UCLA

Existing research highlights TB's economic and social stigma burdens and impacts behavior in households, but virtually unexplored is the dynamics of TB and parenthood. This qualitative study examines the interplay between TB diagnosis and treatment, and parenting experiences, focusing on the intersections of individual agency, societal expectations, and access to resources. From March 2021 to January 2022, we conducted in-depth interviews with 98 TB patients in Buffalo City, South Africa, including 44 mothers and 54 fathers. Guided by the Network-Individual-Resource (NIR) model, we applied layered coding, memoing, and causal mapping to analyze experiences through the lens of agency and societal expectations. Mothers reported primary caregiving responsibilities, drawing motivation from their children, and relying on multigenerational family support for childcare and treatment-adherence. Children frequently provided emotional and practical care, such as cooking and medication reminders. Fathers more often distanced themselves to protect families from infection, faced isolation, and struggled to fulfill provider roles due to job loss. Extended family support for fathers was limited, with financial pressures exacerbating treatment challenges. These results illustrate how gender norms and resources structure the TB experience. Strengthening maternal caregiving networks and reimagining fatherhood beyond economic provision can foster better adherence and more equitable health outcomes.

The oral cavity presents a highly dynamic environment where inter-microbial communications play a pivotal role. Understanding the spatial organization of microbial ecosystems has been highlighted on the microbiome and polymicrobial infection. Furthermore, cross-feeding and modulation by metabolites from the oral microbiota and host cells, such as lactate and reactive oxidative species, impact the stability and functionality of microbial communities. Traditional research focusing solely on the compositional aspects of these communities is insufficient to understand the sophisticated interactions. We evaluated recent advancements in imaging technologies, bolstered by multi-omics analyses and artificial intelligence (AI)-driven approachesinsights, to provide an more integrated understanding of the dynamics and function of the oral microbiome. Real time imaging and resolution-enhancing methods at the single-cell level have unraveled the ecology and dynamics of microbial communities, indicating unique three-dimensional architectures and biogeographical patterns associated with disease status in polymicrobial interplays. Emerging computational techniques can account for the spatial features of oral microbiome by creating image-like representations that capture the complex relationships between host tissues and microbial communities. Spatial multi-omics, help address the limitations of single-cell sequencing, deciphering molecular mechanisms between species in these biogeographical patterns. To process the massive volume of imaging-based data, AI-assisted analysis enables complex dataset integration, predictive capacity, and personalized treatment, bringing a whole new level of understanding of the oral microbiome and its relationships with the host. In this review, we highlight recent imaging-based technologies used to study the spatial biogeography of interspecies and interkingdom relationships within oral microbial communities, focusing on how these interactions and functional/metabolic alterations associated with health and disease. We further outline limitations of AI-generated predictions and imaging-based observational data. Finally, we elaborate on potential biomarkers for early diagnosis and new effective therapeutic strategies to reshape microbial dynamics.

BACKGROUND: The global refugee crisis has reached unprecedented levels, placing millions of forcibly displaced individuals at heightened risk for psychological distress. While the effects of pre-migration trauma on mental health are well established, less is known about how intersecting identity factors and post-migration systemic barriers jointly shape posttraumatic stress severity. OBJECTIVE: Guided by Intersectionality Theory and Ecological Systems Theory, this study examined how pre-migration trauma exposure, identity-related characteristics, and post-migration stressors interact to predict post-traumatic stress symptom severity among refugees and asylum seekers. METHOD: Participants included 610 refugees and asylum seekers who completed standardized assessments at the Boston Center for Refugee Health and Human Rights. PTSD symptom severity was assessed using the Harvard Trauma Questionnaire. Predictors included pre-migration trauma exposure, identity factors (e.g., education level, region of origin, English proficiency), and post-migration stressors (e.g., legal status, employment, housing, community involvement). Classification and Regression Tree (CART) analysis was used to identify hierarchical and nonlinear interactions among predictors. RESULTS: The pruned CART model identified education level, English language proficiency, and region of origin as the strongest predictors of PTSD symptom severity. Individuals with some formal education, higher English proficiency, and origins outside Uganda and Europe reported higher symptom scores. Pre-migration trauma variables were not retained in the final model, highlighting the influence of post-migration structural conditions. CONCLUSIONS: Findings underscore the importance of moving beyond trauma-focused models to address how legal insecurity, social exclusion, and linguistic integration shape refugee mental health and recovery.

Comprehenders in many of the world's languages exhibit a preference or a greater ease in comprehending transitive relative clauses (RCs) when associating the dislocated head with a subject position. Some theories relate this preference to an early prediction that an animate head will serve as a subject. We present a picture selection experiment with eye-tracking investigating the role of such predictions in comprehending transitive RCs in Santiago Laxopa Zapotec, an Oto-Manguean language of southern Mexico where RCs are ambiguous unless they contain a resumptive pronoun. Patterns of offline choices and incremental looking suggest that Santiago Laxopa Zapotec comprehenders avoid forming predictive interpretations of the RC head, and do not take its animacy into account in early processing. Instead, comprehenders exhibit a later, animacy-sensitive bias to interpret the RC-internal co-argument as a subject. Overall, we take this pattern as evidence that a comprehender's structural predictions must be somehow dependent on experience-based tuning, while more universal pressures like similarity-based interference remain fixed. The lingering question for theories of cross-linguistic processing is how much of our predictive mechanism is tunable: do we tune just the distribution of expected structures which directs predictions, or also the practice of deploying predictions itself? We discuss how either of these approaches might explain the pattern we observe in Santiago Laxopa Zapotec, and highlight that an answer to this question will depend on continued investigations across a diverse sample of the world's languages.

Osteogenesis imperfecta (OI) is a genetically heterogenous disorder characterized by brittle bone and recurrent fractures. The majority of OI is clinically categorized into mild, moderate, severe, and perinatal lethal forms. A clinical study on the use of intermittent parathyroid hormone (PTH) as an anabolic therapy for OI found PTH was effective in mild, but not in individuals with moderate/severe OI. However, this sub-group analysis was relatively small and considered preliminary by the authors. Since then, research on PTH therapy in OI has focused on mild OI and no studies of PTH use in moderate/severe OI patients or animal models have been reported. In this work, we used the Aga2/+ mouse, an established autosomal dominant model of moderate/severe OI, to investigate whether PTH treatment has an effect on bone. In vitro, Aga2/+ osteoblasts were sensitive to PTH and exhibited similar transcriptional changes compared to WT. In vivo, PTH treatment improved lumbar trabecular bone, increased cortical bone area fraction in the mid-femur, and, in females, led to improved biomechanical properties. These results demonstrate the Aga2/+ model responded to PTH and support further investigation into whether PTH is effective in adult individuals with moderate/severe OI who have limited treatment options.

As governments expand subsidies for “green” hydrogen (here, H2 produced through electrolysis powered by renewable electricity), competing demands on a scarce resource with several alternative applications are intensifying. This motivates the comparison here of alternative uses of electrolytic hydrogen (for ammonia, oil refining, steel, long-term electricity storage, trucking, sustainable aviation fuel, and methanol) based on multiple indicators of environmental and economic performance. These indicators are calculated using fundamental stoichiometric and thermodynamic calculations, consistent system boundaries and select major drivers of levelized cost of final product in each application. We analyze the sensitivity with respect to three key variables – carbon intensity of electricity for electrolysis, cost of green H2 and price of incumbent product. We identify the more cost-effective applications among those examined here, based on avoided lifecycle emissions per unit H2, avoided emissions per unit product and incremental levelized cost relative to incumbent or displaced product. Current policies worldwide simply provide a flat subsidy per kg of green H2 that is independent of the lifecycle carbon intensity of green H2 or the net avoided carbon emissions per kg of green H2. We discuss how the approach outlined here can help design subsidies or regulations indexed to lifecycle emissions, which can be more cost-effective compared to flat subsidies or regulations when there is variability in avoided lifecycle emissions both within and across applications. Nevertheless, some support for even the currently less cost-effective pathways is justifiable, given potential learning and infrastructure spillovers relevant to hard-to-abate sectors in the longer run.

This study presents physics‐based, 3D simulations using the EQSIM framework for several earthquakes in the Los Angeles region. The primary objective was to assess the ability of deterministic physics‐based ground motion simulations to reproduce the observed motions from historical events. The selected events included the 5.4 2008 Chino Hills, the 4.4 2024 Highland Park, and the 4.3 2021 Carson events. The simulated motions were evaluated by comparing the recorded and simulated seismograms, as well as the Fourier amplitude spectra, across multiple seismic stations. The SCEC 3D velocity model, CVM‐S4.26.M01, was used to represent the regional geology, and ground motion simulations were carried out with a resolution of up to 5 Hz. The results indicate that the simulated motions captured the recorded motions up to approximately 4 Hz. While careful iterations regarding source parameters and corner frequencies were required, and, for the case of the Highland Park event, some of the near‐source stations had relatively low accuracy, the present study established a positive step toward the utilization of physics‐based simulations in practical applications. The computational efficiencies exhibited by EQSIM, especially on GPU clusters, further supported this assertion, as wall‐clock times of simulations involving more than 10 billion grid points were as low as minutes. This permits ensemble simulations for a considered scenario event so that modeling uncertainties (e.g., source and geology) can be bracketed.