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

Differential Gene Expression Profiling in Bed Bug (Cimex Lectularius L.) Fed on Ibuprofen and Caffeine in Reconstituted Human Blood

The recent resurgence of the common bed bug (Cimex lectularius L.) infestations worldwide has created a need for renewed research on biology, behavior, population genetics and management practices. Humans serve as exclusive hosts to bed bugs in urban environments. Since a majority of humans consume Ibuprofen (as pain medication) and caffeine (in coffee and other soft drinks) so bug bugs subsequently acquire Ibuprofen and caffeine through blood feeding. However, the effect of these chemicals at genetic level in bed bug is unknown. Therefore, this research was conducted to determine differential gene expression in bed bugs using RNA-Seq analysis at dosages of 200 ppm Ibuprofen and 40 ppm caffeine incorporated into reconstituted human blood and compared against the control. Total RNA was extracted from a single bed bug per replication per treatment and sequenced. Read counts obtained were analyzed using Bioconductor software programs to identify differentially expressed genes, which were then searched against the non-redundant (nr) protein database of National Center for Biotechnology Information (NCBI). Data on comparison of differentially expressed genes between control and Ibuprofen treatments revealed that 659 genes were significantly differentially regulated and 95% of them returned BLAST hits. Heat stress proteins were among the top significantly differentially down regulated genes. Comparison of the control vs caffeine treatments revealed that 2,161 genes were significantly differently regulated (Pad

Crop DNA extraction with lab-made magnetic nanoparticles

Molecular breeding methods, such as marker-assisted selection and genomic selection, require high-throughput and cost-effective methods for isolating genomic DNA from plants, specifically from crop tissue or seed with high polysaccharides, lipids, and proteins. A quick and inexpensive high-throughput method for isolating genomic DNA from seed and leaf tissue from multiple crops was tested with a DNA isolation method that combines CTAB extraction buffer and lab-made SA-coated magnetic nanoparticles. This method is capable of isolating quality genomic DNA from leaf tissue and seeds in less than 2 hours with fewer steps than a standard CTAB extraction method. The yield of the genomic DNA was 582– 729 ng per 5 leaf discs or 216–1869 ng per seed in soybean, 2.92–62.6 ng per 5 leaf discs or 78.9–219 ng per seed in wheat, and 30.9–35.4 ng per 5 leaf discs in maize. The isolated DNA was tested with multiple molecular breeding methods and was found to be of sufficient quality and quantity for PCR and targeted genotyping by sequencing methods such as molecular inversion probes (MIPs). The combination of SA-coated magnetic nanoparticles and CTAB extraction buffer is a fast, simple, and environmentally friendly, high-throughput method for both leaf tissues and seed(s) DNA preparation at low cost per sample. The DNA obtained from this method can be deployed in applied breeding programs for marker-assisted selection or genomic selection

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

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

The role of lactate-induced protein lactylation in gliomas: implications for preclinical research and the development of new treatments

The most prevalent primary brain tumors in adults are gliomas. In addition to insufficient therapeutic alternatives, gliomas are fatal mostly due to the rapid proliferation and continuous infiltration of tumor cells into the surrounding healthy brain tissue. According to a growing body of research, aerobic glycolysis, or the Warburg effect, promotes glioma development because gliomas are heterogeneous cancers that undergo metabolic reprogramming. Therefore, addressing the Warburg effect might be a useful therapeutic strategy for treating cancer. Lactate plays a critical role in reprogramming energy metabolism, allowing cells to rapidly access large amounts of energy. Lactate, a byproduct of glycolysis, is therefore present in rapidly proliferating cells and tumors. In addition to the protumorigenesis pathways of lactate synthesis, circulation, and consumption, lactate-induced lactylation has been identified in recent investigations. Lactate plays crucial roles in modulating immune processes, maintaining homeostasis, and promoting metabolic reprogramming in tumors, which are processes regulated by the lactate-induced lactylation of the lysine residues of histones. In this paper, we discuss the discovery and effects of lactylation, review the published studies on how protein lactylation influences cancer growth and further explore novel treatment approaches to achieve improved antitumor effects by targeting lactylation. These findings could lead to a new approach and guidance for improving the prognosis of patients with gliomas

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

Knockdown of RNA Interference Pathway Genes in Western Corn Rootworms (\u3ci\u3eDiabrotica virgifera virgifera\u3c/i\u3e Le Conte) Demonstrates a Possible Mechanism of Resistance to Lethal dsRNA

RNA interference (RNAi) is being developed as a potential tool for insect pest management. Increased understanding of the RNAi pathway in target insect pests will provide information to use this technology effectively and to inform decisions related to resistant management strategies for RNAi based traits. Dicer 2 (Dcr2), an endonuclease responsible for formation of small interfering RNA’s and Argonaute 2 (Ago2), an essential catalytic component of the RNA-induced silencing complex (RISC) have both been associated with the RNAi pathway in a number of different insect species including the western corn rootworm, Diabrotica virgifera virgifera (Coleoptera: Chrysomelidae). We identified both genes from a transcriptome library generated from different tissues and developmental stages of the western corn rootworm, an important target pest for transgenic plants expressing dsRNA targeting essential genes. The expression of these genes was suppressed by more than 90% after injecting gene specific dsRNA into adult rootworms. The injected beetles were then fed vATPase A dsRNA which has previously been demonstrated to cause mortality in western corn rootworm adults. The suppression of both RNAi pathway genes resulted in reduced mortality after subsequent exposure to lethal concentrations of vATPase A dsRNA as well as increased vATPase A expression relative to control treatments. Injections with dsRNA for a non-lethal target sequence (Laccase 2) did not affect mortality or expression caused by vATPase A dsRNA indicating that the results observed with Argo and Dicer dsRNA were not caused by simple competition among different dsRNA’s. These results confirm that both genes play an important role in the RNAi pathway for western corn rootworms and indicate that selection pressures that potentially affect the expression of these genes may provide a basis for future studies to understand potential mechanisms of resistance