Quantitative Relationships Between Basalt Geochemistry, Shear Wave Velocity, and Asthenospheric Temperature Beneath Western North America

©2018. American Geophysical Union. All Rights Reserved. Western North America has an average elevation that is ∼2 km higher than cratonic North America. This difference coincides with a westward decrease in average lithospheric thickness from ∼240 to 260 basaltic samples. Forward and inverse modeling of carefully selected major, trace, and rare earth elements were used to determine melt fraction as a function of depth. Basaltic melt appears to have been generated by adiabatic decompression of dry peridotite with asthenospheric potential temperatures of 1340 ± 20 °C. Potential temperatures as high as 1365 °C were obtained for the Snake River Plain. For the youngest (i.e., <5 Ma) basalts with a subplate geochemical signature, there is a positive correlation between shear wave velocities and trace element ratios such as La/Yb. The significance of this correlation is explored by converting shear wave velocity into temperature using a global empirical parameterization. Calculated temperatures agree with those determined by inverse modeling of rare earth elements. We propose that regional epeirogenic uplift of western North America is principally maintained by widespread asthenospheric temperature anomalies lying beneath a lithospheric plate, which is considerably thinner than it was in Late Cretaceous times. Our proposal accounts for the distribution and composition of basaltic magmatism and is consistent with regional heat flow anomalies

Mapping the Spread of Malaria Drug Resistance

Tim Anderson discusses a new study of molecular variation in alleles at the dihydropteroate synthase locus, which underlies resistance to sulfadoxine, in over 5,000 parasites from 50 locations

Nemo: a computational tool for analyzing nematode locomotion

The nematode Caenorhabditis elegans responds to an impressive range of chemical, mechanical and thermal stimuli and is extensively used to investigate the molecular mechanisms that mediate chemosensation, mechanotransduction and thermosensation. The main behavioral output of these responses is manifested as alterations in animal locomotion. Monitoring and examination of such alterations requires tools to capture and quantify features of nematode movement. In this paper, we introduce Nemo (nematode movement), a computationally efficient and robust two-dimensional object tracking algorithm for automated detection and analysis of C. elegans locomotion. This algorithm enables precise measurement and feature extraction of nematode movement components. In addition, we develop a Graphical User Interface designed to facilitate processing and interpretation of movement data. While, in this study, we focus on the simple sinusoidal locomotion of C. elegans, our approach can be readily adapted to handle complicated locomotory behaviour patterns by including additional movement characteristics and parameters subject to quantification. Our software tool offers the capacity to extract, analyze and measure nematode locomotion features by processing simple video files. By allowing precise and quantitative assessment of behavioral traits, this tool will assist the genetic dissection and elucidation of the molecular mechanisms underlying specific behavioral responses.Comment: 12 pages, 2 figures. accepted by BMC Neuroscience 2007, 8:8

Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain

Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Methodology/Principal Findings: Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. Conclusions/Significance: The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available

Regulating, Measuring, and Modeling the Viscoelasticity of Bacterial Biofilms

Biofilms occur in a broad range of environments under heterogeneous physicochemical conditions, such as in bioremediation plants, on surfaces of biomedical implants, and in the lungs of cystic fibrosis patients. In these scenarios, biofilms are subjected to shear forces, but the mechanical integrity of these aggregates often prevents their disruption or dispersal. Biofilms' physical robustness is the result of the multiple biopolymers secreted by constituent microbial cells which are also responsible for numerous biological functions. A better understanding of the role of these biopolymers and their response to dynamic forces is therefore crucial for understanding the interplay between biofilm structure and function. In this paper, we review experimental techniques in rheology, which help quantify the viscoelasticity of biofilms, and modeling approaches from soft matter physics that can assist our understanding of the rheological properties. We describe how these methods could be combined with synthetic biology approaches to control and investigate the effects of secreted polymers on the physical properties of biofilms. We argue that without an integrated approach of the three disciplines, the links between genetics, composition, and interaction of matrix biopolymers and the viscoelastic properties of biofilms will be much harder to uncover

Genetic variation for tuber mineral concentrations in accessions of the Commonwealth Potato Collection

The variation in tuber mineral concentrations amongst accessions of wild tuber-bearing Solanum species in the Commonwealth Potato Collection (CPC) was evaluated under greenhouse conditions. Selected CPC accessions, representing the eco-geographical distribution of wild potatoes, were grown to maturity in peat-based compost under controlled conditions. Tubers from five plants of each accession were harvested, bulked and their mineral composition analysed. Among the germplasm investigated, there was a greater range in tuber concentrations of some elements of nutritional significance to both plants and animals, such as (Ca, Fe and Zn; 6.7, 3.6, and 4.5-fold respectively) than others, such as (K, P and S; all <3-fold). Significant positive correlations were found between mean altitude of the species' range and tuber P, K, Cu and Mg concentrations. The amount of diversity observed in the CPC collection indicates the existence of wide differences in tuber mineral accumulation among different potato accessions. This might be useful in breeding for nutritional improvement of potato tubers

Spectral plots and the representation and interpretation of biological data

It is basic question in biology and other fields to identify the char- acteristic properties that on one hand are shared by structures from a particular realm, like gene regulation, protein-protein interaction or neu- ral networks or foodwebs, and that on the other hand distinguish them from other structures. We introduce and apply a general method, based on the spectrum of the normalized graph Laplacian, that yields repre- sentations, the spectral plots, that allow us to find and visualize such properties systematically. We present such visualizations for a wide range of biological networks and compare them with those for networks derived from theoretical schemes. The differences that we find are quite striking and suggest that the search for universal properties of biological networks should be complemented by an understanding of more specific features of biological organization principles at different scales.Comment: 15 pages, 7 figure

Predictor Model of Root Caries in Older Adults: Reporting of Evidence to the Translational Evidence Mechanism

Compared to younger adults, older adults are at greater risk for root caries. A model of root caries may assist dentists in predicting disease outcomes. OBJECTIVES: Using the Iowa 65+ Oral Health Survey, analysis was done to model the patterns of the root caries development in older adults

A measure of individual role in collective dynamics

Identifying key players in collective dynamics remains a challenge in several research fields, from the efficient dissemination of ideas to drug target discovery in biomedical problems. The difficulty lies at several levels: how to single out the role of individual elements in such intermingled systems, or which is the best way to quantify their importance. Centrality measures describe a node's importance by its position in a network. The key issue obviated is that the contribution of a node to the collective behavior is not uniquely determined by the structure of the system but it is a result of the interplay between dynamics and network structure. We show that dynamical influence measures explicitly how strongly a node's dynamical state affects collective behavior. For critical spreading, dynamical influence targets nodes according to their spreading capabilities. For diffusive processes it quantifies how efficiently real systems may be controlled by manipulating a single node.Comment: accepted for publication in Scientific Report