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  1. Abstract

    Traditional gender roles and gender stereotypes assume different life and career priorities among men and women. Meanwhile, the science profession is commonly considered to abide by a universalist ethos and a meritocracy that is independent of gender. We examined whether men and women scientists held different ideals about the good life and about good science. Furthermore, we investigated if those ideals of good life and of good science were linked in the minds of scientists; and if the linkages differed by gender. This study used a structural topic modeling approach to analyze the interview transcripts of 108 women and 92 men elite scientists who had received highly prestigious postdoctoral fellowships during the 1960s and1970s. In the open-ended interviews, the scientists were asked to describe their ideals of good life and of good science. Regarding the good life, we found that women scientists focused more on enjoying life and relationships and less on intellectual stimulation, relative to men scientists. For good science, women scientists focused more on empirical procedural accuracy and less on basic and fundamental breakthroughs, relative to men scientists. Moreover, we found that women scientists exhibited correlations between life and science ideals, whereas the two domains were completely separate for men scientists. In conclusion, a gendered system of life and science ideals existed even among this group of highly promising scientists.

     
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  2. Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively.

     
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    Free, publicly-accessible full text available December 1, 2024
  3. Metalorganic chemical vapor deposition (MOCVD) is a promising technique for wafer-scale synthesis of MoS2 monolayers for 2D field-effect transistors (2D-FETs) and related devices. Epitaxial growth of MoS2 on sapphire provides films that are crystallographically well-oriented but typically contain low-angle grain boundaries (e.g., mirror twins), voids, and other defects depending on growth conditions and substrate characteristics. In this study, we investigate microstructure, optical properties, and field-effect characteristics of wafer-scale MoS2 monolayers grown by MOCVD on c-plane sapphire over a narrow window of growth temperatures (900–1000 °C). The density of low-angle grain boundaries in the MoS2 monolayer was found to decrease dramatically from 50% areal coverage for films grown at 900 °C to 5% at 1000 °C. This decrease in low-angle grain boundary density is correlated with an increase in the room-temperature photoluminescence intensity of A excitons and a decrease in the full-width-half maximum (FWHM) of the Raman A1g peak, which are typically indicative of a general reduction in defects in MoS2. However, the best transport properties (e.g., mean field-effect mobility mFE = 17.3 cm2/V s) were obtained in MoS2 monolayers grown at an intermediate temperature of 950 °C. It was found that as the growth temperature increased, small regions bound by high-angle boundaries begin to appear within the monolayer and increase in areal coverage, from ∼2% at 900 °C to ∼5% at 950 °C to ∼10% at 1000 °C. The growth temperature of 950 °C, therefore, provides an intermediate condition where the combined effects of low-angle and high-angle boundaries are minimized. The results of this study provide guidance on MOCVD growth and characterization that can be used to further optimize the performance of MoS2 2D-FETs.

     
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    Free, publicly-accessible full text available March 1, 2025
  4. Free, publicly-accessible full text available October 12, 2024
  5. Abstract

    Microelectronic devices can directly communicate with biology, as electronic information can be transmitted via redox reactions within biological systems. By engineering biology’s native redox networks, we enable electronic interrogation and control of biological systems at several hierarchical levels: proteins, cells, and cell consortia. First, electro-biofabrication facilitates on-device biological component assembly. Then, electrode-actuated redox data transmission and redox-linked synthetic biology allows programming of enzyme activity and closed-loop electrogenetic control of cellular function. Specifically, horseradish peroxidase is assembled onto interdigitated electrodes where electrode-generated hydrogen peroxide controls its activity.E. coli’s stress response regulon,oxyRS, is rewired to enable algorithm-based feedback control of gene expression, including an eCRISPR module that switches cell-cell quorum sensing communication from one autoinducer to another—creating an electronically controlled ‘bilingual’ cell. Then, these disparate redox-guided devices are wirelessly connected, enabling real-time communication and user-based control. We suggest these methodologies will help us to better understand and develop sophisticated control for biology.

     
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  6. Abstract Background

    Digital media are pervasive in the lives of young people and provide opportunities for them to learn about STEM. Multiple theories argue that the STEM media environment may shape how youth see a STEM career in their future. Yet, little is known about how pre-college digital media consumption may be related to students’ STEM career interest at the beginning of college. The wide variety of STEM media also raises the question of potentially different effects and pathways by media type. In this study, we collected a nationally representative sample of more than 15,000 students in their first year in U.S. colleges and universities. We asked about their career interests at the beginning of college and also asked them to retrospectively report their STEM media consumption during high school.

    Results

    We found that watching STEM-related TV and online videos, as well as playing STEM-related video games during high school, were positively associated with students’ STEM career interests at the beginning of college. However, we also found that STEM media consumption did not impact directly on STEM career interest, but acted through two intermediaries: STEM identity (I and others see me as a STEM person) and three personal career outcome expectations: a high interest in self-development (enhancement and use of talents), and low interests in material status (money, fame, power) and in interpersonal relationships (helping, and working with, other people).

    Conclusions

    This study finds that STEM media have a significant effect in fostering STEM career interest, with most of the effect coming from STEM TV, STEM video viewing, and STEM video games. The effect is mediated mainly through students’ identity and, to a lesser extent, through personal values, such as self-development, material, and interpersonal relationship values. This study suggests that media communication should be mindful of how different platforms may deliver nuanced and varied messages of what STEM careers may afford and who can succeed in STEM.

     
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  7. Autophagy is a cellular process with important functions that drive neurodegenerative diseases and cancers. Lysosomal hyperacidification is a hallmark of autophagy. Lysosomal pH is currently measured by fluorescent probes in cell culture, but existing methods do not allow for quantitative, transient or in vivo measurements. In the present study, we developed near-infrared optical nanosensors using organic color centers (covalent sp3 defects on carbon nanotubes) to measure autophagy-mediated endolysosomal hyperacidification in live cells and in vivo. The nanosensors localize to the lysosomes, where the emission band shifts in response to local pH, enabling spatial, dynamic and quantitative mapping of subtle changes in lysosomal pH. Using the sensor, we observed cellular and intratumoral hyperacidification on administration of mTORC1 and V-ATPase modulators, revealing that lysosomal acidification mirrors the dynamics of S6K dephosphorylation and LC3B lipidation while diverging from p62 degradation. This sensor enables the transient and in vivo monitoring of the autophagy–lysosomal pathway. 
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    Free, publicly-accessible full text available December 1, 2024
  8. Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients. Although such a mechanism is proven to be effective in various fields, existing works generally assume that each client preserves sufficient data for training. In practice, however, certain clients may only contain a limited number of samples (i.e., few-shot samples). For example, the available photo data taken by a specific user with a new mobile device is relatively rare. In this scenario, existing FL efforts typically encounter a significant performance drop on these clients. Therefore, it is urgent to develop a few-shot model that can generalize to clients with limited data under the FL scenario. In this paper, we refer to this novel problem as federated few-shot learning. Nevertheless, the problem remains challenging due to two major reasons: the global data variance among clients (i.e., the difference in data distributions among clients) and the local data insufficiency in each client (i.e., the lack of adequate local data for training). To overcome these two challenges, we propose a novel federated few-shot learning framework with two separately updated models and dedicated training strategies to reduce the adverse impact of global data variance and local data insufficiency. Extensive experiments on four prevalent datasets that cover news articles and images validate the effectiveness of our framework compared with the state-of-the-art baselines. 
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    Free, publicly-accessible full text available August 1, 2024
  9. Abstract

    In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

     
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  10. Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteria resistant to certain antibiotics, making it difficult to prevent MRSA infections. Among decades of efforts to conquer infectious diseases caused by MRSA, many studies have been proposed to estimate the causal effects of close contact (treatment) on MRSA infection (outcome) from observational data. In this problem, the treatment assignment mechanism plays a key role as it determines the patterns of missing counterfactuals -- the fundamental challenge of causal effect estimation. Most existing observational studies for causal effect learning assume that the treatment is assigned individually for each unit. However, on many occasions, the treatments are pairwisely assigned for units that are connected in graphs, i.e., the treatments of different units are entangled. Neglecting the entangled treatments can impede the causal effect estimation. In this paper, we study the problem of causal effect estimation with treatment entangled in a graph. Despite a few explorations for entangled treatments, this problem still remains challenging due to the following challenges: (1) the entanglement brings difficulties in modeling and leveraging the unknown treatment assignment mechanism; (2) there may exist hidden confounders which lead to confounding biases in causal effect estimation; (3) the observational data is often time-varying. To tackle these challenges, we propose a novel method NEAT, which explicitly leverages the graph structure to model the treatment assignment mechanism, and mitigates confounding biases based on the treatment assignment modeling. We also extend our method into a dynamic setting to handle time-varying observational data. Experiments on both synthetic datasets and a real-world MRSA dataset validate the effectiveness of the proposed method, and provide insights for future applications. 
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    Free, publicly-accessible full text available August 1, 2024