COVID-19 diagnostic prediction on chest CT scan images using hybrid quantum-classical convolutional neural network.

Notwithstanding the extensive research efforts directed towards devising a dependable approach for the diagnosis of coronavirus disease 2019 (COVID-19), the inherent complexity and capriciousness of the virus continue to pose a formidable challenge to the precise identification of affected individuals. In light of this predicament, it is essential to devise a model for COVID-19 prediction utilizing chest computed tomography (CT) scans. To this end, we present a hybrid quantum-classical convolutional neural network (HQCNN) model, which is founded on stochastic quantum circuits that can discern COVID-19 patients from chest CT images. Two publicly available chest CT image datasets were employed to evaluate the performance of our model. The experimental outcomes evinced diagnostic accuracies of 99.39% and 97.91%, along with precisions of 99.19% and 98.52%, respectively. These findings are indicative of the fact that the proposed model surpasses recently published works in terms of performance, thus providing a superior ability to precisely predict COVID-19 positive instances.Communicated by Ramaswamy H. Sarma.

Digitally predicting protein localization and manipulating protein activity in fluorescence images using 4D reslicing GAN.

MOTIVATION: While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One solution is using deep neural networks to model the localization relationship between two proteins so that the localization of one protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflect the modeled relationship. Accordingly, observing the response of the prediction via manipulating input localization could provide an informative way to analyze the modeled relationships between the input and the predicted proteins. RESULTS: We propose a protein localization prediction (PLP) method using a cGAN named 4D Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of input and output proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, based on accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein, in order to observing the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on six pairs of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix, and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins, and the developed DA and DI tools provide guidance to study localization-based protein functions. AVAILABILITY AND IMPLEMENTATION: The open-source code is available at https://github.com/YangJiaoUSA/4DR-GAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data of the constructivist practices in the learning environment survey from engineering undergraduates: An exploratory factor analysis.

This paper presents the dataset of a questionnaire on first-year engineering undergraduates' perceptions of constructivist practices in the learning environment. The questionnaire with a 5-Likert scale was adapted from previous research. The sample consisted of 293 first-year engineering undergraduates in the southwest region of the United States. The online questionnaire was sent to participants who completed it voluntarily at the end of Fall 2019. A total of 274 of 293 participants completed the questionnaire with a response rate of 93.515%. Exploratory factor analysis was conducted to test the underlying factor structure of the questionnaire, which serves as a good reference for future research.

Nanoporous Au-Ag shell with fast kinetics: integrating chemical and plasmonic catalysis.

Nanoporous noble metals and alloys are widely utilized as efficient catalysts, because they have high surface-to-volume ratios for sufficient active sites and induce molecule polarization through plasmon excitation as well. Herein, we demonstrate one approach to fabricate nanoporous Au-Ag shell. Such material represents the dual functions serving as efficient catalysts and high-performance surface-enhanced Raman scattering substrate. In situ spectrum acquisition can track the conversion of p-nitrothiophenol to 4, 4'-dimercapto-azobenzene at ambient temperature. In particular, as a result of chemical catalysis of Ag elements and strong plasmon-molecule coupling, catalytic kinetics of nanoporous Au-Ag shell is 79.2-123.8 times faster than Au nanoparticles (NPs), and 2.2-3.3 times faster than Ag NPs. This investigation offers a route to design superior catalysts to integrate chemical and plasmonic catalysis.

Electrical conduction of nanoparticle monolayer for accurate tracking of mechanical stimulus in finger touch sensing.

A flexible strain gauge is an essential component in advanced human-machine interfacing, especially when it comes to many important mobile and biomedical appliances that require the detection of finger touches. In this paper, we report one such strain gauge made from a strip of nanoparticle monolayer onto a flexible substrate. This proposed gauge operates on the observation that there is a linear relationship between electrical conduction and mechanical displacement in a compressive state. Due to its prompt temporal response, the gauge can accurately track various mechanical stimuli running at the frequencies of interest. Experiments have confirmed that the proposed strain gauge has a strain detection limit as low as 9.4 × 10(-5), and its gauge factor can be as large as 70, making this device particularly suitable for sensitive finger touch sensing. Furthermore, negligible degradation in the gauge's output electrical signal is observed even after 9000 loading/unloading cycles.

MultisHRP-DNA-coated CMWNTs as signal labels for an ultrasensitive hepatitis C virus core antigen electrochemical immunosensor.

An ultrasensitive and selective electrochemical immunosensor was developed for the detection of hepatitis C virus (HCV) core antigen. The immunosensor consists of graphitized mesoporous carbon-methylene blue (GMCs-MB) nanocomposite as an electrode modified material and a horseradish peroxidase-DNA-coated carboxyl multi-wall carbon nanotubes (CMWNTs) as a secondary antibody layer. After modification of the electrode with GMCs-MB nanocomposite, Au nanoparticles were electrodeposited on to the electrode to immobilize the captured antibodies. The bridging probe and secondary antibodies linked to the CMWNTs, and DNA concatamers were obtained by hybridization of the biotin-tagged signal and auxiliary probes. Finally, streptavidin-horseradish peroxidases (HRP) were labeled on the secondary antibody layer via biotin-streptavidin system. The reduction current of MB were generated in the presence of hydrogen peroxide and monitored by square wave voltammetry. Under optimum conditions, the amperometric signal increased linearly with the core antigen concentration (0.25pgmL(-1) to 300pgmL(-1)). The immunosensor exhibites the detection limit as low as 0.01pgmL(-1) and it has a high selectivity. The new protocol showed acceptable stability and reproducibility, as well as favorable recovery for HCV core antigen in human serum. The proposed immunosensor has great potential for clinical applications.

Pressure-driven transport of particles through a converging-diverging microchannel.

Pressure-driven transport of particles through a symmetric converging-diverging microchannel is studied by solving a coupled nonlinear system, which is composed of the Navier-Stokes and continuity equations using the arbitrary Lagrangian-Eulerian finite-element technique. The predicted particle translation is in good agreement with existing experimental observations. The effects of pressure gradient, particle size, channel geometry, and a particle's initial location on the particle transport are investigated. The pressure gradient has no effect on the ratio of the translational velocity of particles through a converging-diverging channel to that in the upstream straight channel. Particles are generally accelerated in the converging region and then decelerated in the diverging region, with the maximum translational velocity at the throat. For particles with diameters close to the width of the channel throat, the usual acceleration process is divided into three stages: Acceleration, deceleration, and reacceleration instead of a monotonic acceleration. Moreover, the maximum translational velocity occurs at the end of the first acceleration stage rather than at the throat. Along the centerline of the microchannel, particles do not rotate, and the closer a particle is located near the channel wall, the higher is its rotational velocity. Analysis of the transport of two particles demonstrates the feasibility of using a converging-diverging microchannel for passive (biological and synthetic) particle separation and ordering.

Transient electrophoretic motion of a charged particle through a converging-diverging microchannel: effect of direct current-dielectrophoretic force.

Transient electrophoretic motion of a charged particle through a converging-diverging microchannel is studied by solving the coupled system of the Navier-Stokes equations for fluid flow and the Laplace equation for electrical field with an arbitrary Lagrangian-Eulerian finite-element method. A spatially non-uniform electric field is induced in the converging-diverging section, which gives rise to a direct current dielectrophoretic (DEP) force in addition to the electrostatic force acting on the charged particle. As a sequence, the symmetry of the particle velocity and trajectory with respect to the throat is broken. We demonstrate that the predicted particle trajectory shifts due to DEP show quantitative agreements with the existing experimental data. Although converging-diverging microchannels can be used for super fast electrophoresis due to the enhancement of the local electric field, it is shown that large particles may be blocked due to the induced DEP force, which thus must be taken into account in the study of electrophoresis in microfluidic devices where non-uniform electric fields are present.

A behavior study of the effects of visual feedback on motor output.

Visual feedback is a crucial factor that impacts the motor function, and a number of parameters, such as gain, delay and frequency, all play a role in regulating the motor output. In this paper, we conduct a behavioral study on 12 volunteers to determine the effects of visual feedback in the physical movement by measuring the grasp force output under different visual feedback gain levels. To this end, two force tracking tasks with different incremental/decremental rates of the force have been designed, and the force deviation and the error rate from the 12 participants are recorded when they are exposed to different visual gains. Further statistical analysis on the experimental data reveals that the gain of visual stimuli has a significant influence on the force output. For the same force tracking task, visual feedback with high gain tends to enhance the regulation of force production. The results also suggest that different visual feedback gains may be mapped onto different cortex function areas governing different motor tasks.

Progressive image transmission for medical applications based on wavelet transform with a non-uniform scalar quantization scheme.

The Progressive Image Transmission (PIT) technique has been used to alleviate the communication problem related to transmit large volume of medical image data. In this study, we propose a novel PIT algorithm based on wavelet transform, DPCM coding and non-uniform scalar quantization. Experimental results have confirmed the efficiency of the proposed scheme. The achieved bit rate for the first recognizable picture can be as low as 0.05 bit/pixel transmitted in less than 1.0 second for a 512x512 256-gray scale medical image. The reconstructed image shows higher quality than that obtained by the Set Partitioning In Hierarchical Trees (SPIHT) algorithm, which makes it a winning choice for medical image transmission through low speed communication channels.

The internet-based knowledge acquisition and management method to construct large-scale distributed medical expert systems.

The Internet offers an unprecedented opportunity to construct powerful large-scale medical expert systems (MES). In these systems, a cost-effective medical knowledge acquisition (KA) and management scheme is highly desirable to handle the large quantities of, often conflicting, medical information collected from medical experts in different medical fields and from different geographical regions. In this paper, we demonstrate that a medical KA/management system can be built upon a three-tier distributed client/server architecture. The knowledge in the system is stored/managed in three knowledge bases. The maturity of the medical know-how controls the knowledge flow through these knowledge bases. In addition, to facilitate the knowledge representation and application in these knowledge bases as well as information retrieval across the Internet, an 8-digit numeric coding scheme with a weight value system is proposed. At present, a medical KA and management system based on the proposed method is being tested in clinics. Current results have showed that the method is a viable solution to construct, modify, and expand a distributed MES through the Internet.