INHIBITOR STUDIES ON MYCOBACTERIUM TUBERCULOSIS MALATE SYNTHASE

The emergence of multidrug-resistant strains of Mycobacterium tuberculosis (Mtb) has intensified efforts to discover novel drugs for tuberculosis (TB) treatment. Targeting the persistent state of Mtb, a condition in which Mtb is resistant to conventional drug therapies, is of particular interest. Persistent bacteria rely on metabolic pathways that are distinct from active infection Mtb as the environmental conditions of the persistent state are different (e.g., low nutrient). Because persistent Mtb are forced to survive in a low nutrient environment, a short, two enzyme pathway that becomes heavily utilized and upregulated is the glyoxylate shunt. Since the glyoxylate shunt enzymes are not present in mammals, they make attractive drug targets. We are studying malate synthase (MS), one of the enzymes in the glyoxylate shunt. We used computational, biochemical, and cellular techniques to identify potential inhibitors of MS. Crystal structures of MS in complex with inhibitors were used to rationally design better MS inhibitors. MS inhibitors validated via an enzyme activity assay, were then tested against whole cells using a non-pathogenic form of mycobacteria, Mycobacterium smegmatis. In this manner, inhibitors against MS have been identified and characterized for further development into potential novel antitubercular drugs

Non-Adaptive Data Structure Bounds for Dynamic Predecessor

In this work, we continue the examination of the role non-adaptivity plays in maintaining dynamic data structures, initiated by Brody and Larsen. We consider non-adaptive data structures for predecessor search in the w-bit cell probe model. In this problem, the goal is to dynamically maintain a subset T of up to n elements from {1, ..., m}, while supporting insertions, deletions, and a predecessor query Pred(x), which returns the largest element in T that is less than or equal to x. Predecessor search is one of the most well-studied data structure problems. For this problem, using non-adaptivity comes at a steep price. We provide exponential cell probe complexity separations between (i) adaptive and non-adaptive data structures and (ii) non-adaptive and memoryless data structures for predecessor search. A classic data structure of van Emde Boas solves dynamic predecessor search in log(log(m)) probes; this data structure is adaptive. For dynamic data structures which make non-adaptive updates, we show the cell probe complexity is O(log(m)/log(w/log(m))). We also give a nearly-matching Omega(log(m)/log(w)) lower bound. We also give an m/w lower bound for memoryless data structures. Our lower bound technique is tailored to non-adaptive (as opposed to memoryless) updates and might be of independent interest

INHIBITOR STUDIES ON MYCOBACTERIUM TUBERCULOSIS MALATE SYNTHASE

The emergence of multidrug-resistant strains of Mycobacterium tuberculosis (Mtb) has intensified efforts to discover novel drugs for tuberculosis (TB) treatment. Targeting the persistent state of Mtb, a condition in which Mtb is resistant to conventional drug therapies, is of particular interest. Persistent bacteria rely on metabolic pathways that are distinct from active infection Mtb as the environmental conditions of the persistent state are different (e.g., low nutrient). Because persistent Mtb are forced to survive in a low nutrient environment, a short, two enzyme pathway that becomes heavily utilized and upregulated is the glyoxylate shunt. Since the glyoxylate shunt enzymes are not present in mammals, they make attractive drug targets. We are studying malate synthase (MS), one of the enzymes in the glyoxylate shunt. We used computational, biochemical, and cellular techniques to identify potential inhibitors of MS. Crystal structures of MS in complex with inhibitors were used to rationally design better MS inhibitors. MS inhibitors validated via an enzyme activity assay, were then tested against whole cells using a non-pathogenic form of mycobacteria, Mycobacterium smegmatis. In this manner, inhibitors against MS have been identified and characterized for further development into potential novel antitubercular drugs

Can LLMs Grade Short-answer Reading Comprehension Questions : Foundational Literacy Assessment in LMICs

This paper presents emerging evidence of using generative large language models (i.e., GPT-4) to reliably evaluate short-answer reading comprehension questions. Specifically, we explore how various configurations of generative (LLMs) are able to evaluate student responses from a new dataset, drawn from a battery of reading assessments conducted with over 150 students in Ghana. As this dataset is novel and hence not used in training runs of GPT, it offers an opportunity to test for domain shift and evaluate the generalizability of generative LLMs, which are predominantly designed and trained on data from high-income North American countries. We found that GPT-4, with minimal prompt engineering performed extremely well on evaluating the novel dataset (Quadratic Weighted Kappa 0.923, F1 0.88), substantially outperforming transfer-learning based approaches, and even exceeding expert human raters (Quadratic Weighted Kappa 0.915, F1 0.87). To the best of our knowledge, our work is the first to empirically evaluate the performance of generative LLMs on short-answer reading comprehension questions, using real student data, and suggests that generative LLMs have the potential to reliably evaluate foundational literacy. Currently the assessment of formative literacy and numeracy is infrequent in many low and middle-income countries (LMICs) due to the cost and operational complexities of conducting them at scale. Automating the grading process for reading assessment could enable wider usage, and in turn improve decision-making regarding curricula, school management, and teaching practice at the classroom level. Importantly, in contrast transfer learning based approaches, generative LLMs generalize well and the technical barriers to their use are low, making them more feasible to implement and scale in lower resource educational contexts

MEMS 411: Swing Energy Demo

A device to demonstrate the energy processes of a swing was created with the intention of display at the St. Louis Science Center

Neutrophils: homing in on the myeloid mechanisms of metastasis

The metastasis cascade is complex and comprises several stages including local invasion into surrounding tissue, intravasation and survival of tumour cells in the circulation, and extravasation and colonisation of a distant site. It is increasingly clear that these processes are driven not only by signals within the tumour cells, but are also profoundly influenced by stromal cells and signals in the tumour microenvironment. Amongst the many cell types within the tumour microenvironment, immune cells such as lymphocytes, macrophages and neutrophils play a prominent role in tumour development and progression. Neutrophils, however, have only recently emerged as important players, particularly in metastasis. Here we review the current evidence suggesting a multi-faceted role for neutrophils in the metastatic cascade

Erosion-Corrosion of Carbon Steel in Complex Flow Geometries in Oil & Gas CO2 Environments

When sand is present in carbon dioxide (CO2) corrosion environments in oil and gas pipe flow, wear rates of carbon steel pipelines can be severe. This wear mechanism is known as erosion-corrosion and consists of erosion and corrosion components, with degradation enhanced by interactions between the mechanisms. A lack of understanding of erosion-corrosion of carbon steel and the mechanisms contributing to enhanced degradation through erosion and corrosion interactions exists. Erosion-corrosion of carbon steel in CO2 conditions was the subject of investigation in this work. A submerged impinging jet (SIJ) was used to complete a case study of erosion-corrosion degradation of X65 carbon steel in field conditions at high flow velocities up to 20 m/s in a 60°C, pH 4.7, 2 wt.% NaCl solution containing up to 1000 mg/L of sand particles with an average diameter of 250 µm. High degradation rates, some in excess of 25 mm/yr, were measured and whilst corrosion inhibitors added to protect the X65 surface did reduce corrosion rates, they did not reduce erosion degradation, resulting in degradation rates remaining greater than 10 mm/yr in the most severe conditions evaluated. An investigation into the mechanisms of erosion-corrosion interactions revealed that work-hardened layers were thick and more refined on samples subject to erosion conditions compared with samples used in erosion-corrosion tests. This was explained by removal of the work-hardened layers, formed after particle impacts, through electrochemical dissolution, resulting in corrosion-enhanced erosion, which accounted for up to 20% of overall erosion-corrosion degradation at a flow velocity of 20 m/s in a 60°C, CO2-saturared solution containing 1000 mg/L of sand. Erosion-enhanced corrosion was shown not to be significant in the conditions tested. Flow geometry was also shown to have a significant influence on the erosion-corrosion degradation rates. A 90° elbow was designed to evaluate erosion-corrosion in pipe flow, CO2-saturated, pH 4 conditions at a flow velocity of 6 m/s that showed small erosion contributions to erosion-corrosion degradation on the outer radius of the elbow, with flow induced corrosion accounting for the majority of degradation. To fully understand erosion-corrosion conditions in both flow geometries, computational fluid dynamics (CFD) was used to predict mass transfer coefficients and sand particle trajectories in the flow. Predictions were used to define the erosion mechanisms in the different geometries and to explain why degradation rates could vary significantly between different flow geometries