Christopher Conrad results 64
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Thermal remote sensing is a valuable tool for assessing Surface Urban Heat Islands (SUHI). To quantify the SUHI intensity, the Urban Thermal Field Variance Index (UTFVI) is increasingly used as a proxy for urban heat distribution, e.g., by public authorities in Germany. The UTFVI is an ordinal-scaled metric that shows the spatial variability of LST in relation to the average LST of an area of interest. Numerous scientific stud...
Expansion of extractive industries to deep‐sea environments will lead to increased stresses on seafloor ecosystems. We examined changes in environmental parameters following direct and indirect experimental benthic disturbance using a modified plough on the Chatham Rise (∼450 m water depth), Aotearoa/New Zealand. Measurements included sediment community oxygen consumption (SCOC), nutrient fluxes, macro‐infauna community compos...
Selecting Relevant Features for Random Forest-Based Crop Type Classifications by Spatial Assessments of Backward Feature ReductionNARA Subscribed
Random Forest (RF) is a widely used machine learning algorithm for crop type mapping. RF’s variable importance aids in dimension reduction and identifying relevant multisource hyperspectral data. In this study, we examined spatial effects in a sequential backward feature elimination setting using RF variable importance in the example of a large-scale irrigation system in Punjab, Pakistan. We generated a reference classificatio...
Voxel-wise insights into early Alzheimer’s disease pathology progression: the association with APOE and memory declineNARA Subscribed
Longitudinal investigation of the Apolipoprotein E (APOE) genotype’s impact on Alzheimer’s disease (AD) biomarker progression, focusing on amyloid beta (Aβ) accumulation and gray matter (GM) atrophy, integrating cognitive decline and baseline levels. Longitudinal florbetapir-PET and T1-weighted MRI data from 100 cognitively normal (CN) and mild cognitive impaired (MCI) participants both with considerable global Aβ accumulation...
“Nature” is a broad term with neither a standard definition nor consistent use, even across federal reports like the National Climate Assessment (NCA). The process of defining complex topics like “nature” is difficult given the broad range in people’s understandings of and relationships with the natural world. To support the development of future nature assessments and NCAs, we analyzed use of nature-related words and themes o...
Machine Learning Approaches for Predicting Progression to Alzheimer’s Disease in Patients with Mild Cognitive ImpairmentNARA Subscribed
Purpose Alzheimer's disease (AD), a neurodegenerative disorder, is a condition that impairs cognition, memory, and behavior. Mild cognitive impairment (MCI), a transitional stage before AD, urgently needs the development of prediction models for conversion from MCI to AD. Method This study used machine learning methods to predict whether MCI subjects would develop AD, highlighting the importance of biomarkers (biological indic...
Complementary value of molecular, phenotypic, and functional aging biomarkers in dementia predictionNARA Subscribed
DNA methylation age (MA), brain age (BA), and frailty index (FI) are putative aging biomarkers linked to dementia risk. We investigated their relationship and combined potential for prediction of cognitive impairment and future dementia risk using the ADNI database. Of several MA algorithms, DunedinPACE and GrimAge2, associated with memory, were combined in a composite MA alongside BA and a data-driven FI in predictive analyse...
Optimizing photosynthetic light-harvesting under stars: simple and general antenna modelsNARA Subscribed
In the next 10–20 years, several observatories will aim to detect the signatures of oxygenic photosynthesis on exoplanets, though targets must be carefully selected. Most known potentially habitable exo-planets orbit cool M-dwarf stars, which have limited emission in the photosynthetically active region of the spectrum (PAR, $$400< \lambda < 700$$ 400 < λ < 700 nm) used by Earth’s oxygenic photoautotrophs. Still, recent experi...
Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodataNARA Subscribed
Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths ( T ), requires root zone depth optimization ( T opt ) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employe...