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

Prolactinoma presenting as chronic anaemia with osteoporosis: a case report

<p>Abstract</p> <p>Introduction</p> <p>Unexplained anaemia is a rare mode of presentation for prolactinoma. We describe a case of a man, with chronic anaemia ascribed to old age. Six years later, he was evaluated and diagnosed with a prolactinoma and resultant osteoporosis. Prolactinoma in old people may present insidiously with chronic anaemia and osteoporosis with or without sexual dysfunction.</p> <p>Case presentation</p> <p>We describe the case of a 70-year-old Caucasian man who presented with mild anaemia and tiredness. His anaemia was investigated and ascribed to senescence. Endocrine causes were not considered or tested for. Six years later, he was again referred. Reassessment and direct questioning revealed long-standing sexual dysfunction. It was also discovered that our patient had fractured his radius twice, with minor trauma, during the preceding year. His serum prolactin was massively increased and a magnetic resonance imaging (MRI) scan of the head demonstrated a pituitary mass consistent with a prolactinoma. Dual X-ray absorptiometry revealed osteoporosis. Treatment of the prolactinoma led to a reduction in his serum prolactin with a rise in his haemoglobin to normal levels. This suggested that the prolactinoma was present during the initial presentation and was the cause of his anaemia.</p> <p>Conclusion</p> <p>This case highlights the importance of fully evaluating and investigating unexplained anaemia in older people and that endocrine causes should be considered. Osteoporosis also requires evaluation with secondary causes considered.</p

Conceptualisation of an Efficient Particle-Based Simulation of a Twin-Screw Granulator

Discrete Element Method (DEM) simulations have the potential to provide particle-scale understanding of twin-screw granulators. This is difficult to obtain experimentally because of the closed, tightly confined geometry. An essential prerequisite for successful DEM modelling of a twin-screw granulator is making the simulations tractable, i.e., reducing the significant computational cost while retaining the key physics. Four methods are evaluated in this paper to achieve this goal: (i) develop reduced-scale periodic simulations to reduce the number of particles; (ii) further reduce this number by scaling particle sizes appropriately; (iii) adopt an adhesive, elasto-plastic contact model to capture the effect of the liquid binder rather than fluid coupling; (iv) identify the subset of model parameters that are influential for calibration. All DEM simulations considered a GEA ConsiGma™ 1 twin-screw granulator with a 60° rearward configuration for kneading elements. Periodic simulations yielded similar results to a full-scale simulation at significantly reduced computational cost. If the level of cohesion in the contact model is calibrated using laboratory testing, valid results can be obtained without fluid coupling. Friction between granules and the internal surfaces of the granulator is a very influential parameter because the response of this system is dominated by interactions with the geometry

A tandem evolutionary algorithm for identifying causal rules from complex data

We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recover the true causal rule sets used to generate the data. Finally, we discuss an application to a complex real-world survey dataset designed to inform possible ecohealth interventions for Chagas disease

Computational Fluid Dynamic Simulations for Determination of Ventricular Workload in Aortic Arch Obstructions

Objective The cardiac workload associated with various types of aortic obstruction was determined using computational fluid dynamic simulations. Methods Computed tomography image data were collected from 4 patients with 4 distinct types of aortic arch obstructions and 4 controls. The categorization of arch hypoplasia corresponded to the “A, B, C” nomenclature of arch interruption; a type “D” was added to represent diffuse arch hypoplasia. Measurements of the vessel diameter were compared against the normal measurements to determine the degree of narrowing. Three-dimensional models were created for each patient, and additional models were created for type A and B hypoplasia to represent 25%, 50%, and 75% diameter narrowing. The boundary conditions for the computational simulations were chosen to achieve realistic flow and pressures in the control cases. The simulations were then repeated after changing the boundary conditions to represent a range of cardiac and vascular adaptations. The resulting cardiac workload was compared with the control cases. Results Of the 4 patients investigated, 1 had aortic coarctation and 3 had aortic hypoplasia. The cardiac workload of the patients with 25% narrowing type A and B hypoplasia was not appreciably different from that of the control. When comparing the different arch obstructions, 75% type A, 50% type B, and 50% type D hypoplasia required a greater workload increase than 75% coarctation. Conclusions The present study has determined the hemodynamic significance of aortic arch obstruction using computational simulations to calculate the cardiac workload. These results suggest that all types of hypoplasia pose more of a workload challenge than coarctation with an equivalent degree of narrowing

Post-processing and visualisation of large-scale DEM simulation data with the open-source VELaSSCo platform

Regardless of its origin, in the near future the challenge will not be how to generate data, but rather how to manage big and highly distributed data to make it more easily handled and more accessible by users on their personal devices. VELaSSCo (Visualization for Extremely Large-Scale Scientific Computing) is a platform developed to provide new visual analysis methods for large-scale simulations serving the petabyte era. The platform adopts Big Data tools/architectures to enable in-situ processing for analytics of engineering and scientific data and hardware-accelerated interactive visualization. In large-scale simulations, the domain is partitioned across several thousand nodes, and the data (mesh and results) are stored on those nodes in a distributed manner. The VELaSSCo platform accesses this distributed information, processes the raw data, and returns the results to the users for local visualization by their specific visualization clients and tools. The global goal of VELaSSCo is to provide Big Data tools for the engineering and scientific community, in order to better manipulate simulations with billions of distributed records. The ability to easily handle large amounts of data will also enable larger, higher resolution simulations, which will allow the scientific and engineering communities to garner new knowledge from simulations previously considered too large to handle. This paper shows, by means of selected Discrete Element Method (DEM) simulation use cases, that the VELaSSCo platform facilitates distributed post-processing and visualization of large engineering datasets

Novel evolutionary algorithm identifies interactions driving infestation of triatoma dimidiata, a chagas disease vector

Chagas disease is a lethal, neglected tropical disease. Unfortunately, aggressive insecticide-spraying campaigns have not been able to eliminate domestic infestation of Triatoma dimidiata, the native vector in Guatemala. To target interventions toward houses most at risk of infestation, comprehensive socioeconomic and entomologic surveys were conducted in two towns in Jutiapa, Guatemala. Given the exhaustively large search space associated with combinations of risk factors, traditional statistics are limited in their ability to discover risk factor interactions. Two recently developed statistical evolutionary algorithms, specifically designed to accommodate risk factor interactions and heterogeneity, were applied to this large combinatorial search space and used in tandem to identify sets of risk factor combinations associated with infestation. The optimal model includes 10 risk factors in what is known as a third-order disjunctive normal form (i.e., infested households have chicken coops AND deteriorated bedroom walls OR an accumulation of objects AND dirt floors AND total number of occupants 3 5 AND years of electricity 3 5 OR poor hygienic condition ratings AND adobe walls AND deteriorated walls AND dogs). Houses with dirt floors and deteriorated walls have been reported previously as risk factors and align well with factors currently targeted by Ecohealth interventions to minimize infestation. However, the tandem evolutionary algorithms also identified two new socioeconomic risk factors (i.e., households having many occupants and years of electricity 3 5). Identifying key risk factors may help with the development of new Ecohealth interventions and/or reduce the survey time needed to identify houses most at risk

Uncovering vector, parasite, blood meal and microbiome patterns from mixed-DNA specimens of the Chagas disease vector Triatoma dimidiata

Chagas disease, considered a neglected disease by the World Health Organization, is caused by the protozoan parasite Trypanosoma cruzi, and transmitted by \u3e140 triatomine species across the Americas. In Central America, the main vector is Triatoma dimidiata, an opportunistic blood meal feeder inhabiting both domestic and sylvatic ecotopes. Given the diversity of interacting biological agents involved in the epidemiology of Chagas disease, having simultaneous information on the dynamics of the parasite, vector, the gut microbiome of the vector, and the blood meal source would facilitate identifying key biotic factors associated with the risk of T. cruzi transmission. In this study, we developed a RADseq-based analysis pipeline to study mixed-species DNA extracted from T. dimidiata abdomens. To evaluate the efficacy of the method across spatial scales, we used a nested spatial sampling design that spanned from individual villages within Guatemala to major biogeographic regions of Central America. Information from each biotic source was distinguished with bioinformatics tools and used to evaluate the prevalence of T. cruzi infection and predominant Discrete Typing Units (DTUs) in the region, the population genetic structure of T. dimidiata, gut microbial diversity, and the blood meal history. An average of 3.25 million reads per specimen were obtained, with approximately 1% assigned to the parasite, 20% to the vector, 11% to bacteria, and 4% to putative blood meals. Using a total of 6,405 T. cruzi SNPs, we detected nine infected vectors harboring two distinct DTUs: TcI and a second unidentified strain, possibly TcIV. Vector specimens were sufficiently variable for population genomic analyses, with a total of 25,710 T. dimidiata SNPs across all samples that were sufficient to detect geographic genetic structure at both local and regional scales. We observed a diverse microbiotic community, with significantly higher bacterial species richness in infected T. dimidiata abdomens than those that were not infected. Unifrac analysis suggests a common assemblage of bacteria associated with infection, which co-occurs with the typical gut microbial community derived from the local environment. We identified vertebrate blood meals from five T. dimidiata abdomens, including chicken, dog, duck and human; however, additional detection methods would be necessary to confidently identify blood meal sources from most specimens. Overall, our study shows this method is effective for simultaneously generating genetic data on vectors and their associated parasites, along with ecological information on feeding patterns and microbial interactions that may be followed up with complementary approaches such as PCR-based parasite detection, 18S eukaryotic and 16S bacterial barcoding

Metered Cryospray™: a novel uniform, controlled, and consistent in vivo application of liquid nitrogen cryogenic spray

Typically, wood-based composite materials have been developed through empirical studies. In these products, the constituent wood elements have broad spectrums regarding species, size, and anatomical orientation relative to their own dimensions. To define special strength and stiffness properties during a long-term study, two types of corrugated wood composite panels were developed for possible structural utilization. The constitutional elements of the newly developed products included Appalachian hardwood veneer residues (side clippings) and/or rejected low quality, sliced veneer sheets. The proposed primary usage of these veneer-based panels is in applications where the edgewise loading may cause buckling (e.g., web elements of I-joists, shear-wall and composite beam core materials). This paper describes the development of flat and corrugated panels, including furnish preparations and laboratory-scale manufacturing processes as well as the determination of key mechanical properties. According to the results in parallel to grain direction bending, tension and compression strengths exceeded other structural panels’ similar characteristics, while the rigidities were comparable. Based on the research findings, sliced veneer clipping waste can be transformed into structural panels or used as reinforcement elements in beams and sandwich-type products

Bostonia: The Boston University Alumni Magazine. Volume 10

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs