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A simulation study reveals the adsorption behavior ofShewanellamultiheme cytochrome protein MtrF on small-sized α-Fe₂O₃nanoparticles in water and its electron transfer across the adsorbed protein. 

Polyamide membranes are widely used in reverse osmosis (RO) water treatment, yet the mechanism of interfacial polymerization during membrane formation is not fully understood. In this work, we perform atomistic molecular dynamics simulations to explore the cross-linking of trimesoyl chloride (TMC) and m-phenylenediamine (MPD) monomers at the aqueous–organic interface. Our studies show that the solution interface provides a function of “concentration and dispersion” of monomers for cross-linking. The process starts with rapid cross-linking, followed by slower kinetics. Initially, amphiphilic MPD monomers diffuse in water and accumulate at the solution interface to interact with TMC monomers from the organic phase. As cross-linking progresses, a precross-linked thin film forms, reducing monomer diffusion and reaction rates. However, the structural flexibility of the amphiphilic film, influenced by interfacial fluctuations and mixed interactions with water and the organic solvent at the solution interface, promotes further cross-linking. The solubility of MPD and TMC monomers in different organic solvents (cyclohexane versus n-hexane) affects the cross-linking rate and surface homogeneity, leading to slight variations in the structure and size distribution of subnanopores. Our study of the interfacial polymerization process in explicit solvents is essential for understanding membrane formation in various solvents, which will be crucial for optimal polyamide membrane design. 

Abstract— Currently available automotive radars are designed to stream real-time 2D image data over high-speed links to a central ADAS (Advance Driver-Assistance System) computer for object recognition, which considerably contributes to the system’s power consumption and complexity. This paper presents a preliminary work for the implementation of a new in-sensor computer architecture to extract representative features from raw sensor data to detect and identify objects with radar signals. Such new architecture makes it possible to reduce the data transferred between sensors and the central ADAS computer significantly, giving rise to significant energy savings and latency reductions, while still maintaining sufficient accuracy and preserving image details. An experimental prototype has been built using the Texas Instruments AWR1243 Frequency-Modulated Continuous Wave (FMCW) radar board. We carried out experiments using the prototype to collect radar images, to preprocess raw data, and to transfer feature vectors to the central ADAS computer for classification and object detection. Two different approaches will be presented in this paper: First, a vanilla autoencoder will demonstrate the possibility of data reduction on radar signals. Second, a convolutional neural network based cross-domain deep learning architecture is presented by using a sample dataset to show the feasibility of computing Range-Angle Heatmaps directly on the sensor board eliminating the need for the raw data preprocessing on the central ADAS computer. We show that the reconstruction of Range-Angle Heatmaps can be predicted with a very high accuracy by leveraging deep learning architectures. Implementation of such a deep learning architecture on the sensor board can reduce the amount of data transferred from sensors to the central ADAS computer implying great potential for an energy efficient deep learning architecture in such environments. 

Allosteric regulation is common in protein–protein interactions and is thus promising in drug design. Previous experimental and simulation work supported the presence of allosteric regulation in the SARS-CoV-2 spike protein. Here the route of allosteric regulation in SARS-CoV-2 spike protein is examined by all-atom explicit solvent molecular dynamics simulations, contrastive machine learning, and the Ohm approach. It was found that peptide binding to the polybasic cleavage sites, especially the one at the first subunit of the trimeric spike protein, activates the fluctuation of the spike protein's backbone, which eventually propagates to the receptor-binding domain on the third subunit that binds to ACE2. Remarkably, the allosteric regulation routes starting from the polybasic cleavage sites share a high fraction (39–67%) of the critical amino acids with the routes starting from the nitrogen-terminal domains, suggesting the presence of an allosteric regulation network in the spike protein. Our study paves the way for the rational design of allosteric antibody inhibitors. 

The acknowledgement to NSF supports was missing from this paper. Authors apologize for this error when submitting the camera-ready paper.