components are parameterized in relation to difficulty as opposed to discrimination parameters. As a result, while simultaneously accounting for passage-related local item dependence, the model permits the assessment of how items based on the same passage may require varying levels of passage comprehension (as well as varying levels of passage-independent proficiency) to obtain a correct response. Through a simulation study, we evaluate the comparative fit of the PB-NM against the bifactor model and also illustrate the relationship between the difficulty parameters of the PB-NM and the discrimination parameters of the bifactor model. We further apply the PB-NM to an actual reading comprehension test to demonstrate the relevance of the model in understanding variation in the relative difficulty of the two components across different item types.