Requirement of the NF- B Subunit p65/RelA for K-Ras-Induced Lung Tumorigenesis

K-Ras-induced lung cancer is a very common disease, for which there are currently no effective therapies. Because therapy directly targeting the activity of oncogenic Ras has been unsuccessful, a different approach for novel therapy design is to identify critical Ras downstream oncogenic targets. Given that oncogenic Ras proteins activate the transcription factor NF-κB, and the importance of NF-κB in oncogenesis, we hypothesized that NF-κB would be an important K-Ras target in lung cancer. To address this hypothesis, we generated an NF-κB-EGFP reporter mouse model of K-Ras-induced lung cancer and determined that K-Ras activates NF-κB in lung tumors in situ. Furthermore, a mouse model was generated where activation of oncogenic K-Ras in lung cells was coupled with inactivation of the NF-κB subunit p65/RelA. In this model, deletion of p65/RelA reduces the number of K-Ras-induced lung tumors both in the presence and absence of the tumor suppressor p53. Lung tumors with loss of p65/RelA have higher numbers of apoptotic cells, reduced spread and lower grade. Using lung cell lines expressing oncogenic K-Ras, we show that NF-κB is activated in these cells in a K-Ras-dependent manner and that NF-κB activation by K-Ras requires IKKβ kinase activity. Taken together, these results demonstrate the importance of the NF-κB subunit p65/RelA in K-Ras induced lung transformation and identify IKKβ as a potential therapeutic target for K-Ras-induced lung cancer

Akt-dependent Activation of mTORC1 Complex Involves Phosphorylation of mTOR (Mammalian Target of Rapamycin) by IκB Kinase α (IKKα)

The serine/threonine protein kinase Akt promotes cell survival, growth, and proliferation through phosphorylation of different downstream substrates. A key effector of Akt is the mammalian target of rapamycin (mTOR). Akt is known to stimulate mTORC1 activity through phosphorylation of tuberous sclerosis complex 2 (TSC2) and PRAS40, both negative regulators of mTOR activity. We previously reported that IκB kinase α (IKKα), a component of the kinase complex that leads to NF-κB activation, plays an important role in promoting mTORC1 activity downstream of activated Akt. Here, we demonstrate IKKα-dependent regulation of mTORC1 using multiple PTEN null cancer cell lines and an animal model with deletion of IKKα. Importantly, IKKα is shown to phosphorylate mTOR at serine 1415 in a manner dependent on Akt to promote mTORC1 activity. These results demonstrate that IKKα is an effector of Akt in promoting mTORC1 activity

Akt-dependent Activation of mTORC1 Complex Involves Phosphorylation of mTOR (Mammalian Target of Rapamycin) by I kappa B Kinase alpha (IKK alpha)

The serine/threonine protein kinase Akt promotes cell survival, growth, and proliferation through phosphorylation of different downstream substrates. A key effector of Akt is the mammalian target of rapamycin (mTOR). Akt is known to stimulate mTORC1 activity through phosphorylation of tuberous sclerosis complex 2 (TSC2) and PRAS40, both negative regulators of mTOR activity. We previously reported that I kappa B kinase alpha (IKK alpha), a component of the kinase complex that leads to NF-kappa B activation, plays an important role in promoting mTORC1 activity downstream of activated Akt. Here, we demonstrate IKK alpha-dependent regulation of mTORC1 using multiple PTEN null cancer cell lines and an animal model with deletion of IKK alpha. Importantly, IKK alpha is shown to phosphorylate mTOR at serine 1415 in a manner dependent on Akt to promote mTORC1 activity. These results demonstrate that IKK alpha is an effector of Akt in promoting mTORC1 activity

Combinations of Cocaine with Other Dopamine Uptake Inhibitors: Assessment of Additivity

Drugs that inhibit dopamine (DA) reuptake through actions at the dopamine transporter (DAT) have been proposed as candidates for development as pharmacotherapies for cocaine abuse. Accordingly, it is important to understand the potential pharmacological interactions of cocaine with other drugs acting at the DAT. Effects of combinations of cocaine with a cocaine analog, 2β-carbomethoxy-3β-(4-fluorophenyl)tropane (WIN 35,428), were compared quantitatively with the combinations of cocaine with the N-butyl,4′,4″-diF benztropine analog, 3-(bis(4-fluorophenyl)methoxy)-8-butyl-8-azabicyclo[3.2.1]octane (JHW 007), to determine whether their effects on DA levels in the shell of the nucleus accumbens (NAC) in mice differed. Each of the drugs alone produced dose-related elevations in NAC DA levels. In contrast to the other drugs, JHW 007 was less effective, producing maximal effects that approached 400% of control versus ∼700% with the other drugs. In addition, the JHW 007 dose-effect curve was not as steep as those for cocaine and WIN 35,428. Combinations of cocaine with its analog, WIN 35,428, were most often greater than those predicted based on dose additivity. In contrast, combinations of cocaine with JHW 007 were most often subadditive. This outcome is consistent with recent studies suggesting that structurally divergent DA uptake inhibitors bind to different domains of the DAT, which can result in different DAT conformations. The conformational changes occurring with JHW 007 binding may result in functional outcomes that alter its abuse liability and its effects in combination with cocaine

Relations between stimulation of mesolimbic dopamine and place conditioning in rats produced by cocaine or drugs that are tolerant to dopamine transporter conformational change

Proces održavanja zrakoplova karakteriziraju velike količine strukturiranih i nestrukturiranih podataka koji se svakodnevno bilježe u obliku pilotskih izvještaja, izvještaja o aktivnostima održavanja, zapisa o nezgodama i zastojima, izvještaja nakon leta, itd. Znanja dobivena na temelju povijesnih podataka se mogu koristiti za unapređenje procesa održavanja zrakoplova, no ekstrakcija tih znanja se ne može obaviti ručno. U tu svrhu se koriste tehnike dubinske analize podataka, koje omogućavaju automatiziranu ili polu-automatiziranu ekstrakciju znanja iz skupova podataka. Predloženo istraživanje bavi se razvojem modela, temeljenih na tehnikama dubinske analize podatka, koji će služiti kao potpora pri odlučivanju o raspoloživosti zrakoplova, a pri modeliranju se koriste tri izvora podataka: podaci prikupljeni iz sustava za nadzor tehničke ispravnosti stanja zrakoplova, zapisi o nepravilnostima u radu sustava/prošlim kvarovima i zapisi o zastojima u eksploataciji zrakoplova (operacijskim zastojima). Podaci su prikupljeni u razdoblju od 76 uzastopnih mjeseci za četiri zrakoplova tipa Airbus A319/20 koji pripadaju floti jednog zračnog prijevoznika. U disertaciji su analizirani kritični sustavi zrakoplova; sustav automatskog leta (ATA 22), sustav upravljanja letjelicom (ATA 27), hidraulički sustav (ATA 29), sustav podvozja (ATA 32) i navigacijski sustav (ATA 34). Klasifikacijski modeli prve grupe izgrađeni su s ciljem predviđanja nastanka pilotskog upisa u tehničku knjigu na temelju prethodno generiranih poruka upozorenja u određenim fazama leta. Razvijen je postupak za strukturiranje podataka prikupljenih iz sustava za nadzor tehničke ispravnosti zrakoplova te postupak integracije tih podataka sa zapisima o nepravilnostima u radu sustava/prošlim kvarovima. Udruživanjem skupova podataka za izgradnju klasifikacijskih modela prve grupe otkriven je rang relevantnih značajki primjenom različitih filterskih postupaka (korelacijskog postupka, gini indeks postupka, informacijskog dobitka i omjera informacijskog dobitka) za selekciju značajki. Dodatno je provedeno istraživanje kako smanjenje udjela ulaznih značajki utječe na učinkovitost modela (F-mjeru, osjetljivosti i specifičnosti) za različite načine uzorkovanja podataka (uravnoteženo i slučajno uzorkovanje). Klasifikacijski modeli druge grupe izgrađeni su s ciljem predviđanja posljedice nastalih tekstualnih pilotskih zapisa na raspoloživost zrakoplova. Izgradnji modela prethodila je aktivnost udruživanja skupova podataka o nepravilnostima u radu sustava/kvarova i operacijskim zastojima. Usvajanjem evaluacijske mjere točnosti, modeli su uspoređeni s postojećim modelom iz sličnih istraživanja te je dokazana njihova primjenjivost.The aircraft maintenance process is characterized by large amounts of structured and unstructured data that are recorded daily in the form of pilot reports, maintenance logs, records of operational interruptions and technical incidents, post-flight reports, etc. The knowledge hidden within this data can potentially be used to improve the maintenance process, but its extraction can hardly be done manually. Therefore, in the last couple of years, a trend of development of predictive models using data mining techniques has been noticed. However, it can be concluded that these techniques are still not sufficiently applied in the process of aircraft maintenance because they require interdisciplinary knowledge, which includes understanding of the database, statistical knowledge, and understanding of machine learning and artificial intelligence techniques and models. This provides motivation for further research in this field. The aim of the work presented in this thesis is to develop a new decision support models based on data mining techniques that will be used for predicting aircraft availability. In modelling process, three independent sources of data will be used; data collected from aircraft health monitoring system (AHMS), information of past faults/defects and information of operational interruptions. These data were collected over a period of 76 consecutive months for four Airbus A319/20 aircraft. Only data from critical aircraft systems were analysed in this dissertation; auto flight system (ATA 22), flight control system (ATA 27), hydraulic system (ATA 29), landing gear system (ATA 32) and navigation system (ATA 34). Based on the data collected from these systems, two groups of classification models were built. The aim of the first group classification models is to determine whether a specific warning message/group of messages, collected from AHMS during different flight phases, will result in a pilot logbook entry. Prior to model development step, an algorithm for structuring AHMS data and algorithm for data fusion was developed. By integrating two data sources (warning messages from AHMS and information of past faults/defects), four filter methods (correlation based method, Gini index, information gain and information gain ratio) for feature selection were applied on a combined data source. In addition, research has been carried out to investigate how the reduction of the data dimensionality (a feature vector), in combination with different sampling techniques (stratified and shuffled), affects model performance measures (F-measure, sensitivity and specificity). The aim of the second group classification models is to determine whether the created pilot logbook record will affect the aircraft availability, i.e. whether it will result in aircraft on ground (AOG) status, delay or flight cancellation status. This group of models is also built on combined data set, i.e. by integrating information of past faults/defects and information of operational interruptions. By adopting the evaluation measure of accuracy, developed models were compared to existing model from similar past research, and its applicability was demonstrated. The research was carried out in several phases, which are summarized in the following chapters. Chapter 1 “Introduction” outlines literature gaps in the field of the aircraft maintenance. Based on the information acquired from published scientific papers and doctoral dissertations, various data mining techniques used for prediction in the field of aircraft maintenance were presented in the second subchapter. The rest of the sections present main aim of this research, hypothesis, expected scientific contributions, domain-specific terminology and an overview of the thesis. Chapter 2 “Prognostics in the aircraft maintenance process” highlights the benefits and challenges of the prognostics approaches currently applied in the literature. It explains in detail knowledge data discovery process, as well as supervised and unsupervised data mining techniques. Cluster analysis, association rules and various classification algorithms (Neural Networks, Decision Trees, Support Vector Machines and Naïve Bayes) were additionally described. Four filtering methods for feature selection were introduced. Different data sampling techniques and measures for evaluation of classification models were also described in this chapter. Chapter 3 “Processing and analysis of collected data” provides a detail explanation of the three independent data sources (AHMS data, information of past faults/defects and information of operational interruptions) used for this research. This chapter also presents a procedure for structuring AHMS data based on algorithm presented in Appendix A. Except for this procedure, two additional procedures for data fusion are shown; a procedure for data fusion of AHMS data and past faults/defects (Appendix B), and procedure for data fusion of past faults/defects and data containing information of operational interruptions. The final result of this procedures presents two combined data sets, which are transformed into a form suitable for modelling. In addition, an exploratory data analysis was conducted on these combined data sets and recommended guidelines were provided for future research phases. Chapter 4 “The process of discovering relevant features and the development of classification models” consists of the concise research steps used for building various data mining models. In the first section, an outline of the process developed within RapidMiner platform for discovering relevant features by applying filtering methods is given. This section also presents a process for applying the association rule mining to the combined data set to identify the features that appear more frequently together. The second section presents a process for building a first group classification models, while the third one presents a process for building a second group classification models. A list of the operators and their parameters used for building the models is presented. A graphical representation of these models is given in Appendix C. Chapter 5 “Research results” outlines the results of the models developed in previous chapters. The resulting models were tested on a verification data set, i.e. a data set that was not used for model building. To determine the performance of the built classification models, evaluation metric has been used. Due to the data imbalance, the first group classification models were evaluated by F-measure, sensitivity and specificity. All models were first built on the original dataset, which contains all the features. During the modelling phase, different data sampling techniques (stratified and shuffled) were used. By applying different filter methods, a rank of the features, representing a feature vector, was obtained (Appendix D). In order to evaluate results of the filter methods, the performance of the first group classification models was observed by gradually decreasing the number of the features from the rank obtained by each filter method. The main metric for the performance evaluation of the second group classification models was accuracy. After obtaining model accuracy, these results were compared with results from the similar model found in the literature. Chapter 6 “Conclusion” presents the final chapter where the original scientific contributions are presented and a summary of the results is provided. Also, theoretical and practical implications are outlined and areas for potential future research are suggested