UCLA

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.

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.

This article engages Führer and Vorhölter’s (2025) reinvigoration of Nancy Scheper–Hughes’s utopian call for liberation medicine in their special issue of Culture, Medicine, and Psychiatry. They offer a practical reconceptualization of liberation medicine as “concrete utopias” in the “medical undercommons”. Inspired by Scheper–Hughes’s humble call for “good-enough” ethnographic engagement in her documentation of a successful solidarity movement of single-mother agricultural laborers in Northeastern Brazil, I highlight ethnographically a (fragile) concrete utopian initiative: a peer-support program for inmates with psychosis spectrum conditions who are swept into the Los Angeles County Jail system’s overcrowded mental health units on a daily basis by routinized law enforcement sweeps of homeless individuals. Developed by former gang members (without mental illness) facing capital punishment charges, this remarkable initiative of solidarity, redemption, and anti-racism through radical caregiving in a claustrophobic carceral environment is carving out a temporarily safe space free from abuse and violence. The larger facility remains gang-controlled and is overwhelmed by what colleagues and I have been calling “predatory accumulation” and “necrogovernance” processes (Hart et al., 2024, 2025). The inmates are powerless, caught within the “carceral mesh” of U.S. political-economic corporate and opportunistic special interests. Furthermore, the discretionary power of “hyperpunitive” U.S. judges allows them to routinely impose Life Without Parole (LWOP) sentences on redeemed inmates incarcerated as adolescents. Inspired by Scheper–Hughes’s practical political optimism coupled with her love for ethnography and political activism, I end with eight practical suggestions for specific state bureaucratic reforms to foment public transparency about the useless cruelty of mass incarceration and the global rise of authoritarianisms in our perilous moment in global history.

Recent DIII-D tokamak experiments have demonstrated the integration of intrinsic low-collisionality grassy edge-localized mode (ELM) regime with high-performance hybrid core scenario, possibly offering a core-edge compatible solution for ITER and future fusion reactors. This regime features grassy ELMs (with ELM energy loss over pedestal stored energy <2%) at ITER-relevant pedestal top collisionality ( νe∗ ∼ 0.1), ITER similar shape, while maintaining high core confinement ( H98y2 ∼ 1.5) in non-inductive hybrid scenarios. A small ELM-focused database is constructed to investigate the parametric dependence of the small/grassy ELM regimes. Access and sustainment of this regime appear to be favored by high poloidal beta ( βp > 1.5), higher ratio of separatrix-to-pedestal density ( ne,sep/ne,ped > 0.4) and low pedestal top collisionality ( νe,ped∗ = 0.1–0.4). In this scenario, the pedestal width exceeds the prediction from EPED–KBM scaling, consistent with the expectation of a turbulence-limited pedestal. Linear modeling using ELITE indicates that this grassy ELM regime is along the peeling boundary. Relative to a large-ELM phase, the grassy-ELM phase exhibits a broader inner-target heat-flux width and a substantially reduced ELM-induced transient heat-flux increment. These results motivate further evaluation of low-collisionality grassy ELMs as a potentially reactor-relevant operating regime, while full-duration sustainment and compatibility with divertor detachment remain unresolved.

Signal transducer and activator of transcription 1 (STAT1) defects present with diverse clinical phenotypes. We describe an unusual genotype/phenotype: disseminated mycobacterial infection without chronic mucocutaneous candidiasis in the context of a pathogenic STAT1 gain-of-function variant (p.Lys388Glu). To our knowledge, this is the first report of such a case.

Site response effects are accounted for in the development of risk-targeted maximum considered earthquake (MCER) ground motions in two ways: (1) as site factors incorporated within ground-motion models, as implemented in the National Seismic Hazard Model, and (2) from the use of site response analyses that model wave propagation through a geotechnical model of the project site. Site response analyses are the subject of Section 21.1 in the NEHRP Provisions and the ASCE-7 Standard. As part of a provisions update cycle concluding in Fall 2026 for the NEHRP Provisions and 2028 for ASCE-7, Section 21.1 was rewritten to permit the use of probabilistic methods for combining site response with reference site ground motions, as well as to incorporate significant improvements related to parameter selection, uncertainty evaluation, and analytical approaches. Included within the updates are procedures that allow for the use of non-ergodic (site-specific) site response, including guardrails.

Frequency-dependent horizontal-to-vertical spectral ratios (HVSRs) of three-component recordings can provide useful information on site resonant frequencies, which may be useful for predicting site response. Despite this potential, HVSR-conditioned site response models have not been adopted in the ground motion models most often employed for ground motion prediction in the United States and most global regions. We suggest that this stems from HVSR data resources being unavailable to support model development until recently, ongoing questions related to how HVSR should be measured and interpreted, and lack of consensus on how HVSR data should be utilized for site response prediction. Despite those challenges, we argue that the stage is set for future implementation of HVSR-conditioned models because a public dataset is now available, it is increasingly recognized that models should be conditioned on microtremor-based HVSR (mHVSR), and several modeling approaches have appeared in recent literature. This brief article focuses on site response modeling approaches, which include machine learning methods, models conditioned on mHVSR ordinates, and models conditioned on attributes of mHVSR peaks (i.e., peak frequency, amplitude, and width) or the lack of peaks.