Deep Spatial-Spectral Joint-Sparse Prior Encoding Network for Hyperspectral Target Detection.

Hyperspectral target detection aims to locate targets of interest in the scene, and deep learning-based detection methods have achieved the best results. However, black box network architectures are usually designed to directly learn the mapping between the original image and the discriminative features in a single data-driven manner, a choice that lacks sufficient interpretability. On the contrary, this article proposes a novel deep spatial-spectral joint-sparse prior encoding network (JSPEN), which reasonably embeds the domain knowledge of hyperspectral target detection into the neural network, and has explicit interpretability. In JSPEN, the sparse encoded prior information with spatial-spectral constraints is learned end-to-end from hyperspectral images (HSIs). Specifically, an adaptive joint spatial-spectral sparse model (AS 2 JSM) is developed to mine the spatial-spectral correlation of HSIs and improves the accuracy of data representation. An optimization algorithm is designed for iteratively solving AS 2 JSM, and JSPEN is proposed to simulate the iterative optimization process in the algorithm. Each basic module of JSPEN one-to-one corresponds to the operation in the optimization algorithm so that each intermediate result in the network has a clear explanation, which is convenient for intuitive analysis of the operation of the network. With end-to-end training, JSPEN can automatically capture the general sparse properties of HSIs and faithfully characterize the features of background and target. Experimental results verify the effectiveness and accuracy of the proposed method. Code is available at https://github.com/Jiahuiqu/JSPEN.

Stress State Classification Based on Deep Neural Network and Electrodermal Activity Modeling.

Electrodermal Activity (EDA) has become of great interest in the last several decades, due to the advent of new devices that allow for recording a lot of psychophysiological data for remotely monitoring patients' health. In this work, a novel method of analyzing EDA signals is proposed with the ultimate goal of helping caregivers assess the emotional states of autistic people, such as stress and frustration, which could cause aggression onset. Since many autistic people are non-verbal or suffer from alexithymia, the development of a method able to detect and measure these arousal states could be useful to aid with predicting imminent aggression. Therefore, the main objective of this paper is to classify their emotional states to prevent these crises with proper actions. Several studies were conducted to classify EDA signals, usually employing learning methods, where data augmentation was often performed to countervail the lack of extensive datasets. Differently, in this work, we use a model to generate synthetic data that are employed to train a deep neural network for EDA signal classification. This method is automatic and does not require a separate step for features extraction, as in EDA classification solutions based on machine learning. The network is first trained with synthetic data and then tested on another set of synthetic data, as well as on experimental sequences. In the first case, an accuracy of 96% is reached, which becomes 84% in the second case, thus demonstrating the feasibility of the proposed approach and its high performance.

Presence and inter-individual variability of carboxylesterases (CES1 and CES2) in human lung.

Lungs are pharmacologically active organs and the pulmonary drug metabolism is of interest for inhaled drugs design. Carboxylesterases (CESs) are enzymes catalyzing the hydrolysis of many structurally different ester, amide and carbamate chemicals, including prodrugs. For the first time, the presence, kinetics, inhibition and inter-individual variations of the major liver CES isozymes (CES1 and CES2) were investigated in cytosol and microsomes of human lungs from 20 individuals using 4-nitrophenyl acetate (pNPA), 4-methylumbelliferyl acetate (4-MUA), and fluorescein diacetate (FD) as substrates the rates of hydrolysis (Vmax) for pNPA and 4-MUA, unlike FD, were double in microsomes than in cytosol. In these cellular fractions, the Vmax of pNPA, as CES1 marker, were much greater (30-50-fold) than those of FD, as a specific CES2 marker. Conversely, the Km values were comparable suggesting the involvement of the same enzymes. Inhibition studies revealed that the FD hydrolysis was inhibited by bis-p-nitrophenylphosphate, phenylmethanesulfonyl fluoride, and loperamide (specific for CES2), whereas the pNPA and 4-MUA hydrolysis inhibition was limited. Inhibitors selective for other esterases missed having any effect on above-mentioned activities. In cytosol and microsomes of 20 lung samples, inter-individual variations were found for the hydrolysis of pNPA (2.5-5-fold), FD or 4-MUA (8-15-fold). Similar variations were also observed in CES1 and CES2 gene expression, although determined in a small number (n = 9) of lung samples. The identification of CES1 and CES2 and their variability in human lungs are important for drug metabolism and design of prodrugs which need to be activated in this organ.