A novel FRET-based screen in high-throughput format to identify inhibitors of malarial and human glucose transporters

The glucose transporter PfHT is essential to the survival of the malaria parasite Plasmodium falciparum and has been shown to be a druggable target with high potential for pharmacological intervention. Identification of compounds against novel drug targets is crucial to combating resistance against current therapeutics. Here, we describe the development of a cell-based assay system readily adaptable to high-throughput screening that directly measures compound effects on PfHT-mediated glucose transport. Intracellular glucose concentrations are detected using a genetically encoded fluorescence resonance energy transfer (FRET)-based glucose sensor. This allows assessment of the ability of small molecules to inhibit glucose uptake with high accuracy (Z′ factor of >0.8), thereby eliminating the need for radiolabeled substrates. Furthermore, we have adapted this assay to counterscreen PfHT hits against the human orthologues GLUT1, -2, -3, and -4. We report the identification of several hits after screening the Medicines for Malaria Venture (MMV) Malaria Box, a library of 400 compounds known to inhibit erythrocytic development of P. falciparum. Hit compounds were characterized by determining the half-maximal inhibitory concentration (IC(50)) for the uptake of radiolabeled glucose into isolated P. falciparum parasites. One of our hits, compound MMV009085, shows high potency and orthologue selectivity, thereby successfully validating our assay for antimalarial screening

A Prototype of a Conversational Virtual University Support Agent Powered by a Large Language Model That Addresses Inquiries About Policies in the Student Handbook

Universities gain a competitive advantage by deliberately improving overall service, student, faculty, and staff experience, leading to attractiveness, retention, and improved outcomes. Quality services are achieved partly by addressing employee satisfaction, specifically in the work environment. This paper presents a prototype study of a virtual university support agent, a system grounded in a Large Language Model (LLM) engineered to address inquiries from university students, faculty and staff related to the student handbook. The study investigates the integration of generative artificial intelligence and natural conversation properties inherent in LLMs to overcome customer service shortcomings identified in previous chatbot applications. The LLMs\u27 susceptibility to \u27hallucination\u27 is mitigated through a combined approach of few-shot learning and chain of thought libraries in the training phase. The information core of this system comprises student handbook PDF files, from which an algorithm extracts and structures data to be utilized by the LLM. As a result, the university support agent facilitates a viable Q&A interface for students, faculty, and administrators to inquire about university guidelines and policies

Supporting Mastery Learning Through a Multiple-Submission Policy for Assignments in a Purely Online Programming Class

The Learning Edge Momentum (LEM) theory suggests that once students fall behind, it gets more difficult to catch up with the course material. It then becomes increasingly more difficult to connect new, higher-level concepts to those solid edges of knowledge with mastery of basic concepts. Learning for Mastery (LFM) acknowledges that students learn at different paces by allowing students unable to master tests the first time to catch up eventually. This paper describes how an online introductory Python programming course offered to business students followed a multiple-submission policy for assignments to support LFM. The multiple submission policy contributed to the students’ mastery by encouraging individual practice and experimentation while also increasing the students’ comfort level and confidence. The research attempts to find relationships between taking advantage of the multiple-submit policy and results of summative assessments. Qualitative data on students’ self-reported progress per week is cross-referenced with quantitative data from the results of a regression analysis performed on LMS logs related to students’ engagement with course material. Performance on summative assessments is used as the regression’s dependent variable, and engagement with formative assessments in terms of the number of attempts and performance per attempt is used as the explanatory variable

Overcoming transactional distance when conducting online classes on programming for business students: a COVID-19 experience

Studies have shown that transactional distance negatively impacts student learning. In the context of learning, distance pertains to the geographic, pedagogical, and psychological gap between instructors and students. This perception of distance is magnified in online learning because instructors and students do not meet face to face. The gaps involve not only the geographic aspect. Another gap is pedagogical, which depends on the online course\u27s design and structure flexibility and how these align with the students\u27 level of autonomy. Still, another gap is psychological, which relates to how students perceive how much the teacher is accessible or disengaged (level of dialogue) and with students\u27 academic self-efficacy assessments. This paper describes how we could reduce the transactional distance between instructor and students by deliberately designing and conducting mostly asynchronous classes on programming for business students but with the right blend of non-lecture synchronous activities during tight lockdown due to COVID-19. We explain what used to work well before the pandemic where classes were onsite and face-to-face and what mechanisms we used to overcome the lockdown-related gaps. The course was held during Intersession and only had less than six weeks. Based on students\u27 grades and general sentiments, the results were in line with expected learning outcomes, and miscellaneous feedback and comments from students were positive

Ethical Education Data Mining Framework for Analyzing and Evaluating Large Language Model-Based Conversational Intelligent Tutoring Systems for Management and Entrepreneurship Courses

Educational data mining (EDM) can be used to design better and smarter learning technology by finding and predicting aspects of learners. Amend if necessary. Insights from EDM are based on data collected from educational environments. Among these educational environments are computer-based educational systems (CBES) such as learning management systems (LMS) and conversational intelligent tutoring systems (CITSs). The use of large language models (LLMs) to power a CITS holds promise due to their advanced natural language understanding capabilities. These systems offer opportunities for enriching management and entrepreneurship education. Collecting data from classes experimenting with these new technologies raises some ethical challenges. This paper presents an EDM framework for analyzing and evaluating the impact of these LLM-based CITS on learning experiences in management and entrepreneurship courses and also places strong emphasis on ethical considerations. The different learning experience aspects to be tracked are (1) learning outcomes and (2) emotions or affect and sentiments. Data sources comprise Learning Management System (LMS) logs, pre-post-tests, and reflection papers gathered at multiple time points. This framework aims to deliver actionable insights for course and curriculum design and development through design science research (DSR), shedding light on the LLM-based system’s influence on student learning, engagement, and overall course efficacy. Classes targeted to apply this framework have 30–40 students on average, grouped between 2 and 6 members. They will involve sophomore to senior students aged 18–22 years. One entire semester takes about 14 weeks. Designed for broad application across diverse courses in management and entrepreneurship, the framework aims to ensure that the utilization of LLMs in education is not only effective but also ethically sound

Exploratory Prompting of Large Language Models to Act as Co-Pilots for Augmenting Business Process Work in Document Classification

Businesses deal with different types of documents containing unstructured documents. The data in these documents must be converted into digital forms other automated systems could only process. One generic use case is document classification, which usually involves manual transformation due to human understanding needed in the process. These documents go beyond those generated through regular business transactions and operations and also include web-based content such as online news, blogs, e-mails, and various digital libraries. Recent developments in robotic process automation (RPA) and artificial intelligence (AI) aim to automate the otherwise expensive, time-consuming, and repetitive manual steps. Through more powerful natural language processing (NLP) and natural language understanding (NLU) capabilities, large language models (LLMs) may come as a big boost in applying AI to RPA initiatives. This study proposes a general approach to using LLMs as document classifier co-pilots for knowledge workers in charge of classifying documents to be useful. The manner of prompt engineering and refinement involving labeled health insurance documents to achieve better results is discussed and evaluated through early, iterative classification attempts. However, early tests with a complex sample use case show unsatisfactory results. The study ends with recommendations for future work to improve precision and recall performance

Graph-Partitioning Entity Resolution for Resolving Noisy Product Names in OCR Scans of Retail Receipts

In business intelligence for retail, it is critical to ensure consistent and unambiguous product dimension information. This is challenging, especially if an organization does not have full control over the source of either transaction or master data. Such lack of control is the case when brands rely on data provided directly by consumers through images of receipts. Product name strings obtained from the digitization of receipts often contain substitution, insertion, and deletion errors. These errors prevent product names from serving as a useful dimension for further analysis. This paper proposes a clustering-based approach to link error-laden product names to underlying SKUs to remove this noise. The problem can be modeled as an entity resolution problem: each digitized product name is a reference to an underlying entity SKU. The entity resolution problem can further be modeled as a clique-partitioning problem that can be solved in a reasonable time with an agglomerative clustering heuristic. The results of clustering a synthetic data set show that the approach can successfully resolve product references to reveal coarse-grained (i.e., category, generic product) groupings. Future work may be done on implementing blocking strategies, optimizing the model parameters, and understanding the limits of the model for fine-grained (i.e., size variation) groupings

Designing a Multiple Submission Policy Supporting Mastery Learning for a Design Thinking Class in a Purely Online Learning Environment

Mastery learning is defined as an approach where students are equipped with complex skills required in the VUCA world instead of simple skills that only apply to traditional classrooms. One way to encourage mastery learning in the classroom is through repeated assessment, specifically formative ones. In this paper, we describe our experience in designing a multiple submission policy to support mastery learning for a design thinking class taught purely online amidst lockdowns due to COVID. The transition to online learning and today’s context presented an opportunity to target mastery learning instead of traditional learning outcomes, which we achieved in two ways. First, we elevated the assessments’ level on Bloom’s taxonomy and encouraged iteration by providing feedback to guide metacognition. Second, we built creative confidence providing a safety net for graded assessments, which helped address fears of judgment and lack of control. In the process, we also overcame transactional distance to help promote self-efficacy, especially those with initially low grades. The policy was implemented with the aid of technology, which served as the medium for learning and dialogue. The use of technology in this study allowed for practices that were otherwise not implemented or even considered in previous trials of the class. The study resulted in positive feedback and improved quality of submissions from participants

Integrating RTI case management within LGU Health Centers: An intervention study

In 1994, the Philippines’ Department of Health began assessing the feasibility of syndromic management—utilizing flowcharts and algorithms—for reproductive tract infections (RTIs) in the country’s public health facilities. This intervention, in seven primary health care clinics, trained service providers on RTI case management, improved clinical facilities and laboratories, provided supportive supervision and TA, along with developing training materials. Clinical case management was observed for a six month period. The project also included a cost analysis