A global climatology of wind–wave interaction

Generally, ocean waves are thought to act as a drag on the surface wind so that momentum is transferred downwards, from the atmosphere into the waves. Recent observations have suggested that when long wavelength waves, characteristic of remotely generated swell, propagate faster than the surface wind momentum can also be transferred upwards. This upward momentum transfer acts to accelerate the near-surface wind, resulting in a low-level wave-driven wind jet. Previous studies have suggested that the sign reversal of the momentum flux is well predicted by the inverse wave age, the ratio of the surface wind speed to the speed of the waves at the peak of the spectrum. ECMWF ERA-40 data has been used here to calculate the global distribution of the inverse wave age to determine whether there are regions of the ocean that are usually in the wind-driven wave regime and others that are generally in the wave-driven wind regime. The wind-driven wave regime is found to occur most often in the mid-latitude storm tracks where wind speeds are generally high. The wave-driven wind regime is found to be prevalent in the tropics where wind speeds are generally light and swell can propagate from storms at higher latitudes. The inverse wave age is also a useful indicator of the degree of coupling between the local wind and wave fields. The climatologies presented emphasise the non-equilibrium that exists between the local wind and wave fields and highlight the importance of swell in the global oceans

Metacaspase gene function in the mushroom fungus Schizophyllum commune

The overall goal of this project was to investigate the biological role of a putative metacaspase gene present in the mushroom fungus Schizophyllum commune. For this study, we have utilized a strain of S. commune that is unable to integrate DNA via the non-homologous end joining pathway. This forces transforming DNA to integrate homologously, as is required for the purposes of gene knockout. The gene Scp1 encodes a likely member of the metacaspase protein family, which are suspected to have activity similar to caspases, the latter crucial to programmed cell death. A knockout construct containing a non-functional version of Scp1 was previously generated in our laboratory. This DNA was then transformed into Schizophyllum commune in an attempt to knockout the native Scp1 gene. At present a likely knockout (null) strain has been identified, and analysis by polymerase chain reaction has supported its status as a true knockout. A homozygous null mutant of Scp1 will then be generated, and will be compared to a wild-type strain for any alterations in colony growth and/or mushroom development. The role(s) of other members of the metacaspase family will eventually be examined by a similar approach

A New Evolutionary Algorithm For Mining Noisy, Epistatic, Geospatial Survey Data Associated With Chagas Disease

The scientific community is just beginning to understand some of the profound affects that feature interactions and heterogeneity have on natural systems. Despite the belief that these nonlinear and heterogeneous interactions exist across numerous real-world systems (e.g., from the development of personalized drug therapies to market predictions of consumer behaviors), the tools for analysis have not kept pace. This research was motivated by the desire to mine data from large socioeconomic surveys aimed at identifying the drivers of household infestation by a Triatomine insect that transmits the life-threatening Chagas disease. To decrease the risk of transmission, our colleagues at the laboratory of applied entomology and parasitology have implemented mitigation strategies (known as Ecohealth interventions); however, limited resources necessitate the search for better risk models. Mining these complex Chagas survey data for potential predictive features is challenging due to imbalanced class outcomes, missing data, heterogeneity, and the non-independence of some features. We develop an evolutionary algorithm (EA) to identify feature interactions in Big Datasets with desired categorical outcomes (e.g., disease or infestation). The method is non-parametric and uses the hypergeometric PMF as a fitness function to tackle challenges associated with using p-values in Big Data (e.g., p-values decrease inversely with the size of the dataset). To demonstrate the EA effectiveness, we first test the algorithm on three benchmark datasets. These include two classic Boolean classifier problems: (1) the majority-on problem and (2) the multiplexer problem, as well as (3) a simulated single nucleotide polymorphism (SNP) disease dataset. Next, we apply the EA to real-world Chagas Disease survey data and successfully archived numerous high-order feature interactions associated with infestation that would not have been discovered using traditional statistics. These feature interactions are also explored using network analysis. The spatial autocorrelation of the genetic data (SNPs of Triatoma dimidiata) was captured using geostatistics. Specifically, a modified semivariogram analysis was performed to characterize the SNP data and help elucidate the movement of the vector within two villages. For both villages, the SNP information showed strong spatial autocorrelation albeit with different geostatistical characteristics (sills, ranges, and nuggets). These metrics were leveraged to create risk maps that suggest the more forested village had a sylvatic source of infestation, while the other village had a domestic/peridomestic source. This initial exploration into using Big Data to analyze disease risk shows that novel and modified existing statistical tools can improve the assessment of risk on a fine-scale