Mitigating systematic error in topographic models for geomorphic change detection: Accuracy, precision and considerations beyond off‐nadir imagery

Unmanned aerial vehicles (UAVs) and structure-from-motion photogrammetry enable detailed quantification of geomorphic change. However, rigorous precision-based change detection can be compromised by survey accuracy problems producing systematic topographic error (e.g. 'doming'), with error magnitudes greatly exceeding precision estimates. Here, we assess survey sensitivity to systematic error, directly correcting topographic data so that error magnitudes align more closely with precision estimates. By simulating conventional grid-style photogrammetric aerial surveys, we quantify the underlying relationships between survey accuracy, camera model parameters, camera inclination, tie point matching precision and topographic relief, and demonstrate a relative insensitivity to image overlap. We show that a current doming-mitigation strategy of using a gently inclined ( 0 center dot 3 m, representing accuracy issues an order of magnitude greater than precision-based error estimates. For higher-relief topography, and for nadir-imaging surveys of the lower-relief topography, systematic error was <0 center dot 09 m. Modelling and subtracting the systematic error directly from the topographic data successfully reduced error magnitudes to values consistent with twice the estimated precision. Thus, topographic correction can provide a more robust approach to uncertainty-based detection of event-scale geomorphic change than designing surveys with small off-nadir camera inclinations and, furthermore, can substantially reduce ground control requirements. (c) 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Lt

Global Developmental Gene Expression and Pathway Analysis of Normal Brain Development and Mouse Models of Human Neuronal Migration Defects

Heterozygous LIS1 mutations are the most common cause of human lissencephaly, a human neuronal migration defect, and DCX mutations are the most common cause of X-linked lissencephaly. LIS1 is part of a protein complex including NDEL1 and 14-3-3ε that regulates dynein motor function and microtubule dynamics, while DCX stabilizes microtubules and cooperates with LIS1 during neuronal migration and neurogenesis. Targeted gene mutations of Lis1, Dcx, Ywhae (coding for 14-3-3ε), and Ndel1 lead to neuronal migration defects in mouse and provide models of human lissencephaly, as well as aid the study of related neuro-developmental diseases. Here we investigated the developing brain of these four mutants and wild-type mice using expression microarrays, bioinformatic analyses, and in vivo/in vitro experiments to address whether mutations in different members of the LIS1 neuronal migration complex lead to similar and/or distinct global gene expression alterations. Consistent with the overall successful development of the mutant brains, unsupervised clustering and co-expression analysis suggested that cell cycle and synaptogenesis genes are similarly expressed and co-regulated in WT and mutant brains in a time-dependent fashion. By contrast, focused co-expression analysis in the Lis1 and Ndel1 mutants uncovered substantial differences in the correlation among pathways. Differential expression analysis revealed that cell cycle, cell adhesion, and cytoskeleton organization pathways are commonly altered in all mutants, while synaptogenesis, cell morphology, and inflammation/immune response are specifically altered in one or more mutants. We found several commonly dysregulated genes located within pathogenic deletion/duplication regions, which represent novel candidates of human mental retardation and neurocognitive disabilities. Our analysis suggests that gene expression and pathway analysis in mouse models of a similar disorder or within a common pathway can be used to define novel candidates for related human diseases

Molecular Modeling and Simulation: Force Field Development, Evaporation Processes and Thermophysical Properties of Mixtures

To gain physical insight into the behavior of fluids on a microscopic level as well as to broaden the data base for thermophysical properties especially for mixtures, molecular modeling and simulation is utilized in this work. Various methods and applications are discussed, including a procedure for the development of new force field models. The evaporation of liquid nitrogen into a supercritical hydrogen atmosphere is presented as an example for large scale molecular dynamics simulation. System-size dependence and scaling behavior are discussed in the context of Kirkwood-Buff integration. Further, results for thermophysical mixture properties are presented, i.e. the Henry’s law constant of aqueous systems and diffusion coefficients of a ternary mixture