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Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase
No full textDespite the success of AlphaFold methods in predicting single protein structures, these methods showed intrinsic limitations in the characterization of multiple functional conformations of allosteric proteins. The recent NMR-based structural determination of the unbound ABL kinase in the active state and discovery of the inactive low-populated functional conformations that are unique for ABL kinase present an ideal challenge for the AlphaFold2 approaches. In the current study, we employ several adaptations of the AlphaFold2 methodology to predict protein conformational ensembles and allosteric states of the ABL kinase including randomized alanine sequence scanning combined with the multiple sequence alignment subsampling proposed in this study. We show that the proposed new AlphaFold2 adaptation combined with local frustration profiling of conformational states enables accurate prediction of the protein kinase structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AlphaFold2 predictions and detection of hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AlphaFold2. The results of this study uncovered previously unappreciated fundamental connections between local frustration profiles of the functional allosteric states and the ability of AlphaFold2 methods to predict protein structural ensembles of the active and inactive states. This study showed that integration of the randomized sequence scanning adaptation of AlphaFold2 with a robust landscape-based analysis allows for interpretable atomistic predictions and characterization of protein conformational ensembles, providing a physical basis for the successes and limitations of current AlphaFold2 methods in detecting functional allosteric states that play a significant role in protein kinase regulation
Multidisciplinary Travel Health Education: Current Status and Rationale for Standardized Competencies
No full textThis perspective highlights the inconsistent integration of travel health education across medical, nursing and pharmacy educational programmes globally and calls for standardized travel health competencies based on the International Society of Travel Medicine’s (ISTM) Body of Knowledge to help improve patient care outcomes as global travel returns to pre-pandemic levels
Goodbye Copyright? The Rise of Trademark and Rights of Publicity in the Hip-Hop Music Industry
Get PDFHip-hop music dominates popular culture and fuels the global entertainment industry, from music to dance, film, advertising, television, social media, and the internet.8 Hip-hop music, also known as rap, is an art form created by African American artists, but largely controlled through distribution and intellectual property (\u27IP\u27) transfers by majority white-led corporations.9 From its inception, hip-hop presented a challenge to prevailing theories and doctrines of intellectual property, especially copyright law. Today, the gauntlet of rules regarding who is an IP owner, what is and is not protected, and the law’s bias toward the sophisticated continues to burden hip-hop artists
M3T-LM: A Multi-modal Multi-task Learning Model for Jointly Predicting Patient Length of Stay and Mortality
No full textEnsuring accurate predictions of inpatient length of stay (LoS) and mortality rates is essential for enhancing hospital service efficiency, particularly in light of the constraints posed by limited healthcare resources. Integrative analysis of heterogeneous clinic record data from different sources can hold great promise for improving the prognosis and diagnosis level of LoS and mortality. Currently, most existing studies solely focus on single data modality or tend to single-task learning, i.e., training LoS and mortality tasks separately. This limits the utilization of available multi-modal data and prevents the sharing of feature representations that could capture correlations between different tasks, ultimately hindering the model’s performance. To address the challenge, this study proposes a novel Multi-Modal Multi-Task learning model, termed as M3T-LM, to integrate clinic records to predict inpatients’ LoS and mortality simultaneously. The M3T-LM framework incorporates multiple data modalities by constructing sub-models tailored to each modality. Specifically, a novel attention-embedded one-dimensional (1D) convolutional neural network (CNN) is designed to handle numerical data. For clinical notes, they are converted into sequence data, and then two long short-term memory (LSTM) networks are exploited to model on textual sequence data. A two-dimensional (2D) CNN architecture, noted as CRXMDL, is designed to extract high-level features from chest X-ray (CXR) images. Subsequently, multiple sub-models are integrated to formulate the M3T-LM to capture the correlations between patient LoS and modality prediction tasks. The efficiency of the proposed method is validated on the MIMIC-IV dataset. The proposed method attained a test of 5.54 for LoS prediction and a test 1 of 0.876 for mortality prediction. The experimental results demonstrate that our approach outperforms state-of-the-art (SOTA) methods in tackling mixed regression and classification tasks
Incorporating AI Literacy into Music Library Instruction: An Interactive Discussion
Get PDFTaylor Greene gave a presentation connecting AI Literacy to his work not only as the liaison to the Hall-Musco Conservatory of Music but also more broadly in his role as Chair of Research and Instructional Services. He began by providing an overview of Chapman University’s cautious approach to embracing generative AI and highlighted the library’s role in supporting faculty, staff, librarians, and students in better understanding these technologies and their potential impact on higher education. He summarized the work of the AI Task Force and offered a general overview of the AI Literacy lectures that he and Dr. Doug Dechow usually present. Greene then described how he has incorporated AI Literacy into his two music courses—Music Information Literacy and Research Methods for Performers—sharing examples of music-specific applications of generative AI technologies discussed in those classes.
Following this, Greene facilitated an interactive discussion by posing a series of questions aimed at generating new ideas and best practices, drawing on both his own experiences and those of his music librarian colleagues at other institutions. The discussion revealed that many participants were still grappling with how to address generative AI and anticipate its effects on music studies. One MLACC member in attendance, who is deeply involved with MLA’s cataloging groups, shared insights into how AI is currently being tested and evaluated for applications such as linked data development
The Price of Mathematical Scepticism
No full textI argue that, insofar as we doubt the bivalence of the Continuum Hypothesis or the truth of the Axiom of Choice, we should also doubt the consistency of third-order arithmetic, both the classical and intuitionistic versions. Thus scepticism about mathematical reality comes at a price.
Underlying this argument is the following philosophical view. Mathematical belief springs from certain intuitions, each of which can be either accepted or doubted in its entirety, but not half-accepted. Therefore, our beliefs about reality, bivalence, choice and consistency should all be aligned.
A key part of the talk is the “bivalence questionnaire”, designed to help us think about our intuitions and beliefs. By examining the different ways to answer it, we are led to a spectrum of positions, dubbed ultrafinitism, finitism, countabilism, sequentialism, particularism and totalism. Though they are very different, each maintains the belief alignment that I advocate.
Based on a paper in Philosophia Mathematica: https://academic.oup.com/philmat/article/30/3/283/662201
Alternative Robust Ways of Witnessing Nonclassicality in the Simplest Scenario
Get PDFIn this paper we relate notions of nonclassicality in what is known as the simplest nontrivial scenario (a prepare and measure scenario composed of four preparations and two binary-outcome tomographically complete measurements). Specifically, we relate the established method developed by Pusey [M. F. Pusey, Phys. Rev. A 98, 022112 (2018)] to witness a violation of preparation noncontextuality, that is not suitable in experiments where the operational equivalences to be tested are specified in advance, with an approach based on the notion of bounded ontological distinctness for preparations, defined by Chaturvedi and Saha [A. Chaturvedi and D. Saha, Quantum 4, 345 (2020)]. In our approach, we test bounded ontological distinctness for two particular preparations that are relevant in certain information processing tasks in that they are associated with the even and odd parity of the bits to communicate. When there exists an ontological model where this distance is preserved we talk of parity preservation. Our main result provides a noise threshold under which violating parity preservation (and so bounded ontological distinctness) agrees with the established method for witnessing preparation contextuality in the simplest nontrivial scenario. This is achieved by first relating the violation of parity preservation to the quantification of contextuality in terms of inaccessible information as developed by Marvian (I. Marvian, arXiv:2003.05984.), that we also show, given the way we quantify noise, to be more robust in witnessing contextuality than Pusey\u27s noncontextuality inequality. As an application of our findings, we treat the case of two-bit parity-oblivious multiplexing in the presence of noise. In particular, given that we have a noise threshold below which preparation contextuality holds, we use it to establish a condition for which preparation contextuality is present in the case where the probability of success exceeds that achieved by any classical strategy
Moving Beyond Transactions: Understanding the Relationships between College Access Professionals and Underrepresented College-Bound Families
Get PDFFramed by family engagement frameworks, this study presents four types of interactions college access professionals (CAPs) have with the families of underrepresented college-going students—inconsistent communication, transactional exchanges, student-family mediation, and trusting relationships—to explore the nature of family-educator partnerships for students’ college access. Drawing from in-depth qualitative interviews with a diverse sample of 20 CAPs, this study demonstrates that the nature of these interactions and their corresponding family engagement practices are influenced by CAPs’ job requirements and previous experiences working with families. This ultimately shapes their ability to invest in and develop strong, trusting partnerships with students’ families. By understanding these family-educator interactions, college access programming can work towards benefitting from strong and trusting partnerships, which can ultimately lead to successful college acceptance and matriculation for underrepresented college-bound students
