Toward trait-based mortality models for tropical forests

Tree mortality in tropical forests is a complex ecological process for which modelling approaches need to be improved to better understand, and then predict, the evolution of tree mortality in response to global change. The mortality model introduced here computes an individual probability of dying for each tree in a community. The mortality model uses the ontogenetic stage of the tree because youngest and oldest trees are more likely to die. Functional traits are integrated as proxies of the ecological strategies of the trees to permit generalization among all species in the community. Data used to parametrize the model were collected at Paracou study site, a tropical rain forest in French Guiana, where 20,408 trees have been censused for 18 years. A Bayesian framework was used to select useful covariates and to estimate the model parameters. This framework was developed to deal with sources of uncertainty, including the complexity of the mortality process itself and the field data, especially historical data for which taxonomic determinations were uncertain. Uncertainty about the functional traits was also considered, to maximize the information they contain. Four functional traits were strong predictors of tree mortality: wood density, maximum height, laminar toughness and stem and branch orientation, which together distinguished the light-demanding, fast-growing trees from slow-growing trees with lower mortality rates. Our modelling approach formalizes a complex ecological problem and offers a relevant mathematical framework for tropical ecologists to process similar uncertain data at the community level

Music Convocation

https://dc.ewu.edu/music_performances/1757/thumbnail.jp

D-cis-Diltiazem Can Produce Oxidative Stress in Healthy Depolarized Rods In Vivo

PurposeNew perspectives are needed to understand decades of contradictory reports on the neuroprotective effects of the Cav1.2 L-type calcium channel blocker d-cis-diltiazem in retinitis pigmentosa (RP) models. Here, we address, in vivo, the following two knowledge gaps regarding d-cis-diltiazem's actions in the murine outer retina: (1) do normal mouse rods contain d-cis-diltiazem-insensitive Cav1.2 L-type calcium channels? (2) Can d-cis-diltiazem modify the normal rod redox environment?MethodsFirst, transretinal Cav1.2 L-type calcium channels were noninvasively mapped with manganese-enhanced magnetic resonance imaging (MRI) following agonist Bay K 8644 in C57BL/6 (B6) and in Cav1.2 L-type calcium channel BAY K 8644-insensitive mutant B6 mice. Second, d-cis-diltiazem-treated oxidative stress-vulnerable (B6) or -resistant [129S6 (S6)] mice were examined in vivo (QUEnch-assiSTed [QUEST] MRI) and in whole retina ex vivo (lucigenin). Retinal thickness was measured using MRI.ResultsThe following results were observed: (1) manganese uptake patterns in BAY K 8644-treated controls and mutant mice identified in vivo Cav1.2 L-type calcium channels in inner and outer retina; and (2) d-cis-diltiazem induced rod oxidative stress in dark-adapted B6 mice but not in light-adapted B6 mice or dark-adapted S6 mice (QUEST MRI). Oxidative stress in vivo was limited to inferior outer retina in dark-adapted B6 mice approximately 1-hour post d-cis-diltiazem. By approximately 4 hours post, only superior outer retina oxidative stress was observed and whole retinal superoxide production was supernormal. All groups had unremarkable retinal thicknesses.ConclusionsD-cis-diltiazem's unexpectedly complex spatiotemporal outer retinal oxidative stress pattern in vivo was dependent on genetic background and rod membrane depolarization, but not apparently dependent on Cav1.2 L-type calcium channels, providing a potential rationale for contradictory results in different RP models

Image-Guided Precision Medicine in the Diagnosis and Treatment of Pheochromocytomas and Paragangliomas

In this comprehensive review, we aimed to discuss the current state-of-the-art medical imaging for pheochromocytomas and paragangliomas (PPGLs) diagnosis and treatment. Despite major medical improvements, PPGLs, as with other neuroendocrine tumors (NETs), leave clinicians facing several challenges; their inherent particularities and their diagnosis and treatment pose several challenges for clinicians due to their inherent complexity, and they require management by multidisciplinary teams. The conventional concepts of medical imaging are currently undergoing a paradigm shift, thanks to developments in radiomic and metabolic imaging. However, despite active research, clinical relevance of these new parameters remains unclear, and further multicentric studies are needed in order to validate and increase widespread use and integration in clinical routine. Use of AI in PPGLs may detect changes in tumor phenotype that precede classical medical imaging biomarkers, such as shape, texture, and size. Since PPGLs are rare, slow-growing, and heterogeneous, multicentric collaboration will be necessary to have enough data in order to develop new PPGL biomarkers. In this nonsystematic review, our aim is to present an exhaustive pedagogical tool based on real-world cases, dedicated to physicians dealing with PPGLs, augmented by perspectives of artificial intelligence and big data

Providing Risk of the Environment’s Changing Climate Threats for Galleries, Libraries, Archives, & Museums (PROTECCT-GLAM) Data File

The data file was created as part of the IMLS-funded project, PROTECCT-GLAM: Risk of The Environment’s Changing Climate Threats for Galleries, Libraries, Archives, and Museums in an effort to gather the identities and georeferences of all galleries, libraries, archives, and museums located within the United States. The data file includes 22,388 archives, 21,189 libraries, and 29,781 museums