Notes

Oncocytic adjustments to pleomorphic adenoma: Document of an unusual situation.
This study investigated associations between perceived stress and sexual communication, considering supportive dyadic coping as a potential mediator and whether being male or female moderated associations. Data from 2,529 couples from Wave 5 of the German Family Panel (pairfam) were used in the analyses. Structural equation modeling results showed higher levels of stress were linked with lower levels of dyadic coping and higher levels of dyadic coping were associated with higher levels of sexual communication. There was no direct association between stress and sexual communication, but there was an indirect relationship between higher levels of perceived stress and less sexual communication via supportive dyadic coping. Sex did not moderate these associations. These results highlight supportive dyadic coping as an important protective factor against the effects of perceived stress on sexual communication and call for further investigation of how couples can maintain a healthy sex life in the face of stress.Spending time with a romantic partner by going on dates is important for promoting closeness in established relationships; however, not all date nights are created equally, and some people might be more adept at planning dates that promote closeness. Drawing from the self-expansion model and relationship goals literature, we predicted that people higher (vs. lower) in approach relationship goals would be more likely to plan dates that are more exciting and, in turn, experience more self-expansion from the date and increased closeness with the partner. In Study 1, people in intimate relationships planned a date to initiate with their partners and forecasted the expected level of self-expansion and closeness from engaging in the date. In Study 2, a similar design was employed, but we also followed up with participants 1 week later to ask about the experience of engaging in their planned dates (e.g., self-expansion, closeness from the date). Taken together, the results suggest that people with higher (vs. lower) approach relationship goals derive more closeness from their dates, in part, because of their greater aptitude for planning dates that are more exciting and promote self-expansion.Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.In many real world situations, the design of social rankings over agents or items from a given raking over groups or coalitions, to which these agents or items belong to, is of big interest. With this aim, we revise the lexicographic excellence solution and introduce two novel solutions which, moreover, take into account the size of the groups. We present some desirable axioms which are interpreted in this context. Next, a comparable axiomatization of these three solutions is established, revealing the main differences among the two new social rankings and the lexicographic excellence solution. Finally, we apply the three social rankings under study to a real scenario. Specifically, the performance of some football players of Paris Saint-Germain during the UEFA Champions League according to these three rules is analyzed.This paper introduces new methods to study the changing dynamics of COVID-19 cases and deaths among the 50 worst-affected countries throughout 2020. First, we analyse the trajectories and turning points of rolling mortality rates to understand at which times the disease was most lethal. We demonstrate five characteristic classes of mortality rate trajectories and determine structural similarity in mortality trends over time. Next, we introduce a class of virulence matrices to study the evolution of COVID-19 cases and deaths on a global scale. Finally, we introduce three-way inconsistency analysis to determine anomalous countries with respect to three attributes countries' COVID-19 cases, deaths and human development indices. We demonstrate the most anomalous countries across these three measures are Pakistan, the United States and the United Arab Emirates.Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic behavior of our estimators and provide simulation studies that highlight the need for and usefulness of the proposed method in practice. We use the data from the "Extending Treatment Effectiveness of Naltrexone" multi-stage randomized trial to illustrate our proposed methods.As individuals undergoing a developmental process characterized by identity exploration, Jewish young adults are particularly vulnerable to the disruption of social connections related to the COVID-19 pandemic. Recent research has demonstrated that young adults, including young Jews, have experienced higher rates of mental health difficulties than older individuals during the pandemic. Using data from a survey of Jewish young adults who applied to participate in Birthright Israel summer 2020 trips but were unable to participate due to the pandemic, we examined the factors contributing to young adults' mental health difficulties. Rucaparib We found that loneliness, rather than financial worries or concerns about the health impacts of COVID-19, was the single most important driver of reported emotional or mental health difficulties. Results also suggested that simply increasing the frequency of contacts between individuals is unlikely to reduce loneliness, unless these are positive, substantial connections, such as those among members of a "social support network." Building and rebuilding deep, meaningful social connections between Jewish young adults should be a top priority for Jewish organizations going forward.Social scientists routinely rely on methods of interpolation to adjust available data to their research needs. Spatial data from different sources often are based on different geographies that need to be reconciled, and some boundaries (e.g., administrative or political boundaries) change frequently. This study calls attention to the potential for substantial error in efforts to harmonize data to constant boundaries using standard approaches to areal and population interpolation. The case in point is census tract boundaries in the United States, which are redefined before every decennial census. Research on neighborhood effects and neighborhood change rely heavily on estimates of local area characteristics for a consistent area of time, for which they now routinely use estimates based on interpolation offered by sources such as the Neighborhood Change Data Base (NCDB) and Longitudinal Tract Data Base (LTDB). We identify a fundamental problem with how these estimates are created, and we reveal an alarming leveare of tracts that experienced complex boundary changes.Monthly, high-resolution (∼2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality clusters were identified with k-means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower midsummer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL-AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial-resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground-based network sites.Many durable goods firms use price promotion strategies and advertising simultaneously to impact consumer preferences among vertically differentiated product offerings. In this research, we use a large secondary dataset of automotive purchases (N = 323,959) to investigate how advertising spending differentially moderates the positive impact of both customer- and retailer-directed price incentives on consumers' premium level of purchase for vertically differentiated products. We find that higher advertising spending magnifies the positive impact of customer-directed price incentives on consumers' preference for more premium purchases. In contrast, higher advertising spending attenuates the positive impact of retailer-directed price incentives on consumers' preference for more premium purchases. Our work is distinct from previous research, which has almost exclusively focused on the CPG industry and the effects of advertising and price promotions on general demand metrics-instead of consumers' preferences for premium products. Our work has important implications for practitioners and consumer welfare.An essential feature common to all empirical social research is variability across units of analysis. Individuals differ not only in background characteristics, but also in how they respond to a particular treatment, intervention, or stimulation. Moreover, individuals may self-select into treatment on the basis of their anticipated treatment effects. To study heterogeneous treatment effects in the presence of self-selection, Heckman and Vytlacil (1999, 2001a, 2005, 2007b) have developed a structural approach that builds on the marginal treatment effect (MTE). In this paper, we extend the MTE-based approach through a redefinition of MTE. Specifically, we redefine MTE as the expected treatment effect conditional on the propensity score (rather than all observed covariates) as well as a latent variable representing unobserved resistance to treatment. As with the original MTE, the new MTE can also be used as a building block for evaluating standard causal estimands. However, the weights associated with the new MTE are simpler, more intuitive, and easier to compute.
Here's my website: https://www.selleckchem.com/products/rucaparib.html