Fossil fungal ascospores assignable to the genus Potamomyces have been recovered from Early Pliocene deposits at the Gray Fossil Site, a Fossil-Lagerstätte in Tennessee, USA. Characterized by four prominent equatorial verrucae, these ascospores closely resemble the fossil-species Potamomyces invaginatus, P. batii, and P. pontidiensis, as well as the extant species P. armatisporus. Modern species of Potamomyces are primarily saprotrophic fungi that occur on decaying wood in freshwater and brackish environments, as well as in moist terrestrial habitats. During the Pliocene, Potamomyces formed part of the late Neogene wetland ecosystem associated with sinkhole ponds at the Gray Fossil Site, which developed under a warm temperate to subtropical humid climate. The remains described here represent the northernmost known occurrence of Potamomyces in North America, based on both fossil and extant records. These findings further suggest that Potamomyces appears to have considerable potential as a non-pollen palynomorph proxy for reconstructing past environments and climates.
The interactions between vegetation and hydro-climatic factors play a critical role in key geophysical processes, including carbon and hydrological cycles. However, the interaction between vegetation and hydro-climatic factors in China remains unclear. Here, nonlinear Granger causality tests were employed to analyze the bidirectional relationships between vegetation and key hydro-climatic variables from 2002 to 2021. The results indicate that temperature was the dominant Granger-causal factor influencing vegetation growth (43.49 % of grid cells), followed by terrestrial water storage (16.49 %), soil moisture (11.44 %), precipitation (10.92 %), and solar radiation (3.11 %). In the reverse direction, vegetation exerted the strongest feedback on terrestrial water storage (31.18 %) and precipitation (28.77 %), with weaker effects on soil moisture (20.68 %), solar radiation (9.67 %), and temperature (3.79 %). Bidirectional Granger causality was observed in 33.57 %-46.77 % of the assessed areas, with grasslands showing the highest proportion of significant causal relationships. These findings improve our understanding between vegetation and hydro-climatic factors across China, providing valuable insights that can guide the refinement and optimization of these processes in ecological and hydrological models.
Cortical folding shows substantial inter-individual variability yet contains stable anatomical landmarks that can support fine-scale characterization of cortical organization. Among these landmarks, the three-hinge gyrus (3HG) is a particularly informative folding primitive, exhibiting strong intra-species consistency alongside meaningful variations in morphology, connectivity, and functional relevance. However, prior landmark-based approaches typically model each 3HG in isolation, overlooking the fact that 3HGs form higher-order folding communities that capture mesoscale organizational structure. Ignoring this community-level organization oversimplifies gyral architecture and makes one-to-one landmark matching highly sensitive to fine-scale positional variability and noise. We propose a spectral graph representation learning framework that explicitly models community-level folding units rather than isolated landmarks. Each 3HG is characterized using a dual-profile representation integrating its topological surface context and structural connectivity fingerprint. A subject-specific spectral clustering module identifies coherent folding communities, followed by a topological refinement step that ensures anatomical continuity. To establish cross-subject correspondence, we introduce Joint Morphological-Geometric Matching (JMGM), which aligns community-level representations by jointly optimizing geometric and morphometric similarity. Across more than 1000 Human Connectome Project subjects, the resulting folding communities exhibit substantially reduced morphometric variance, stronger modular organization and superior cross-subject alignment, together with improved hemispheric consistency, compared to atlas-based and existing landmark- or embedding-based baselines. These results demonstrate that community-level modeling of gyral landmarks provides a robust, anatomically grounded foundation for individualized cortical characterization, enabling more reliable correspondence and high-resolution subject-specific analyses.
Family members caring for individuals with Alzheimer's disease and related dementias (AD/ADRD) provide the foundation of long-term care worldwide. In 2023, more than 11 million U.S. family and friends contributed 18 billion hours of unpaid care, often at the cost of their own physical and mental health. These informal caregivers – also referred as the "invisible second patients" – experience elevated rates of mental health problems. Yet research commonly reduces their complex psychosocial experiences to a single construct of caregiver burden, obscuring which specific needs are unmet or effectively supported. At the same time, digital and AI-enabled technologies are rapidly expanding, from smartphone apps and videoconferencing to sensor platforms and AI chatbots. However, the absence of shared frameworks across medicine, psychology, and technology research limits cumulative progress. This study introduces a Caregiver Mental Health and Technology Taxonomy that systematically links AD/ADRD caregiver needs with corresponding classes of technology-based interventions. Drawing from an interdisciplinary literature review and two qualitative studies with caregivers, the taxonomy identifies mismatches between caregiver priorities and existing technological support, highlights under-served domains such as relational strain and compassion fatigue, and proposes design directions for adaptive, responsive systems. The framework offers a shared vocabulary to guide clinicians, researchers, and technology designers in developing more person-centered and clinically grounded innovation in dementia care.
Caregivers often turn to online communities for informational and emotional support. In these spaces, peer supporters frequently draw on personal narratives to respond to emotionally complex caregiving situations. As LLMs are increasingly designed as peer-like sources of support, they introduce a critical tension: AI can provide immediate, private, and nonjudgmental support, but it cannot authentically possess the lived experiences that make human peer support meaningful. Yet, when prompted to sound peer-like, LLMs may generate language that implies lived experience. This creates a synthetic lived experience paradox: the same experiential language that may make AI support feel warm, relatable, and peer-like can also falsely position the system as someone with lived experience. We examine this paradox in the context of family caregivers of people living with Alzheimer's Disease and Related Dementias (ADRD). Drawing on caregiver support exchanges from online communities and prompted peer-like responses from three LLMs – LLaMA, GPT-4o-mini, and MedGemma – we analyze how human peers use personal narratives and how AI incorporates similar narrative forms. Psycholinguistic analysis shows that peer responses used significantly more first-person and past-focused language than peer-like AI responses. Qualitatively, we identify seven types of personal narratives in human peer support and show that AI often captures their emotional work, but can fabricate experiential grounding. These findings reveal a narrative authenticity gap: peer-like AI can generate synthetic lived experience without the real experience that makes peer support meaningful. We argue that caregiver-support AI systems need mechanisms to distinguish supportive peer-like framing from fabricated lived experience, ensuring that models can offer warmth and validation without falsely positioning themselves as experiential peers.
