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The concept of passenger-centric design has become increasingly significant in the intelligent automotive industry. Enhancing in-cabin thermal comfort while maintaining energy efficiency is crucial, and to this end, we leverage real-time passenger thermal sensation as a basis for HVAC control strategies. This study introduces a non-invasive, dual-phase prediction model that evaluates passenger thermal comfort by utilizing facial thermal imaging and environmental factors. The proposed model employs a deep learning neural network to extract pertinent features from the facial thermal images for an initial prediction. Then this prediction is refined by integrating it with environmental data through a machine learning algorithm, facilitating real-time personal thermal comfort assessment. Data collection for the model was performed across two distinct seasons, with 22 participants in summer and 18 in winter, gathering facial thermal images, environmental parameters, and thermal sensation votes to construct a comprehensive dataset. Analysis revealed a set of critical environmental factors that correlate strongly with thermal sensation votes, which were used as auxiliary features in the model. The performance of the proposed dual-phase approach, combining deep learning and machine learning techniques, demonstrated robust effectiveness on both summer and winter datasets. This substantiates the method's practicality for real-world application within vehicle environments.

Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models.

By mimicking the hierarchical structure of human brain, deep spiking neural networks (DSNNs) can extract features from a lower level to a higher level gradually, and improve the performance for the processing of spatio-temporal information. Due to the complex hierarchical structure and implicit nonlinear mechanism, the formulation of spike train level supervised learning methods for DSNNs remains an important problem in this research area. Based on the definition of kernel function and spike trains inner product (STIP) as well as the idea of error backpropagation (BP), this paper firstly proposes a deep supervised learning algorithm for DSNNs named BP-STIP. Furthermore, in order to alleviate the intrinsic weight transport problem of the BP mechanism, feedback alignment (FA) and broadcast alignment (BA) mechanisms are utilized to optimize the error feedback mode of BP-STIP, and two deep supervised learning algorithms named FA-STIP and BA-STIP are also proposed. In the experiments, the effectiveness of the proposed three DSNN algorithms is verified on the MNIST digital image benchmark dataset, and the influence of different kernel functions on the learning performance of DSNNs with different network scales is analyzed. Experimental results show that the FA-STIP and BP-STIP algorithms can achieve 94.73% and 95.65% classification accuracy, which apparently possess better learning performance and stability compared with the benchmark algorithm BP-STIP.

In this study, 45 complete mitochondrial genomes of Sciaenidae species obtained from GenBank were analyzed. Our results suggested that mitochondrial genomes could be an effective marker for resolving the phylogeny of Sciaenidae species. Phylogenetic relationships indicated that Johnius species are more ancient species among all the species that were used in this analysis because Johnius species stands on the root of the polygenetic tree. However, Collichthys niveatus could not separate from Larimichthys polyactis based on the complete mtDNA which indicate that these two species needs further study in the future.

Neuroscience research confirms that the synaptic delays are not constant, but can be modulated. This paper proposes a supervised delay learning algorithm for spiking neurons with temporal encoding, in which both the weight and delay of a synaptic connection can be adjusted to enhance the learning performance. The proposed algorithm firstly defines spike train kernels to transform discrete spike trains during the learning phase into continuous analog signals so that common mathematical operations can be performed on them, and then deduces the supervised learning rules of synaptic weights and delays by gradient descent method. The proposed algorithm is successfully applied to various spike train learning tasks, and the effects of parameters of synaptic delays are analyzed in detail. Experimental results show that the network with dynamic delays achieves higher learning accuracy and less learning epochs than the network with static delays. The delay learning algorithm is further validated on a practical example of an image classification problem. The results again show that it can achieve a good classification performance with a proper receptive field. Therefore, the synaptic delay learning is significant for practical applications and theoretical researches of spiking neural networks.

Neurons are the basic building and computational units of the nervous system, and have complex and diverse spatial geometric structures. By solving the neuronal classification problem, we can further understand the characteristics of neurons and the process of information transmission. This paper presents a neuronal morphology classification approach based on locally cumulative connected deep neural networks, where 43 geometric features were extracted from two different neuron datasets and applied to classify types of neurons. Then, the effects of different parameters of deep learning networks on the performance of neuron classification were analyzed including mini-batch size, number of intermediate layers, and number of building blocks. The accuracy of the approach was also compared with that of the other mainstream machine learning approaches. The experimental results showed that the proposed approach is effective for solving complex neuronal morphology classification problems.

Kangfuxin liquid (KFX), an extract of the American cockroach, has been clinically proven to be effective in various skin damage disorders, but there are no reports on its use in photodamage. We explored the effect of KFX on ultraviolet B (UVB)-induced photodamage and whether its mechanism was related to autophagy. We found that KFX treatment reduced UVB-induced reactive oxygen species production and improved the vitality of cells inhibited by UVB irradiation. The expression of LC3 (A/B), which was inhibited after UVB irradiation, could be rescued by KFX treatment. Furthermore, KFX may upregulate the level of cellular autophagy by regulating the AMPK-mTOR signaling pathway. When the autophagy inhibitor wortmannin was used to inhibit autophagy, the protective effect of KFX on cells was diminished or even disappeared. Our study suggests that KFX may resist UVB-mediated oxidative stress damage of HaCaT through the induction of autophagy. [ABSTRACT FROM AUTHOR]

Photoaging is mainly caused by the exposure of the skin to ultraviolet (UV) radiation. Among them, damage to human dermal fibroblast (HDF) cells caused by ultraviolet A (UVA) is the main cause of skin aging. Researchers have dedicated to identifying natural compounds from plants to fight against UV radiation-induced photoaging. We previously found that extracts from wild chrysanthemum could prevent acute damage and photoaging induced by UV irradiation. As one of the most abundant ingredients in wild chrysanthemum extract, handelin was hypothesized to have the potential to prevent UVA-induced photoaging of skin fibroblast. In the present study, we report the great potential of handelin in combating UVA-induced photoaging of fibroblasts. We firstly demonstrated that handelin was safe for skin fibroblast as high as a concentration of 0.0125 µ M, showing no toxicity on the cells and improved cell viability. Furthermore, handelin can reduce UVA-induced cellular senescence, indicated by a reduced proportion of senescence-associated beta-galactose positive cells and the expression of P21. We then verified that handelin pretreatment markedly attenuated the production of reactive oxygen species (ROS) generation after UVA irradiation. Meanwhile, we found that handelin enhances autophagy after UVA irradiation, and autophagy is involved in the quality control of intracellular proteins after UV-induced damage (partially indirectly via ROS). Therefore, these results suggest that handelin has a very high potential as an effective ingredient against UVA-induced skin aging. Moreover, this provides an important basis for further research on the photoprotective mechanism of handelin. [ABSTRACT FROM AUTHOR]

The feedback alignment provides a biologically plausible learning mechanism, which can directly transmit error signals with a random weight matrix to multiple layers of a neural network. This paper proposes an online supervised learning algorithm based on the feedback alignment mechanism for multilayer spiking neural networks, named Multi-OSLFA, which can support real-time learning for the spatio-temporal pattern of spike trains. The online learning rule is represented by the kernel function of spike trains and adjusts the synaptic weights when the output neuron fires a spike during the running process of spiking neural networks. The Multi-OSLFA algorithm is successfully applied to spike train learning tasks and nonlinear pattern classification problems on two UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms. It shows that the proposed learning algorithm is effective for solving spatio-temporal pattern learning problems. [ABSTRACT FROM AUTHOR]

The ocean fishery is an important part of the marine economy and an important source of human food. The leaders at all levels attach great importance to the development and utilization of ocean fishery resources. Using the regression analysis method, we forecast the main indicators for the development of ocean fishery during the Twelfth Five-Year Plan period. Through the study, it is found that in 2015, the value added of ocean fishery in China is expected to exceed 360 billion yuan, and the output of marine products will exceed 30 million tons. The marine products make stable contribution to the marine economy and the residents' nutrient composition, conducive to maintaining the stable supply of the market and meeting the residents' daily needs for marine products. [ABSTRACT FROM AUTHOR]

Abstract Through reviewing the current international market for traditional Chinese medicine (TCM), this paper identified the internationalization challenges for TCM, including unclear therapeutic material basis and mechanism, difficulty of quality control, low preparation level, registration/policy barriers, and shortage of intellectual property. To deal with these challenges, suggestions were given including: (1) product innovation of TCM (study the TCM by using the methods and means of western medicine; innovate the basic theory of TCM; develop TCM health product); (2) standard innovation of TCM; (3) building big data platform of Chinese medicine (big data platform of TCM preparation; big data platform on the quality of TCM).