This study bridges research on interdisciplinary research and field-of-study mismatch to examine how interdisciplinary doctoral training is associated with early-career job satisfaction, and whether this association varies by field-of-study mismatch. Using linked data from the Survey of Earned Doctorates and the Survey of Doctorate Recipients, I analyze U.S.-employed science and engineering doctorate recipients who earned their first doctoral degree from U.S. institutions between 2010 and 2017. Doctorate recipients with interdisciplinary dissertations (hereafter, interdisciplinary doctorates) report lower overall job satisfaction than mono-disciplinary peers and are more likely to work in occupations outside their primary doctoral field (field-of-study mismatch). Field-of-study mismatch moderates the association between interdisciplinary doctorates and job satisfaction: the job-satisfaction gap between interdisciplinary doctorates and mono-disciplinary peers is smaller among those in mismatched jobs than among those in matched jobs. Working outside one's primary doctoral field is not uniformly associated with worse outcomes and may be linked to higher job satisfaction among some doctorate recipients, particularly interdisciplinary doctorates. These findings suggest that universities and institutions promoting interdisciplinarity should also strengthen interdisciplinary career advising, and that doctorate recipients with interdisciplinary dissertation research may benefit from exploring a wider set of career opportunities beyond their primary field.
As the use of head-mounted displays (HMDs) becomes increasingly common, concerns about visual fatigue in virtual reality (VR) environments have grown, as HMDs are known to induce greater fatigue than traditional 2D displays. To enable timely mitigation of this issue, it is essential to predict subjective visual fatigue in real time from continuously monitored objective measures. To address this need, we designeda passive HMDbased video-viewing experiment that manipulated motion (high/low) and brightness (high/low) to induce varying levels of visual fatigue and aimed to predict dynamic visual fatigue using eye-tracking data. Fiftytwo participants took part in the study, providing continuous and post-task fatigue ratings while their eye movements were recorded as they watched video content in VR. Based on the recorded data, we developed a temporal attention based deep learning model that predicts fatigue 10 s ahead using a 30-second window of eye-related metrics.The proposed BiGRU-Attn model achieved competitive predictive performance-a mean absolute error (1.156), root mean square error (1.253), and two-point ordinal accuracy of 91% under leave-one-subject-out cross-validation-indicating that the predictive signal resides primarily in the eye-tracking features. Moreover, explainable feature importance analysis converged on the number of blinks (NB) as the single most informative ocular signal across zero-order correlation, Integrated Gradients, and feature ablation, with fixation duration (FD) emerging as a supporting contributor through temporal multivariate interactions. These findings demonstrate that eye-tracking data alone can predict dynamic visual fatigue duringpassive HMD-based video viewing and highlight the potential for a real-time fatigue prediction system that can enable timely application of fatigue reduction techniques.
With recent advances in cryo-electron microscopy/cryo-electron tomography (cryoEM/cryoET), computational capabilities and machine learning, there has been an incredible increase in the speed of structure determination for large biomolecular assemblies. In order to connect these static descriptions with biological function, one needs reliable and accurate approaches to quantify the factors that regulate dynamics. For this, models that employ simplified energetics and/or coarse-grained representations have become indispensable tools for integrating structural insights, biochemical analyses and physical principles. Here we discuss examples where molecular simulations have provided a framework for understanding dynamics in assemblies of increasing size and complexity. We focus on three classes for which there have been notable advances in recent years: ribosomes, viral capsids and nuclear pore complexes. Each possesses unique physical attributes that have required distinct theoretical and computational strategies. Together, these studies provide a rich conceptual and technical foundation that can help shape our perspective on the machinery of the cell.
Focused ultrasound (FUS) generates acoustic forces that activate cellular mechanotransduction, including calcium signaling via mechanosensitive ion channels, and modulates nanoparticle behavior through physical and chemical perturbations. These coupled effects have been leveraged to enhance anti-cancer drug delivery and reshape tumor transport dynamics. Integration of FUS with nanodrug systems enables coordinated modulation of vascular permeability, intratumoral distribution, and tumor microenvironment (TME) remodeling. Advanced cancer-on-a-chip platforms provide physiologically relevant in vitro models for systematically evaluating these multiscale interactions under controlled conditions. Together, these elements form an integrated framework linking FUS-induced physicochemical mechanisms to biological responses, nanoparticle behavior, and chip-based evaluation. This review presents the biological and transport-associated roles of FUS in cancer therapy, discusses its integration with nanodrug systems to modulate TME dynamics, and highlights the application of cancer-on-a-chip technologies to assess FUS-mediated transport modulation and nanodrug performance in preclinical settings.
