ECONOMIC COSTS AND HEALTH BURDEN OF VISION PROBLEMS IN SINGAPORE

Ph.DDOCTOR OF PHILOSOPH

Next generation nitric oxide-releasing polyurethane membranes for implantable glucose biosensors

Herein, fabrication of polyurethane glucose sensor membranes doped with next generation biocompatible macromolecular nitric oxide-releasing scaffolds (hyperbranched polyesters, hyperbranched polyurethanes, alginate) was reported. Nitric oxide-releasing S-nitrosothiol-modified hyperbranched polyesters-doped membranes achieved extended nitric oxide-release (> 48 h) and promising in vitro sensor performance (sensitivity 20.9 nA/mM, linear dynamic range 0 – 15 mM). Nitric oxide-releasing N-diazeniumdiolate-modified hyperbranched polyurethanes-doped membranes showed desirable smooth membrane morphology, but had relatively limited nitric oxide-releasing duration (up to 18 h) and half-life (~ 0.4 h). Nitric oxide-releasing N-diazeniumdiolate-modified alginate-doped membranes showed significantly longer nitric oxide-releasing half-life (~ 1.2 h) with hyperbranched polyurethanes, but their nitric oxide-releasing duration (< 20 h) was still limited. Challenges for future studies on this subject lie in extending the nitric oxide-releasing lifetimes of the membranes and obtaining more control over the nitric oxide-release kinetics.Bachelor of Scienc

Synchronization of reaction–diffusion Hopfield neural networks with s-delays through sliding mode control

Synchronization of reaction–diffusion Hopfield neural networks with s-delays via sliding mode control (SMC) is investigated in this paper. To begin with, the system is studied in an abstract Hilbert space C([–r; 0];U) rather than usual Euclid space Rn. Then we prove that the state vector of the drive system synchronizes to that of the response system on the switching surface, which relies on equivalent control. Furthermore, we prove that switching surface is the sliding mode area under SMC. Moreover, SMC controller can also force with any initial state to reach the switching surface within finite time, and the approximating time estimate is given explicitly. These criteria are easy to check and have less restrictions, so they can provide solid theoretical guidance for practical design in the future. Three different novel Lyapunov–Krasovskii functionals are used in corresponding proofs. Meanwhile, some inequalities such as Young inequality, Cauchy inequality, Poincaré inequality, Hanalay inequality are applied in these proofs. Finally, an example is given to illustrate the availability of our theoretical result, and the simulation is also carried out based on Runge–Kutta–Chebyshev method through Matlab

Biogenesis of iron–sulfur clusters and their role in DNA metabolism

Iron–sulfur (Fe/S) clusters (ISCs) are redox-active protein cofactors that their synthesis, transfer, and insertion into target proteins require many components. Mitochondrial ISC assembly is the foundation of all cellular ISCs in eukaryotic cells. The mitochondrial ISC cooperates with the cytosolic Fe/S protein assembly (CIA) systems to accomplish the cytosolic and nuclear Fe/S clusters maturation. ISCs are needed for diverse cellular functions, including nitrogen fixation, oxidative phosphorylation, mitochondrial respiratory pathways, and ribosome assembly. Recent research advances have confirmed the existence of different ISCs in enzymes that regulate DNA metabolism, including helicases, nucleases, primases, DNA polymerases, and glycosylases. Here we outline the synthesis of mitochondrial, cytosolic and nuclear ISCs and highlight their functions in DNA metabolism

The neurological and non-neurological roles of the primary microcephaly-associated protein ASPM

Primary microcephaly (MCPH), is a neurological disorder characterized by small brain size that results in numerous developmental problems, including intellectual disability, motor and speech delays, and seizures. Hitherto, over 30 MCPH causing genes ( MCPHs ) have been identified. Among these MCPHs , MCPH5 , which encodes abnormal spindle-like microcephaly-associated protein (ASPM), is the most frequently mutated gene. ASPM regulates mitotic events, cell proliferation, replication stress response, DNA repair, and tumorigenesis. Moreover, using a data mining approach, we have confirmed that high levels of expression of ASPM correlate with poor prognosis in several types of tumors. Here, we summarize the neurological and non-neurological functions of ASPM and provide insight into its implications for the diagnosis and treatment of MCPH and cancer

Cytokinesis and cancer: polo loves ROCK'n' Rho(A)

Cytokinesis is the last step of the M (mitosis) phase, yet it is crucial for the faithful division of one cell into two. Cytokinesis failure is often associated with cancer. Cytokinesis can be morphologically divided into four steps: cleavage furrow initiation, cleavage furrow ingression, midbody formation and abscission. Molecular studies have revealed that RhoA as well as its regulators and effectors are important players to ensure a successful cytokinesis. At the same time, Polo-like kinase 1 (Plk1) is an important kinase that can target many substrates and carry out different functions during mitosis, including cytokinesis. Recent studies are beginning to unveil a closer tie between Plk1 and RhoA networks. More specifically, Plk1 phosphorylates the centralspindlin complex Cyk4 and MKLP1/CHO1, thus recruiting RhoA guanine nucleotide-exchange factor (GEF) Ect2 through its phosphopeptide-binding BRCT domains. Ect2 itself can be phosphorylated by Plk1 in vitro. Plk1 can also phosphorylate another GEF MyoGEF to regulate RhoA activity. Once activated, RhoA-GTP will activate downstream effectors, including ROCK1 and ROCK2. ROCK2 is among the proteins that associate with Plk1 Polo-binding domain (PBD) in a large proteomic screen, and Plk1 can phosphorylate ROCK2 in vitro. We review current understandings of the interplay between Plk1, RhoA proteins and other proteins (e.g., NudC, MKLP2, PRC1, CEP55) involved in cytokinesis, with particular emphasis of its clinical implications in cancer

The neurological and non-neurological roles of the primary microcephaly-associated protein ASPM

Primary microcephaly (MCPH), is a neurological disorder characterized by small brain size that results in numerous developmental problems, including intellectual disability, motor and speech delays, and seizures. Hitherto, over 30 MCPH causing genes (MCPHs) have been identified. Among these MCPHs, MCPH5, which encodes abnormal spindle-like microcephaly-associated protein (ASPM), is the most frequently mutated gene. ASPM regulates mitotic events, cell proliferation, replication stress response, DNA repair, and tumorigenesis. Moreover, using a data mining approach, we have confirmed that high levels of expression of ASPM correlate with poor prognosis in several types of tumors. Here, we summarize the neurological and non-neurological functions of ASPM and provide insight into its implications for the diagnosis and treatment of MCPH and cancer

Efficient production of human acidic fibroblast growth factor in pea (Pisum sativum L.) plants by agroinfection of germinated seeds

<p>Abstract</p> <p>Background</p> <p>For efficient and large scale production of recombinant proteins in plants transient expression by agroinfection has a number of advantages over stable transformation. Simple manipulation, rapid analysis and high expression efficiency are possible. In pea, Pisum sativum, a Virus Induced Gene Silencing System using the pea early browning virus has been converted into an efficient agroinfection system by converting the two RNA genomes of the virus into binary expression vectors for Agrobacterium transformation.</p> <p>Results</p> <p>By vacuum infiltration (0.08 Mpa, 1 min) of germinating pea seeds with 2-3 cm roots with <it>Agrobacteria </it>carrying the binary vectors, expression of the gene for Green Fluorescent Protein as marker and the gene for the human acidic fibroblast growth factor (aFGF) was obtained in 80% of the infiltrated developing seedlings. Maximal production of the recombinant proteins was achieved 12-15 days after infiltration.</p> <p>Conclusions</p> <p>Compared to the leaf injection method vacuum infiltration of germinated seeds is highly efficient allowing large scale production of plants transiently expressing recombinant proteins. The production cycle of plants for harvesting the recombinant protein was shortened from 30 days for leaf injection to 15 days by applying vacuum infiltration. The synthesized aFGF was purified by heparin-affinity chromatography and its mitogenic activity on NIH 3T3 cells confirmed to be similar to a commercial product.</p

Benchmarking automated cell type annotation tools for single-cell ATAC-seq data

As single-cell chromatin accessibility profiling methods advance, scATAC-seq has become ever more important in the study of candidate regulatory genomic regions and their roles underlying developmental, evolutionary, and disease processes. At the same time, cell type annotation is critical in understanding the cellular composition of complex tissues and identifying potential novel cell types. However, most existing methods that can perform automated cell type annotation are designed to transfer labels from an annotated scRNA-seq data set to another scRNA-seq data set, and it is not clear whether these methods are adaptable to annotate scATAC-seq data. Several methods have been recently proposed for label transfer from scRNA-seq data to scATAC-seq data, but there is a lack of benchmarking study on the performance of these methods. Here, we evaluated the performance of five scATAC-seq annotation methods on both their classification accuracy and scalability using publicly available single-cell datasets from mouse and human tissues including brain, lung, kidney, PBMC, and BMMC. Using the BMMC data as basis, we further investigated the performance of these methods across different data sizes, mislabeling rates, sequencing depths and the number of cell types unique to scATAC-seq. Bridge integration, which is the only method that requires additional multimodal data and does not need gene activity calculation, was overall the best method and robust to changes in data size, mislabeling rate and sequencing depth. Conos was the most time and memory efficient method but performed the worst in terms of prediction accuracy. scJoint tended to assign cells to similar cell types and performed relatively poorly for complex datasets with deep annotations but performed better for datasets only with major label annotations. The performance of scGCN and Seurat v3 was moderate, but scGCN was the most time-consuming method and had the most similar performance to random classifiers for cell types unique to scATAC-seq