378 research outputs found

    Multiple Gene Variants Linked to Alzheimer\u27s-Type Clinical Dementia via GWAS are Also Associated with Non-Alzheimer\u27s Neuropathologic Entities

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    The classic pathologic hallmarks of Alzheimer’s disease (AD) are amyloid plaques and neurofibrillary tangles (AD neuropathologic changes, or ADNC). However, brains from individuals clinically diagnosed with “AD-type” (amnestic) dementia usually harbor heterogeneous neuropathologies in addition to, or other than, ADNC. We hypothesized that some AD-type dementia associated genetic single nucleotide variants (SNVs) identified from large genomewide association studies (GWAS) were associated with non-ADNC neuropathologies. To test this hypothesis, we analyzed data from multiple studies with available genotype and neuropathologic phenotype information. Clinical AD/dementia risk alleles of interest were derived from the very large GWAS by Bellenguez et al. (2022) who reported 83 clinical AD/dementia-linked SNVs in addition to the APOE risk alleles. To query the pathologic phenotypes associated with variation of those SNVs, National Alzheimer’s disease Coordinating Center (NACC) neuropathologic data were linked to AD Sequencing Project (ADSP) and AD Genomics Consortium (ADGC) data. Separate data were obtained from the harmonized Religious Orders Study and the Rush Memory and Aging Project (ROSMAP). A total of 4811 European participants had at least ADNC neuropathology data and also genotype data available; data were meta-analyzed across cohorts. As expected, a subset of dementia-associated SNVs were associated with ADNC risk in Europeans—e.g., BIN1, PICALM, CR1, MME, and COX7C. Other gene variants linked to (clinical) AD dementia were associated with non-ADNC pathologies. For example, the associations of GRN and TMEM106B SNVs with limbic-predominant age-related TDP-43 neuropathologic changes (LATE-NC) were replicated. In addition, SNVs in TNIP1 and WNT3 previously reported as ADrelated were instead associated with hippocampal sclerosis pathology. Some genotype/neuropathology association trends were not statistically significant at P \u3c 0.05 after correcting for multiple testing, but were intriguing. For example, variants in SORL1 and TPCN1 showed trends for association with LATE-NC whereas Lewy body pathology trended toward association with USP6NL and BIN1 gene variants. A smaller cohort of non-European subjects (n = 273, approximately one-half of whom were African-Americans) provided the basis for additional exploratory analyses. Overall, these findings were consistent with the hypothesis that some genetic variants linked to AD dementia risk exert their affect by influencing non-ADNC neuropathologies

    Analysis of Genes (\u3ci\u3eTMEM106B\u3c/i\u3e, \u3ci\u3eGRN\u3c/i\u3e, \u3ci\u3eABCC9\u3c/i\u3e, \u3ci\u3eKCNMB2\u3c/i\u3e, and \u3ci\u3eAPOE\u3c/i\u3e) Implicated in Risk for LATE-NC and Hippocampal Sclerosis Provides Pathogenetic Insights: A Retrospective Genetic Association Study

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    Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is the most prevalent subtype of TDP-43 proteinopathy, affecting up to 1/3rd of aged persons. LATE-NC often co-occurs with hippocampal sclerosis (HS) pathology. It is currently unknown why some individuals with LATE-NC develop HS while others do not, but genetics may play a role. Previous studies found associations between LATE-NC phenotypes and specific genes: TMEM106B, GRN, ABCC9, KCNMB2, and APOE. Data from research participants with genomic and autopsy measures from the National Alzheimer’s Coordinating Center (NACC; n = 631 subjects included) and the Religious Orders Study and Memory and the Rush Aging Project (ROSMAP; n = 780 included) were analyzed in the current study. Our goals were to reevaluate disease-associated genetic variants using newly collected data and to query whether the specific genotype/phenotype associations could provide new insights into disease-driving pathways. Research subjects included in prior LATE/HS genome-wide association studies (GWAS) were excluded. Single nucleotide variants (SNVs) within 10 kb of TMEM106B, GRN, ABCC9, KCNMB2, and APOE were tested for association with HS and LATE-NC, and separately for Alzheimer’s pathologies, i.e. amyloid plaques and neurofibrillary tangles. Significantly associated SNVs were identified. When results were meta-analyzed, TMEM106B, GRN, and APOE had significant gene-based associations with both LATE and HS, whereas ABCC9 had significant associations with HS only. In a sensitivity analysis limited to LATE-NC + cases, ABCC9 variants were again associated with HS. By contrast, the associations of TMEM106B, GRN, and APOE with HS were attenuated when adjusting for TDP-43 proteinopathy, indicating that these genes may be associated primarily with TDP-43 proteinopathy. None of these genes except APOE appeared to be associated with Alzheimer’s-type pathology. In summary, using data not included in prior studies of LATE or HS genomics, we replicated several previously reported gene-based associations and found novel evidence that specific risk alleles can differentially affect LATE-NC and HS

    Predictors of linkage to care following community-based HIV counseling and testing in rural Kenya

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    Despite innovations in HIV counseling and testing (HCT), important gaps remain in understanding linkage to care. We followed a cohort diagnosed with HIV through a community-based HCT campaign that trained persons living with HIV/AIDS (PLHA) as navigators. Individual, interpersonal, and institutional predictors of linkage were assessed using survival analysis of self-reported time to enrollment. Of 483 persons consenting to follow-up, 305 (63.2%) enrolled in HIV care within 3 months. Proportions linking to care were similar across sexes, barring a sub-sample of men aged 18–25 years who were highly unlikely to enroll. Men were more likely to enroll if they had disclosed to their spouse, and women if they had disclosed to family. Women who anticipated violence or relationship breakup were less likely to link to care. Enrolment rates were significantly higher among participants receiving a PLHA visit, suggesting that a navigator approach may improve linkage from community-based HCT campaigns.Vestergaard Frandse

    Brain arteriolosclerosis

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    Brain arteriolosclerosis (B-ASC), characterized by pathologic arteriolar wall thickening, is a common finding at autopsy in aged persons and is associated with cognitive impairment. Hypertension and diabetes are widely recognized as risk factors for B-ASC. Recent research indicates other and more complex risk factors and pathogenetic mechanisms. Here we describe aspects of the unique architecture of brain arterioles, histomorphologic features of B-ASC, relevant neuroimaging findings, epidemiology and association with aging, established genetic risk factors, and the co-occurrence of B-ASC with other neuropathologic conditions such as Alzheimer’s disease and limbic-predominant age-related TDP-43 encephalopathy (LATE). There may also be complex physiologic interactions between metabolic syndrome (e.g. hypertension and inflammation) and brain arteriolar pathology. Although there is no universally applied diagnostic methodology, several classification schemes and neuroimaging techniques are used to diagnose and categorize cerebral small vessel disease pathologies that include B-ASC, microinfarcts, microbleeds, lacunar infarcts, and cerebral amyloid angiopathy (CAA). In clinical-pathologic studies that include consideration of comorbid diseases, B-ASC is independently associated with impairments in global cognition, episodic memory, working memory, and perceptual speed, and has been linked to autonomic dysfunction and motor symptoms including parkinsonism. We conclude by discussing critical knowledge gaps related to B-ASC and suggest that there are probably subcategories of B-ASC that differ in pathogenesis. Observed in over 80% of autopsied individuals beyond 80 years of age, B-ASC is a complex and under-studied contributor to neurologic disability

    Toward a Generalizable Framework of Disturbance Ecology Through Crowdsourced Science

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    © 2021 Graham, Averill, Bond-Lamberty, Knelman, Krause, Peralta, Shade, Smith, Cheng, Fanin, Freund, Garcia, Gibbons, Van Goethem, Guebila, Kemppinen, Nowicki, Pausas, Reed, Rocca, Sengupta, Sihi, Simonin, Słowiński, Spawn, Sutherland, Tonkin, Wisnoski, Zipper and Contributor Consortium.Disturbances fundamentally alter ecosystem functions, yet predicting their impacts remains a key scientific challenge. While the study of disturbances is ubiquitous across many ecological disciplines, there is no agreed-upon, cross-disciplinary foundation for discussing or quantifying the complexity of disturbances, and no consistent terminology or methodologies exist. This inconsistency presents an increasingly urgent challenge due to accelerating global change and the threat of interacting disturbances that can destabilize ecosystem responses. By harvesting the expertise of an interdisciplinary cohort of contributors spanning 42 institutions across 15 countries, we identified an essential limitation in disturbance ecology: the word ‘disturbance’ is used interchangeably to refer to both the events that cause, and the consequences of, ecological change, despite fundamental distinctions between the two meanings. In response, we developed a generalizable framework of ecosystem disturbances, providing a well-defined lexicon for understanding disturbances across perspectives and scales. The framework results from ideas that resonate across multiple scientific disciplines and provides a baseline standard to compare disturbances across fields. This framework can be supplemented by discipline-specific variables to provide maximum benefit to both inter- and intra-disciplinary research. To support future syntheses and meta-analyses of disturbance research, we also encourage researchers to be explicit in how they define disturbance drivers and impacts, and we recommend minimum reporting standards that are applicable regardless of scale. Finally, we discuss the primary factors we considered when developing a baseline framework and propose four future directions to advance our interdisciplinary understanding of disturbances and their social-ecological impacts: integrating across ecological scales, understanding disturbance interactions, establishing baselines and trajectories, and developing process-based models and ecological forecasting initiatives. Our experience through this process motivates us to encourage the wider scientific community to continue to explore new approaches for leveraging Open Science principles in generating creative and multidisciplinary ideas.This research was supported by the U.S. Department of Energy (DOE), Office of Biological and Environmental Research (BER), as part of Subsurface Biogeochemical Research Program’s Scientific Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL). PNNL is operated for DOE by Battelle under contract DE-AC06-76RLO 1830

    Refining trait resilience: identifying engineering, ecological, and adaptive facets from extant measures of resilience

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    The current paper presents a new measure of trait resilience derived from three common mechanisms identified in ecological theory: Engineering, Ecological and Adaptive (EEA) resilience. Exploratory and confirmatory factor analyses of five existing resilience scales suggest that the three trait resilience facets emerge, and can be reduced to a 12-item scale. The conceptualization and value of EEA resilience within the wider trait and well-being psychology is illustrated in terms of differing relationships with adaptive expressions of the traits of the five-factor personality model and the contribution to well-being after controlling for personality and coping, or over time. The current findings suggest that EEA resilience is a useful and parsimonious model and measure of trait resilience that can readily be placed within wider trait psychology and that is found to contribute to individual well-bein

    A Survey of Methods for Volumetric Scene Reconstruction from Photographs

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    Scene reconstruction, the task of generating a 3D model of a scene given multiple 2D photographs taken of the scene, is an old and difficult problem in computer vision. Since its introduction, scene reconstruction has found application in many fields, including robotics, virtual reality, and entertainment. Volumetric models are a natural choice for scene reconstruction. Three broad classes of volumetric reconstruction techniques have been developed based on geometric intersections, color consistency, and pair-wise matching. Some of these techniques have spawned a number of variations and undergone considerable refinement. This paper is a survey of techniques for volumetric scene reconstruction
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