Rutgers Sexual and Reproductive Health and Rights

Endophenotypes (intermediate phenotypes) are traits hypothesized to represent genetic risk for disorders such as OCD. However, it is unclear if disgust conditioning abnormalities may function as an OCD endophenotype. The present study sought to examine the extent which disgust conditioning abnormalities characterize OCD in a manner that is consistent with an endophenotype. Participants with OCD (n=30), first-degree relatives (n=27), and unrelated age-matched healthy controls (n=25) completed a disgust conditioning task in which one neutral food item (conditioned stimulus; CS+) was followed by disgusting videos of individuals vomiting (unconditioned stimulus; US) and another neutral food item (CS-) was not reinforced with the disgusting video. Following this acquisition phase, there was an extinction phase in which both CSs were presented unreinforced. As predicted, the CS+ was rated as significantly more disgusting than the CS- after two blocks of acquisition and this pattern was eliminated after extinction. Although no group differences emerged as a function of the CS+ compared to the CS-, the OCD group reported statistically significantly higher disgust ratings to both CSs during acquisition (but not at habituation or extinction) than relatives and controls. However, relatives and controls did not significantly differ from each other. Additionally, disgust proneness explained the group difference between those with and without OCD in disgust responding to both CSs at acquisition. These findings suggest a readiness for disgust conditioning in OCD that may be the product of heightened disgust proneness but provide no evidence that it functions as an endophenotype.

Practicing CBT skills outside of therapy sessions is a core component of effective treatment, yet meaningful engagement can be difficult during periods of elevated psychological distress. Smartphone-based ecological momentary interventions (EMIs) offer a promising approach to support real-time skill use, but most research has focused on frequency rather than quality of engagement. This study examined the quality of CBT skills practice in a 28-day smartphone-based EMI following inpatient discharge and explored its associations with internal context and intervention effectiveness. Twenty-three adults hospitalized for suicidal thoughts and behaviors participated, selecting and practicing one of three guided CBT skills exercises (Mindful Emotional Awareness, Countering Emotional Behavior, or Cognitive Flexibility) up to three times daily. A structured coding system was used to rate the completeness and relevance of 945 total exercises (0-2 scale). Quality varied primarily within individuals and was lower during moments of high suicidal intent and urge. When combining across all skills, quality was not associated with proximal change. However, higher-quality Countering Emotional Behavior practice predicted greater reductions in suicidal intent, and higher-quality Countering Emotional Behavior and Cognitive Flexibility practice predicted greater reductions in anger. These findings suggest that momentary distress can impact engagement quality, and that the short-term impact of skills practice may depend on both the specific CBT strategy and type of distress targeted. Understanding and enhancing engagement quality is a key step toward optimizing mobile interventions for individuals in the midst of psychological distress.

Cancer cachexia is a complex metabolic syndrome characterized by involuntary weight loss, skeletal muscle atrophy, adipose tissue remodeling, anorexia, and systemic inflammation. Affecting up to 80% of patients with advanced cancer, it is associated with poor prognosis and lacks effective therapies. This review focuses on metabolic reprogramming occurring between organs, integrating recent mechanistic insights into cross talk between central and peripheral tissues. Key mediators, including CCL2, IL-6, LIF, and GDF-15, are discussed in the context of neuroimmune and metabolic pathways that drive tissue wasting. Particular attention is given to the CCR2/CCL2 axis, which mediates immune cell infiltration into the brain, liver, and tumors, contributing to anorexia, muscle loss, and hepatic dysfunction. Understanding these integrated interorgan pathways highlights potential therapeutic targets to preserve metabolic homeostasis, improve functional outcomes, and extend survival in patients with cancer-associated cachexia.

Housing constitutes one of the most significant assets for ordinary citizens in China, yet its political implications remain underexplored. Existing political economy theories, largely developed in democratic contexts, predict that property ownership reduces support for redistribution. This study examines whether these expectations hold in China by analyzing the heterogeneous effects of homeownership on policy support for policies related to redistribution. Using nationally representative survey data and combining matching techniques with instrumental variable analysis, we find that homeownership increases support for policies related to redistribution. This positive effect is significantly stronger among employees of state-owned enterprises than among private-sector workers. Mediation analysis further suggests that this relationship operates through channels including family wealth, perceptions of inequality, and marital status. These findings challenge dominant asset-based theories of redistribution and demonstrate that the political consequences of homeownership are fundamentally shaped by institutional context. More broadly, the study highlights how housing functions not only as an economic asset but also as a socio-political mechanism through which states mediate welfare expectations and social stability. • Homeownership increases support for redistribution policies in China. • The impact is stronger for employees of state-owned enterprises compared to private-sector workers. • Socioeconomic factors like income and inequality perceptions mediate this relationship. • Matching algorithms and instrumental variable methods address endogeneity concerns. • Findings connect housing policies to broader welfare and urban governance debates.

Close social relationships are critical for emotional well-being. The COVID-19 pandemic severely disrupted in-person contact with friends, particularly among young adults, for whom friendships support key developmental goals. In a longitudinal study of U.S. college students (N = 205; 10,088 observations), we examined how close friendship networks related to emotional well-being during the early months of the pandemic (May-October 2020). Leveraging prepandemic social network data and 28 days of ecological momentary assessments of affect and social interactions, we found that students with more close college friends reported higher positive affect and lower negative affect in daily life, even while physically separated from those friends. These individuals were buffered from the emotional toll of pandemic-related stressors, a pattern not explained by personality, interaction frequency, or living conditions. Rather, participants with more close friends experienced higher quality online interactions. Additionally, personal disclosures, whether in-person or online, were consistently associated with greater feelings of closeness. Notably, individuals with fewer close friends showed the largest boost in closeness following partner disclosures, suggesting that emotional sharing may play a compensatory role for those with limited social ties. These findings illustrate how friendships can continue to shape affective experiences from afar and highlight disclosure as a key mechanism through which closeness and its emotional benefits can be cultivated. Integrating social network structure, daily affect, and interaction-level processes, this work advances affective science by providing evidence of how the social regulation of emotion extends beyond physical proximity. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Agricultural regions in rural areas face damage from climate-related risks, including droughts, heavy rainfall, and shifting weather patterns. Prior research calls for adaptive risk-management solutions and decision-making strategies. To this end, artificial intelligence (AI), particularly agentic AI, offers a promising path forward. Agentic AI systems consist of autonomous, specialized agents capable of solving complex, dynamic tasks. While past systems have relied on single-agent models or have used multi-agent frameworks only for static functions, there is a growing need for architectures that support dynamic collaborative reasoning and context-aware outputs. To bridge this gap, we present AgroAskAI, a multi-agent reasoning system for climate adaptation decision support in agriculture, with a focus on vulnerable rural communities. AgroAskAI features a modular, role-specialized architecture that uses a chain-of-responsibility approach to coordinate autonomous agents, integrating real-time tools and datasets. The system has built-in governance mechanisms that mitigate hallucination and enable internal feedback for coherent, locally relevant strategies. The system also supports multilingual interactions, making it accessible to non-English-speaking farmers. Experiments on common agricultural queries related to climate adaptation show that, with additional tools and prompt refinement, AgroAskAI delivers more actionable, grounded, and inclusive outputs. Our experimental results highlight the potential of agentic AI for sustainable and accountable decision support in climate adaptation for agriculture.

For the past two decades, sustainability and carbon reduction have emerged as critical factors for data center (DC) design and operation. A set of advances has been encapsulated in green DCs with the onsite generation of renewable energy and efficient cooling systems. In this paper, we study how to apply Deep Reinforcement Learning (DRL) to optimize green DC operation. Green DC management is typically an infinite-horizon problem with exogenous stochastic input processes. We propose EA, a framework that applies DRL to the typical infinite-horizon problem without discounting. EA approximates the infinite-horizon problem with a finite-horizon one. In this approach, it is important to avoid actions optimized for the end of the finite-horizon problem but inappropriate for the true infinite-horizon one. EA addresses this challenge by combining a stationary policy with the fact that green DC management has repeating patterns (e.g., daily temperature, solar energy generation, and workload). We apply EA to the management of a green DC with onsite solar energy generation and a hybrid cooling system that includes ``free'' cooling. Evaluation results show that EA successfully learns important principles such as delaying deferrable jobs to solar-rich times and gracefully maintaining inside temperature. Further, EA outperforms three state-of-the-art DRL algorithms, realizing the greatest benefits on days with high outside temperature and high solar generation. While we evaluate EA in the specific context of a green DC, we believe that EA is a promising approach for more general system management settings.