6 research outputs found
Biomechanical signals and the C-type natriuretic peptide counteract catabolic activities induced by IL-1? in chondrocyte/agarose constructs
No full textIntroduction: The present study examined the effect of C-type natriuretic peptide (CNP) on the anabolic and catabolic activities in chondrocyte/agarose constructs subjected to dynamic compression. Methods: Constructs were cultured under free-swelling conditions or subjected to dynamic compression with low (0.1 to 100 pM) or high concentrations (1 to 1,000 nM) of CNP, interleukin-1? (IL-1?), and/or KT-5823 (inhibits cyclic GMP-dependent protein kinase II (PKGII)). Anabolic and catabolic activities were assessed as follows: nitric oxide (NO) and prostaglandin E2 (PGE2) release, and [3H]-thymidine and 35SO4 incorporation were quantified by using biochemical assays. Gene expression of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), aggrecan, and collagen type II were assessed with real-time quantitative PCR (qPCR). Two-way ANOVA and the post hoc Bonferroni-corrected t tests were used to examine data. Results: CNP reduced NO and PGE2 release and partially restored [3H]-thymidine and 35SO4 incorporation in constructs cultured with IL-1?. The response was dependent on the concentration of CNP, such that 100 pM increased [3H]-thymidine incorporation (P < 0.001). This is in contrast to 35SO4 incorporation, which was enhanced with 100 or 1000 nM CNP in the presence and absence of IL-1? (P < 0.001). Stimulation by both dynamic compression and CNP and/or the PKGII inhibitor further reduced NO and PGE2 release and restored [3H]-thymidine and 35SO4 incorporation. In the presence and absence of IL-1?, the magnitude of stimulation for [3H]-thymidine and 35SO4 incorporation by dynamic compression was dependent on the concentration of CNP and the response was inhibited with the PKGII inhibitor. In addition, stimulation by CNP and/or dynamic compression reduced IL-1?-induced iNOS and COX-2 expression and restored aggrecan and collagen type II expression. The catabolic response was not further influenced with the PKGII inhibitor in IL-1?-treated constructs. Conclusions: Treatment with CNP and dynamic compression increased anabolic activities and blocked catabolic effects induced by IL-1?. The anabolic response was PKGII mediated and raises important questions about the molecular mechanisms of CNP with mechanical signals in cartilage. Therapeutic agents like CNP could be administered in conjunction with controlled exercise therapy to slow the OA disease progression and to repair damaged cartilage. The findings from this research provide the potential for developing novel agents to slow the pathophysiologic mechanisms and to treat OA in the young and old. <br/
Choice modeling and recommendation optimization in presence of context effects
No full textRandom Utility choice models are supervised learning tools that can be used to estimate the choice behavior of customers facing multiple options. This is accomplished through assigning a utility value to each option and deriving a choice probability for each option. In the presence of context effects, the utility perceived from individual options is not fixed and depends on other options that are offered beside them. While context effects are well explored in the marketing and psychology literature, very little work has been done on incorporating these effects in revenue management systems and product recommendation modules. In this thesis, we propose three sets of machine learning models in order to capture these effects in different settings with different input data structures. For these settings, we also study combinatorial problems concerned with finding the optimal set of products to offer to the customer including (i) assortment optimization problem or reward maximization problem, (ii) click through rate optimization problem, and (iii) customer surplus optimization problem.
The first model we propose is a random utility discrete choice model which captures context effects in sparse choice/click data sets and under single-choice outcome assumption. In the proposed model, the perceived utilities from products are dependent on the whole choice set recommended to the customer, and choice probabilities have Multinomial Logistic Regression-type structure. We show the prediction power of this model by testing it on a relevant real data set and prove the NP-hardness of the assortment optimization problem under the proposed model. Several polynomially solvable special cases of the model are identified that also perform well in our empirical validation for our data set. We obtain some easily verifiable conditions for the monotonicity and submodularity of the assortment optimization objective in order to provide some approximation guarantees.
Second, we propose a utility based listwise logistic regression model, which is applicable in estimating the context effects in dense data sets with a multi-choice outcome assumption. We show the predictive and descriptive power of this model through an extensive empirical study on real click data sets chosen from diverse categories of products. We prove the NP-hardness of the Assortment Optimization Problem (AOP) under the general CL model, and show that when some specific types of contextual interactions are dominant in the data, the AOP is tractable.
Third, we propose a featurized choice model, in order to capture context effects when the input data is featurized. We study the top- retrieval problem which focuses on finding relevant products/documents for a given query. We train a featurized estimator that can measure the context effects among the objects through mapping their features to contextual interaction terms by using an underlying neural net structure. We empirically validate the estimator on a real data set and prove the NP-hardness of the top- retrieval problem for the proposed model. For all three sets of models, to circumvent NP-hardness, we design heuristic algorithms and test their efficiency through extensive numerical studies.
Different models proposed in this thesis and the relevant empirical studies, as well as the recommendation optimization results, shed more light on the contextual behavioral patterns observed in customers' choice behavior in e-commerce platforms, and how to further optimize the recommender systems by considering these patterns. To the best of our knowledge, this thesis is the first systematic study and its findings can help in designing operational recommender systems that capture complex contextual patterns in large scale data sets.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01The student, Reza Yousefi Maragheh, accepted the attached license on 2021-07-13 at 12:32.The student, Reza Yousefi Maragheh, submitted this Dissertation for approval on 2021-07-13 at 12:48.This Dissertation was approved for publication on 2021-07-13 at 21:10.DSpace SAF Submission Ingestion Package generated from Vireo submission #16907 on 2022-01-12 at 12:54:50Made available in DSpace on 2022-01-12T22:35:13Z (GMT). No. of bitstreams: 3
YOUSEFIMARAGHEH-DISSERTATION-2021.pdf: 4931100 bytes, checksum: 282b82c2ce2ec1bff41954f6b18069ae (MD5)
LICENSE.txt: 4218 bytes, checksum: 2c4767cfd215edba19698115c5031439 (MD5)
PROQUEST_LICENSE.txt: 4564 bytes, checksum: e55b2e26e39bf9eed0017455fc99ad6f (MD5)
Previous issue date: 2021-07-13Embargo set by: Seth Robbins for item 121110
Lift date: 2024-01-12T22:35:30Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Onl
Health Worker Factors Associated with Prescribing of Artemisinin Combination Therapy for Uncomplicated Malaria in Rural Tanzania.
No full textImproving malaria case management is partially dependent on health worker compliance with clinical guidelines. This study assessed health worker factors associated with correct anti-malarial prescribing practices at two sites in rural Tanzania. Repeated cross-sectional health facility surveys were conducted during high and low malaria transmission seasons in 2010 and collected information on patient consultations and health worker characteristics. Using logistic regression, the study assessed health worker factors associated with correct prescription for uncomplicated malaria defined as prescription of artemisinin-based combination therapy (ACT) for patients with fever and Plasmodium falciparum asexual infection based on blood slide or malaria rapid diagnostic test (RDT) according to national treatment guidelines. The analysis included 685 patients with uncomplicated malaria who were seen in a health facility with ACT in stock, and 71 health workers practicing in 30 health facilities. Overall, 58% of malaria patients were correctly treated with ACT. Health workers with three or more years' work experience were significantly more likely than others to prescribe correctly (adjusted odds ratio (aOR) 2.9; 95% confidence interval (CI) 1.2-7.1; p = 0.019). Clinical officers (aOR 2.2; 95% CI 1.1-4.5; p = 0.037), and nurse aide or lower cadre (aOR 3.1; 95% CI 1.3-7.1; p = 0.009) were more likely to correctly prescribe ACT than medical officers. Training on ACT use, supervision visits, and availability of job aids were not significantly associated with correct prescription. Years of working experience and health worker cadre were associated with correct ACT prescription for uncomplicated malaria. Targeted interventions to improve health worker performance are needed to improve overall malaria case management
Learning anywhere, anytime: Student motivators for m-learning
No full textThis paper documents the outcomes of a study that focused on identifying what motivates stu-dents to use mobile devices for learning and to engage in m-learning. An outcome of this study was to provide a better understanding of what educators should consider when adapting their course for mobile learners. The research included seven classes from three Australian universi-ties. The students in this study used laptops or tablet PCs, and in three of the classes, these were provided by the university as part of a laptop/tablet program. The findings indicated that mobility was the key motivator for the use of laptops, and the learning tasks that students found to be most motivating involved accessing information, authoring (e.g., writing, blogging, note taking) and communication
M-learning and student engagement: Factors that support students' engagement in m-learning
No full textMobile learning (m-learning) is learning that takes place in a variety of contexts, within and beyond traditional learning environments, utilising any type of mobile device. The availability of mobile devices with the potential to be used in university education has vastly increased over the past decade, and therefore m-learning has become increasingly common in university settings. M-learning is usually supported by a mobile device which offers some level of mobile connectivity to learning resources or access to communication technologies, or both, and can facilitate or support learning. M-learning thus involves participating in learning activities not confined to a set place or time.
In some institutions, m-learning has been encouraged through initiatives such as laptop programs, but often m-learning occurs incidentally with students utilising laptops and other mobile devices that they have acquired to help them with their studies.
Despite attempts by these institutions to develop such laptop programs, there have been limited resources on how to integrate m-learning into tertiary education for use by lecturers who have the desire to tap into the possibilities of taking advantage of students’ ad hoc access to laptops and other mobile devices. Models with practical applicability to m-learning have been slow to emerge, and very few practical guidelines are available for educators on the effective implementation of mobile devices in university teaching and learning, and lecturers are often left guessing as to what might promote students’ engagement in m-learning.
The aim of this study was to gain an understanding of the factors that influence students’ engagement in m-learning, specifically where mobile devices facilitate engagement in both learning activities (M-learning Task Engagement) and interaction with others (Online Social Engagement) in ways that contribute to learning outcomes (e.g. ACER, 2010; Coates, 2006; Kearsley & Shneiderman, 1998). A secondary aim was to identify what motivates students to use laptops for learning. Finally, the study aimed to develop a new m-learning design model and guidelines for lecturers developing learning designs for use in m-learning contexts.
The study included both formal m-learning and laptop programs, and classes where laptop use is ad hoc driven mostly by students’ personal needs. The research was conducted using seven case studies at three Australian universities. In one case, students were participating in a laptop program; in two cases, tablet computers were used in classroom settings; and in four cases, students’ use of laptops was based on student ownership and personal initiative. Data were collected by two surveys: one to gather students’ perceptions of their m-learning experiences and the other focussing on lecturers’ perceptions of m-learning.
The research explored what may motivate students to use their laptops and engage in m-learning. As could be expected, mobility was the key motivator for the use of laptops, and the learning tasks that students found to be most motivating involved accessing information, authoring (e.g. writing, blogging, note taking) and communication. Other categories of motivators identified in this study were: student productivity; performance outcomes; the learning experience; information access; the lecturer; entertainment; and social interaction.
The results of this study indicated that both Online Social Engagement and M-learning Task Engagement were influenced by students’ Goal Orientation. Online Social Engagement was also influenced by Technology Focus, which is learners’ orientation towards utilising technologies for learning. Students’ Perceived Mobility also influenced engagement in m-learning, but specifically in the area of M-Learning Task Engagement.
The results of this research led to a model and guidelines for lecturers planning to implement m-learning in a student-centred learning context which maximises students’ engagement in m-learning. The m-learning design model and guidelines lead lecturers towards considering student motivators for m-learning and the factors that influence students’ engagement in m-learning. The resulting learning designs, that address students’ requirements for mobility, and mesh with their Goal Orientation and Technology Focus, are therefore likely to contribute towards both M-Learning Task and Online Social Engagement in m-learning.
The outcomes of this study have important practical implications for educators and institutions as they provide a planned approach to integrating the use of mobile technologies in the curriculum with the aim of achieving increased engagement in learning
