Software defined networking for radio telescopes: a case study on the applicability of SDN for MeerKAT

Scientific instruments like radio telescopes depend on high-performance networks for internal data exchange. The high bandwidth data exchange between the components of a radio telescope makes use of multicast networking. Complex multicast networks are hard to maintain and grow, and specific installations require modified network switches. This study evaluates Software Defined Networking (SDN) for use in the MeerKAT radio telescope to alleviate the management complexity and allow for a vendor-neutral implementation. The purpose of this dissertation is to verify that an SDN multicast network can produce suitable paths for data flow through the network and to see if such an implementation is easier to maintain and grow. There is little literature regarding SDN for radio telescope networks; however, there is considerable work where different aspects of SDN are discussed and demonstrated for video streaming. SDN with multicast for video streaming, although simpler, forms the background research. Considerable work was put into understanding and documenting the different aspects of a radio telescope affecting the data network. The telescope network controller generates the OpenFlow rules required by the SDN controller and is a new concept introduced in this work. The telescope network controller is fitted with two placement algorithms to demonstrate its flexibility. Both algorithms are suitable for the expected workload, but they produce very different traffic patterns. The two algorithms are not compared to one another, they were created to demonstrate the ease of adding domain specific knowledge to an SDN. The telescope network controller makes it easy to introduce and use new flow placement algorithms, thus making traffic engineering feasible for the radio telescope. Complex multicast networks are easier to maintain and grow with SDN. SDN allows customised packet forwarding rules typically unattainable with standard routing and other standard network protocols and implementations. A radio telescope with a software-defined data network is resilient, easier to maintain, vendor-neutral, and possesses advanced traffic engineering mechanisms

A Study of Client-based Caching for Parallel I/O

The trend in parallel computing toward large-scale cluster computers running thousands of cooperating processes per application has led to an I/O bottleneck that has only gotten more severe as the the number of processing cores per CPU has increased. Current parallel file systems are able to provide high bandwidth file access for large contiguous file region accesses; however, applications repeatedly accessing small file regions on unaligned file region boundaries continue to experience poor I/O throughput due to the high overhead associated with accessing parallel file system data. In this dissertation we demonstrate how client-side file data caching can improve parallel file system throughput for applications performing frequent small and unaligned file I/O. We explore the impacts of cache page size and cache capacity using the popular FLASH I/O benchmark and explore a novel cache sharing approach that leverages the trend toward multi-core processors. We also explore a technique we call progressive page caching that represents cache data using dynamic data structures rather than fixed-size pages of file data. Finally, we explore a cache aggregation scheme that leverages the high-level file I/O interfaces provided by the PVFS file system to provide further performance enhancements. In summary, our results indicate that a correctly configured middleware-based file data cache can dramatically improve the performance of I/O workloads dominated by small unaligned file accesses. Further, we demonstrate that a well designed cache can offer stable performance even when the selected cache page granularity is not well matched to the provided workload. Finally, we have shown that high-level file system interfaces can significantly accelerate application performance, and interfaces beyond those currently envisioned by the MPI-IO standard could provide further performance benefits

Transcriptomic Analyses Reveal Novel Genes with Sexually Dimorphic Expression in the Zebrafish Gonad and Brain

Background Our knowledge on zebrafish reproduction is very limited. We generated a gonad-derived cDNA microarray from zebrafish and used it to analyze large-scale gene expression profiles in adult gonads and other organs. Methodology/Principal Findings We have identified 116638 gonad-derived zebrafish expressed sequence tags (ESTs), 21% of which were isolated in our lab. Following in silico normalization, we constructed a gonad-derived microarray comprising 6370 unique, full-length cDNAs from differentiating and adult gonads. Labeled targets from adult gonad, brain, kidney and ‘rest-of-body’ from both sexes were hybridized onto the microarray. Our analyses revealed 1366, 881 and 656 differentially expressed transcripts (34.7% novel) that showed highest expression in ovary, testis and both gonads respectively. Hierarchical clustering showed correlation of the two gonadal transcriptomes and their similarities to those of the brains. In addition, we have identified 276 genes showing sexually dimorphic expression both between the brains and between the gonads. By in situ hybridization, we showed that the gonadal transcripts with the strongest array signal intensities were germline-expressed. We found that five members of the GTP-binding septin gene family, from which only one member (septin 4) has previously been implicated in reproduction in mice, were all strongly expressed in the gonads. Conclusions/Significance We have generated a gonad-derived zebrafish cDNA microarray and demonstrated its usefulness in identifying genes with sexually dimorphic co-expression in both the gonads and the brains. We have also provided the first evidence of large-scale differential gene expression between female and male brains of a teleost. Our microarray would be useful for studying gonad development, differentiation and function not only in zebrafish but also in related teleosts via cross-species hybridizations. Since several genes have been shown to play similar roles in gonadogenesis in zebrafish and other vertebrates, our array may even provide information on genetic disorders affecting gonadal phenotypes and fertility in mammals

Genetic and Molecular Investigation of the Schnitzler Syndrome

The Schnitzler Syndrome (SchS) is a rare, autoinflammatory condition with an unknown pathological mechanism, but treatment with IL-1 inhibition provides remarkable efficacy. Exhibiting two main defining features: (1) an urticarial rash and (2) an IgM gammopathy, this IL-1 mediated disease phenotypically bears stark resemblance to NLRP3-associated inflammatory disease. The latter monogenic entity is known to show gain-of-function and pathological mutations in the NACHT, LRR and PYD domains-containing protein 3 (NLRP3) inflammasome. Furthermore, 20% of SchS patients go on to develop overt lymphoproliferative diseases, namely Waldenström's Macroglobulinaemia (WM). This condition presents with a specific mutation in the Myeloid Differentiation Primary Response (MYD88) gene in over 90% of patients. Against the backdrop of these imperative findings, the work presented in this thesis therefore examines the role of these immunological constituents in SchS, via the assessment of mutations in NLRP3 and MYD88 alongside a panel of genes frequently mutated in haematological malignancies. Identification of a causative gene would not only improve molecular diagnosis, but allows for potential unearthing of genotype-phenotype correlations. Since the identification of this condition in 1972, the features and consequences of the IgM gammopathy has remained elusive. In a bid to delineate the latter, examination of the heavy chain of the immunoglobulin repertoire would therefore indicate aspects of the adaptive immune response integral to formation of the monoclonal component. A biased repertoire would therefore indicate the existence of a clonal B-cell population. Additionally, isolation of SchS-IgM and interrogation of a protein array comprising of over 15,700 human proteins further indicates whether this element causes pathological effects. Exploration of the genetic and molecular components did not expose a common mechanism through which SchS manifests, ruling out the notion that NLRP3, MYD88 and other associated genes are universally causative of this enigmatic disease. Assessment of the IgH repertoire indicated that SchS patients show evidence of expanded B-cell populations, and together with protein array analysis demonstrating the preference of IgM binding to nuclear antigens, this study supports the theory that SchS is a clonal disorder. The breadth and depth of these findings broadens the currently limited scientific knowledge pertaining to SchS, forming the basis upon which further investigations can commence

Multi-angle valve seat machining: experimental analysis and numerical modelling

Modern automotive manufacturers operate in highly competitive markets, heavily influenced by Government regulation and ever more environmentally conscious consumers. Modern high-temperature, high-pressure engines that use high hardness multi-angle valve seats are an attractive environmental option, but one that manufacturers find requires more advanced materials and tighter geometric tolerances to maintain engine performance.Tool manufacturers meet these increasingly tougher demands by using, higher hardness cutting materials such as polycrystalline cubic boron nitride (pcBN), that on paper, promise to wear at a lower rate, require less coolant and deliver tighter tolerances than their carbide counterparts.The low brittle fracture toughness of pcBN makes tools that use it vulnerable to minute chipping. A review of literature for this work pointed to no clear answer to this problem, although suggestions range from manufacturing defects, dynamic and flexibility problems with the production line machinery and fixtures, and radial imbalances in the cutting loads.This work set about experimentally investigating those potential explanations, coming to the conclusion that the high radial imbalance of the cutting loads is responsible for pcBN cutting insert failure during multi-angle valve seat machining, and that by simply relocating the cutting inserts around the multi angle cutting tool, the imbalance can be reduced, thus extending the life of the cutting inserts.It is not always easy to predict the imbalance due to the multiple flexibilities in the system, and simulating such a system in 3D with all its associated cutting phenomena such as friction, thermal expansion, chip flow and shearing, would call upon extraordinary computational power and extremely precise experimental inputs to reduce cumulative error.This thesis proves that such a 3D simulation can be made, that runs in exceptionally short durations compared to traditional methods, by making a number of simplifications.MSC Marc was used to host the simulation, with a parametric script written in Python responsible for generating the model geometry and cutter layout. A Fortran program was developed that is called upon by Marc to calculate the required cutting load outputs and generate new workpiece meshes as material is removed.</div

Recent Trends in Computational Research on Diseases

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level