Viruses in the genus Orthobunyavirus (family Bunyaviridae) are classified serologically into 18 serogroups. The viruses have a tripartite genome of negative sense RNA composed of large (L), medium (M) and small (S) segments. The L segment encodes the polymerase protein, the M segment encodes two glycoproteins, Gc and Gn, and a non-structural protein (NSm), and the S segment encodes nucleocapsid (N) and NSs proteins, in overlapping reading frames (ORF). The NSs proteins of Bunyamwera and California serogroup viruses have been shown to play a role in inhibiting host cell protein synthesis and preventing induction of interferon in infected cells. To-date, viruses in only 4 serogroups: Bunyamwera, California, Group C and Simbu, have been studied intensively. Therefore, this study was conducted with the aim to sequence the S RNA segments of representative viruses in the other 14 orthobunyavirus serogroups, to analyse virus-encoded proteins synthesised in infected cells, and to investigate their ability to cause shutoff of host protein synthesis. S RNA segment sequences were obtained from cloned RT-PCR products. They were compared with the available sequences and each other. Complete S RNA sequences of Anopheles A (ANAV) and Tacaiuma virus (TCMV) [Anopheles A serogroup], Anopheles B (ANBV) and Boraceia virus (BORV) [Anopheles B serogroup], Eretmapodites (E147V) and Nyando virus (NDV)[Nyando serogroup], Bwamba virus (BWAV) [Bwamba serogroup], M’Poko virus (MPOV) [Turlock serogroup], Tete (TETEV) and Batama virus (BMAV) [Tete serogroup], and Gamboa (GAMV) and San Juan 2441 virus (SJ244V) [Gamboa serogroup], and partial sequences of Patois virus (PATV) [Patois serogroup], Guama (GMAV) and Bertioga virus (BERV) [Guama serogroup], Capim virus (CAPV) [Capim serogroup] and Palestina virus (PLSV) [Minatitlan serogroup] were obtained. Complete S segment sequences revealed that viruses in the same serogroup have same length of N and NSs proteins, except for the viruses in Gamboa serogroup which were found to have two lengths of NSs protein. Viruses in 4 serogroups (Anopheles A, Anopheles B, Tete and Capim) were found not to encode an NSs ORF, presenting the first report of naturally isolated orthobunyaviruses without an NSs protein. Most of these viruses were found to have longer N proteins compared to those with NSs protein, with the largest N protein observed to date in TETEV and BMAV (258 amino acids). Other viruses 3 (EREV, NDV, GAMV, SJ2441V, BWAV and MPOV) were found to encode both N and NSs proteins in their S segment with the largest and smallest NSs protein detected to date in SJ2441V (137 amino acids) and MPOV (70 amino acids) respectively. The conserved CA rich motif in 5’ non coding region (NCR) of Bunyamwera and California serogroups viruses was absent in BWAV and MPOV, while ANBV and BORV were found to have two copies of this motif. Repeated sequences, as observed previously in the 5’ NCR of genomic-sense RNA of Lumbo virus (LUMV), were also detected in BWAV and TCMV S RNA segments. Sequence comparisons and phylogenetic analyses of the sequences determined in this study were in agreement with previous serological classification of the viruses, except for BERV and TCMV. BERV, in the Guama serogroup, was found to have a closer relationship with CAPV compared to GMAV. However high sequence identities (>70%) were observed between these 3 viruses, suggesting that they are derived from the same ancestor. N protein and nucleotide sequence identities of TCMV with ANAV were only 53% and 59% respectively. However, Neighbour-Joining (NJ) plot based on complete N amino acid sequence and Maximum Parsimony (MP) plot based on partial N sequence supported previous serological classification which placed this virus in the same clade as ANAV. This study first reports on the proteins synthesised by Bakau, Bwamba, Koongol, Gamboa, Minatitlan, Olifantsvlei and Tete serogroup viruses. Analysis of radio-labelled cell extracts revealed similar protein migration patterns for all the studied viruses compared with other viruses in the genus Orthobunyavirus. Shutoff of host cell protein synthesis, similar to that seen in Bunyamwera virus (BUNV)-infected cells was only observed in ACAV, BAKV, BWAV, CAPV, PAHV, PATV and WONV-infected cells. However, this shutoff was found not related to the presence of NSs protein. In general, viruses in the same serogroup were found to have almost same size of plaque and plaque-size did not correlate with the presence of NSs protein and the virulence of the virus in the mice. In vitro transcription and translation (TnT) using rabbit reticulocyte and wheat germ lysate expression systems further confirmed the sequencing results that no NSs protein was expressed from S cDNA clones of ANAV, TCMV, ANBV, BORV, BMAV and TETEV. S RNA segments shutoff almost similar to BUNV-infected cells was observed in A549 cells infected with TCMV, suggesting that TCMV might use a different mechanism to induce shutoff. No significant shutoff was observed in Hep2, Hep2/V and C6/36 cells infected with any of the viruses. RT-PCR specific for IFN- ß mRNA in 293 infected cells and IFN reporter gene assays revealed that TCMV was capable of counteracting IFN production similar to wt BUNV, whereas the other NSs minus viruses (ANAV, ANBV, BORV, TETEV and BMAV) were found to be capable of inducing IFN in infected cells. However, only low level of IFN- ß mRNA and weak activation of the IFN- ß promoter was detected in ANAV and BMAV- infected cells.

Finding frequent patterns plays a vital role in many analytics tasks such as finding itemsets, associations, correlations, and sequences. In recent decades, spatiotemporal frequent pattern mining has emerged with the main goal focused on developing data-driven analysis frameworks for understanding underlying spatial and temporal characteristics in massive datasets. In this thesis, we will focus on discovering spatiotemporal event sequences from large-scale region trajectory datasetes with event annotations. Spatiotemporal event sequences are the series of event types whose trajectory-based instances follow each other in spatiotemporal context. We introduce new data models for storing and processing evolving region trajectories, provide a novel framework for modeling spatiotemporal follow relationships, and present novel spatiotemporal event sequence mining algorithms.

by Sik Lok Lam. / The "2" in the title is subscript. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 184-197). / ABSTRACT --- p.iii / ACKNOWLEDGEMENTS --- p.v / Chapter CHAPTER ONE: --- LITERATURE SURVEY OF SEQUENCE-SPECIFIC LOCAL STRUCTURAL VARIATIONS IN DEOXYRIBONUCLEIC ACID MOLECULES --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- General Review of DNA --- p.1 / Chapter 1.2.1 --- "Nomenclature, Symbols and Atomic Numbering Scheme of DNA" --- p.2 / Chapter 1.2.2 --- Conformations of DNAs --- p.6 / Chapter 1.2.3 --- Helix-to-Random-Coil Transition --- p.9 / Chapter 1.3 --- Sequence-Specific Local Structural Studies --- p.11 / Chapter 1.4 --- Purpose of This Work --- p.14 / Chapter CHAPTER TWO: --- DETERMINATION OF STRUCTURES OF SOLUTION DNA MOLECULES --- p.17 / Chapter 2.1 --- Introduction --- p.17 / Chapter 2.2 --- Optimization of Conditions --- p.17 / Chapter 2.3 --- Resonance Assignments --- p.19 / Chapter 2.4 --- Extraction of Structural Constraints --- p.22 / Chapter 2.4.1 --- Interproton Distances --- p.23 / Chapter 2.4.2 --- Endocyclic Sugar Torsion Angles --- p.25 / Chapter 2.4.3 --- Phosphate Backbone Torsion Angles --- p.29 / Chapter 2.4.4 --- Hydrogen Bonds --- p.31 / Chapter 2.5 --- Structural Refinement --- p.31 / Chapter CHAPTER THREE: --- SIGNIFICANCE OF DIFFERENT TYPES OF STRUCTURAL CONSTRAINTS IN STRUCTURAL REFINEMENT PROCESS --- p.35 / Chapter 3.1 --- Introduction --- p.35 / Chapter 3.2 --- Experimental --- p.36 / Chapter 3.2.1 --- DNA Model Building --- p.36 / Chapter 3.2.2 --- Generation of Structural Constraints --- p.37 / Chapter 3.2.3 --- Structural Refinement --- p.40 / Chapter 3.3 --- Results and Discussion --- p.41 / Chapter 3.3.1 --- Endocyclic Sugar Torsion Angle Constraints --- p.45 / Chapter 3.3.2 --- Phosphate Backbone Torsion Angle Constraints --- p.49 / Chapter 3.3.3 --- Hydrogen Bond Constraints --- p.50 / Chapter 3.4 --- Summary --- p.50 / Chapter CHAPTER FOUR: --- EFFECTS OF DIFFERENT VARIABLES IN THE RESTRAINED MOLECULAR DYNAMICS PROCESS --- p.52 / Chapter 4.1 --- Introduction --- p.52 / Chapter 4.2 --- Experimental --- p.53 / Chapter 4.3 --- Results and Discussion --- p.55 / Chapter 4.3.1 --- Variables in the Temperature Profile --- p.58 / Chapter 4.3.2 --- Variables in the Force Constant Profile --- p.62 / Chapter 4.4 --- Summary --- p.65 / Chapter CHAPTER FIVE: --- THE J-COUPLING RESTRAINED MOLECULAR MECHANICS PROTOCOL - AN EFFICIENT AND RELIABLE ALTERNATIVE IN DERIVING ENDOCYCLIC SUGAR TORSION ANGLE CONSTRAINTS --- p.66 / Chapter 5.1 --- Introduction --- p.66 / Chapter 5.2 --- Methodology --- p.71 / Chapter 5.2.1 --- "Establishment of the Correlation of 3J1'2, withvi" --- p.71 / Chapter 5.2.2 --- Sample Preparation --- p.73 / Chapter 5.2.3 --- NMR Analysis --- p.73 / Chapter 5.2.4 --- Theoretical Testing of the Protocol --- p.74 / Chapter 5.2.5 --- Experimental Testing of the Protocol --- p.75 / Chapter 5.3 --- Results and Discussion --- p.76 / Chapter 5.3.1 --- Selection of the Appropriate JrMM-derived Torsion Angles --- p.85 / Chapter 5.3.2 --- Theoretical Testing of the Protocol --- p.88 / Chapter 5.3.3 --- Experimental Testing of the Protocol --- p.93 / Chapter 5.4 --- Summary --- p.98 / Chapter CHAPTER SIX: --- HETERONUCLEAR SINGLE QUANTUM COHERENCE DERIVED BACKBONE TORSION ANGLE CONSTRAINTS --- p.99 / Chapter 6.1 --- Introduction --- p.99 / Chapter 6.2 --- Experimental --- p.102 / Chapter 6.3 --- Results and Discussion --- p.103 / Chapter 6.3.1 --- Determination of the Backbone Torsion Angles β and E --- p.103 / Chapter 6.3.2 --- Error Estimation on 3JC4'p- and 3JH3'p-derived E --- p.109 / Chapter 6.4 --- Summary --- p.110 / Chapter CHAPTER SEVEN: --- SOLUTION STRUCTURES OF d(CGXX，CG)2 AND d(CAXX´ةTG)2 --- p.111 / Chapter 7.1 --- Introduction --- p.111 / Chapter 7.2 --- Experimental --- p.111 / Chapter 7.2.1 --- Sample Preparation --- p.112 / Chapter 7.2.2 --- Resonance Assignment --- p.112 / Chapter 7.2.3 --- Melting Profile Study --- p.112 / Chapter 7.2.4 --- Extraction of Structural Constraints --- p.113 / Chapter 7.2.5 --- Structural Refinement --- p.115 / Chapter 7.2.6 --- Structural Parameter Analysis --- p.116 / Chapter 7.3 --- Results and Discussion --- p.116 / Chapter 7.3.1 --- Melting Profile Study --- p.117 / Chapter 7.3.2 --- Structural Constraints --- p.120 / Chapter 7.3.3 --- Structural Refinement --- p.129 / Chapter 7.3.4 --- Structural Features --- p.135 / Chapter CHAPTER EIGHT: --- SEQUENCE-SPECIFIC LOCAL STRUCTURAL STUDY --- p.156 / Chapter 8.1 --- Introduction --- p.156 / Chapter 8.2 --- Predictions from the Calladine's Rules --- p.156 / Chapter 8.3 --- Predictions from Olson's Base-Pair Morphology Dependent Clash Function --- p.160 / Chapter 8.4 --- Re-formulation of Calladine's Idea and its Relationship to Sequence-Specific Local Structural Function ΣLS --- p.163 / Chapter 8.4.1 --- Sequence-Specific Base-Pair Geometry Analysis --- p.164 / Chapter 8.4.2 --- Sequence-Specific Base-Pair Step Geometry Analysis --- p.166 / Chapter 8.4.3 --- Sequence-Specific Local Structural Function ΣLS --- p.167 / Chapter 8.5 --- Summary --- p.173 / Chapter CHAPTER NINE: --- CONCLUSIONS AND FURTHER WORK --- p.174 / APPENDIX I The Base Proton Regions of the lH NMR Spectra of the Hexamers --- p.177 / APPENDIX II 2D NOESY Spectra (Tm = 200 ms) of the Hexamers --- p.178 / "APPENDIX III The H1'-H27H2"" Regions of the DQF-COSY Spectra of the Hexamers" --- p.180 / APPENDIX IV The C4'-H4' Regions of the HSQC Spectra of the Hexamers --- p.182 / REFERENCES --- p.184

This dissertation summarizes the development of experimental and analytical tools for single cell RNA sequencing (scRNA-Seq), including 1) scPLATE-Seq, a FACS- and plate-based scRNASeq platform, which is accurate, robust, fully automated and cost-efficient; 2) metaVIPER, an algorithm for transcriptional regulator activity inference based on scRNA-Seq profiles; and 3) iterClust, a statistical framework for iterative clustering analysis, especially suitable for dissecting hierarchy of heterogeneity among single cells. Further this dissertation summarizes biological questions answered by combining these tools, including 1) understanding inter- and intra-tumor heterogeneity of human glioblastoma; 2) elucidating regulators of β-cell de-differentiation in type-2 diabetes; and 3) developing novel therapeutics targeting cell-state regulators of breast cancer stem cells.

by Sze-wan Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 41-42). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- The probability model --- p.8 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.2 --- The coding system --- p.11 / Chapter 2.3 --- The likelihood estimates of hexamer codes --- p.13 / Chapter 2.4 --- A cross validation study --- p.15 / Chapter Chapter 3 --- An application of the likelihood ratio --- p.21 / Chapter 3.1 --- Introduction --- p.21 / Chapter 3.2 --- The Needleman-Wunsch algorithm --- p.21 / Chapter 3.2.1 --- Background --- p.21 / Chapter 3.2.2 --- The principle of the algorithm --- p.21 / Chapter 3.2.3 --- The algorithm --- p.23 / Chapter 3.3 --- Application of the structural information --- p.25 / Chapter 3.3.1 --- Basic idea --- p.25 / Chapter 3.3.2 --- Comparison between pairs of hexamer sequences --- p.25 / Chapter 3.3.3 --- The score of similarity of a pair of hexamer sequences --- p.26 / Chapter Chapter 4 --- Application of the modified Needleman-Wunsch algorithm --- p.27 / Chapter 4.1 --- The structurally homologous pair --- p.27 / Chapter 4.1.1 --- The horse hemoglobin beta chain --- p.28 / Chapter 4.1.2 --- The antartic fish hemoglobin beta chain --- p.29 / Chapter 4.2 --- Other proteins --- p.31 / Chapter 4.2.1 --- The acetylchoinesterase --- p.31 / Chapter 4.2.2 --- The lipase --- p.32 / Chapter 4.2.3 --- The two A and D chains of the deoxyribonuclease --- p.33 / Chapter 4.3 --- Evaluation of the significance of the maximum match --- p.36 / Chapter Chapter 5 --- Conclusion and discussion --- p.38 / References --- p.41 / Tables --- p.43

Combinatorial techniques can frequently provide satisfying “explanations” of various mathematical phenomena. In this thesis, we seek to explain a number of well known number theoretic congruences using combinatorial methods. Many of the results we prove involve the Fibonacci sequence and its generalizations.

Graduation date: 1995

Thesis (M.S.)--Baylor University, 2005. / Includes bibliographical references (p. 142-147).

Despite the known benefits of performing aerobic and resistance exercise independently, the metabolic effects of performing aerobic and resistance exercise in succession, remain unclear. Several studies suggest that the alteration of exercise sequence may influence carbohydrate and lipid oxidation and energy expenditure during exercise and in recovery. High intensity resistance exercise performed prior to a bout of aerobic exercise has been shown to augment fat oxidation during the subsequent bout of aerobic exercise. Changes in hormone and metabolite concentrations from prior resistance exercise could potentially influence substrate selection and energy expenditure in a subsequent bout of aerobic exercise. However, an exercise session whereby aerobic exercise is followed by a bout of resistance exercise has yet to be evaluated to determine the metabolic effects (specifically, the differences in substrate selection for energy provision) when exercise sequence is altered. It was hypothesized that when resistance exercise was performed prior to a bout of aerobic exercise, sympathetic nervous system activity would be elevated, leading to an increase in non-esterified fatty acid (NEFA) and glycerol concentrations and resultant increase in lipid oxidation during the aerobic portion of the exercise compared to the opposite sequence. It was also hypothesized that during recovery there would be an increased reliance on fat oxidation for energy provision with a resistance-aerobic exercise sequence compared to an aerobic-resistance exercise sequence. Additionally, the differences in metabolite concentrations and respiratory parameters between two identical bouts of aerobic exercise performed on separate days (~1 week apart) were measured and it was hypothesized that day-to-day variability would be non-significant (p>0.05). Plasma glucose, lactate, NEFA, glycerol, insulin, C-peptide, glucagon, epinephrine and norepinephrine concentrations in addition to oxygen consumption (VO2) and respiratory exchange ratio (RER) were measured in nine healthy, recreationally active males that participated in 3 different, randomized exercise trials (Trial A: aerobic exercise; Trial AR: aerobic exercise followed by a bout of resistance exercise; Trial RA: resistance exercise followed by an aerobic exercise bout). The aerobic exercise bout was performed at 60% VO2 max for 30 min while the resistance exercise bout consisted of 5 exercises (overhead squat, chest press, triceps extension, shoulder press, and dead-lift) performed for 3 sets of 8 repetitions at 70% 1-RM. Contrary to the primary hypothesis, NEFA concentrations and lipid oxidation rates were similar for the aerobic exercise bout of both the AR and RA trials. During recovery, lipid oxidation was elevated immediately post-exercise in the RA trial compared to the AR trial, however there were no differences between trials by 15 min post-exercise. Furthermore, only epinephrine, and not norepinephrine, concentrations were significantly higher after aerobic exercise in the RA trial compared to the AR trial. VO2 and energy expenditure values were similar for the duration of the 30 min recovery. These results suggest that while exercise sequence may influence carbohydrate and lipid oxidation immediately post exercise, substrate selection and utilization are similar during aerobic exercise bouts irrespective of the sequence in which aerobic and resistance exercise are performed. Thus, when resistance exercise is performed prior to aerobic exercise, compared to the opposite sequence, overall energy provision is not altered at the volume and intensity of exercise performed in this study.

Multiple sequence alignment is an important bioinformatics problem, with applications in diverse types of biological analysis, such as structure prediction, phylogenetic analysis and critical sites identification. In recent years, the quality of multiple sequence alignment was improved a lot by newly developed methods, although it remains a difficult task for constructing accurate alignments, especially for divergent sequences. In this dissertation, we propose three new methods (PSAlign, ISPAlign, and NRAlign) for further improving the quality of multiple sequences alignment. In PSAlign, we propose an alternative formulation of multiple sequence alignment based on the idea of finding a multiple alignment which preserves all the pairwise alignments specified by edges of a given tree. In contrast with traditional NP-hard formulations, our preserving alignment formulation can be solved in polynomial time without using a heuristic, while still retaining very good performance when compared to traditional heuristics. In ISPAlign, by using additional hits from database search of the input sequences, a few strategies have been proposed to significantly improve alignment accuracy, including the construction of profiles from the hits while performing profile alignment, the inclusion of high scoring hits into the input sequences, the use of intermediate sequence search to link distant homologs, and the use of secondary structure information. In NRAlign, we observe that it is possible to further improve alignment accuracy by taking into account alignment of neighboring residues when aligning two residues, thus making better use of horizontal information. By modifying existing multiple alignment algorithms to make use of horizontal information, we show that this strategy is able to consistently improve over existing algorithms on all the benchmarks that are commonly used to measure alignment accuracy.