A large number of human diseases result from disruptions to protein structure and function caused by missense mutations. Computational methods are frequently employed to assist in the prediction of protein stability upon mutation. These methods utilize a combination of protein sequence data, protein structure data, empirical energy functions, and physicochemical properties of amino acids. In this work, we present the first use of dynamic protein structural features in order to improve stability predictions upon mutation. This is achieved through the use of a set of timeseries extracted from microsecond timescale atomistic molecular dynamics simulations of proteins. Standard machine learning algorithms using mean, variance, and histograms of these timeseries were found to be 60-70% accurate in stability classification based on experimental G or protein-chaperone interaction measurements. A recurrent neural network with full treatment of timeseries data was found to be 80% accurate according the F1 score. The performance of our models was found to be equal or better than two recently developed machine learning methods for binary classification as well as two industry-standard stability prediction algorithms. In addition to classification, understanding the molecular basis of protein stability disruption due to disease-causing mutations is a significant challenge that impedes the development of drugs and therapies that may be used treat genetic diseases. The use of dynamic structural features allows for novel insight into the molecular basis of protein disruption by mutation in a diverse set of soluble proteins. To assist in the interpretation of machine learning results, we present a technique for determining the importance of features to a recurrent neural network using Garson's method. We propose a novel extension of neural interpretation diagrams by implementing Garson's method to scale each node in the neural interpretation diagram according to its relative importance to the network.
The rising population of heritage speakers (HS) in university courses in the US has increased the need for instructors who understand the linguistic, social, and cultural profiles of their students. Recent research has discussed the need for specialized courses and their differentiation from second-language (L2) classes, as well as the intersection between HS and language attitudes. However, prior studies have not examined HS students’ language attitudes toward the sociolinguistic background of the instructors and their effect on classroom interactions. Therefore, this study explores HS students’ overall language attitudes and perceptions of their instructors’ sociolinguistic background. In a survey, HS university students (N = 92) across the US assessed four instructor profiles along five dimensions. Results showed that students rated more favorably instructors born and raised in Latin America, followed by those from Spain. Furthermore, HS favored these two profiles over HS or L2 profiles as their course instructors. However, preferences were less marked in the online context. These findings demonstrate that to design supportive learning spaces with—rather than for—HS students, programs must first acknowledge how classroom dynamics are shaped by the perspectives brought into the learning space and by the context of the learning space itself.
This monograph will report on the results of a series of case studies conducted across several campuses of the City University of New York within a university-sponsored project entitled Futures Initiative (FI). The FI project advocates for greater equity and innovation in higher education through several actions, including research and student and teacher development initiatives. In this monograph, the authors of the contributions came together in an interdisciplinary doctoral seminar on educational language policy, which was chosen to take on an active role in the FI project. The seminar was led by Dr. Ofelia García and Dr. Carmina Makar. The issues raised in the seminar, coupled with the goals of the FI project, invited deeper awareness, criticality and increased agency in terms of linguistic diversity, language policies and equity as they were understood and experienced by different populations at the university. The participants in the seminar thus engaged in an innovative pedagogical process of becoming sociolinguistic ethnographers, thereby developing tools and agency to contribute to heightened awareness of salient issues within their local university context and to the potential transformation of language-related injustices through their research.The invited author for this monograph is Dr. Camina Makar, who was one of the professors guiding the research discussed in the articles. In her contribution, Dr. Makar provides a contextual background to the FI project and discusses the role of multilingualism in higher education policy and pedagogy. She further provides an overview of the methodological approach taken in the different case studies included in the monograph and the relationship between the research conducted and the pedagogical development of the doctoral seminar. Finally, she reflects on some of the implications of this research for the City University of New York and beyond.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.