No abstract

R67 dihydrofolate reductase (DHFR) is an R-plasmid encoded protein that confers clinical resistance to the antibacterial drug trimethoprim. To determine whether an acidic titration in kinetic pH profiles is related to titration of histidines 62, 162, 262, and 362, the stability of tetrameric R67 DHFR has been monitored as a function of pH. For the pH range 5-8, tetrameric R67 DHFR reversibly dissociates into dimers, as monitored by ultracentrifugation and molecular sieving techniques. From the crystal structures of dimeric and tetrameric R67 DHFR [Matthews et al. (1986) Biochemistry 25, 4194-4204] (Narayana, Matthews, and Xuong, personal communication), symmetry-related histidines 62, 162, 262, and 362 occur at the two dimer-dimer interfaces and protonation of these residues could destabilize tetrameric R67 DHFR. Ionization of these histidines was confirmed by monitoring the chemical shifts of the C2 proton in NMR experiments, and best fits of an incomplete titration curve yield a pKa of 6.77. Since tryptophans 38, 138, 238, and 338 also occur at the dimer-dimer interfaces, fluorescence additionally monitors the tetramer-two dimers equilibrium. When fluorescence was monitored over the pH range 5-8, a protein concentration dependence of fluorescence was observed and global fitting of three titration curves yielded Kd = 9.72 nM and pKa = 6.84 for the linked reactions: [formula: see text] Modification of H62, H162, H262, and H362 by diethyl pyrocarbonate stabilizes dimeric R67 DHFR and causes a 200-600-fold decrease in catalytic efficiency. Decreased catalytic activity in dimeric R67 DHFR is presumably due to loss of the putative single active site pore found in tetrameric R67 DHFR.

No abstract

No abstract

Enhanced oil recovery (EOR) using nanofluids has been proposed in recent years, but the mechanism of oil recovery enhancement through nanofluid injection still needs further study. In this study, the pore-scale performance and mechanism of nanofluid EOR were investigated based on a micromodel experiment. The micromodel sample was designed to compare the silica-based homogeneous water-wet sandstone reservoirs. The behavior of 0.1% wt water-base silica nanofluid-displacing oil (dodecane) was compared to the deionized (DI) water-displacing case. Residual oil saturation gradually decreases from 50% to 43% as the DI water injection flow rate increases from 0.5 to 5.0 μL/min. In the nanofluid-injection case, residual oil saturation decreases from 24% to 20% as the flow rate varies in the same range. About 25% saturation of incremental oil recovery is obtained by nanofluid injection compared to DI water injection. This implies significant improvement in oil recovery performance from nanofluid injection. Through investigation of detailed pore-scale fluid distribution, the wettability alteration of the oil-bearing pore wall from a strongly water-wet condition to the neutrally wet condition is observed in the presence of nanoparticles. The wettability alteration behavior in a natural sandstone sample was investigated via a spontaneous imbibition test. The imbibition rate slows down significantly in the presence of nanoparticles indicating that the wettability alteration mechanism observed in the micromodel experiment is also valid in the case of natural water-wet sandstones and consequently can enhance the oil recovery. The analysis based on Wenzel’s model indicates that the nanoparticle adsorption-induced nonuniform pore wall roughness change is the possible mechanism for wettability alteration.

Counterfeit electronics are a growing problem for the electronic information industry worldwide, so developing unbreakable security tags is crucial to ensure the trustworthiness and traceability of electronics. Traditional anticounterfeiting and trace solutions rely on reproducible deterministic processes and additional labels, which can still be copied or faked by counterfeiters. Herein, physical unclonable functions enabled by spontaneously formed plasmonic core–shell nanoparticles on electrodes are proposed to ensure label‐free traceable electronics, giving a practical solution to fight against counterfeit electronics. Random hemispherical core–shell nanoparticles are intentionally introduced on the metal electrode of different semiconductors (Si, GaAs, and GaN) from Ni/Au bilayer heterofilms by rapid thermal annealing, which can be integrated with electronics seamlessly, with no negative effect on electrical properties. The position, size, and shape of nanoparticles are random and uncontrollable; the corresponding scattering patterns, intensity, and spectra can work as nanofingerprints of the electrode, proving multidimensional unclonable labels with large encoding capacity suitable for electrodes smaller than several micrometers. It can be further combined with machine vision and artificial intelligence to identify and track electronics automatically and efficiently. The anticounterfeiting electrodes also show good thermal robustness and mechanical stability, opening up a prospect for practical anticounterfeiting of electronics.

Fine-grained image classification methods often suffer from the challenge that the subordinate categories within an entry-level category can only be distinguished by subtle differences. Crop disease classification is affected by various visual interferences, including uneven illumination, dew, and equipment jitter. It demands an effective algorithm to accurately discriminate one category from the others. Thus, the representational ability of algorithm needs to be strengthened to learn a robust domain-specific discrimination through an effective way. To address this challenge, a unified convolutional neural network (CNN) denoting the matrix-based convolutional neural network (M-bCNN) was proposed. Its hallmark is the convolutional kernel matrix, whose convolutional layers are arranged parallelly in the form of a matrix, and integrated with DropConnect, exponential linear unit, local response normalization, and so on to defeat over-fitting and vanishing gradient. With a tolerable addition of parameters, it can effectively increase the data streams, neurons, and link channels of the model compared with the commonly used plain networks. Therefore, it will create more non-linear mappings and will enhance the representational ability with a tolerable growth of parameters. The images of winter wheat leaf diseases were utilized as experimental samples for their strong similarities among sub-categories. A total of 16 652 images containing eight categories were collected from Shandong Province, China, and were augmented into 83 260 images. The M-bCNN delivered significant improvements and achieved an average validation accuracy of 96.5% and a testing accuracy of 90.1%; this outperformed AlexNet and VGG-16. The M-bCNN demonstrated accuracy gains with a convolutional kernel matrix in fine-grained image classification. INDEX TERMS Convolutional neural network, fine-grained image classification, deep learning, convolutional kernel matrix, wheat leaf diseases.

Information security is of great importance for modern society with all things connected. Physical unclonable function (PUF) as a promising hardware primitive has been intensively studied for information security. However,...