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Search Results: 1 - 10 of 53065 matches for " David Tuck "
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Pre-Operative Perihepatic Lymph Node Assessment in Colorectal Cancer Liver Metastasis—A Review of Current Literature  [PDF]
Tuck Leong Yong, David Burrows, Chris Christophi
Open Journal of Medical Imaging (OJMI) , 2017, DOI: 10.4236/ojmi.2017.73009
Abstract: Objective: Perihepatic lymph node involvement in colorectal cancer liver metastases is a negative prognostic factor. Resection of certain nodal stations around the liver has been shown to possibly improve survival. The aim of this review is to interrogate current literature on pre-operative investigations in diagnosing lymph node involvement. Method: A systematic review was conducted of articles published since 2006 to determine usefulness of pre-operative imaging in diagnosing lymph node involvement in colorectal cancer liver metastases. Results: Only 2 papers met the inclusion criteria for this study. Computed tomography (CT) scans were found to have sensitivities of 33% and 40%, specificities of 94% and 92%, positive predictive values (PPV) of 56% and 30%, and negative predictive values (NPV) of 85% and 95%. Positron emission tomography (PET) was studied in one of the paper and was found to have sensitivity, specificity, PPV and NPV of 57%, 100%, 100%, and 88% respectively. Conclusion: There is a significant lack of research on pre-operative investigations of perihepatic lymph node involvement in colorectal cancer liver metastases. Pre-operative CT and PET scans in assessing perihepatic lymph nodes were shown to be inaccurate. Newer pre-operative imaging modalities and research would be needed.
An Effective Tri-Clustering Algorithm Combining Expression Data with Gene Regulation Information
Ao Li and David Tuck
Gene Regulation and Systems Biology , 2012,
Abstract: Motivation: Bi-clustering algorithms aim to identify sets of genes sharing similar expression patterns across a subset of conditions. However direct interpretation or prediction of gene regulatory mechanisms may be difficult as only gene expression data is used. Information about gene regulators may also be available, most commonly about which transcription factors may bind to the promoter region and thus control the expression level of a gene. Thus a method to integrate gene expression and gene regulation information is desirable for clustering and analyzing. Methods: By incorporating gene regulatory information with gene expression data, we define regulated expression values (REV) as indicators of how a gene is regulated by a specific factor. Existing bi-clustering methods are extended to a three dimensional data space by developing a heuristic TRI-Clustering algorithm. An additional approach named Automatic Boundary Searching algorithm (ABS) is introduced to automatically determine the boundary threshold. Results: Results based on incorporating ChIP-chip data representing transcription factor-gene interactions show that the algorithms are efficient and robust for detecting tri-clusters. Detailed analysis of the tri-cluster extracted from yeast sporulation REV data shows genes in this cluster exhibited significant differences during the middle and late stages. The implicated regulatory network was then reconstructed for further study of defined regulatory mechanisms. Topological and statistical analysis of this network demonstrated evidence of significant changes of TF activities during the different stages of yeast sporulation, and suggests this approach might be a general way to study regulatory networks undergoing transformations.
An Effective Tri-Clustering Algorithm Combining Expression Data with Gene Regulation Information
Ao Li,David Tuck
Gene Regulation and Systems Biology , 2009,
Abstract: Motivation: Bi-clustering algorithms aim to identify sets of genes sharing similar expression patterns across a subset of conditions. However direct interpretation or prediction of gene regulatory mechanisms may be difficult as only gene expression data is used. Information about gene regulators may also be available, most commonly about which transcription factors may bind to the promoter region and thus control the expression level of a gene. Thus a method to integrate gene expression and gene regulation information is desirable for clustering and analyzing.Methods: By incorporating gene regulatory information with gene expression data, we define regulated expression values (REV) as indicators of how a gene is regulated by a specific factor. Existing bi-clustering methods are extended to a three dimensional data space by developing a heuristic TRI-Clustering algorithm. An additional approach named Automatic Boundary Searching algorithm (ABS) is introduced to automatically determine the boundary threshold.Results: Results based on incorporating ChIP-chip data representing transcription factor-gene interactions show that the algorithms are efficient and robust for detecting tri-clusters. Detailed analysis of the tri-cluster extracted from yeast sporulation REV data shows genes in this cluster exhibited significant differences during the middle and late stages. The implicated regulatory network was then reconstructed for further study of defined regulatory mechanisms. Topological and statistical analysis of this network demonstrated evidence of significant changes of TF activities during the different stages of yeast sporulation, and suggests this approach might be a general way to study regulatory networks undergoing transformations.
An Effective Tri-Clustering Algorithm Combining Expression Data with Gene Regulation Information
Ao Li,David Tuck
Gene Regulation and Systems Biology , 2009,
Abstract: Motivation: Bi-clustering algorithms aim to identify sets of genes sharing similar expression patterns across a subset of conditions. However direct interpretation or prediction of gene regulatory mechanisms may be difficult as only gene expression data is used. Information about gene regulators may also be available, most commonly about which transcription factors may bind to the promoter region and thus control the expression level of a gene. Thus a method to integrate gene expression and gene regulation information is desirable for clustering and analyzing. Methods: By incorporating gene regulatory information with gene expression data, we define regulated expression values (REV) as indicators of how a gene is regulated by a specific factor. Existing bi-clustering methods are extended to a three dimensional data space by developing a heuristic TRI-Clustering algorithm. An additional approach named Automatic Boundary Searching algorithm (ABS) is introduced to automatically determine the boundary threshold. Results: Results based on incorporating ChIP-chip data representing transcription factor-gene interactions show that the algorithms are efficient and robust for detecting tri-clusters. Detailed analysis of the tri-cluster extracted from yeast sporulation REV data shows genes in this cluster exhibited significant differences during the middle and late stages. The implicated regulatory network was then reconstructed for further study of defined regulatory mechanisms. Topological and statistical analysis of this network demonstrated evidence of significant changes of TF activities during the different stages of yeast sporulation, and suggests this approach might be a general way to study regulatory networks undergoing transformations.
Prolonged Disease Stabilization and Tolerability in a Nuclear Protein in Testis Midline Carcinoma Patient Treated with Dual Histone Deacetylase and Phosphoinositide 3-Kinase Inhibitor CUDC-907  [PDF]
Pamela Munster, Nilson Wu, Meaghan McMahon, Robert Gharavi, David Tuck
Case Reports in Clinical Medicine (CRCM) , 2018, DOI: 10.4236/crcm.2018.77039
Abstract: Introduction: Nuclear protein in testis midline carcinoma (NMC) is a rare and extremely aggressive carcinoma (median survival < 7 months) with no effective treatment options. CUDC-907 is a novel small molecule inhibitor of histone deacetylase (HDAC) and phosphoinositide 3-kinase (PI3K) enzymes currently being investigated in multiple tumor types, including NMC. Case Report: A 61-year old female NMC patient enrolled on study CUDC-907-102 (NCT02307240) after rapidly progressing through two prior treatments. The patient’s assessable sites of disease consisted of right pleural effusion, right hilar soft tissue, and segment IV liver. Treatment was well tolerated with toxicities primarily consisting of manageable diarrhea and thrombocytopenia. The patient remains on active treatment after more than 32 months of stable disease. Discussion: Dysregulation of MYC in NMC is believed to play a central role in pathogenesis. CUDC-907 has demonstrated potent suppression of MYC expression and anti-tumor activity in preclinical NMC models, providing a mechanistic rationale for the prolonged disease stabilization observed here. The treatment of additional NMC patients with CUDC-907 is needed to further evaluate this promising report. Conclusion: This case demonstrates a rare success in the treatment of a devastating disease using only a novel small molecule, warranting further investigation of CUDC-907 in NMC.
Modeling the clonal heterogeneity of stem cells
David P Tuck, Willard Miranker
Theoretical Biology and Medical Modelling , 2010, DOI: 10.1186/1742-4682-7-44
Abstract: The promise of therapeutic applications of stem cells depends on expansion, purification and differentiation of cells of specific types required for different clinical purposes. Stem cells are defined by the capacity to either self-renew or differentiate into multiple cell lineages. These characteristics make stem cells candidates for cell therapies and tissue engineering. Stem cell-based technologies will require the ability to generate large numbers of cells with specific characteristics. Thus, understanding and manipulating stem cell dynamics has become an increasingly important area of biomedical research. Genomic and technological advances have led to strategies for such manipulations by targeting key molecular pathways with biological and pharmacological interventions [1-3], as well as by niche or microenvironmental manipulations [4].Recent conceptual and mathematical models of stem cells have been proposed [5-9] that extend the relevance of earlier ones [10] by focusing on the intrinsic properties of cells and effects of the microenvironment, and address new concepts of stem cell plasticity. Sieburg et al have provided evidence for a clonal diversity model of the stem cell compartment in which functionally discrete subsets of stem cells populate the stem cell pool [11]. In this model, heterogeneous properties of these clones that regulate self-renewal, growth, differentiation, and apoptosis informed by epigenetic mechanisms are maintained and passed onto daughter cells. Experimental evidence supports this notion that tissue stem cell pools are composed of such functionally diverse epigenetic clones [11]. Roeder at al, by extending their previous model to include clonal heterogeneity, have demonstrated through agent based model simulations that clonally fixed differences are necessary to explain the experimental data in hematopoietic stem cells from Sieburg [12].Metapopulation models concentrate on collections of populations characterized by expansion and exti
MixHMM: Inferring Copy Number Variation and Allelic Imbalance Using SNP Arrays and Tumor Samples Mixed with Stromal Cells
Zongzhi Liu,Ao Li,Vincent Schulz,Min Chen,David Tuck
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0010909
Abstract: Genotyping platforms such as single nucleotide polymorphism (SNP) arrays are powerful tools to study genomic aberrations in cancer samples. Allele specific information from SNP arrays provides valuable information for interpreting copy number variation (CNV) and allelic imbalance including loss-of-heterozygosity (LOH) beyond that obtained from the total DNA signal available from array comparative genomic hybridization (aCGH) platforms. Several algorithms based on hidden Markov models (HMMs) have been designed to detect copy number changes and copy-neutral LOH making use of the allele information on SNP arrays. However heterogeneity in clinical samples, due to stromal contamination and somatic alterations, complicates analysis and interpretation of these data.
Characterizing disease states from topological properties of transcriptional regulatory networks
David P Tuck, Harriet M Kluger, Yuval Kluger
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-236
Abstract: We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links.We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity.Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.The study of mammalian transcription based on high throughput gene expression data has primarily focused on the identification of individual differentially expressed genes, co-regulated gene sets and genes with inferred functional similarity based on co-expression under various conditions. Investigators have identified functional modules from gene expression data using a reverse-engineering approach to
Web Archiving in the UK: Cooperation, Legislation and Regulation
John Tuck
Liber Quarterly : The Journal of European Research Libraries , 2008,
Abstract: The author presents an overview of web archiving in an international context, focussing on web archiving initiatives in the United Kingdom from 2001 onwards.
The Rights of War and Peace. Political Thought and the International Order from Grotius to Kant, Oxford University Press, Nueva York, 1999.
Richard TUCK
Relaciones Internacionales , 2006,
Abstract: Richard Tuck, en este fragmento de su obra, plantea una innovadora visión de la tradición filosófica que da origen a unos planteamientos liberales, esenciales en nuestra época. El autor plantea como el escepticismo y la razón de estado, recuperados por los teóricos del Renacimiento y aplicados al comportamiento a los estados en Relaciones Internacionales en plena expansión europea, suprimen todo vestigio de cualidad afectiva o moral en éstos y son trasladados al ámbito de la sociedad civil para que los individuos puedan comportarse como verdaderos agentes liberales. In this part of his book, Richard Tuck reveals a new perspective on the philosophical tradition that gave birth to some of the most fundamental liberal principles. The author explains how the skepticism and the raison d′etat recovered by Renaissance theorists are applied to the behavior of the nation-states in International Relations during the European expansion, suppressing all kind of affective or moral quality and are translated to the civil society in order to convert the individuals into perfect liberal agents.
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