Proteins interact with numerous water molecules to perform their physiological functions in biological organisms. Most water molecules act as solvent media; hence, their roles may be considered ...implicitly in theoretical treatments of protein structure and function. However, some water molecules interact intimately with proteins and require explicit treatment to understand their effects. Most physics-based computational methods are limited in their ability to accurately locate water molecules on protein surfaces because of inaccurate energy functions. Instead of relying on an energy function, this study attempts to learn the locations of water molecules from structural data. GalaxyWater-convolutional neural network (CNN) predicts water positions on protein chains, protein–protein interfaces, and protein–compound binding sites using a 3D-CNN model that is trained to generate a water score map on a given protein structure. The training data are compiled from high-resolution protein crystal structures resolved together with water molecules. GalaxyWater-CNN shows improved water prediction performance both in the coverage of crystal water molecules and in the accuracy of the predicted water positions when compared with previous energy-based methods. This method shows a superior performance in predicting water molecules that form hydrogen-bond networks precisely. The web service and the source code of this water prediction method are freely available at https://galaxy.seoklab.org/gwcnn and https://github.com/seoklab/GalaxyWater-CNN, respectively.
We propose a condition-adaptive representation learning framework for driver drowsiness detection based on a 3D-deep convolutional neural network. The proposed framework consists of four models: ...spatio-temporal representation learning, scene condition understanding, feature fusion, and drowsiness detection. Spatio-temporal representation learning extracts features that can describe motions and appearances in video simultaneously. Scene condition understanding classifies the scene conditions related to various conditions about the drivers and driving situations, such as statuses of wearing glasses, illumination condition of driving, and motion of facial elements, such as head, eye, and mouth. Feature fusion generates a condition-adaptive representation using two features extracted from the above models. The drowsiness detection model recognizes driver drowsiness status using the condition-adaptive representation. The condition-adaptive representation learning framework can extract more discriminative features focusing on each scene condition than the general representation so that the drowsiness detection method can provide more accurate results for the various driving situations. The proposed framework is evaluated with the NTHU drowsy driver detection video dataset. The experimental results show that our framework outperforms the existing drowsiness detection methods based on visual analysis.
Die casting is a suitable process for producing complex and high precision parts, but it faces challenges in terms of quality degradation due to inevitable defects. The casting parameters play a ...significant role in quality, and in many cases, producers rely on their experience to manage these parameters. In order to address this, domestic small and medium sized die casting companies have established smart factories (MES) and collected data. This study aims to utilize this data to construct a machine learning based optimal casting parameter model to enhance quality. During the model development process, distinct important features were identified for each company. This indicates the necessity of deriving tailored models for each site, aligning with the make to order (MTO) environment, rather than a generalized model.
This review covers both fundamental aspects and applications of electrochemically mediated atom transfer radical polymerization (eATRP). eATRP setup is discussed in detail, together with the ...advantages and limitations of this technique. All relevant parameters that can influence eATRP outcome are evaluated (e.g. applied current and potential, stirring and diffusion, solvents and supporting electrolytes). Various materials prepared by eATRP are described, including homopolymers, block copolymers, star polymers, and surface grafted polymer brushes. In addition, other electrochemical techniques conceptually similar to eATRP are discussed, including copper-catalyzed azide-alkyne cycloaddition, electrochemical micropatterning, reversible addition-fragmentation chain transfer polymerization using redox-sensitive initiators, and catalyst removal by electrochemical reduction. The increasing research activity in the last decade indicates that electrochemically regulated methods are becoming valuable tools in the design and synthesis of advanced polymer materials.
An electrochemically mediated reversible addition–fragmentation chain-transfer polymerization (eRAFT) of (meth)acrylates was successfully carried out via electroreduction of either benzoyl peroxide ...(BPO) or 4-bromobenzenediazonium tetrafluoroborate (BrPhN2 +) which formed aryl radicals, acting as initiators for RAFT polymerization. Direct electroreduction of chain transfer agents was unsuccessful since it resulted in the formation of carbanions by a two-electron-transfer process. Reduction of BrPhN2 + under a fixed potential showed acceptable control but limited conversion due to the generation of a passivating organic layer grafted on the working electrode surface. However, by use of fixed current conditions, easier to implement than fixed potential conditions, conversions >80% were achieved. Well-defined homopolymers and block copolymers with a broad range of targeted degrees of polymerization were prepared.
Abstract
Eukaryotic genome and methylome encode DNA fragments’ propensity to form nucleosome particles. Although the mechanical properties of DNA possibly orchestrate such encoding, the definite link ...between ‘omics’ and DNA energetics has remained elusive. Here, we bridge the divide by examining the sequence-dependent energetics of highly bent DNA. Molecular dynamics simulations of 42 intact DNA minicircles reveal that each DNA minicircle undergoes inside-out conformational transitions with the most likely configuration uniquely prescribed by the nucleotide sequence and methylation of DNA. The minicircles’ local geometry consists of straight segments connected by sharp bends compressing the DNA’s inward-facing major groove. Such an uneven distribution of the bending stress favors minimum free energy configurations that avoid stiff base pair sequences at inward-facing major grooves. Analysis of the minicircles’ inside-out free energy landscapes yields a discrete worm-like chain model of bent DNA energetics that accurately account for its nucleotide sequence and methylation. Experimentally measuring the dependence of the DNA looping time on the DNA sequence validates the model. When applied to a nucleosome-like DNA configuration, the model quantitatively reproduces yeast and human genomes’ nucleosome occupancy. Further analyses of the genome-wide chromatin structure data suggest that DNA bending energetics is a fundamental determinant of genome architecture.
An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel-prediction ...algorithms that address this goal by integrating transfer and meta-learning with a reduced-rank parametrization of the channel. The proposed methods optimize linear predictors by utilizing data from previous frames, which are generally characterized by distinct propagation characteristics, in order to enable fast training on the time slots of the current frame. The proposed predictors rely on a novel long short-term decomposition (LSTD) of the linear prediction model that leverages the disaggregation of the channel into long-term space-time signatures and fading amplitudes. We first develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Then, we introduce transfer and meta-learning algorithms for LSTD-based prediction models that build on equilibrium propagation (EP) and alternating least squares (ALS). Numerical results under the 3GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization.
In the semiconductor industry, achieving a high production yield is a very important issue. Wafer bin maps (WBMs) provide critical information for identifying anomalies in the manufacturing process. ...A WBM forms a certain defect pattern according to the error occurring during the process, and by accurately classifying the defect pattern existing in the WBM, the root causes of the anomalies that have occurred during the process can be inferred. Therefore, WBM defect pattern recognition and classification tasks are important for improving yield. In this paper, we propose a deep convolutional generative adversarial network (DCGAN)-based data augmentation method to improve the accuracy of a convolutional neural network (CNN)-based defect pattern classifier in the presence of extremely imbalanced data. The proposed method forms various defect patterns compared to the data augmentation method by using a convolutional autoencoder (CAE), and the formed defect patterns are classified into the same pattern as the original pattern through a CNN-based defect pattern classifier. Here, we introduce a new quantitative index called PGI to compare the effectiveness of the augmented models, and propose a masking process to refine the augmented images. The proposed method was tested using the WM-811k dataset. The proposed method helps to improve the classification performance of the pattern classifier by effectively solving the data imbalance issue compared to the CAE-based augmentation method. The experimental results showed that the proposed method improved the accuracy of each defect pattern by about 5.31% on average compared to the CAE-based augmentation method.
Naturally occurring hemin, an iron‐containing porphyrin, and its synthetic derivatives were used as atom transfer radical polymerization (ATRP; see picture) catalysts. The effects of the halide salt ...concentration, attachment of poly(ethylene glycol) moieties, and hydrogenation of the hemin vinyl groups on the catalyst performance were studied.
Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate scale quantum (NISQ) computers. A fundamental ...challenge in quantum machine learning is generalization, as the designer targets performance under testing conditions, while having access only to limited training data. Existing generalization analyses, while identifying important general trends and scaling laws, cannot be used to assign reliable and informative "error bars" to the decisions made by quantum models. In this article, we propose a general methodology that can reliably quantify the uncertainty of quantum models, irrespective of the amount of training data, of the number of shots, of the ansatz, of the training algorithm, and of the presence of quantum hardware noise. The approach, which builds on probabilistic conformal prediction, turns an arbitrary, possibly small, number of shots from a pre-trained quantum model into a set prediction, e.g., an interval, that provably contains the true target with any desired coverage level. Experimental results confirm the theoretical calibration guarantees of the proposed framework, referred to as quantum conformal prediction .
PDF
