Characterisation of the vibrio cholerae antibiotic resistance var operon

The discovery and use of antibiotics in the chemotherapy of bacterial infections has revolutionised medicine as it is today. Unfortunately, the progressive use of antibiotics has promoted the evolution of bacterial defences against these mediators and thus the emergence of antibiotic resistance. Multidrug resistance (MDR) in bacterial pathogens has grown with such rapid progression that it now threatens to compromise the effective chemotherapy of a plethora of diseases. This thesis aspires to elucidate the molecular resistance mechanisms adopted by these bacteria, in order to expand our knowledge and to assist in the development of new therapeutic approaches to circumvent these mechanisms. On this basis, this thesis presents insights into a novel Vibrio cholerae antibiotic resistance, var, operon that encodes a metallo- β -lactamase (Mßl), VarG, and a tripartite ATP-binding cassette-type (ABC-type) transport system, VarACDEF that has substrate specificities for antimicrobial peptides and macrolide antibiotics. Mßls are fast emerging as a primary resistance mechanism, possibly as a consequence of the introduction of newer ß-lactam antibiotics such as the carbapenems in response to increasing Gram-negative bacterial resistance. Fascinatingly, the ABC transporter, through secondary structure predictions, has been envisaged to adopt a tripartite structure similar to the MDR transporter, AcrAB-TolC, from the resistance nodulation and cell division (RND) family. The structural characterisation of this system would be the first such tripartite system to be elucidated and may bring new insights into how Gram-negative bacteria may have evolved to tackle the issue that threatens its existence. The resistance mechanisms in the var Operon are believed to be under the control of a LysR-type transcriptional regulatory protein (LTTR), VarR. LTTR proteins form one of the largest transcriptional regulatory families with extremely diverse functions ranging from amino acid biosynthesis to CO(_2) fixation. VarR binds to three distinct promoter regions, varRG, varGA and varBC located upstream and adjacent to VarG, VarA an AcrA-like membrane fusion protein and VarC a TolC-like outer membrane protein, respectively. VarR has also been shown to act as a repressor at the varRG promoter region in the absence of its substrate. Interestingly, the mechanism of regulation by VarR is strikingly similar to the well documented LTTR, AmpR and serine ß-lactamase AmpC system that are found in many pathogenic bacteria. It could be that V. cholerae has evolved from this regular system and developed a ß-lactamase that would prove more beneficial in light of current selective pressures. Contrary to LTTRs being notoriously recalcitrant to purification due to their low solubility, this thesis reports the successful purification and crystallisation of full-length VarR in the presence and absence of its cognate promoter DNA. Elucidating the structural characteristics of VarR would be the first such regulator associated with MDR in the LTTR family. This would advance the knowledge on the only currently existing full-length crystal structure of a LTTR, CbnR, and will provide further insights into how structural conformations may lead to dissociation from the promoter and induction of gene expression. Understanding the mechanism by which VarR induces expression of these resistance mechanisms is paramount for future strategies to prevent the emergence of MDR microorganisms. Although these mechanisms of MDR maybe elucidated in V. cholerae, the evolutionary relatedness and conservation of structure and function in all families will enable this information to be related to similar systems in alternative bacterial species

WEATHERING COVID-19: Lessons from Wuhan and Milan for Urban Governance and Sustainability

The global spread of COVID-19 has exposed the world’s largest and densest urban centres to bearing the brunt of this pandemic. The invisible virus has forced thriving metropolises to empty their streets and shops to dead spaces absent of people and activity. It even triggers the doomsday question of, “Does COVID-19 mean the end of cities?” In this article, we compare how two great cities of the East and West – Wuhan and Milan – have responded to the deadly virus, with their internal and external strengths and constraints. We also take the reader deep into the two cities’ neighbourhoods for a realistic sense of how their local residents have dealt with COVID-19. We end by drawing critical lessons for urban governance and sustainability

Electrical conductance of molecular junctions by a robust statistical analysis

We propose an objective and robust method to extract the electrical conductance of single molecules connected to metal electrodes from a set of measured conductance data. Our method roots in the physics of tunneling and is tested on octanedithiol using mechanically controllable break junctions. The single molecule conductance values can be deduced without the need for data selection.Comment: 4 figure

Experiment Design and Training Data Quality of Inverse Model for Short-term Building Energy Forecasting

For data-driven building energy forecasting modeling, the quality of training data strongly affects a model’s accuracy and cost-effectiveness. In order to obtain high-quality training data within a short time period, experiment design, active learning, or excitation is becoming increasingly important, especially for nonlinear systems such as building energy systems. Experiment design and system excitation have been widely studied and applied in fields such as robotics and automobile industry for their model development. But these methods have hardly been applied for building energy modeling. This paper presents an overall discussion on the topic of applying system excitation for developing building energy forecasting models. For gray-box and white-box models, a model’s physical representations and theories can be applied to guide their training data collections. However, for black-box (pure-data-driven) models, the training data’s quality is sensitive to the model structure, leading to a fact that there is no universal theory for data training.  The focus of black-box modeling has traditionally been on how to represent a data set well. The impact of how such a data set represents the real system and how the quality of a training data set affect the performances of black-box models have not been well studied. In this paper, the system excitation method, which is used in system identification area, is used to excite zone temperature set-points to generate training data. These training data from system excitation are then used to train a variety of black-box building energy forecasting models. The models’ performances (accuracy and extendibility) are compared among different model structures. For the same model structure, its performances are also compared between when it is trained using typical building operational data and when it is trained using exited training data. Results show that the black-box models trained by normal operation data achieve better performance than that trained by excited training data but have worse model extendibility; Training data obtained from excitation will help to improve performances of system identification models

Correlation Enhanced Distribution Adaptation for Prediction of Fall Risk

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different data sources with labeled and unlabeled samples to predict a patient\u27s condition poses a significant challenge. Traditional machine learning models assume that data for new patients follow a similar distribution. If the data does not satisfy this assumption, the trained models do not achieve the expected accuracy, leading to potential misdiagnosing risks. To address this issue, we utilize domain adaptation (DA) techniques, which employ labeled data from one or more related source domains. These DA techniques promise to tackle discrepancies in multiple data sources and achieve a robust diagnosis for new patients. In our research, we have developed an unsupervised DA model to align two domains by creating a domain-invariant feature representation. Subsequently, we have built a robust fall-risk prediction model based on these new feature representations. The results from simulation studies and real-world applications demonstrate that our proposed approach outperforms existing models

ENTRNA: A Framework to Predict RNA Foldability

RNA molecules play many crucial roles in living systems. The spatial complexity that exists in RNA structures determines their cellular functions. Therefore, understanding RNA folding conformations, in particular, RNA secondary structures, is critical for elucidating biological functions. Existing literature has focused on RNA design as either an RNA structure prediction problem or an RNA inverse folding problem where free energy has played a key role