A Neuroimaging Web Interface for Data Acquisition, Processing and Visualization of Multimodal Brain Images

Structural and functional brain images are generated as essential modalities for medical experts to learn about the different functions of the brain. These images are typically visually inspected by experts. Many software packages are available to process medical images, but they are complex and difficult to use. The software packages are also hardware intensive. As a consequence, this dissertation proposes a novel Neuroimaging Web Services Interface (NWSI) as a series of processing pipelines for a common platform to store, process, visualize and share data. The NWSI system is made up of password-protected interconnected servers accessible through a web interface. The web-interface driving the NWSI is based on Drupal, a popular open source content management system. Drupal provides a user-based platform, in which the core code for the security and design tools are updated and patched frequently. New features can be added via modules, while maintaining the core software secure and intact. The webserver architecture allows for the visualization of results and the downloading of tabulated data. Several forms are ix available to capture clinical data. The processing pipeline starts with a FreeSurfer (FS) reconstruction of T1-weighted MRI images. Subsequently, PET, DTI, and fMRI images can be uploaded. The Webserver captures uploaded images and performs essential functionalities, while processing occurs in supporting servers. The computational platform is responsive and scalable. The current pipeline for PET processing calculates all regional Standardized Uptake Value ratios (SUVRs). The FS and SUVR calculations have been validated using Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) results posted at Laboratory of Neuro Imaging (LONI). The NWSI system provides access to a calibration process through the centiloid scale, consolidating Florbetapir and Florbetaben tracers in amyloid PET images. The interface also offers onsite access to machine learning algorithms, and introduces new heat maps that augment expert visual rating of PET images. NWSI has been piloted using data and expertise from Mount Sinai Medical Center, the 1Florida Alzheimer’s Disease Research Center (ADRC), Baptist Health South Florida, Nicklaus Children\u27s Hospital, and the University of Miami. All results were obtained using our processing servers in order to maintain data validity, consistency, and minimal processing bias

Partitioning the effects of habitat loss hunting and climate change on the endangered Chacoan peccary

Aim: Land-use change and overexploitation are major threats to biodiversity, and cli mate change will exert additional pressure in the 21st century. Although there are strong interactions between these threats, our understanding of the synergistic and compensatory effects on threatened species' range geography remains limited. Our aim was to disentangle the impact of habitat loss, hunting and climate change on spe cies, using the example of the endangered Chacoan peccary (Catagonus wagneri). Location: Gran Chaco ecoregion in South America. Methods: Using a large occurrence database, we integrated a time-calibrated species distribution model with a hunting pressure model to reconstruct changes in the distri bution of suitable peccary habitat between 1985 and 2015. We then used partitioning analysis to attribute the relative contribution of habitat change to land-use conver sion, climate change and varying hunting pressure. Results: Our results reveal widespread habitat deterioration, with only 11% of the habitat found in 2015 considered suitable and safe. Hunting pressure was the strong est single threat, yet most habitat deterioration (58%) was due to the combined, rather than individual, effects of the three drivers we assessed. Climate change would have led to a compensatory effect, increasing suitable habitat area, yet this effect was ne gated by the strongly negative and interacting threats of land-use change and hunting. Main Conclusions: Our study reveals the central role of overexploitation, which is often neglected in biogeographic assessments, and suggests that addressing overex ploitation has huge potential for increasing species' adaptive capacity in the face of climate and land-use change. More generally, we highlight the importance of jointly assessing extinction drivers to understand how species might fare in the 21st century. Here, we provide a simple and transferable framework to determine the separate and joint effects of three main drivers of biodiversity loss.Fil: Torres, Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Diversidad y Ecología Animal ; Argentina.Fil: Kuemmerle, Tobias. Humboldt-University Berlin. Integrative Research Institute for Transformations in Human Environment Systems. Geography Department; AlemaniaFil: Baumann, Matthias. Humboldt-University. Geography Department; Alemania.Fil: Romero Muñoz, Alfredo. Humboldt University. Geography Departament; Alemania. University of British Columbia. Institute for Resources, Environment and Sustainability (IRES); Canada. Helmholtz Centre for Environmental Research. Department Computational Landscape Ecology; Alemania. Transformations of Human-Environment Systems (IRI THESys). Integrative Research Institute; AlemaniaFil: Altrichter, Mariana. IUCN SSC Peccary Specialist Group; Suiza. Prescott College. Environmental Studies; Estados UnidosFil: Boaglio, Gabriel Ivan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Cabral, Hugo. Universidade Estadual Paulista. Programa de Pós-Graduação em Biologia Animal; Brasil. Instituto de Investigación Biológica del Paraguay; ParaguayFil: Camino, Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Ecología del Litoral. Laboratorio de Biología de la Conservación; ArgentinaFil: Campos Kraver, Juan M. University of Florida. College of Veterinary Medicine & Department of Wildlife Ecology and Conservation. Department of Large Animal Clinical Sciences; Estados UnidosFil: Giordano, Anthony. Society for the Preservation of Endangered Carnivores and their International Ecological Study (S.P.E.C.I.E.S); Estados Unidos. University of Los Angeles. Institute of the Environment and Sustainability. Center for Tropical Research; Estados UnidosFil: Cartes, José L. Guyra Paraguay, Parque del Río; ParaguayFil: Cuéllar, Rosa L. Fundación para la Conservación del Bosque Chiquitano; BoliviaFil: Decarre, Julieta. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; ArgentinaFil: Gallegos, Marcelo. Provincia de Salta. Secretaría de Ambiente; ArgentinaFil: Lizárraga, Leónidas. Administración De Parques Nacionales. Dirección Regional Noroeste. Salta; Argentina.Fil: Maffei, Leonardo. Biósfera Consultores Ambientales, Lima, Perú.Fil: Neris, Nora N. Secretaria del Ambiente; ParaguayFil: Quiroga, Verónica. Universidad Nacional de Córdoba. Inst. de Diversidad y Ecología Animal (IDEA – CONICET), Centro de Zoología Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Saldivar, Silvia. ITAIPU Binacional. Dirección de Coordinación. División de Áreas Protegidas; ParaguayFil: Tamburini, Daniela Maria. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Centro de Ecología y Recursos Naturales Renovables; Argentin

Deep reinforcement learning for hybrid beamforming in multi-user millimeter wave wireless systems

Publisher Copyright: © 2021 IEEE.This paper proposes a Machine Learning (ML) algorithm for hybrid beamforming in millimeter-wave wireless systems with multiple users. The time-varying nature of the wireless channels is taken into account when training the ML agent, which identifies the most convenient hybrid beamforming matrix with the aid of an algorithm that keeps the amount of signaling information low, avoids sudden changes in the analog beamformers radiation patterns when scheduling different users (flashlight interference), and simplifies the hybrid beamformer update decisions by adjusting the phases of specific analog beamforming vectors. The proposed hybrid beamforming algorithm relies on Deep Reinforcement Learning (DRL), which represents a practical approach to embed the online adaptation feature of the hybrid beamforming matrix into the channel states of continuous nature in which the multiuser MIMO system can be. Achievable data rate curves are used to analyze performance results, which validate the advantages of DRL algorithms with respect to solutions relying on conventional/deterministic optimization tools.Peer reviewe

Utilizing semantic intrusions to identify amyloid positivity in mild cognitive impairment

Objective Semantic intrusion (SI) errors may highlight specific breakdowns in memory associated with preclinical Alzheimer disease (AD); however, there have been no investigations to determine whether SI errors occur with greater frequency in persons with amnestic mild cognitive impairment (aMCI) confirmed as amyloid positive (Amy+) vs those who have clinical symptoms of aMCI-AD with negative amyloid scans (suspected non-AD pathology [SNAP]) or persons who are diagnosed with other brain disorders affecting cognition. Methods Eighty-eight participants with aMCI underwent brain amyloid PET and MRI scans and were classified as early AD (Amy+), SNAP (Amy−), or other neurological/psychiatric diagnosis (Amy−). We focused on SI on the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L) targeting proactive semantic interference (PSI; old semantic learning interferes with new semantic learning), failure to recover from PSI after an additional learning trial (frPSI), and retroactive semantic interference (new semantic learning interferes with memory for old semantic learning). Results SIs on measures of PSI and frPSI distinguished between Amy+ AD and SNAP and other non-AD cases. PSI and frPSI intrusions evidenced moderately high associations with reduced volumes in the entorhinal cortex, superior temporal regions, and supramarginal gyrus. No such associations were observed in cases with SNAP. Conclusions SIs on the LASSI-L related to PSI and frPSI uniquely differentiated Amy+ and Amy− participants with aMCI and likely reflect deficits with inhibition and source memory in preclinical AD not captured by traditional cognitive measures. This may represent a specific, noninvasive test successful at distinguishing cases with true AD from those with SNAP.Fil: Loewenstein, David A.. University of Miami; Estados UnidosFil: Curiel, Rosie E.. University of Miami; Estados UnidosFil: DeKosky, Steven. University of Miami; Estados UnidosFil: Bauer, Russell M.. University of Miami; Estados UnidosFil: Rosselli, Monica. University of Miami; Estados UnidosFil: Guinjoan, Salvador Martín. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Salud Mental; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; ArgentinaFil: Adjouadi, Malek. University of Miami; Estados UnidosFil: Peñate, Ailyn. University of Miami; Estados UnidosFil: Barker, William W.. University of Miami; Estados UnidosFil: Goenaga, Sindy. University of Miami; Estados UnidosFil: Golde, Todd. University of Miami; Estados UnidosFil: Greig Custo, Maria T.. University of Miami; Estados UnidosFil: Hanson, Kevin S.. University of Miami; Estados UnidosFil: Li, Chunfei. University of Miami; Estados UnidosFil: Lizarraga, Gabriel. University of Miami; Estados UnidosFil: Marsiske, Michael. University of Miami; Estados UnidosFil: Duara, Ranjan. University of Miami; Estados Unido