MACHINE LEARNING FOR CLASSIFICATION OF AN ERODING SCARP SURFACE USING TERRESTRIAL PHOTOGRAMMETRY WITH NIR AND RGB IMAGERY

Abstract. Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius

A Multi-State Model of the CaMKII Dodecamer Suggests a Role for Calmodulin in Maintenance of Autophosphorylation

Ca²⁺/calmodulin-dependent protein kinase II (CaMKII) accounts for up to 2 percent of all brain protein and is essential to memory function. CaMKII activity is known to regulate dynamic shifts in the size and signaling strength of neuronal connections, a process known as synaptic plasticity. Increasingly, computational models are used to explore synaptic plasticity and the mechanisms regulating CaMKII activity. Conventional modeling approaches may exclude biophysical detail due to the impractical number of state combinations that arise when explicitly monitoring the conformational changes, ligand binding, and phosphorylation events that occur on each of the CaMKII holoenzyme’s subunits. To manage the combinatorial explosion without necessitating bias or loss in biological accuracy, we use a specialized syntax in the software MCell to create a rule-based model of a twelve-subunit CaMKII holoenzyme. Here we validate the rule-based model against previous experimental measures of CaMKII activity and investigate molecular mechanisms of CaMKII regulation. Specifically, we explore how Ca²⁺/CaM-binding may both stabilize CaMKII subunit activation and regulate maintenance of CaMKII autophosphorylation. Noting that Ca²⁺/CaM and protein phosphatases bind CaMKII at nearby or overlapping sites, we compare model scenarios in which Ca²⁺/CaM and protein phosphatase do or do not structurally exclude each other’s binding to CaMKII. Our results suggest a functional mechanism for the so-called “CaM trapping” phenomenon, wherein Ca²⁺/CaM may structurally exclude phosphatase binding and thereby prolong CaMKII autophosphorylation. We conclude that structural protection of autophosphorylated CaMKII by Ca²⁺/CaM may be an important mechanism for regulation of synaptic plasticity

New Perspectives on Glacial Geomorphology in Earth's Deep Time Record

International audienceThe deep time (pre-Quaternary) glacial record is an important means to understand the growth, development, and recession of the global cryosphere on very long timescales (10 6-10 8 Myr). Sedimentological description and interpretation of outcrops has traditionally played an important role. Whilst such data remain vital, new insights are now possible thanks to freely accessible aerial and satellite imagery, the widespread availability and affordability of Uncrewed Aerial Vehicles, and accessibility to 3D rendering software. In this paper, we showcase examples of glaciated landscapes from the Cryogenian, Ediacaran, Late Ordovician and Late Carboniferous where this approach is revolutionizing our understanding of deep time glaciation. Although some problems cannot be overcome (erosion or dissolution of the evidence), robust interpretations in terms of the evolving subglacial environment can be made. Citing examples from Australia (Cryogenian), China (Ediacaran), North and South Africa (Late Ordovician, Late Carboniferous), and Namibia (Late Carboniferous), we illustrate how the power of glacial geomorphology can be harnessed to interpret Earth's ancient glacial record

Are luminescent bacteria suitable for online detection and monitoring of toxic compounds in drinking water and its sources?

Biosensors based on luminescent bacteria may be valuable tools to monitor the chemical quality and safety of surface and drinking water. In this review, an overview is presented of the recombinant strains available that harbour the bacterial luciferase genes luxCDABE, and which may be used in an online biosensor for water quality monitoring. Many bacterial strains have been described for the detection of a broad range of toxicity parameters, including DNA damage, protein damage, membrane damage, oxidative stress, organic pollutants, and heavy metals. Most lux strains have sensitivities with detection limits ranging from milligrams per litre to micrograms per litre, usually with higher sensitivities in compound-specific strains. Although the sensitivity of lux strains can be enhanced by various molecular manipulations, most reported detection thresholds are still too high to detect levels of individual contaminants as they occur nowadays in European drinking waters. However, lux strains sensing specific toxic effects have the advantage of being able to respond to mixtures of contaminants inducing the same effect, and thus could be used as a sensor for the sum effect, including the effect of compounds that are as yet not identified by chemical analysis. An evaluation of the suitability of lux strains for monitoring surface and drinking water is therefore provided

Development of a Dynamic Self-Configuring Data Acquisition System

NRC publication: Ye