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Amphiphilic Tripodal Metalloligands: Synthesis, Coordination Chemistry and Preliminary Catalysis
Get PDFTriscatecholate binding has been widely studied in the context of biological systems, supramolecular chemistry, and weak aggregate interactions. However, the utilization of the triscatecholate binding motif for the preparation of metalloligands remains unexplored. Herein, we report the synthesis, structural characterization, and functional studies of a triscatecholate ligand with an appended pyridyl residue as a candidate for such applications. Our findings reveal that the selective formation of cis-triscatecholate-based metalloligands is influenced by electronic effects on the catecholate anions, enabling the targeted synthesis of Al(III), Ga(III), and In(III) triscatecholate metalloligands capable of binding Zn(II) and Ru(II) at the appended pyridyl moiety for applications in catalysis
Swinging Lever Mechanism of Myosin Directly Shown by Time-Resolved Cryo-EM
Get PDFMyosins produce force and movement in cells through interactions with F-actin1. Generation of movement is thought to arise through actin-catalysed conversion of myosin from an ATP-generated primed (pre-powerstroke) state to a post-powerstroke state, accompanied by myosin lever swing2,3. However, the initial, primed actomyosin state has never been observed, and the mechanism by which actin catalyses myosin ATPase activity is unclear. Here, to address these issues, we performed time-resolved cryogenic electron microscopy (cryo-EM)4 of a myosin-5 mutant having slow hydrolysis product release5,6. Primed actomyosin was predominantly captured 10 ms after mixing primed myosin with F-actin, whereas post-powerstroke actomyosin predominated at 120 ms, with no abundant intermediate states detected. For detailed interpretation, cryo-EM maps were fitted with pseudo-atomic models. Small but critical changes accompany the primed motor binding to actin through its lower 50-kDa subdomain, with the actin-binding cleft open and phosphate release prohibited. Amino-terminal actin interactions with myosin promote rotation of the upper 50-kDa subdomain, closing the actin-binding cleft, and enabling phosphate release. The formation of interactions between the upper 50-kDa subdomain and actin creates the strong-binding interface needed for effective force production. The myosin-5 lever swings through 93°, predominantly along the actin axis, with little twisting. The magnitude of lever swing matches the typical step length of myosin-5 along actin7. These time-resolved structures demonstrate the swinging lever mechanism, elucidate structural transitions of the power stroke, and resolve decades of conjecture on how myosins generate movement
Multi-Grade Deep Learning
Get PDFDeep learning requires solving a nonconvex optimization problem of a large size to learn a deep neural network (DNN). The current deep learning model is of a single-grade, that is, it trains a DNN end-to-end, by solving a single nonconvex optimization problem. When the layer number of the neural network is large, it is computationally challenging to carry out such a task efficiently. The complexity of the task comes from learning all weight matrices and bias vectors from one single nonconvex optimization problem of a large size. Inspired by the human education process which arranges learning in grades, we propose a multi-grade learning model: instead of solving one single optimization problem of a large size, we successively solve a number of optimization problems of small sizes, which are organized in grades, to learn a shallow neural network (a network having a few hidden layers) for each grade. Specifically, the current grade is to learn the leftover from the previous grade. In each of the grades, we learn a shallow neural network stacked on the top of the neural network, learned in the previous grades, whose parameters remain unchanged in training of the current and future grades. By dividing the task of learning a DDN into learning several shallow neural networks, one can alleviate the severity of the nonconvexity of the original optimization problem of a large size. When all grades of the learning are completed, the final neural network learned is a stair-shape neural network, which is the superposition of networks learned from all grades. Such a model enables us to learn a DDN much more effectively and efficiently. Moreover, multi-grade learning naturally leads to adaptive learning. We prove that in the context of function approximation if the neural network generated by a new grade is nontrivial, the optimal error of a new grade is strictly reduced from the optimal error of the previous grade. Furthermore, we provide numerical examples which confirm that the proposed multi-grade model outperforms significantly the standard single-grade model and is much more robust to noise than the single-grade model. They include three proof-of-concept examples, classification on two benchmark data sets MNIST and Fashion MNIST with two noise rates, which is to find classifiers, functions of 784 dimensions, and as well as numerical solutions of the one-dimensional Helmholtz equation
Student Perspectives on Universal Design for Learning (UDL): Q-Methodology
Get PDFThis study aims to examine community college students’ unique viewpoints of the Universal Design for Learning (UDL) 3.0 guidelines through Q-methodology. This study will provide the unique perspectives students bring toward UDL to better inform faculty when implementing UDL into the instructional process and instructional designers when designing learning for community college students. The findings revealed student viewpoints toward UDL. Due to the findings, it is recommended that curriculum designers and instructors consider these viewpoints when communicating about the course and the design of the learning
A Fast Framework for Generating Radioactive Mixture Spectra and Its Application to Remote High-Performance Mixture Identification
Get PDFRemote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework
Check Your Data Before You Wreck Your Model: The Impact of Careless Responding on Substance Use Data Quality
Get PDFBackground: The accuracy of survey responses is a concern in research data quality, especially in college student samples. However, examination of the impact of removing participants from analyses who respond inaccurately or carelessly is warranted given the potential for loss of information or sample diversity. This study aimed to understand if careless responding varies across a number of demographic indices, substance use behaviors, and the timing of survey completion.
Method: College students (N = 5809; 70.7% female; 75.7% White, non-Hispanic) enrolled in psychology classes from six universities completed an online survey assessing a variety of demographic and substance use-related information, which included four attention check questions dispersed throughout the hour-long survey. Differences in careless responding were assessed across multiple demographic groups, and we examined the impact of careless responding on data quality via a confirmatory factor analysis of a validated substance use measure, the Drinking Motives Questionnaire-Revised Short Form.
Results: Careless responding varied significantly by participant race, sex, gender, sexual orientation, and socioeconomic status. Substance use was generally unassociated with careless responding, though careless responding was associated with experiencing more alcohol-related problems. Careless responding was more prevalent when the survey was completed near the end of the semester. Finally, the factor structure of the drinking motives measure was affected by the inclusion of those who failed two or more attention check questions.
Conclusions: Including attention checks in surveys is an effective method to detect and address careless responding. However, omitting participants from analyses who evidence any careless responding may bias the sample demographics. We discuss recommendations for the use of attention check questions in undergraduate substance use cross-sectional surveys, including retaining participants who fail only one attention check, as this has a minimal impact on data quality while preserving sample diversity
VAIM-CFF: A Variational Autoencoder Inverse Mapper Solution to Compton Form Factor Extraction from Deeply Virtual Compton Scattering
Get PDFWe develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted CFFs, use of uncertainty quantification technique, and inclusion of prior physics information. We then use dimensionality reduction techniques such as principal component analysis to visualize the missing physics information tracked in the latent space of the VAIM framework. Through re-framing the extraction of CFFs as an inverse problem, we gain access to fundamental properties of the problem not comprehensible in standard fitting methodologies: exploring the limits of the information encoded in deeply virtual exclusive experiments
Contemporary Student Activism in a Digital Age
Get PDFHigher education has as part of its purpose to support students becoming active citizens (Barnhardt, 2015). One way that this is practiced is through student activism. With many opportunities for development, student activism has manifested differently across the ages going through periods of high activity and others of apparent latency (Levine, 1980). Arthur Levine (1980) shared a framework for student activism motivation where there were oscillating periods of community and individual ascendency. However, this model has not been considered with the attributes of contemporary college students and with the advance of new media being accessible to the masses.
This multi-site case study will explore how contemporary college students engage in student-led activism, what motivates their efforts, and how new media has been utilized. Utilizing focus groups comprised of members of student organizations, individual interviews of campus administrators, observation, and researcher memos focused on four public institutions of higher education within the same regional area
Mapping Perspectives: Community College Career Advisors’ Viewpoints for Enhancing Economic Mobility and Career Selection—A Q Methodology Study
Get PDFIn an era of rapid workforce transformation and increasing demand for postsecondary career readiness, community college career advisors play a critical role in supporting students’ economic mobility and informed career decision-making. Traditional academic advising, which primarily focuses on course registration and degree completion, may not fully address students’ evolving career development needs. This study employed Q methodology to examine the subjective viewpoints of career advisors in North Carolina community colleges regarding strategies to enhance students’ career outcomes. Grounded in human capital theory and career construction theory, the study investigated how advisors integrate labor market data, wage trends, and workforce expectations into their advising practices. A 55-item Q sample was developed through a comprehensive review of literature on academic advising, career counseling, and workforce development. Participants sorted the statements using a forced distribution Q-sort, and factor analysis identified five distinct advising perspectives: Student Centered Career Advising, Data-Driven Career Advising, Workforce-Aligned Career Advising, Skills-Driven Career Advising, and Personalized and Flexible Career Advising. These perspectives varied in their emphasis on labor market alignment, transferable skill development, and individualized student support. The findings highlight the multidimensional nature of career advising and document the diverse strategies advisors use to prepare students for employment and long-term career planning. The results have implications for institutional policy, advisor training, and workforce development, and offer direction for future research exploring long-term advising outcomes
Man With An Itchy Back
Get PDF[First paragraph] A 61-year old man with no significant past medical history presented with painful bug bites on his lower back 1 day after a 1.5-week-long visit to Africa. He noticed bug bites on his lower back 3 days ago; since then, they have increased in size and become more painful. He was prescribed doxycycline at urgent care earlier that morning and had taken 1 dose. He is taking prophylactic medications for malaria and has had no fever at home. Vital signs, exquisitely tender papular lesions with a central punctum and surrounding erythema in the bilateral paralumbar regions