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The importance of studying suicidal behavior cannot be overstated given the concerning prevalence. Despite a wide range of studies on suicidal behaviors, three major limitations remain. First, most studies are cross-sectional. Second, most studies examined risk factors of suicidal ideation in isolation. A meta-analysis covering the past 50 years of suicidal research found that prediction was only slightly better than chance because researchers rarely tested the combined effect of multiple risk factors. Researchers recommended utilizing machine learning (ML) approaches to study suicidal ideation instead because ML can address complex classification problems. Third, many studies have ignored the possibility that suicidal ideation can shift throughout different developmental stages. Disregarding the fact that there are different trajectories of suicidal ideation can lead to biased results. In this paper, I addressed the limitations of past research by first investigating different trajectories of suicidal ideation using Latent Class Growth Analysis on a large, nationally representative longitudinal dataset (n = 7,295). I then used two ML approaches, classification trees and random forests, to examine which risk factors are predictive of the identified trajectories. Two hundred and eighty-one predictors were included in the ML models, spanning demographic, psychological, behavioral, economic, social, and environmental variables linked to suicidal behaviors, such as mental health, violence exposure, family, peer, and school functioning, neighborhood characteristics, community engagement, negative life events, expectations for the future, risky behaviors, self-esteem, and substance use. The Latent Class Growth Analysis identified three distinct trajectories: (a) Resilient Class, (b) Declining Class, and (c) Escalating Class. When comparing the predictive performance, machine learning models, specifically classification tree (AUC = 0.73) and random forest (AUC = 0.79), out

Maternal mortality remains a significant global health challenge, with roughly 300,000 maternal deaths occurring annually, most of which are preventable. The relationship between socioeconomic status (SES) and maternal mortality rates (MMR) has been widely studied, with a growing body of evidence suggesting that lower SES is associated with higher MMR. This meta-analysis aimed to investigate the relationship between SES and MMR across different countries. We conducted a comprehensive search of electronic databases and included studies that reported the association between SES and MMR. We used a random-effects model to estimate the overall effect size and explored potential sources of heterogeneity using subgroup analyses. Our findings suggest that there is a significant inverse association between SES and MMR, with higher SES being associated with lower MMR. However, the magnitude of the effect varied across different regions, with the strongest association observed in low and middle-income countries. While previous studies have examined the relationship between various variables, they often do so within a narrow context, focusing on specific regions or communities. As a result, the findings of such studies may not be generalizable or applicable to other settings. Our research, therefore, takes a more comprehensive approach, examining the interplay between SES and MMR across different regions and countries, and considering a range of social, economic, and health-related factors that could potentially influence this relationship. Our research employs a meta-analysis of research papers and scientific data, this approach allowed for a more comprehensive and rigorous examination of the research question and can help identify patterns and trends across studies. A systematic literature search/screening coupled with data extraction from online databases informed our results

A classifier may be limited by its conditional misclassification rates more than its overall misclassification rate. In the case that one or more of the conditional misclassification rates are high, a neutral zone may be introduced to decrease and possibly balance the misclassification rates. In this paper, a neutral zone is incorporated into a three‐class classifier with its region determined by controlling conditional misclassification rates. The neutral zone classifier is illustrated with a text mining application that classifies written comments associated with student evaluations of teaching.

The simultaneous optimization of multiple objective functions is needed in many particle accelerator applications. In this paper, we present a parallel evolution based multi-objective optimizer that uses a variable population from generation to generation and an external storage to save good solutions. Two heuristic optimization methods, one uses the unified differential evolution and the other uses the real-coded genetic algorithm, are included in the optimizer to generate next generation candidate solutions, and are compared in the test examples. As an application, we applied this optimizer to the beam dynamics design optimization of a photoinjector and attained the optimal front solutions after 200 generations with the unified differential evolution offspring production scheme.

Video Games are a media platform that cannot be ignored - yet when we ask what methods can we use to make a successful video game, the answers are blurry. Players seem to care about the emergent properties of games that science traditionally struggles with: fun, balance, feel, and so on. These terms tend to be stigmatized in a research community - yet they also seem to be some of the strongest indicators of a game’s success. This thesis presents two papers that offer a framework to understand how we might start getting good design recommendations for emergent properties through a deep analysis of the conscious experience of players. In the first, we present an automated game design system (AGD) that aims to generate “balanced” games. In the second, we provide a theoretical discussion on why we might need to take unconventional routes of analysis to produce impactful games. Together, these papers present a picture not only of how to think about design strategies of emergent properties in games, but also a case-study of developing such a system and deploying it amongst users.

This review aims to summarize the putative role of histone deacetylases (HDACs) in rhabdomyosarcoma (RMS) and the effects of HDAC inhibitors (HDACi) on RMS by elucidating and highlighting known oncogenic pathways, mechanisms of resistance, and the synergistic potential of histone deacetylase inhibitors. We searched two databases (PubMed and Google Scholar) for the keywords Rhabdomyosarcoma, histone deacetylase, histone deacetylase inhibitors. We excluded three publications that did not permit access to the full text to review and those that focus exclusively on pleiomorphic RMS in adults. Forty-seven papers met the inclusion criteria. This review highlights that HDACi induce cytotoxicity, cell-cycle arrest, and oxidative stress in RMS cells. Ultimately, HDACi have been shown to increase apoptosis and the cessation of embryonal and alveolar RMS proliferation in vivo and in vitro, both synergistically and on its own. HDACi contain potent therapeutic potential against RMS. This review discusses the significant findings and the biological mechanisms behind the anti-cancer effects of HDACi. Additionally, this review highlights important clinical trials assessing the efficacy of HDACi in sarcomas.

Youth in the United States are experiencing mental health concerns at alarming rates. Considering the nation's legacy of racism and growing recognition of the impact of social determinants of health on educational and mental health inequities, it is imperative to re-envision how we approach mental health screening in schools to center equity. A focus on mental health screening for the sole purpose of identifying individual at-risk students ignores key contextual considerations, is ineffective in addressing health and educational inequities, and has the potential to perpetuate oppressive practices in schools. Equity-focused mental health screening requires a shift from individual- and deficit-focused approaches to systems- and holistic-focused approaches that (a) identify strengths and stressors among individuals, groups, and communities; (b) dismantle structural forms of oppression; and (c) promote positive mental health outcomes for minoritized youth. Integrating recommendations from the educational equity literature and critical school mental health frameworks, this paper identifies core considerations for equitable school mental health screening and provides guiding principles for each phase of the screening process, from screening readiness to execution to follow up. To implement these recommendations and transform school-based mental health care, schools should (a) incorporate multiple perspectives; (b) prioritize student, family, and community voices; and (c) build collaborative partnerships to co-construct a vision for equitable school mental health.

This paper offers a model of the semantic content of spatial nouns as generic terms in place names (e.g. Square in Trafalgar Square) and as descriptors for places ("place nouns", e.g. street in the second street). The model is based on a variant of Frame Semantics in which different context- and community-based uses (e.g. general, daily uses; specialised uses; legal, normative uses) are modelled as as sets/matrices of attribute-value pairs, or frames. The attributes forming these frames are based on data extraction from corpora (general uses), Wikipedia articles (specialised uses), and professional geographical dictionary (legal uses) as contexts. It is shown that uses associated to each context define frames varying considerably in content; however, a semantic overlap relation connects these frames. Consequences for a general theory of the semantics of place and geonames are discussed.

We consider a spatial model of cancer in which cells are points on the d-dimensional torus $\mathcal{T}=[0,L]^d$ , and each cell with $k-1$ mutations acquires a kth mutation at rate $\mu_k$ . We assume that the mutation rates $\mu_k$ are increasing, and we find the asymptotic waiting time for the first cell to acquire k mutations as the torus volume tends to infinity. This paper generalizes results on waiting for $k\geq 3$ mutations in Foo et al. (2020), which considered the case in which all of the mutation rates $\mu_k$ are the same. In addition, we find the limiting distribution of the spatial distances between mutations for certain values of the mutation rates.

In 1879, James McNeill Whistler created The Gold Scab, a painting depicting Frederick R. Leyland, his former friend and patron, as an anthropomorphized peacock sitting atop a white house in front of a piano covered with bags of gold. The Gold Scab has been described as a self-portrait, a satirical portrait, a “Japanese grotesquerie,” and a monumental caricature. Regardless of what one categorizes it as, the work is an unsatisfactory misfit that deviates from the conventions of these genres. By analyzing how The Gold Scab borrows from the genres of portraiture and caricature yet deviates from the conventions and intentions of these genres, this paper situates The Gold Scab within a larger movement, away from the rigidity of the genre of nineteenth-century portraiture. In my examination of The Gold Scab through the lens of anti-portraiture, a framework that deprioritizes likeness as the defining convention of portraiture and instead prioritizes consideration of the artist’s intentions, it becomes clear that The Gold Scab is not a misfit; rather, it serves as a more than satisfactory example of the shift in the ideological discourse regarding likeness and portraiture taking place in the nineteenth century.