Can nonvolatile tastants be smelled during food oral processing?

While accumulating evidence implied the involvement of retro-nasal sensation in the consumption of nonvolatile taste compounds, it is still unclear whether it was caused by the taste compounds themselves, and if so, how can they migrate from the oral to nasal cavity. At first, we proposed aerosol particles as an alternative oral-nasal mass transfer mechanism. The high-speed camera approved that aerosol particles could be generated by the typical oral and pharynx actions during food oral processing; while the narrow-band imaging of nasal cleft and mass spectrometry of nostril-exhaled air approved the migration of aerosol within the oral-nasal route. Then, the "smelling" of taste compounds within the aerosol particles was testified. The four-alternative forced choices (4AFC) approved that the potential volatile residues or contaminants within the headspace air of pure taste solution cannot arouse significant smell, while the taste compounds embedded in the in vitro prepared aerosol particles can be "smelled" via the ortho route. The "smell" of sucrose is very different from its taste and the "smell" of quinine, implying its actual olfaction. The sweetness intensity of sucrose solution was also reduced when the volunteers' noses were clipped, indicating the involvement of retro-nasal sensation during its drinking. At last, the efficiency of aerosol as a mechanism of oral-nasal mass transfer was demonstrated to be comparable with the volatile molecules under the experimental condition, giving it the potential to be a substantial and unique source of retro-nasal sensation during food oral processing.

Identification of the regulatory network and potential markers for type 2 diabetes mellitus related to internal exposure to metals in Chinese adults.

People intake metals from their environment. This study investigated type 2 diabetes mellitus (T2DM) related to internal exposure to metals and attempted to identify possible biomarkers. A total of 734 Chinese adults were enrolled, and urinary levels of ten metals were measured. Multinomial logistic regression model was used to assess the association between metals and impaired fasting glucose (IFG) and T2DM. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction were used to explore the pathogenesis of T2DM related to metals. After adjustment, lead (Pb) was positively associated with IFG (odds ratio [OR] 1.31, 95% confidence interval [CI] 1.06-1.61) and T2DM (OR 1.41, 95% CI 1.01-1.98), but cobalt was negatively associated with IFG (OR 0.57, 95% CI 0.34-0.95). Transcriptome analysis showed 69 target genes involved in the Pb-target network of T2DM. GO enrichment indicated that the target genes are enriched mainly in the biological process category. KEGG enrichment indicated that Pb exposure leads to non-alcoholic fatty liver disease, lipid and atherosclerosis, and insulin resistance. Moreover, there is alteration of four key pathways, and six algorithms were used to identify 12 possible genes in T2DM related to Pb. SOD2 and ICAM1 show strong similarity in expression, suggesting a functional correlation between these key genes. This study reveals that SOD2 and ICAM1 may be potential targets of Pb exposure-induced T2DM and provides novel insight into the biological effects and underlying mechanism of T2DM related to internal exposure to metals in the Chinese population.

A method to determine the protection zone of chemical industrial park considering air quality, health risk and environmental risk: a case study.

In China, chemical enterprises are required to cluster into a large number of chemical industrial parks (CIPs), which increase risks and threats to the environment and human being's health due to aggregation of the complicated chemical process and huge unit scale. Setting a scientific and reasonable protection zone around CIP is a very efficient way to protect surrounding people's health. A method was designed to determine the comprehensive protection zone of CIP, taking into account multiple factors: air quality, health risk and environmental risk. By establishing a comprehensive and multi-levels index system, the protection zone and the corresponding environmental risk management countermeasures can be proposed hierarchically, which are very important to the development and environmental risk management of CIP. A CIP located in coastal area of Shandong Province was studied, and it is turned out that the method to determine the protection zone of chemical industrial park considering air quality, health risk and environmental risk has great advantages compared with other methods.

ST-Phys: Unsupervised Spatio-Temporal Contrastive Remote Physiological Measurement.

Remote photoplethysmography (rPPG) is a non-contact method that employs facial videos for measuring physiological parameters. Existing rPPG methods have achieved remarkable performance. However, the success mainly profits from supervised learning over massive labeled data. On the other hand, existing unsupervised rPPG methods fail to fully utilize spatio-temporal features and encounter challenges in low-light or noise environments. To address these problems, we propose an unsupervised contrast learning approach, ST-Phys. We incorporate a low-light enhancement module, a temporal dilated module, and a spatial enhanced module to better deal with long-term dependencies under the random low-light conditions. In addition, we design a circular margin loss, wherein rPPG signals originating from identical videos are attracted, while those from distinct videos are repelled. Our method is assessed on six openly accessible datasets, including RGB and NIR videos. Extensive experiments reveal the superior performance of our proposed ST-Phys over state-of-the-art unsupervised rPPG methods. Moreover, it offers advantages in parameter reduction and noise robustness.

Model Behavior Preserving for Class-Incremental Learning.

Deep models have shown to be vulnerable to catastrophic forgetting, a phenomenon that the recognition performance on old data degrades when a pre-trained model is fine-tuned on new data. Knowledge distillation (KD) is a popular incremental approach to alleviate catastrophic forgetting. However, it usually fixes the absolute values of neural responses for isolated historical instances, without considering the intrinsic structure of the responses by a convolutional neural network (CNN) model. To overcome this limitation, we recognize the importance of the global property of the whole instance set and treat it as a behavior characteristic of a CNN model relevant to model incremental learning. On this basis: 1) we design an instance neighborhood-preserving (INP) loss to maintain the order of pair-wise instance similarities of the old model in the feature space; 2) we devise a label priority-preserving (LPP) loss to preserve the label ranking lists within instance-wise label probability vectors in the output space; and 3) we introduce an efficient derivable ranking algorithm for calculating the two loss functions. Extensive experiments conducted on CIFAR100 and ImageNet show that our approach achieves the state-of-the-art performance.

Brain Cognition-Inspired Dual-Pathway CNN Architecture for Image Classification.

Inspired by the global-local information processing mechanism in the human visual system, we propose a novel convolutional neural network (CNN) architecture named cognition-inspired network (CogNet) that consists of a global pathway, a local pathway, and a top-down modulator. We first use a common CNN block to form the local pathway that aims to extract fine local features of the input image. Then, we use a transformer encoder to form the global pathway to capture global structural and contextual information among local parts in the input image. Finally, we construct the learnable top-down modulator where fine local features of the local pathway are modulated by global representations of the global pathway. For ease of use, we encapsulate the dual-pathway computation and modulation process into a building block, called the global-local block (GL block), and a CogNet of any depth can be constructed by stacking a necessary number of GL blocks one after another. Extensive experimental evaluations have revealed that the proposed CogNets have achieved the state-of-the-art performance accuracies on all the six benchmark datasets and are very effective for overcoming the "texture bias" and the "semantic confusion" problems faced by many CNN models.

Association of using biomass fuel for cooking with depression and anxiety symptoms in older Chinese adults.

BACKGROUNDS: Household air pollution exposure is a crucial public concern and have the potential to seriously affect human health. Using biomass fuels for cooking is the main contributor to household air pollution. However, current evidence linked between cooking with biomass fuels and mental health remains limited. OBJECTIVES: To explore whether cooking with biomass fuels is associated with depression and anxiety symptoms among older adults in China. METHODS: We obtained data from Chinese Longitudinal Healthy Longevity Survey (CLHLS). Depressive and anxiety symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10) and the 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively. Primary cooking fuel type was self-reported. We used logistic regression and linear regression to evaluate the effects of cooking with biomass fuels on depression and anxiety. RESULTS: A total of 13,361 participants aged 65 years and older (mean age, 84.2 ± 11.5 years) were included in the presented study. A positive association was found between cooking with biomass fuels and both depression symptoms (adjusted odds ratio 1.23, 95% CI 1.03 to 1.47) and anxiety symptoms (adjusted odds ratio 1.31, 95% CI 1.02 to 1.68). Biomass fuel users had a higher depression scores (0.33, 95% CI 0.03 to 0.61) and a higher anxiety scores (0.20, 95% CI 0.02 to 0.38) compared to clean fuel users. We found no significant interactions between participant characteristics and biomass fuel use on either depression or anxiety symptoms. CONCLUSIONS: Cooking with biomass fuels was associated with depression and anxiety symptoms in order adults. Further large prospective cohort studies are warranted to confirm this association.

Temporal Hierarchical Dictionary Guided Decoding for Online Gesture Segmentation and Recognition.

Online segmentation and recognition of skeleton- based gestures are challenging. Compared with offline cases, the inference of online settings can only rely on the current few frames and always completes before whole temporal movements are performed. However, incompletely performed gestures are ambiguous and their early recognition is easy to fall into local optimum. In this work, we address the problem with a temporal hierarchical dictionary to guide the hidden Markov model (HMM) decoding procedure. The intuition is that, gestures are ambiguous with high uncertainty at early performing phases, and only become discriminate after certain phases. This uncertainty naturally can be measured by entropy. Thus, we propose a measurement called "relative entropy map" (REM) to encode this temporal context to guide HMM decoding. Furthermore, we introduce a progressive learning strategy with which neural networks could learn a robust recognition of HMM states in an iterative manner. The performance of our method is intensively evaluated on three challenging databases and achieves state-of-the-art results. Our method shows the abilities of both extracting the discriminate connotations and reducing large redundancy in the HMM transition process. It is verified that our framework can achieve online recognition of continuous gesture streams even when they are halfway performed.

Hallucinating Face Image by Regularization Models in High-Resolution Feature Space.

In this paper, we propose two novel regularization models in patch-wise and pixel-wise respectively, which are efficient to reconstruct high-resolution (HR) face image from low-resolution (LR) input. Unlike the conventional patch-based models which depend on the assumption of local geometry consistency in LR and HR spaces, the proposed method directly regularizes the relationship between the target patch and corresponding training set in the HR space. It avoids to deal with the tough problem of preserving local geometry in various resolutions. Taking advantage of kernel function in efficiently describing intrinsic features, we further conduct the patch-based reconstruction model in the high-dimensional kernel space for capturing nonlinear characteristics. Meanwhile, a pixel-based model is proposed to regularize the relationship of pixels in the local neighborhood, which can be employed to enhance the fuzzy details in the target HR face image. It privileges the reconstruction of pixels along the dominant orientation of structure, which is useful for preserving high-frequency information on complex edges. Finally, we combine the two reconstruction models into a unified framework. The output HR face image can be finally optimized by performing an iterative procedure. Experimental results demonstrate that the proposed face hallucination method produces superior performance than the state-of-the-art methods.

Domain Regeneration for Cross-Database Micro-Expression Recognition.

Recently, micro-expression recognition has attracted lots of researchers' attention due to its potential value in many practical applications, e.g., lie detection. In this paper, we investigate an interesting and challenging problem in micro-expression recognition, i.e., cross-database micro-expression recognition, in which the training and testing samples come from different micro-expression databases. Under this problem setting, the consistent feature distribution between the training and testing samples originally existing in conventional micro-expression recognition would be seriously broken and hence the performance of most current well-performing micro-expression recognition methods may sharply drop. In order to overcome it, we propose a simple yet effective framework called Domain Regeneration (DR) in this paper. DR framework aims at learning a domain regenerator to regenerate the micro-expression samples from source and target databases respectively such that they can abide by the same or similar feature distributions. Thus, we are able to use the classifier learned based on the labeled source micro-expression samples to predict the label information of the unlabeled target micro-expression samples. To evaluate the proposed DR framework, we conduct extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases. Experimental results show that compared with recent state-of-the-art cross-database emotion recognition methods, the proposed DR framework has more promising performance.

Characterization of the interaction between surfactants and enzymes by fluorescence probe.

In order to investigate the mechanism of the different stimulatory effects of the biosurfactant rhamnolipid and the chemical surfactant Tween 80 on enzymatic hydrolysis of lignocellulose, the interaction between surfactants and enzymes was analyzed by the fluorescence probe method using pyrene as probe. Based on the evolution law of pyrene fluorescence spectroscopy in the "surfactants-enzymes" systems, the interaction relationship between surfactants and enzymes was analyzed and discussed in this paper. The results show that enzyme molecules bind with rhamnolipid molecules, participate in the formation of rhamnolipid micelles, and increase the inner hydrophobic polarity of micelles, but do not change the properties of rhamnolipid micelles above the CMC (Critical Micelle Concentration). Nevertheless, for Tween 80, enzyme molecules also participate in the forming of micelles, however, they exhibit a stronger interaction with enzymes above the CMC. Both rhamnolipid and Tween 80 bind more strongly with xylanase than cellulase. Considering also previous experimental results, it can be concluded that the interaction between surfactants and enzymes improve enzyme stability and activity, and, therefore, the efficiency of enzymatic hydrolysis of lignocellulose is enhanced. The findings further provide theoretical knowledge about the mechanism of the stimulative effects of surfactants on enzymatic hydrolysis of lignocellulose.

[Application of biosurfactant in composting of agricultural waste].

The biosurfactant of rhamonolipid (Rh) obtained from Pseudomonas aeruginosa was used in co-composting of rice straw and wheat bran, the efficiency of the promoter was studied under temperature control during the first stage of composting. Composting was done in an aerobic static bed of composting during which moisture content of mixture materials was controlled between 60% 70%, and the control of aeration adopted the timer by turns of 0.18m3/h for 20 minutes and pause for 40 minutes. Changes were studied including pH, organic matter, dissolved organic carbon (DOC), the biomass of microorganism, hemicellulase activity, carboxymethyl cellulose enzyme (CMCase) activity, hemi-cellulose and cellulose contents. The experimental results show that the degradation rate of organic matter is 13.4% higher than that of the control, and the average of DOC is improved by 2.2g/kg. The degradation contents of hemi-cellulose and cellulose are increased by 5.7%, 10.7% compared with the control. This indicate that the addition of Rh can improve microenvironment, strengthen polymers hydration, accelerate the co-composting process and improve the quality of composting production.