Compiler and runtime techniques for optimizing dynamic scripting languages

This thesis studies the compilation and runtime techniques to improve the performance of dynamic scripting languages using R programming language as a test case. The R programming language is a convenient system for statistical computing. In this era of big data, R is becoming increasingly popular as a powerful data analytics tool. But the performance of R limits its usage in a broader context. The thesis introduces a classification of R programming styles into Looping over data(Type I), Vector programming(Type II), and Glue codes(Type III), and identified the most serious overhead of R is mostly manifested in Type I R codes. It proposes techniques to improve the performance R. First, it uses interpreter level specialization to do object allocation removal and path length reduction, and evaluates its effectiveness for GNU R VM. The approach uses profiling to translate R byte-code into a specialized byte-code to improve running speed, and uses data representation specialization to reduce the memory allocation and usage. Secondly, it proposes a lightweight approach that reduces the interpretation overhead of R through vectorization of the widely used Apply class of operations in R. The approach combines data transformation and function vectorization to transform the looping-over-data execution into a code with mostly vector operations, which can significantly speedup the execution of Apply operations in R without any native code generation and still using only a single-thread of execution. Thirdly, the Apply vectorization technique is integrated into SparkR, a widely used distributed R computing system, and has successfully improved its performance. Furthermore, an R benchmark suite has been developed. It includes a collection of different types of R applications, and a flexible benchmarking environment for conducting performance research for R. All these techniques could be applied to other dynamic scripting languages. The techniques proposed in the thesis use a pure interpretation approach (the system based on the techniques does not generate native code) to improve the performance of R. The strategy has the advantage of maintaining the portability and compatibility of the VM, simplify the implementation. It is also a very interesting problem to see the potential of an interpreter

Antibacterial Activities of Nepetalactones Against Public Health-Related Pathogens

The antimicrobial activities of (Z,E)- and (E,Z)-nepetalactones, 2 major compositional compounds from the essential oil of catnip (Nepeta cataria), were first discovered from fly larval development media studies with over 98% inhibition of bacterial growth. Further investigation demonstrated inhibition of the growth of various bacterial species of public health significance. Catnip oil showed antibacterial activity against 5 Gram-positive and 9 Gram-negative bacteria. The antimicrobial activity varied among the original essential oil from the plant and its major compositional compounds as a blended mixture or an individual compound. Growth inhibition was observed against 5 Neisseria species, with particularly strong inhibition against Neisseria sicca (with MICs ranging from 0.5 to 5 mg/mL) that provided comparable or increased levels of growth control produced by 2 antibiotics (Ceftiofur and Cephalothin). The development of plant-based antibacterial agents to prevent or delay the emergence of antimicrobial resistance in bacteria is discussed

Generating High Density, Low Cost Genotype Data in Soybean [\u3ci\u3eGlycine max\u3c/i\u3e (L.) Merr.]

Obtaining genome-wide genotype information for millions of SNPs in soybean [Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage. Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI Short Read Archive. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using GATK’s Haplotype Caller tool. Whole genome resequencing at approximately 1X depth was performed on 114 previously ungenotyped experimental soybean lines. Coverages down to 0.1X were analyzed by randomly subsetting raw reads from the original 1X sequence data. SNPs discovered in the reference panel were genotyped in the experimental lines after aligning to the soybean reference genome, and missing markers imputed using Beagle 4.1. Sequencing depth of the experimental lines could be reduced to 0.3X while still retaining an accuracy of 97.8%. Accuracy was inversely related to minor allele frequency, and highly correlated with marker linkage disequilibrium. The high accuracy of skim sequencing combined with imputation provides a low cost method for obtaining dense genotypic information that can be used for various genomics applications in soybean

Soil pH, total phosphorus, climate and distance are the major factors influencing microbial activity at a regional spatial scale

Considering the extensive functional redundancy in microbial communities and great difficulty in elucidating it based on taxonomic structure, studies on the biogeography of soil microbial activity at large spatial scale are as important as microbial community structure. Eighty-four soil samples were collected across a region from south to north China (about 1,000 km) to address the questions if microbial activity displays biogeographic patterns and what are driving forces. These samples represented different soil types, land use and climate. Redundancy analysis and nonmetric multidimensional scaling clearly revealed that soil microbial activities showed distinct differentiation at different sites over a regional spatial scale, which were strongly affected by soil pH, total P, rainfall, temperature, soil type and location. In addition, microbial community structure was greatly influenced by rainfall, location, temperature, soil pH and soil type and was correlated with microbial activity to some extent. Our results suggest that microbial activities display a clear geographic pattern that is greatly altered by geographic distance and reflected by climate, soil pH and total P over large spatial scales. There are common (distance, climate, pH and soil type) but differentiated aspects (TP, SOC and N) in the biogeography of soil microbial community structure and activity

European populations of \u3ci\u3eDiabrotica virgifera virgifera\u3c/i\u3e are resistant to aldrin, but not to methyl-parathion

The western corn rootworm, Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), is a major pest of cultivated corn in North America and has recently begun to invade Europe. In addition to crop rotation, chemical control is an important option for D. v. virgifera management. However, resistance to chemical insecticides has evolved repeatedly in the USA. In Europe, chemical control strategies have yet to be harmonized and no surveys of insecticide resistance have been carried out. We investigated the resistance to methyl-parathion and aldrin of samples from nine D. v. virgifera field populations originating from two European outbreaks thought to have originated from two independent introductions from North America. Diagnostic concentration bioassays revealed that all nine D. v. virgifera field populations were resistant to aldrin but susceptible to methyl-parathion. Aldrin resistance was probably introduced independently, at least twice, from North America into Europe, as there is no evident selection pressure to account for an increase of frequency of aldrin resistance in each of the invasive outbreaks in Europe. Our results suggest that organophosphates, such as methyl-parathion, may still provide effective control of both larval and adult D. v. virgifera in the European invasive outbreaks studied