Survivin and AURKA, key determinants of mitotic cell fate

Defects in chromosomal segregation and genome stability are two of the main hallmarks of cancer and their occurrence can often be due to aberrant expression of cell cycle regulators. Survivin is a multi-interactor protein that play fundamental dual roles as a regulator of cell division and an inhibitor of apoptosis. The family of Aurora kinases (AURKs) are amongst the many interactors that associate with survivin during mitosis, which in mammals, comprises Aurora kinase A (AURKA), Aurora kinase B (AURKB) and Aurora kinase C (AURKC). Deregulation of survivin and AURKs is linked to various abnormalities in cancer cell proliferation and strongly correlated with chemoresistance. Survivin plays a substantial role in regulating proper chromosomal alignment and segregation through its cooperation with AURKB, via the chromosomal passenger complex (CPC). It can also form a complex with AURKC, that can complement AURKB mitotic function as a CPC member during mitosis. AURKA shares functional and structural similarities with AURKB/C, however, it is still unknown whether AURKA can associate with survivin. Here, we show for the first time, that AURKA binds to survivin during mitosis and regulates its function. We report that AURKA interacts directly with the BIR domain of survivin and indirectly via its C-terminus, and their interaction is at its highest during early mitosis.  We also suggest a novel function of AURKA as a regulator of AURKB expression and activity, and as a regulator of survivin localisation and function during early and late mitosis. We further show that survivin plays a major role in controlling cell fate during mitotic arrest and its mislocalisation in the absence of AURKA leads to mitotic slippage. We show that upregulating survivin levels in both normal and transformed human cells results in premature mitotic exit when AURKA is inhibited, and that AURKA activity is essential for maintaining the localisation of the checkpoint protein BubR1 at the unattached kinetochores during mitotic arrest. These finding show that elevated survivin levels in cancer provide a risk factor to inappropriate mitotic exit and genetic instability (aneuploidy), and regulating its levels may alter the approach to utilising AURKA inhibitors in cancer treatment

Morphological and structural characterization of soot-in-oil samples and their impact on marginally lubricated engine components

The major drivers in the development of the latest generation of engines are environmental. For diesel engines, mitigating the effects of soot contamination remains a significant factor in meeting these challenges. There is general consensus of soot impacting oil performance. Considerable efforts have been made towards a greater understanding of soot-lubricant interaction and its effects on engine performance. However, with evolution of engine designs resulting in changes to soot composition/properties, the mechanisms of soot-lubricant interaction in the internal combustion engine continue to evolve. A variety of mechanisms have been proposed to explain soot-induced wear in engine components. The first part of this work aims to critically review and discuss the current understanding of soot-induced mechanisms in heavy-duty diesel engines, as reported in the literature. Emphasis will be given to the aspects of wear, friction, and viscosity, critically highlighting the main pathways for future research. Different hypotheses on wear and the potential mechanisms behind the soot-lubricant interaction are also discussed, showing potential issues related to soot contamination as well as the strong relationship with oil formulation. Multiple soot properties are responsible for wear and their impact seems to depend on the boundary lubrication conditions achieved during the test. Therefore, a systematic soot characterization from different engine test conditions is required for a comprehensive assessment of soot impact on engine components. Thus, the morphology, nanostructure, and composition of soot extracted from the oil sump of different heavy-duty engines operated under dynamometer and field conditions were investigated. Soot characteristics were then compared to three carbon black materials. Soot was extracted from used oils for Transmission Electron Microscopy (TEM) analysis. Energy Dispersive X-Ray (EDX) and X-ray photoelectron spectroscopy (XPS) analysis were also performed to assess soot composition. Two soot classes, I and II, can be identified based on how they appear under the TEM. Carbon blacks and class I particles have graphitic structures, while class II samples have a more sludge-like appearance. Similar aggregate sizes were observed among the samples. Primary particle size shows a unimodal distribution in all samples, with a median particle diameter from 18 nm to 22 nm. Differences in the length and tortuosity of the graphitic fringes between the samples were observed. The findings suggest a greater degree of interaction between class II samples and the lubricating oil, and consequently, a different wear behaviour is expected depending on the specific mechanism involved. Additional research was conducted on artificially aged oil samples loaded with 1% carbon black (Monarch and Vulcan) to investigate the observed sludge-like formations in used oils. Results show that when 1 wt.% Monarch was present in the oil during the ageing process, a thick amorphous layer was observed on CB particles in comparison to the test in which the same amount of CB was added to the aged oil. To further investigate the impact of soot on real engine components, the most similar CB sample was used as soot replica during chassis dynamometer testing. A chain wear test rig is used to motor a 1.3 L diesel engine following the speed profile of a Worldwide Harmonized Light Vehicle Test Cycle (WLTC). The lubricant oil was loaded with 3% carbon black of known morphology. The chain length is measured at regular intervals of 20 WLTC cycles (i.e. 10 hours) and the wear is expressed as a percentage of total elongation. Oil samples were collected and analysed with the same frequency as the chain measurements. Carbon black morphology and nanostructure were investigated using Dynamic Light Scattering (DLS) and TEM. DLS data revealed that carbon black particle size did not change substantially in the first 10 hours, however, during the remaining test cycles a reduction in agglomerates size over time was observed. The wear results show that adding carbon black to the lubricating oil promotes chain elongation by up to 0.10%. Significant chain elongation occurred within the first 10 hours (+0.06%), with further increase in elongation occurring in the remaining 40 hours (+0.04%) but under a reduced wear rate. The overall results suggest that dynamically changing carbon black size distributions and nanostructure could be linked over time

On the evaluation of conversational search

The rapid growth of speech technology has significantly influenced the way that users interact with search systems to satisfy their information needs. Unlike the mode of textual interaction, Voice User Interfaces (VUIs) often encourage multiple rounds of interaction for reasons such as clarifying user input, error handling, progressive information disclosure, personalization, and completing complex tasks. On the other hand, VUIs also promote natural interaction by ensuring accurate speech recognition, maintaining context throughout conversations, offering clear prompts and guidance, and providing feedback and confirmation. Meanwhile, with the widespread application of large language models (LLMs), information retrieval systems increasingly engage in dialogue with users, such as Bing Chat and Google Bard. In comparison to the traditional query-document paradigm, conversational search systems often encourage users to express their search tasks using natural language and to interact over multiple rounds. By adopting this dialogue-like form, these systems can improve accuracy and better understand user intent. However, it is worth noting that the essence of a conversational search system is still an information retrieval (IR) system, which aims to provide users with the information that they want in order to satisfy their information needs. This interaction paradigm is more natural for humans and can help the users to better articulate their information needs. In turn, however, this search paradigm increases the complexity for systems to understand users’ intent and underlying information needs across the multi-turn interactions. Further, the added complexities mean that the evaluation of conversational search systems is also not straightforward. One of the challenges in evaluating conversational search systems is that the number of possible user utterances and system responses is infinite, thus it is difficult, if not impossible, to use a static, finite set of relevance labels to evaluate their effectiveness. Therefore, the evaluation of a conversational search system remains an open, non-trivial challenge. This thesis aims to address some aspects of this challenge and make contributions towards building a comprehensive and replicable evaluation framework for conversational search. In the previous work, a typical evaluation framework (e.g., Cranfield paradigm) for IR research often comprises both the construction of test collection and the design of evaluation metrics. In the context of conversational search, the metrics should not only focus on the similarity between the candidate responses and the provided reference, but also consider the underlying information in the dialogue context. Unlike traditional query-document IR collections (e.g., TREC), the test collections for conversational search should contain dialogue context, queries, reliable references for the queries and quality assessments. This thesis consists of three parts: meta-evaluating existing metrics, developing a set of metrics and building test collections. Firstly, we initiate the work by reviewing several representative metrics and engaging in a meta-evaluation of these metrics in conversational search scenarios. We aim to gain a better understanding of the evaluation process of conversational search and explore the potential limitations of existing metrics. Secondly, according to the analysis of the meta-evaluation, we aim to investigate the underlying factors that influence quality assessments, with the purpose of designing robust and reliable metrics that have a better alignment with human annotations. In this part, we first explore the impact of the syntactic structures (e.g., Part of Speech) in the reference and find the effectiveness of Part-of-Speech (POS) in distinguishing the quality of the responses. We further proposed Part-of-Speech based metrics (POSSCORE) to effectively capture such syntactic matching and achieve significantly better alignments with human preferences than baseline metrics. Moving forward, we further consider the ongoing context of conversation search and develop a context-aware evaluation framework to effectively capture the novelty and coherence of the responses. By thoroughly analyzing the relationship between the ongoing utterance context and the quality of responses, we propose to improve the evaluation of conversational search by extending existing metrics into context-aware modes and proposing novel context-aware metrics (COSS). After that, given that references are still an important part of the evaluation process, we aim to propose a general framework to improve the effectiveness of reference-based metrics. By utilizing the chain of thought prompting, we demonstrate the effectiveness of the chain of thought in improving the performance of reference-based metrics in conversational search. Apart from the design of evaluation metrics, building test collection is another essential component of the evaluation framework. Since human annotations remain the most reliable method for labelling, the process of constructing conversational search test collections still needs laborious and time-consuming efforts. This increased the difficulty in constructing the collections and significantly limited the scale of the collections. To bridge this gap, we start this work by formalizing the user behaviour patterns. The goal of this work is to reuse the existing collections and extend the pseudo-relevance labels via some rule-based methods. After that, we further explore the possibility of employing large language models (LLMs) to reuse the existing datasets. By leveraging LLMs and relevant documents, we design efficient approaches to obtain reliable pseudo references and quality pseudo-labelling for the candidate responses. After constructing the dataset, we further engage in meta-evaluating the performance of existing metrics over LLMs’ responses. In conclusion, this thesis centres on demonstrating the feasibility of developing automatic evaluation approaches for conversational search, aiming to achieve reproducible and reliable evaluation and establish a robust alignment with human annotations. One of our best contributions in this thesis is the development of a context-aware evaluation framework for conversational search, which effectively addresses the challenge of evaluating conversational search systems that need to generate relevant, informative, and engaging responses that consider the ongoing context of the conversation

Investigating the impact of copper leaching on the combustion and emissions characteristics of a direct injection diesel engine

In recent years, an increasing effort towards reducing the use of diesel engine as a consequence to the “Volkswagen scandal” has arisen. Despite those efforts, it is very difficult to replace diesel engines due to their excellent characteristics such us high power to weight ratio and reliability. Especially some sectors such as HDV and ships have not reliable replacement to diesel engines. In this scenario, much interest was drawn to replace diesel with carbon neutral substitutes (e.g. biodiesel, HVO) and/or to improve diesel through the addition of additives. Additives are added as nanomaterials and mixed to diesel/biodiesel. Although additives successfully improve combustion and emissions, a few challenges have still not been met such as having even smaller additive particle size, reducing the PN emissions and reducing the production of nanofuels from a two-step method to a one-step method. This research has proven that it is possible to control the rate of copper release, therefore achieving a one-step method with reduced size of the additive. Copper was released in diesel through a fuel conditioning device made of copper that was subjected to electromagnetic field. This device was installed in a 2.2 L Ford Puma Duratorq diesel engine. The engine was run at 3 conditions representing low to medium speed/load. The addition of 0.2 ppm of copper in diesel has led to a reduction in combustion duration up to 1 CAD and in soot emissions up to 28%. When copper was replaced with Teflon and only the electromagnetic field was left, no improvement in emissions nor combustion were observed, therefore demonstrating that the increasing concentration of copper played a central role in the observed improvement. Subsequently, a small fuel rig was made to assess how temperature and magnetic field were affecting the corrosion. Increasing temperature of copper to 60 ℃ increased corrosion rate by 44% compared to baseline conditions (no heat or magnetic field applied). However, the application of a magnetic field increased the corrosion rate of copper in diesel by 2-3.5 times, therefore suggesting that the application of the electromagnetic field was boosting copper contamination in diesel. Besides, 3 sets of permanent magnets were investigated and the corrosion rate increased with increasing magnetic field strength. The combined effect of temperature and magnetic field provided the highest corrosion rate (257% more than baseline). Other metals were not successfully leached in diesel, although it was possible to deposit platinum oxides onto copper plates and then leach platinum oxides in diesel

The inter-relationship between p53, survivin and XIAP in colorectal cancer cells response to apoptotic stimuli

Colorectal cancer (CRC) also known as colon cancer usually starts in the inner lining of the colon, grows as a non-cancerous polyp and then develops into an adenoma after several years. Overexpression of two inhibitor of apoptosis (IAP) proteins, XIAP and survivin is prevalent in cancers including CRC. Under hypoxia, these cells exhibit even greater resistance to apoptosis due to the expression of HIF-1α, a transcriptional activator of survivin and many other proteins. The increased expression of survivin and XIAP has been associated with chemotherapeutic resistance in hypoxic conditions. P53, a sensor of genotoxic stress and an initiator of apoptosis, is a tumour suppressor protein that regulates transcription of apoptosis genes. This thesis aims to understand the inter-relationship between p53, survivin and XIAP in colorectal cancer cells response to apoptotic stimuli. Using colorectal cancer cells with specific stable knockouts of p53 and XIAP, in conjunction with quantitative real-time PCR and western blotting, the presented data show that survivin was repressed at both the transcriptional and post-translational levels in HCT 116 p53+/+ and XIAP-/- cells, which exhibited high expression of p53 protein. Whereas in p53 -/- cells, survivin mRNA and protein levels were highly expressed. In contrast, no difference was observed in either gene or protein expression levels of XIAP in the p53+/+ and p53-/- cells. Thus, p53 represses survivin independent of XIAP. However, p53 does not repress XIAP. Furthermore, the role of survivin in apoptosis inhibition in these cells was investigated in normoxia and hypoxia. The hypothesis that survivin inhibition of apoptosis was dependent on XIAP/or p53 was tested. Using apoptotic stimuli TRAIL and etoposide flow cytometry analysis of caspase3/7 activity revealed that in TRAIL-mediated apoptosis, survivin functions independently of XIAP, while in the etoposide-induced cell death, the role of survivin was XIAP dependent. Moreover, TRAIL- mediated apoptosis was independent of p53 while etoposide-induced cell death was p53 dependent. The effect of a small molecule, YM 155, which has been described as an inhibitor of survivin was examined in several cancer lines. Quantitative real-time PCR and western blotting analysis showed that survivin was only repressed at the transcriptional level in U2OS cells. Whereas, post-translational elimination of survivin was seen in all the cell types. Interestingly, we unveiled a novel link between YM155 and survivin degradation via the autophagy-lysosomal pathway. In addition, flow cytometry analysis showed the cytotoxic effect of YM 155 was reduced under hypoxia. This thesis shows that p53 repression of survivin is XIAP independent. The role of survivin in apoptosis inhibition is XIAP dependent, but this dependency varies depending on the pathway of cell death. YM 155 reduces survivin via different mechanism of actions

Machine Learning Systems as Integrative Objects: Towards a Generic Epistemology of Predictive Systems

This thesis is concerned with machine learning, focusing in particular on high-stakes implementations of predictive systems that have been increasingly recognized as leading to social harm and deepening discrimination. The expanding range of machine learning applications, including examples such as predictive policing, the primary case study in this thesis, means that these technologies are no longer predominantly within the purview of computer science, and a wide range of critical scholarship is now also invested in discussing their societal impact. However, the resulting research landscape remains fragmented, and efforts to combine critical and computational perspectives in order to address problems with predictive systems often culminate in reductive metrics for ‘fairness’ or ‘bias’. Drawing on generic epistemology, an approach developed primarily by philosopher Anne-Françoise Schmid, this project proposes a framing of machine learning systems as ‘integrative objects’, meaning objects which exceed the productive capacities of singular disciplines as well as their synthesis. Generic epistemology posits that when faced with integrative objects, the operative logics and priorities of distinct disciplines often lead to an impasse in interdisciplinary work. Schmid and her collaborators advocate a more heterogeneous approach, where fragments of disciplinary knowledge can be used in new contexts without the wholesale import of the epistemic machinery of their source domains, in order to enable new conceptual formations. To this end, this thesis explores some of the limitations of the perspectives on machine learning produced by critical theory and computer science, and proposes an engagement with theories of induction (principally John D. Norton’s material theory of induction), with the philosophy of models in science (focusing on the tension between prediction and explanation, and the role of idealization in scientific models), and with theories of causality (in particular the interventionist approach to causation as advanced by Judea Pearl and James Woodward), as conceptual material capable of illuminating crucial parameters of high-stakes predictions. I argue that recognizing machine learning systems as integrative objects and adopting the research paradigm of generic epistemology can offer a more nuanced approach to contesting problematic uses of these technologies

Do algorithmic trading impact the quality of the United States financial market?

This paper aims to examine the effects of algorithmic trading on the market quality in the US market. Over the past decades, the introduction to sophisticated and complex algorithms into the financial sector has seduced many institutions and traders. Indeed, they are willing to pay large amount of money in research and development in order to improve their decision making of just some milliseconds. However, the rise of these new technologic tools came with some interrogations about its impact on the market quality. Many studies have been done all over the world without clearly agreeing on whether it was beneficial for the market or no. This study use aggregates values of 20 NYSE listed stocks to create a set of proxies for algorithmic trading such as the cancel-to-trade ratio, odd-lot volume ratio and trade-to-order volume ratio and analyse their relationships to another set of proxies representing the different measures of market quality (Liquidity, volatility, and price discovery). The analysis is made through a panel data regression and the findings concluded that AT proxies had a positive impact on the liquidity except for the Odd-lot volume ratio that was insignificant. The volatility in the opposite way is worsened and increased by the algorithmic trading activity. Finally, the results admit a strong positive relationship between odd-lot volume ratio and price discovery process and a strong inversely related correlation between trade-to-order volume ratio (negatively related to algorithmic trading activity) as AT proxy and the price discovery meaning that price efficiency was improved due to AT. This study draws the inference that algorithmic trading is beneficial for the market quality and its participants

AdvantAGE: a start-up of recruitment service for mature talent

Malaysia is expected to become an aged society by 2044 with 14% of the population aged above 65. Only 45.2% of people aged 55-64 are employed, which is lower than other high-income economies. This aging trend would bring unique challenges to the nation especially in areas of employment, income security and aged care. The main aim of this start-up is to assist retirees in Malaysia in gaining access to a wide variety of job opportunities while allowing them to embrace active ageing. A survey was conducted through questionnaire to ascertain the employers' acceptance towards hiring of senior citizens in their organization, the willingness of retirees to return to the workforce. Internal and external analysis using PESTLE, Porter’s Five Forces, SWOT, TOWS, VRIO theories were conducted. Findings from primary research data show strong intention of working post-retirement among participants aged 50 and above. The result also shows participants’ acceptance of our platforms offering. Sales forecast and projection indicated the feasibility of the idea, and it is worthwhile exploring further

Does AI-based recruitment influence an employer’s attractiveness to potential employees?

This research investigates the key factors, specifically the new AI-based Recruitment Process which can affect the Employer’s Attractiveness and Joining Intentions from potential candidate’s perspective in Malaysia. Kaplan and Haenlein (2019) describes AI recruiting as a tech-advanced procedure which helps firms in their recruitment and selection processes. It uses a system’s capability to infer the external data accurately to implement the knowledge gained to attain determined goals with its adaptability. Firms using new, innovative and cutting-edge technologies such as AI, Machine Learning, Deep learning technologies etc. are deemed to be attractive as they have great capabilities to widen the reach of realtime engagements for businesses with its stakeholders and offer business continuity even during challenging times like the pandemic (Watters,2023). AI-tools are highly attractive in the recruitment industry as they offer higher speed, superior business insights, reduce human errors, automate tedious repetitive tasks, provides efficiency gains by increases productivity, candidate experience and is also time and cost saving. Hence, it has emerged as a valued asset over traditional recruitment practices(VanEsch and Black, 2019). Other studies like Gartner, InfoTech Research Group report also suggests that AI and other modern technologies are gaining popularity. HR Managers and Firms thus, need to understand the needs of its potential employees when strategically approaching the job market for competitive advantage and to attract skilled talents to join them. In this study, Berthon’s EmpAt scale (Sivertzen et.al, 2013) was used as a basis to create a conceptual model since it’s renowned and its reliability is proved by many scholars for Brand Reputation and Employer Attractiveness leading to Intention to Join a firm. Besides investigating the new proposed concept of AI-based Recruitment process, other key factors like Firm’s Attributes such as Symbolic, Instrumental and P.E Fit as identified through literatures were also studied. The research was conducted by means of a quantitative survey and 275 responses were gathered out of which 220 responses were used for the study using SPSS software for analysis. The target sample comprised of Working Professionals, recent Graduates and Full Time, Part-time students enrolled in Post-Graduate and UnderGraduate Programs in Malaysian Universities who are looking for a new job or a career change. Structural Equation Modelling (SEM) technique using SmartPLS4 was then used to analyse the findings of the research critically to understand the main factors specifically AI-based Recruitment Process affecting the Employer’s Attractiveness and Brand Reputation. And its influence on Joining Intentions from a potential candidate’s perspective in Malaysia. The results showed that both AI-based recruitment and Firm’s Attributes(Symbolic Attributes(FAS),Instrumental Attributes- (FAI) and Person-Environment (P.E) Fit) directly affects the Brand Reputation and Employer’s Attractiveness which leads to positively influencing the Joining Intentions of the prospective candidates in Malaysia.AI-Based Recruitment also has an effect on increasing the Brand Reputation which results in influencing the Joining decisions of the potential employees