VictorJuiz/thesis huggingface dataset:
Cellular Mechanisms of the Systemic Inflammatory Response Following Resuscitated Hemorrhagic Shock: The Role of Reactive Oxygen Species and Toll-like Receptor 4
Keywords: TLR4 Macrophage Trauma Shock Resuscitation Lipopolysaccharide ARDS MODS SIRS

Acute Respiratory Distress Syndrome (ARDS) following hemorrhagic shock/resuscitation (S/R) is an important contributor to late morbidity and mortality in trauma patients. S/R promotes ARDS by inducing oxidative stress that primes cells of the innate immune system for excessive responsiveness to small inflammatory stimuli, termed the “twohit” hypothesis. Activated alveolar macrophages (AM) play a central role and when recovered from S/R animals exhibit an exaggerated responsiveness to lipopolysaccharide (LPS) with increased activation of the proinflammatory transcription factor NF-κB, and augmented expression of cytokines. LPS triggers AM signalling through Toll like receptor 4 (TLR4), which resides in plasma membrane lipid rafts. The objective of this work is to define cellular mechanisms of macrophage priming by oxidative stress following shock resuscitation. The main hypothesis investigated is that altered cellular distribution of TLR4 can lead to macrophage priming and antioxidant resuscitation strategies can diminish these effects. AM of rodents, exposed in vivo to oxidant stress following S/R, increase their surface levels of TLR4, which in turn results in augmented NF-κB translocation in response to small doses of LPS. Furthermore, in vitro H2O2 treatment of RAW 264.7 macrophages results in similar TLR4 surface translocation. Depletion of intracellular calcium, disruption of the cytoskeleton or inhibition of the Src kinases prevents the H2O2-induced TLR4 translocation, suggesting the involvement of receptor exocytosis. Further, fluorescent resonance energy iii transfer between TLR4 and lipid rafts as well as biochemical raft analysis demonstrated that oxidative stress redistributes TLR4 to surface lipid rafts. Preventing the oxidant-induced movement of TLR4 to lipid rafts using methyl-ß-cyclodextrin precluded the increased responsiveness of cells to LPS after H2O2 treatment. Further, AM priming by oxidative stress can be diminished by early exposure to resuscitation regimens with direct or indirect systemic antioxidant effects, such as 25% albumin, N-acetylcysteine and hypertonic saline. Hyperosmolarity was found to modulate AM TLR4 gene and protein expression. Collectively, these studies suggest a novel mechanism whereby oxidative stress might prime the responsiveness of cells of the innate immune system. Targeting the TLR4 signalling pathway early during shock resuscitation may represent an anti-inflammatory strategy able to ameliorate late morbidity and mortality following S/R.
The Development of an Adaptable Surface Modification Architecture for Microfluidic Applications
Keywords: surface modification
microfluidic
adaptable architecture
quartz crystal microbalance

A framework to compartmentalize microfluidic surfaces was developed. Substrates are separated from surface modifying agents with an intermediate binding layer (IBL). The IBL is comprised of two compounds which bind together using a non-covalent interaction; a host compound is immobilized on the substrate, and a guest compound is conjugated to the surface modifying agent. The primary benefit of the IBL architecture is adaptability: substrates and surface compounds become modular components with standard connectors. Beta-Cyclodextrin (BCD) and adamantane (AD) were selected as the model immobilized host and conjugated guest, respectively. A quartz crystal microbalance (QCM) was assembled and developed to study the BCD/AD complexation interaction. Kinetic, thermodynamic, and Langmuir isotherm data were reported for AD-derivatives binding with immobilized BCD. QCM was also used to investigate neutravidin (NA) binding onto AD-PEG and AD-PEG-biotin coatings immobilized to t-BCD surfaces. QCM was an effective platform to validate the use of BCD/AD as the IBL interaction prior to microfluidic implementation. The BCD/AD IBL was successfully demonstrated in a microfluidic environment. Microfluidic devices were fabricated using the soft-lithographic technique. Adapted surface modifications were visualized using fluorescein isothiocyanate (FITC) probes within the microfluidic device and detected using confocal laser scanning microscopy (CLSM). Surface modifications were applied to demonstrate the fundamental functions of surface passivation, specific binding, and visualization using the IBL architecture. Consistent with QCM data, AD-PEG passivated the surface and AD-PEG-biotin specifically bound NA to the BCD surface. Thus, an adaptable surface modification architecture for microfluidic applications was developed and demonstrated.
Design and Analysis of Sequential Clinical Trials using a Markov Chain Transition Rate Model with Conditional Power
Keywords: Markov chain
clinical trial
conditional power
phase II

Background: There are a plethora of potential statistical designs which can be used to evaluate efficacy of a novel cancer treatment in the phase II clinical trial setting. Unfortunately, there is no consensus as to which design one should prefer, nor even which definition of efficacy should be used and the primary endpoint conclusion can vary depending on which design is chosen. It would be useful if an all-encompassing methodology was possible which could evaluate all the different designs simultaneously and allow investigators an understanding of the trial results under the varying scenarios. Methods: Finite Markov chain imbedding is a method which can be used in the setting of phase II oncology clinical trials but never previously evaluated in this scenario. Simple variations to the transition matrix or end-state probability definitions can be performed which allow for evaluation of multiple designs and endpoints for a single trial. A computer program is written in R which allows for computation of p-values and conditional power, two common statistical measures used for evaluation of trial results. A simulation study is performed on data arising from an actual phase II clinical trial performed recently in which the study conclusion regarding the efficacy of the potential treatment was debatable. Results: Finite Markov chain imbedding is shown to be useful for evaluating phase II oncology clinical trial results. The R code written for evaluating the simulation study is demonstrated to be fast and useful for investigating different trial designs. Further detail regarding the clinical trial results are presented, including the potential prolongation of stable disease of the treatment, which is a potentially useful marker of efficacy for this cytostatic agent. Conclusions: This novel methodology may prove to be an useful investigative technique for the evaluation of phase II oncology clinical trial data. Future studies which have disputable conclusions might become less controversial with the aid of finite Markov chain imbedding and the possible multiple evaluations which is now viable. Better understanding of activity for a given treatment might expedite the drug development process or help distinguish active from inactive treatments
Cell Targeted Ribosome Inactivating Proteins Derived from Protein Combinatorial Libraries
Keywords: Protein libraries
cytotoxicity
anti-cancer agents
shiga like toxin 1
ribosome inactivating protein

Combinatorial protein libraries based on a protein template offer a vast potential for deriving protein variants harboring new receptor specificity while retaining other tem-plate functions to serve as library search-engines, cell-routing sequences and therapeutic domains. This concept was tested with the design and synthesis of protein libraries where short random peptide motifs were embedded directly within the catalytic A subunit of the bacterial ribosome-inactivating protein (RIP) known as Shiga-like toxin 1 (SLT-1). More precisely, a seven amino acid peptide epitope (PDTRPAP) was inserted between residues 245-246 of its A subunit (SLT-1APDTRPAP) and shown to preserve catalytic function while exposing the epitope. SLT-1 A chain libraries harboring tripep-tide and heptapeptide random elements were subsequently constructed, screened and shown to express more than 90% of expected cytotoxic A chain variants. Finally, more than 9,000 purified SLT-1 A chain variants were screened using their ribosome-inactivating function in a cell-based assay to identify mutants that are able to kill human melanoma 518-A2 cells. This search led to the striking discovery of a single chain RIP that displays selectivity for a panel of human melanoma cell lines as well as minimal immunogenicity when injected repeatedly into mice. This directed evolution of a RIP template provides a broad platform for identifying cell type specific cytotoxic agents.
Sparse Canonical Correlation Analysis
Keywords: Public Health Sciences
Biostatistics
Statistics

Large scale genomic studies of the association of gene expression with multiple phenotypic or genotypic measures may require the identification of complex multivariate relationships. In multivariate analysis a common way to inspect the relationship between two sets of variables based on their correlation is Canonical Correlation Analysis, which determines linear combinations of all variables of each type with maximal correlation between the two linear combinations. However, in high dimensional data analysis, when the number of variables under consideration exceeds tens of thousands, linear combinations of the entire sets of features may lack biological plausibility and interpretability. In addition, insufficient sample size may lead to computational problems, inaccurate estimates of parameters and non-generalizable results. These problems may be solved by selecting sparse subsets of variables, i.e. obtaining sparse loadings in the linear combinations of variables of each type. However, available methods providing sparse solutions, such as Sparse Principal Component Analysis, consider each type of variables separately and focus on the correlation within each set of measurements rather than between sets. We introduce new methodology - Sparse Canonical Correlation Analysis (SCCA), which examines the relationships of many variables of different types simultaneously. It solves the problem of biological interpretability by providing sparse linear combinations that include only a small subset of variables. SCCA maximizes the correlation between the subsets of variables of different types while performing variable selection. In large scale genomic studies sparse solutions also comply with the belief that only a small proportion of genes are expressed under a certain set of conditions. In this thesis I present methodology for SCCA and evaluate its properties using simulated data. I illustrate practical use of SCCA by applying it to the study of natural variation in human gene expression for which the data have been provided as problem 1 for the fifteenth Genetic Analysis Workshop (GAW15). I also present two extensions of SCCA - adaptive SCCA and modified adaptive SCCA. Their performance is evaluated and compared using simulated data and adaptive SCCA is applied to the GAW15 data.
The Mechanisms Underlying Free Fatty Acid-induced Hepatic Insulin Resistance
Keywords: Hepatic Insulin Resistance
Diabetes Mellitus
Free Fatty Acids
Oxidative Stress
PKC-Delta
Inflammation
Obesity

Elevated circulating free fatty acids (FFA) cause hepatic insulin resistance; however, the mechanisms for this process are incompletely understood. The objective of the studies in the thesis was to examine whether protein kinase C (PKC)-delta (d), oxidative stress, and the serine kinase IkBa kinase (IKK) B are causally involved in FFA-induced hepatic insulin resistance. To test this, we infused rats with lipid with or without inhibitors of the aforementioned factors for 7h, during the last 2h of which a hyperinsulinemic-euglycemic clamp was performed. In Study 1, inhibition of hepatic PKC-d using antisense oligonucleotide prevented FFA-induced membrane translocation of PKC-d, which is a marker of its activation, in parallel with prevention of lipid-induced hepatic insulin resistance, without affecting lipid-induced peripheral insulin resistance. These results implicate PKC-d as a causal mediator of FFA-induced hepatic insulin resistance. In Study 2, the antioxidant N-acetyl-L-cysteine (NAC) prevented lipid-induced hepatic insulin resistance in conjunction with reversal of lipid-induced increase in markers of IKKB and c-Jun NH2-terminal kinase 1 (JNK1) activation, and of impairment of insulin signaling, without affecting PKC-d membrane translocation and increase in phosphorylated p38 mitogen-activated protein kinase (MAPK) induced by lipid infusion. These findings suggested that oxidative stress is a causal mediator of lipid-induced hepatic insulin resistance upstream of IKKB and JNK1, and potentially downstream of PKC-d and p38 MAPK. In Study 3, sodium salicylate, an IKKB inhibitor, prevented FFA-induced hepatic insulin resistance via restoration of hepatic insulin signaling, thus implicating IKKB as a causal factor in the process. Together, the results from these studies demonstrate that PKC-d, oxidative stress, and IKKB are causally involved in FFA-induced hepatic insulin resistance and suggest that the sequence for the process is: FFA -> PKC-d -> oxidative stress -> IKKB -> impaired hepatic insulin signaling.
Knowledge Management Systems and Customer Knowledge Use in Organizations
Keywords: Information Systems
Knowledge Management

The objective of the research was to understand how customer knowledge was used in an organization and the role knowledge management systems (KMS) played in this use. Traditionally, organizations have relied on internal knowledge to shape their corporate strategy. Recently however they are tapping new sources of knowledge that are external to the firm. One important source of organizational knowledge is a company’s customers, as they present a source of knowledge that may provide new insights, innovations and ideas that are not necessarily found within the organization. The study examined the perceptions and beliefs of customer knowledge held by an organization’s employees, the types of customer knowledge available to the firm, the use of a KMS, and its impact on an organization’s use of knowledge. We posed the question of how these perceptions and beliefs influenced the types of customer knowledge available and the design and implementation of a KMS. Furthermore, we investigated the impact of customer knowledge types and knowledge management systems on how an organization uses customer knowledge in its regular operational routines. To answer these questions, a case study was conducted in a Canadian health care systems organization. By taking the approach that individuals in an organization are the key to sharing knowledge with customers, this research was able to gain an in-depth understanding of how employees view customer knowledge, including both positive and negative attitudes towards this new source of knowledge. The types of customer knowledge available to the study organization were identified, as were the ways that the newly implemented KMS helped and hindered knowledge sharing. The results of the research demonstrate how the types of customer knowledge available to an organization can be categorized by the perceived quality and the perceived accessibility of the knowledge. These findings contribute to the field of knowledge management by moving towards a theory of how customer knowledge is used by an organization, and how internal and external factors affect this use. Furthermore, this study raises awareness of the importance of a KMS in managing customer knowledge, including key aspects of its design and implementation.
Upregulation of CaMKIIβ and Nogo-C mRNA in Schizophrenia and the Prevalence of CAA Insert in the 3’UTR of the Nogo Gene
Keywords: CaMKII
schizophrenia
Nogo
RTN4
human
frontal cortex
gene variant
gene expression

Schizophrenia may result from altered gene expression leading to abnormal neurodevelopment. In a search for genes with altered expression in schizophrenia, cDNA library subtractive hybridization experiments using post-mortem human frontal cerebral cortices from schizophrenia individuals and neurological controls were performed. I found the mRNA of two neurodevelopmentally important genes, Nogo (RTN4) and calcium/calmodulin-dependent protein kinase II beta (CaMKIIβ), to be overexpressed in post-mortem frontal cortex tissues from patients who suffered with schizophrenia. I used the quantitative real-time polymerase chain reaction method to determined the mRNA levels of these genes in tissues from age- and sex-matched individuals. Nogo is a myelin-associated protein which inhibits the outgrowth of neurites and nerve terminals. The gene produces three splice variants, Nogo-A, B and C. I found Nogo-C mRNA to be overexpressed by 26% in schizophrenia. I also found a 17% reduction of Nogo-B mRNA in samples from individuals who had been diagnosed with severe depression. Furthermore, I showed that there is a direct correlation between the expression of both Nogo-A and -C and the presence of a CAA insert in the 3’UTR of the Nogo gene. CaMKII is a kinase localized at the postsynaptic density. The holoenzyme is primarily composed of the subunits α and β, encoded by two separate genes. It influences the expression of many neuroreceptors, in particular receptors of the glutamatergic pathway. CaMKII also mediates neural maturation during puberty, a time of onset of schizophrenia. The expression of CaMKIIα was elevated 29% in frontal cortex tissues of patients who suffered from depression. The expression of CaMKIIβ was elevated 27% in tissues of schizophrenia patients and 36% in tissues of patients diagnosed with depression. Upregulation of CaMKIIβ was associated with the presence of the CAA insert in at least one copy of the Nogo gene in a group containing both healthy subjects and patients with mental illness, possibly linking the CaMKII and Nogo pathways. The values for the expression of Nogo, CaMKIIα and CaMKIIβ were normalized to β-glucuronidase expression to minimize the effects of mRNA degradation. These results confirm that upregulation of Nogo-C and CaMKIIβ is likely associated with schizophrenia.
The Search for Genetic Structure and Patterns in Vietnamese Frogs
Keywords: Evolution
Anurans

Vietnam has the greatest biodiversity of any country in Indochina. This diversity may be due to its topographically complex nature, with hills and mountains, drained by several independent river systems, covering three quarters of its area. Topographic complexity has undoubtedly had profound effects on the flora and fauna of the region. Recent surveys have uncovered several cryptic species in what were previously considered single widespread species. These discoveries have led some researchers to propose that widespread forest species do not, in fact, exist in Southeast Asia. To test these hypotheses, I examined patterns of mitochondrial phylogeny in several groups of frogs, both at and below the species level. Additionally, these analyses helped clarify the otherwise chaotic picture of anuran taxonomy and systematics. The stream–tied waterfall frogs of the genera Amolops and Odorrana were examined, the monophyly of the ranid subfamily Amolopinae was rejected, and taxonomic adjustments were made. The phylogeny of the Vietnamese narrow–mouthed frogs of the genus Microhyla was recovered and the current taxonomy examined. Patterns of maternal dispersal and genetic differentiation in mitochondrial DNA were further examined within Microhyla heymonsi, revealing geographic structuring and the existence of two sympatric lineages. Lastly, frogs of the Polypedates leucomystax complex were examined and two major, largely sympatric lineages recovered. Within these groups, 11 separate mitochondrial lineages identified. These represented separate species on the basis of advertisement call and allozyme evidence. The relationship of genetic differentiation and river systems was also investigated and common patterns among the different groups were explored. Clear genetic breaks occurred across both the Red River and the Annamite Mountain range, though most common patterns were groupings of populations along river drainages. While several cryptic species were identified, widespread groups likely representing single species still exist, and a phylogenetic component to broad distribution were noted.
Measuring Semantic Distance using Distributional Profiles of Concepts
Keywords: Computational Linguistics
Natural Language Processing
Lexical semantics
semantic distance
distributional similarity
semantic similarity
semantic relatedness
word concept co-occurrence matrix
distributional profiles of concepts
thesaurus
corpus-based techniques
word senses
cross-lingual techniques
word sense dominance
word sense disambiguation
wordnet

Semantic distance is a measure of how close or distant in meaning two units of language are. A large number of important natural language problems, including machine translation and word sense disambiguation, can be viewed as semantic distance problems. The two dominant approaches to estimating semantic distance are the WordNet-based semantic measures and the corpus-based distributional measures. In this thesis, I compare them, both qualitatively and quantitatively, and identify the limitations of each. This thesis argues that estimating semantic distance is essentially a property of concepts (rather than words) and that two concepts are semantically close if they occur in similar contexts. Instead of identifying the co-occurrence (distributional) profiles of words (distributional hypothesis), I argue that distributional profiles of concepts (DPCs) can be used to infer the semantic properties of concepts and indeed to estimate semantic distance more accurately. I propose a new hybrid approach to calculating semantic distance that combines corpus statistics and a published thesaurus (Macquarie Thesaurus). The algorithm determines estimates of the DPCs using the categories in the thesaurus as very coarse concepts and, notably, without requiring any sense-annotated data. Even though the use of only about 1000 concepts to represent the vocabulary of a language seems drastic, I show that the method achieves results better than the state-of-the-art in a number of natural language tasks. I show how cross-lingual DPCs can be created by combining text in one language with a thesaurus from another. Using these cross-lingual DPCs, we can solve problems in one, possibly resource-poor, language using a knowledge source from another, possibly resource-rich, language. I show that the approach is also useful in tasks that inherently involve two or more languages, such as machine translation and multilingual text summarization. The proposed approach is computationally inexpensive, it can estimate both semantic relatedness and semantic similarity, and it can be applied to all parts of speech. Extensive experiments on ranking word pairs as per semantic distance, real-word spelling correction, solving Reader's Digest word choice problems, determining word sense dominance, word sense disambiguation, and word translation show that the new approach is markedly superior to previous ones.