Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning.

Temporomandibular joint (TMJ) disorders have been misinterpreted by various normal TMJ features leading to treatment failure. This study assessed deep learning algorithms, DenseNet-121 and InceptionV3, for multi-class classification of TMJ normal variations and disorders in 1,710 panoramic radiographs. The overall accuracy of DenseNet-121 and InceptionV3 were 0.99 and 0.95, respectively. The AUC from 0.99 to 1.00, indicating high performance for TMJ disorders classification in panoramic radiographs.

Sensing Mechanisms of Rough Plasmonic Surfaces for Protein Binding of Surface Plasmon Resonance Detection.

Surface plasmon resonance (SPR) has been utilized in various optical applications, including biosensors. The SPR-based sensor is a gold standard for protein kinetic measurement due to its ultrasensitivity on the plasmonic metal surface. However, a slight change in the surface morphology, such as roughness or pattern, can significantly impact its performance. This study proposes a theoretical framework to explain sensing mechanisms and quantify sensing performance parameters of angular surface plasmon resonance detection for binding kinetic sensing at different levels of surface roughness. The theoretical investigation utilized two models, a protein layer coating on a rough plasmonic surface with and without sidewall coatings. The two models enable us to separate and quantify the enhancement factors due to the localized surface plasmon polaritons at sharp edges of the rough surfaces and the increased surface area for protein binding due to roughness. The Gaussian random surface technique was employed to create rough metal surfaces. Reflectance spectra and quantitative performance parameters were simulated and quantified using rigorous coupled-wave analysis and Monte Carlo simulation. These parameters include sensitivity, plasmonic dip position, intensity contrast, full width at half maximum, plasmonic angle, and figure of merit. Roughness can significantly impact the intensity measurement of binding kinetics, positively or negatively, depending on the roughness levels. Due to the increased scattering loss, a tradeoff between sensitivity and increased roughness leads to a widened plasmonic reflectance dip. Some roughness profiles can give a negative and enhanced sensitivity without broadening the SPR spectra. We also discuss how the improved sensitivity of rough surfaces is predominantly due to the localized surface wave, not the increased density of the binding domain.

Generalized figure of merit for plasmonic dip measurement-based surface plasmon resonance sensors.

We propose a theoretical framework to analyze quantitative sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip position, and figure of merits for different surface plasmon operating conditions for a Kretschmann configuration. Several definitions and expressions of the figure of merit have been reported in the literature. Moreover, the optimal operating conditions for each figure of merit are, in fact, different. In addition, there is still no direct figure of merit comparison between different expressions and definitions to identify which definition provides a more accurate performance prediction. Here shot-noise model and Monte Carlo simulation mimicking the noise behavior in SPR experiments have been applied to quantify standard deviation in the SPR plasmonic dip measurements to evaluate the performance responses of the figure of merits. Here, we propose and formulate a generalized figure of merit definition providing a good performance estimation to the detection limit. The measurement parameters employed in the figure of merit formulation are identified by principal component analysis and machine learning. We also show that the proposed figure of merit can provide a good estimation for the surface plasmon resonance performance of plasmonic materials, including gold and aluminum, with no need for a resource-demanding computation.

Cuff-Less Blood Pressure Prediction from ECG and PPG Signals Using Fourier Transformation and Amplitude Randomization Preprocessing for Context Aggregation Network Training.

This research proposes an algorithm to preprocess photoplethysmography (PPG) and electrocardiogram (ECG) signals and apply the processed signals to the context aggregation network-based deep learning to achieve higher accuracy of continuous systolic and diastolic blood pressure monitoring than other reported algorithms. The preprocessing method consists of the following steps: (1) acquiring the PPG and ECG signals for a two second window at a sampling rate of 125 Hz; (2) separating the signals into an array of 250 data points corresponding to a 2 s data window; (3) randomizing the amplitude of the PPG and ECG signals by multiplying the 2 s frames by a random amplitude constant to ensure that the neural network can only learn from the frequency information accommodating the signal fluctuation due to instrument attachment and installation; (4) Fourier transforming the windowed PPG and ECG signals obtaining both amplitude and phase data; (5) normalizing both the amplitude and the phase of PPG and ECG signals using z-score normalization; and (6) training the neural network using four input channels (the amplitude and the phase of PPG and the amplitude and the phase of ECG), and arterial blood pressure signal in time-domain as the label for supervised learning. As a result, the network can achieve a high continuous blood pressure monitoring accuracy, with the systolic blood pressure root mean square error of 7 mmHg and the diastolic root mean square error of 6 mmHg. These values are within the error range reported in the literature. Note that other methods rely only on mathematical models for the systolic and diastolic values, whereas the proposed method can predict the continuous signal without degrading the measurement performance and relying on a mathematical model.

Analysis of Effects of Surface Roughness on Sensing Performance of Surface Plasmon Resonance Detection for Refractive Index Sensing Application.

This paper provides a theoretical framework to analyze and quantify roughness effects on sensing performance parameters of surface plasmon resonance measurements. Rigorous coupled-wave analysis and the Monte Carlo method were applied to compute plasmonic reflectance spectra for different surface roughness profiles. The rough surfaces were generated using the low pass frequency filtering method. Different coating and surface treatments and their reported root-mean-square roughness in the literature were extracted and investigated in this study to calculate the refractive index sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip intensity, plasmonic dip position, and figure of merit. Here, we propose a figure-of-merit equation considering optical intensity contrast and signal-to-noise ratio. The proposed figure-of-merit equation could predict a similar refractive index sensing performance compared to experimental results reported in the literature. The surface roughness height strongly affected all the performance parameters, resulting in a degraded figure of merit for surface plasmon resonance measurement.

Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics.

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

Heterologously expressed Aspergillus aculeatus ß-glucosidase in Saccharomyces cerevisiae is a cost-effective alternative to commercial supplementation of ß-glucosidase in industrial ethanol production using Trichoderma reesei cellulases.

Trichoderma reesei is a filamentous organism that secretes enzymes capable of degrading cellulose to cellobiose. The culture supernatant of T. reesei, however, lacks sufficient activity to convert cellobiose to glucose using ß-glucosidase (BGL1). In this study, we identified a BGL (Cel3B) from T. reesei (TrCel3B) and compared it with the active ß-glucosidases from Aspergillus aculeatus (AaBGL1). AaBGL1 showed higher stability and conversion of sugars to ethanol compared to TrCel3B, and therefore we chose to express this recombinant protein for use in fermentation processes. We expressed the recombinant protein in the yeast Saccharomyces cerevisiae, combined it with the superb T. reesei cellulase machinery and used the combination in a simultaneous saccharification and fermentation (SSF) process, with the hope that the recombinant would supplement the BGL activity. As the sugars were processed, the yeast immediately converted them to ethanol, thereby eliminating the problem posed by end product inhibition. Recombinant AaBGL1 activity was compared with Novozyme 188, a commercially available supplement for BGL activity. Our results show that the recombinant protein is as effective as the commercial supplement and can process sugars with equal efficiency. Expression of AaBGL1 in S. cerevisiae increased ethanol production effectively. Thus, heterologous expression of AaBGL1 in S. cerevisiae is a cost-effective and efficient process for the bioconversion of ethanol from lignocellulosic biomass.

Utilization of recombinant Trichoderma reesei expressing Aspergillus aculeatus ß-glucosidase I (JN11) for a more economical production of ethanol from lignocellulosic biomass.

The capacity of Trichoderma reesei cellulase to degrade lignocellulosic biomass has been enhanced by the construction of a recombinant T. reesei strain expressing Aspergillus aculeatus ß-glucosidase I. We have confirmed highly efficient ethanol production from converge-milled Japanese cedar by recombinant T. reesei expressing A. aculeatus ß-glucosidase I (JN11). We investigated the ethanol productivity of JN11 and compared it with the cocktail enzyme T. reesei PC-3-7 with reinforced cellobiase activity by the commercial Novozyme 188. Results showed that the ethanol production efficiency under enzymatic hydrolysis of JN11 was comparable to the cocktail enzyme both on simultaneous saccharification and fermentation (SSF) or separate hydrolysis and fermentation (SHF) processes. Moreover, the cocktail enzyme required more protein loading for attaining similar levels of ethanol conversion as JN11. We propose that JN11 is an intrinsically economical enzyme that can eliminate the supplementation of BGL for PC-3-7, thereby reducing the cost of industrial ethanol production from lignocellulosic biomass.