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When investigating epidemic dynamics through differential models, the parameters needed to understand the phenomenon and to simulate forecast scenarios require a delicate calibration phase, often made even more challenging by the scarcity and uncertainty of the observed data reported by official sources. In this context, Physics-Informed Neural Networks (PINNs), by embedding the knowledge of the differential model that governs the physical phenomenon in the learning process, can effectively address the inverse and forward problem of data-driven learning and solving the corresponding epidemic problem. In many circumstances, however, the spatial propagation of an infectious disease is characterized by movements of individuals at different scales governed by multiscale partial differential equations. This reflects the heterogeneity of a region or territory in relation to the dynamics within cities and in neighboring zones. In presence of multiple scales, a direct application of PINNs generally leads to poor results due to the multiscale nature of the differential model in the loss function of the neural network. To allow the neural network to operate uniformly with respect to the small scales, it is desirable that the neural network satisfies an Asymptotic-Preservation (AP) property in the learning process. To this end, we consider a new class of AP neural networks for multiscale hyperbolic transport models of epidemic spread that, thanks to an appropriate AP formulation of the loss function, is capable of working uniformly at the different scales of the system. A series of numerical tests for different epidemic scenarios confirms the validity of the proposed approach, highlighting the importance of the AP property in the neural network when dealing with multiscale problems especially in presence of sparse and partially observed systems. [ABSTRACT FROM AUTHOR]

Lung cancer is the cancer with the highest mortality, and non‐small cell lung cancer (NSCLC) accounts for more than 80%. Tumor cells often have high reactive oxygen species (ROS) and antioxidant capacity. Redox balance is very important for tumor. The decline of antioxidant capacity and excessive ROS will induce the death of tumor cells. Destroying the redox balance of tumor cells is a promising tumor treatment strategy. Xanthatin is an active sesquiterpene lactone isolated from Xanthium strumarium L. We observed that xanthatin induced the up regulation of mitochondrial ROS and mitochondrial damage. Meanwhile, our results showed that xanthatin could inhibit system xc− and reduce glutathione (GSH) synthesis. Antioxidant GSH and N‐acetyl‐ l‐cysteine (NAC) significantly reversed cell proliferation inhibition and apoptosis induced by xanthatin. β‐Mercaptoethanol (β‐ME) which can avoid inhibition of system xc− can also reverse the inhibition of cell proliferation induced by xanthatin, si‐SLC7A11 was the opposite. Based on these results, we believe that the inhibition of xanthatin on the proliferation of NSCLC cells may be related to breaking the intracellular redox balance. Our data suggest that xanthatin is a promising antitumor candidate for the treatment of NSCLC. [ABSTRACT FROM AUTHOR]

Uncertainty in data is certainly one of the main problems in epidemiology, as shown by the recent COVID-19 pandemic. The need for efficient methods capable of quantifying uncertainty in the mathematical model is essential in order to produce realistic scenarios of the spread of infection. In this paper, we introduce a bi-fidelity approach to quantify uncertainty in spatially dependent epidemic models. The approach is based on evaluating a high-fidelity model on a small number of samples properly selected from a large number of evaluations of a low-fidelity model. In particular, we will consider the class of multiscale transport models recently introduced in [ 13 , 7 ] as the high-fidelity reference and use simple two-velocity discrete models for low-fidelity evaluations. Both models share the same diffusive behavior and are solved with ad-hoc asymptotic-preserving numerical discretizations. A series of numerical experiments confirm the validity of the approach. Correction: This article is copyrighted in 2022. We apologize for any inconvenience this may cause. [ABSTRACT FROM AUTHOR]

Acrylonitrile (AN) is a known animal carcinogen and suspected human carcinogen. Recently, occupational exposure to AN has considerably increased. Previously, we demonstrated that streptozotocin-induced diabetes potentiates AN-induced acute toxicity in rats and that the induced cytochrome P450 2E1 (CYP2E1) is responsible for this effect. In the present study, we examined whether induction of CYP2E1 is also the underlying mechanism for the potentiation of AN-induced acute toxicity in type 2 diabetes in db/db mice. The effect of phenethyl isothiocyanate (PEITC) in reducing potentiation was also investigated. The mice were randomly divided into the normal control, diabetic control, AN, diabetes + AN, PEITC + AN, and diabetes + PEITC + AN groups. PEITC (40 mg/kg) was orally administered to rats for 3 days, and 1 h after the last PEITC gavage, 45 mg/kg AN was intraperitoneally injected. Time to death was observed. The CYP2E1 level and enzymatic activity, cytochrome c oxidase (CCO) activity, and reactive oxygen species (ROS) levels were measured. The survival rate was decreased in AN-treated db/db mice compared with that in AN-treated wild-type mice. The hepatic CYP2E1 level and enzymatic activity remained unaltered in db/db mice. Phenethyl isothiocyanate alleviated AN-induced acute toxicity in db/db mice as evident in the increased survival rate, restored CCO activity, and decreased ROS level in both the liver and brain. The study results suggested that CYP2E1 may not be responsible for the sensitivity to AN-induced acute toxicity in db/db mice and that PEITC reduced the potentiation of AN-induced acute toxicity in db/db mice. [ABSTRACT FROM AUTHOR]

Recently, the colloidal quantum dot spectrometer has received much attention due to its advantages in cost, size, and operation. Yet, just like many other filter-based miniature spectrometers, spectrum reconstruction for the colloidal quantum dot spectrometer is typically prone to the measurement noise due to the correlation of the filters. In this paper, we propose an effective spectrum reconstruction method for the colloidal quantum dot spectrometer, which can recover high-quality spectra in noisy environments. Specifically, we employ a denoising autoencoder, a machine-learning approach, to reduce noise in the filters’ raw measurements before performing the reconstruction. After that, we reconstruct the spectra with the denoised data by a sparse recovery algorithm. We investigate the feasibility of the proposed reconstruction approach on a synthetic dataset and an experimental dataset collected by the colloidal quantum dot spectrometer. The results demonstrate that the proposed approach could deliver accurate reconstruction results even when data are corrupted with the measurement noise. [ABSTRACT FROM AUTHOR]

In this article, we present one numerical approach to infer the model parameters and state variables of acoustic wave equations. The method we consider is based on the recently proposed method-the so-called hidden physics model. With placing Gaussian process (GP) prior on the state variables, the structure and model parameters of acoustic wave equations are encoded into the kernel function of a multioutput GP. The purpose of this article includes: 1) testing the applicability of hidden physics model to infer the velocity, density, and state variables of the acoustic wave equation, which is important for many applications in geophysics; 2) adapting the method to handle for both homogeneous and heterogeneous media that are of practical interest; and 3) exploring efficient sequential sampling methods to improve the sampling efficiency. We suggest that the expected-improvement-based sequential sampling method would be effective for most practical problems related to acoustic wave propagation. Besides, we demonstrate the performance of the proposed scheme via several benchmark problems. [ABSTRACT FROM AUTHOR]

This paper introduces a new flexible capacitive force sensor for cardiopulmonary resuscitation (CPR) to evaluate the quality of chest compression (CC). Compared with other feedback devices, this sensing element is flexible, which helps avoid the risk of tissue injury to the patient. We describe the relationship between the compressive force and the capacitance of the sensor element, and design a circuit to transduce the capacitance into a voltage. Finally, we tested the system during CPR training with a manikin. The experiment shows that the sensing element can help rescuers distinguish between effective and ineffective CC due to inadequate compression rate and/or depth. [ABSTRACT FROM AUTHOR]

Deep learning models combining spectral and spatial features have been proven to be effective for hyperspectral image (HSI) classification. However, most spatial feature integration methods only consider a single input spatial scale regardless of various shapes and sizes of objects over the image plane, leading to missing scale-dependent information. In this paper, we propose a hierarchical multi-scale convolutional neural networks (CNNs) with auxiliary classifiers (HMCNN-AC) to learn hierarchical multi-scale spectral–spatial features for HSI classification. First, to better exploit the spatial information, multi-scale image patches for each pixel are generated at different spatial scales. These multi-scale patches are all centered at the same central spectrum but with shrunken spatial scales. Then, we apply multi-scale CNNs to extract spectral–spatial features from each scale patch. The obtained multi-scale convolutional features are considered as structured sequential data with spectral–spatial dependency, and a bidirectional LSTM is proposed to capture the correlation and extract a hierarchical representation for each pixel. To better train the whole network, weighted auxiliary classifiers are employed for the multi-scale CNNs and optimized together with the main loss function. Experimental results on three public HSI datasets demonstrate the superiority of our proposed framework over some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports on a high-repetition-rate dual signal-wave (DSW) optical parametric oscillator (OPO) operating at the 1.5μm band with tunable wavelength intervals from 2.5nm to 69.1nm. Two periodically poled crystals, a periodically poled lithium niobate (PPLN) with multiple gratings and a single grating MgO-doped PPLN (PPMgOLN), are cascaded in the same OPO cavity to generate dual signal-waves by using quasi-phase-matched (QPM) technique. The pump source was a Q-switched diode-pumped Nd:YVO4 laser operating at 50kHz. At an incident pump power of 3W, an average output power of 169.6mW at 1489.2nm and 1558.3nm has been achieved. [Copyright &y& Elsevier]

Large-capacity information encryption has attracted significant interest in the information age. The diversity and controllability of spectra have positioned them to be widely applied for information encryption. Current spectra-based information encryption methods commonly rely on either spectral alteration induced by external stimuli or the utilization of narrowband channels within spectra. However, these methods encounter a common challenge in attaining both high security and large capacity simultaneously. To address these issues, we propose a multiple-channel information encryption system based on quantum dot (QD) absorption spectra. The diversity of QD absorption spectra and their broadband features ensure that the encrypted spectra can hardly be decrypted without knowing the correct channel matrix. Meanwhile, the large capacity is realized through the combination of multiple QD spectral channels with a theoretical maximum capacity of 24.0 bits in a single spectrum. In order to optimize the performance of our proposed system, the selection principle of the channel matrix is established to achieve the rapid identification of the optimal channel matrix in several milliseconds. The additivity of QD spectral channels and the consistency of QD spectra are also explored to minimize the impact of errors on information decryption. Furthermore, two spectral encryption scenarios of spatial pattern and spectral pattern are applied to demonstrate the feasibility, showcasing their ability to achieve both a high level of security and large capacity. Owing to the advantages offered by QD spectra, the QD spectra-based information system exhibits excellent potential for broader applications in information storage, authentication, and computing.

Introduction: Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface., Methods: To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model., Results: Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed., Discussion: The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Zhang, Zhu, Wang and Gutierrez-Farewik.)

In this work, we predicted ankle joint torque by combining a neuromusculoskeletal (NMS) solver-informed artificial neural network (hybrid-ANN) model with transfer learning based on joint angle and muscle electromyography signals. The hybrid-ANN is an ANN augmented with two kinds of features: 1) experimental measurements - muscle signals and joint angles, and 2) informative physical features extracted from the underlying NMS solver, such as individual muscle force and joint torque. The hybrid-ANN model accuracy in torque prediction was studied in both intra- and inter-subject tests, and compared to the baseline models (NMS and standard-ANN). For each prediction model, seven different cases were studied using data from gait at different speeds and from isokinetic ankle dorsi/plantarflexion motion. Additionally, we integrated a transfer learning method in inter-subject models to improve joint torque prediction accuracy by transferring the learned knowledge from previous participants to a new participant, which could be useful when training data is limited. Our results indicated that better accuracy could be obtained by integrating informative NMS features into a standard ANN model, especially in inter-subject cases; overall, the hybrid-ANN model predicted joint torque with higher accuracy than the baseline models, most notably in inter-subject prediction after adopting the transfer learning technique. We demonstrated the potential of combining physics-based NMS and standard-ANN models with a transfer learning technique in different prediction scenarios. This procedure holds great promise in applications such as assistance-as-needed exoskeleton control strategy design by incorporating the physiological joint torque of the users.

Surgical removal is the primary treatment for liver cancer, but frequent recurrence caused by residual malignant tissue remains an important challenge, as recurrence leads to high mortality. It is unreliable to distinguish tumors from normal tissues merely under visual inspection. Hyperspectral imaging (HSI) has been proved to be a promising technology for intra-operative use by capturing the spatial and spectral information of tissue in a fast, non-contact and label-free manner. In this work, we investigated the feasibility of HSI for liver tumor delineation on surgical specimens using a multi-task U-Net framework. Measurements are performed on 19 patients and a dataset of 36 specimens was collected with corresponding pathological results serving as the ground truth. The developed framework can achieve an overall sensitivity of 94.48% and a specificity of 87.22%, outperforming the baseline SVM method by a large margin. In particular, we propose to add explanations on the well-trained model from the spatial and spectral dimensions to show the contribution of pixels and spectral channels explicitly. On that basis, a novel saliency-weighted channel selection method is further proposed to select a small subset of 5 spectral channels which provide essentially as much information as using all 224 channels. According to the dominant channels, the absorption difference of hemoglobin and bile content in the normal and malignant tissues seems to be promising markers that could be further exploited., Competing Interests: The authors declare no conflicts of interest., (© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

Objective: A meta-analysis of the screened data was performed to investigate the survival status of patients with second primary non-small cell lung cancer (SPNSCLC) after pulmonary lobectomy and partial pulmonary lobectomy, and assess the applicability of such option in the treatment of such disease., Methods: Based on the databases of PubMed, Ovid, and CNKI, relevant reports on postoperative survival of patients with SPNSCLC were obtained, and the information, including authors, publication year, research type, tumor staging, tumor diameter, surgical options, reasons for resection, and overall survival (OS), was extracted. The data were analyzed using Review Manager 5.3., Results: A total of 11 studies published from 2010 to 2017 were collected. Patients with SPNSCLC were mainly in stage Ia and Ib, 51.4% of whom underwent pulmonary lobectomy and 34.7% of whom underwent partial pulmonary lobectomy. The analysis results showed that after the second lobectomy, the mortality rate of the patients undergoing intensive surgery was 8%, the incidence of postoperative complications was 29%, and the 5-year OS rate was 53% after the second lobectomy for patients with SPNSCLC., Conclusion: The survival rate of patients with SPNSCLC is lower than that of patients with early lung cancer. In view of this, surgical resection is feasible. There are remarkable differences between pulmonary lobectomy and partial pulmonary lobectomy., Competing Interests: None., (AJTR Copyright © 2021.)

Recently, the miniature spectrometer based on the optical filter array has received much attention due to its versatility. Among many open challenges, designing efficient and stable algorithms to recover the input spectrum from the raw measurements is the key to success. Of many existing spectrum reconstruction algorithms, regularization-based algorithms have emerged as practical approaches to the spectrum reconstruction problem, but the reconstruction is still challenging due to ill-posedness of the problem. To alleviate this issue, we propose a novel reconstruction method based on a solver-informed neural network (NN). This approach consists of two components: (1) an existing spectrum reconstruction solver to extract the spectral feature from the raw measurements (2) a multilayer perceptron to build a map from the input feature to the spectrum. We investigate the reconstruction performance of the proposed method on a synthetic dataset and a real dataset collected by the colloidal quantum dot (CQD) spectrometer. The results demonstrate the reconstruction accuracy and robustness of the solver-informed NN. In conclusion, the proposed reconstruction method shows excellent potential for spectral recovery of filter-based miniature spectrometers.

Objective: To establish a HPLC fingerprint for quality evaluation of fructus Euodiae rutaecarpa, and to perform qualitative and quantitative analysis of its main constituents., Method: The 10 batches of samples were analyzed by HPLC linear gradient elution method and evaluated by similarity evaluation and system cluster analysis. The common peaks in chromatographic fingerprint were identified by LC-DAD-MS., Result: The HPLC fingerprint of Euodiae Fructus showed 32 characteristic peaks, among them 13 peaks were identified, and 2 constituents were quantified., Conclusion: The HPLC fingerprint established could reflect the main constituents of Euodiae Fructus, and could combine with content determination to take an overall control of Euodiae Fructus.

A high-conversion-efficiency, low-threshold, quasi-continuous-wave optical parametric generator (OPG) based on a periodically poled lithium niobate (PPLN) crystal is presented. Pumped by an acousto-optically Q-switched 1064 nm Nd:YAG laser with a power output of 848 mW, the OPG generated an output power of 452 mW for the signal and the idle waves, achieving an internal conversion efficiency of 62.7% and a slope efficiency of 75.6%. To the best of our knowledge, this is the highest efficiency ever reported for single-pass, quasi-continuous-wave OPGs by using periodically poled crystals.