Topography and Tilt at Volcanoes

For optimal monitoring of the deformation of a volcano, instrumentation should be deployed at the location most sensitive to changes at the suspected deformation source. The topographic effect on tilt depends strongly on the orientation of the deformation field relative to the surface on which the instrument is deployed. This fact has long been understood and corrected for in tilt measurements related to body tides and referred to as “cavity” or “topographic effects” (Harrison, 1976). Despite this, and whilst topography at volcanoes is often significant, until now the topographic effect on tilt at volcanoes has not been systematically explored. Here, we investigate the topographic effect on tilt produced by either the pressurization of a reservoir or conduit, or shear stress as magma ascends through a conduit, using 2D axisymmetric and 3D finite element deformation modeling. We show that topography alone can amplify or reduce the tilt by more than an order of magnitude, and control the orientation of the maximum tilt. Therefore, a decrease in tilt can even be caused by an increase in deformation at the source. Hence, inverting for the source stress using simple analytical models that neglect topography could potentially lead to a misinterpretation of how the volcanic system is evolving. Since topographic features can amplify the tilt signal, they can be exploited when deciding upon an installation site

Combining Magma Flow and Deformation Modeling to Explain Observed Changes in Tilt

The understanding of magma ascent dynamics is essential in forecasting the scale, style and timing of volcanic eruptions. The monitoring of near-field deformation is widely used to gain insight into these dynamics, and has been linked to stress changes in the upper conduit. The ascent of magma through the conduit exerts shear stress on the conduit wall, pulling up the surrounding edifice, whilst overpressure in the upper conduit pushes the surrounding edifice outwards. How much shear stress and pressure is produced during magma ascent, and the relative contribution of each to the deformation, has until now only been explored conceptually. By combining flow and deformation modeling using COMSOL Multiphysics, we for the first time present a quantitative model that links magma ascent to deformation. We quantify how both shear stress and pressure vary spatially within a cylindrical conduit, and show that shear stress generally dominates observed changes in tilt close to the conduit. However, the relative contribution of pressure is not insignificant, and both pressure and shear stress must be considered when interpreting deformation data. We demonstrate that significant changes in tilt can be driven by changes in the driving pressure gradient or volatile content of the magma. The relative contribution of shear stress and pressure to the tilt varies considerably depending on these parameters. Our work provides insight into the range of elastic moduli that should be considered when modeling edifice-scale rock masses, and we show that even where the edifice is modeled as weak, shear stress generally dominates the near field deformation over pressurization of the conduit. While our model addresses cyclic tilt changes observed during activity at Tungurahua volcano, Ecuador, between 2013 and 2014, it is also applicable to silicic volcanoes in general

Studying Fake News via Network Analysis: Detection and Mitigation

Social media for news consumption is becoming increasingly popular due to its easy access, fast dissemination, and low cost. However, social media also enable the wide propagation of "fake news", i.e., news with intentionally false information. Fake news on social media poses significant negative societal effects, and also presents unique challenges. To tackle the challenges, many existing works exploit various features, from a network perspective, to detect and mitigate fake news. In essence, news dissemination ecosystem involves three dimensions on social media, i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types and how these networks can be used to detect and mitigation fake news on social media.Comment: Submitted as a invited book chapter in Lecture Notes in Social Networks, Springer Pres

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

Metabolic analysis of the interaction between plants and herbivores

Insect herbivores by necessity have to deal with a large arsenal of plant defence metabolites. The levels of defence compounds may be increased by insect damage. These induced plant responses may also affect the metabolism and performance of successive insect herbivores. As the chemical nature of induced responses is largely unknown, global metabolomic analyses are a valuable tool to gain more insight into the metabolites possibly involved in such interactions. This study analyzed the interaction between feral cabbage (Brassica oleracea) and small cabbage white caterpillars (Pieris rapae) and how previous attacks to the plant affect the caterpillar metabolism. Because plants may be induced by shoot and root herbivory, we compared shoot and root induction by treating the plants on either plant part with jasmonic acid. Extracts of the plants and the caterpillars were chemically analysed using Ultra Performance Liquid Chromatography/Time of Flight Mass Spectrometry (UPLCT/MS). The study revealed that the levels of three structurally related coumaroylquinic acids were elevated in plants treated on the shoot. The levels of these compounds in plants and caterpillars were highly correlated: these compounds were defined as the ‘metabolic interface’. The role of these metabolites could only be discovered using simultaneous analysis of the plant and caterpillar metabolomes. We conclude that a metabolomics approach is useful in discovering unexpected bioactive compounds involved in ecological interactions between plants and their herbivores and higher trophic levels.

Midgut microbiota of the malaria mosquito vector Anopheles gambiae and Interactions with plasmodium falciparum Infection

The susceptibility of Anopheles mosquitoes to Plasmodium infections relies on complex interactions between the insect vector and the malaria parasite. A number of studies have shown that the mosquito innate immune responses play an important role in controlling the malaria infection and that the strength of parasite clearance is under genetic control, but little is known about the influence of environmental factors on the transmission success. We present here evidence that the composition of the vector gut microbiota is one of the major components that determine the outcome of mosquito infections. A. gambiae mosquitoes collected in natural breeding sites from Cameroon were experimentally challenged with a wild P. falciparum isolate, and their gut bacterial content was submitted for pyrosequencing analysis. The meta-taxogenomic approach revealed a broader richness of the midgut bacterial flora than previously described. Unexpectedly, the majority of bacterial species were found in only a small proportion of mosquitoes, and only 20 genera were shared by 80% of individuals. We show that observed differences in gut bacterial flora of adult mosquitoes is a result of breeding in distinct sites, suggesting that the native aquatic source where larvae were grown determines the composition of the midgut microbiota. Importantly, the abundance of Enterobacteriaceae in the mosquito midgut correlates significantly with the Plasmodium infection status. This striking relationship highlights the role of natural gut environment in parasite transmission. Deciphering microbe-pathogen interactions offers new perspectives to control disease transmission.Institut de Recherche pour le Developpement (IRD); French Agence Nationale pour la Recherche [ANR-11-BSV7-009-01]; European Community [242095, 223601]info:eu-repo/semantics/publishedVersio

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Attracting and retaining health workers in rural areas: investigating nurses’ views on rural posts and policy interventions

<p>Abstract</p> <p>Background</p> <p>Kenya has bold plans for scaling up priority interventions nationwide, but faces major human resource challenges, with a lack of skilled workers especially in the most disadvantaged rural areas.</p> <p>Methods</p> <p>We investigated reasons for poor recruitment and retention in rural areas and potential policy interventions through quantitative and qualitative data collection with nursing trainees. We interviewed 345 trainees from four purposively selected Medical Training Colleges (MTCs) (166 pre-service and 179 upgrading trainees with prior work experience). Each interviewee completed a self-administered questionnaire including likert scale responses to statements about rural areas and interventions, and focus group discussions (FGDs) were conducted at each MTC.</p> <p>Results</p> <p>Likert scale responses indicated mixed perceptions of both living and working in rural areas, with a range of positive, negative and indifferent views expressed on average across different statements. The analysis showed that attitudes to working in rural areas were significantly positively affected by being older, but negatively affected by being an upgrading student. Attitudes to living in rural areas were significantly positively affected by being a student at the MTC furthest from Nairobi.</p> <p>During FGDs trainees raised both positive and negative aspects of rural life. Positive aspects included lower costs of living and more autonomy at work. Negative issues included poor infrastructure, inadequate education facilities and opportunities, higher workloads, and inadequate supplies and supervision. Particular concern was expressed about working in communities dominated by other tribes, reflecting Kenya’s recent election-related violence.</p> <p>Quantitative and qualitative data indicated that students believed several strategies could improve rural recruitment and retention, with particular emphasis on substantial rural allowances and the ability to choose their rural location. Other interventions highlighted included provision of decent housing, and more rapid career advancement. However, recently introduced short term contracts in named locations were not favoured due to their lack of pension plans and job security.</p> <p>Conclusions</p> <p>This study identified a range of potential interventions to increase rural recruitment and retention, with those most favored by nursing students being additional rural allowances, and allowing choice of rural location. Greater investment is needed in information systems to evaluate the impact of such policies.</p