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Biometric speech recognition systems are often subject to various spoofing attacks, the most common of which are speech synthesis and speech conversion attacks. These spoofing attacks can cause the biometric speech recognition system to incorrectly accept these spoofing attacks, which can compromise the security of this system. Researchers have made many efforts to address this problem, and the existing studies have used the physical features of speech to identify spoofing attacks. However, recent studies have shown that speech contains a large number of physiological features related to the human face. For example, we can determine the speaker’s gender, age, mouth shape, and other information by voice. Inspired by the above researches, we propose a spoofing attack recognition method based on physiological-physical features fusion. This method involves feature extraction, a densely connected convolutional neural network with squeeze and excitation block (SE-DenseNet), and feature fusion strategies. We first extract physiological features in audio from a pre-trained convolutional network. Then we use SE-DenseNet to extract physical features. Such a dense connection pattern has high parameter efficiency, and squeeze and excitation blocks can enhance the transmission of the feature. Finally, we integrate the two features into the classification network to identify the spoofing attacks. Experimental results on the ASVspoof 2019 data set show that our model is effective for voice spoofing detection. In the logical access scenario, our model improves the tandem decision cost function and equal error rate scores by 5% and 7%, respectively, compared to existing methods. [ABSTRACT FROM AUTHOR]

Abstract Background Golden leaf in autumn is a prominent feature of deciduous tree species like Ginkgo biloba L., a landscape tree widely cultivated worldwide. However, little was known about the molecular mechanisms of leaf yellowing, especially its dynamic regulatory network. Here, we performed a suite of comparative physiological and dynamic transcriptional analyses on the golden-leaf cultivar and the wild type (WT) ginkgo to investigate the underlying mechanisms of leaf yellowing across different seasons. Results In the present study, we used the natural bud mutant cultivar with yellow leaves “Wannianjin” (YL) as materials. Physiological analysis revealed that higher ratios of chlorophyll a to chlorophyll b and carotenoid to chlorophyll b caused the leaf yellowing of YL. On the other hand, dynamic transcriptome analyses showed that genes related to chlorophyll metabolism played key a role in leaf coloration. Genes encoding non-yellow coloring 1 (NYC1), NYC1-like (NOL), and chlorophyllase (CLH) involved in the degradation of chlorophyll were up-regulated in spring. At the summer stage, down-regulated HEMA encoding glutamyl-tRNA reductase functioned in chlorophyll biosynthesis, while CLH involved in chlorophyll degradation was up-regulated, causing a lower chlorophyll accumulation. In carotenoid metabolism, genes encoding zeaxanthin epoxidase (ZEP) and 9-cis-epoxy carotenoid dioxygenase (NCED) showed significantly different expression levels in the WT and YL. Moreover, the weighted gene co-expression network analysis (WGCNA) suggested that the most associated transcriptional factor, which belongs to the AP2/ERF-ERF family, was engaged in regulating pigment metabolism. Furthermore, quantitative experiments validated the above results. Conclusions By comparing the golden-leaf cultivar and the wide type of ginkgo across three seasons, this study not only confirm the vital role of chlorophyll in leaf coloration of YL but also provided new insights into the seasonal transcriptome landscape and co-expression network. Our novel results pinpoint candidate genes for further wet-bench experiments in tree species.

Background: Golden leaf in autumn is a prominent feature of deciduous tree species like Ginkgo biloba L., a landscape tree widely cultivated worldwide. However, little was known about the molecular mechanisms of leaf yellowing, especially its dynamic regulatory network. Here, we performed a suite of comparative physiological and dynamic transcriptional analyses on the golden-leaf cultivar and the wild type (WT) ginkgo to investigate the underlying mechanisms of leaf yellowing across different seasons. Results: In the present study, we used the natural bud mutant cultivar with yellow leaves "Wannianjin" (YL) as materials. Physiological analysis revealed that higher ratios of chlorophyll a to chlorophyll b and carotenoid to chlorophyll b caused the leaf yellowing of YL. On the other hand, dynamic transcriptome analyses showed that genes related to chlorophyll metabolism played key a role in leaf coloration. Genes encoding non-yellow coloring 1 (NYC1), NYC1-like (NOL), and chlorophyllase (CLH) involved in the degradation of chlorophyll were up-regulated in spring. At the summer stage, down-regulated HEMA encoding glutamyl-tRNA reductase functioned in chlorophyll biosynthesis, while CLH involved in chlorophyll degradation was up-regulated, causing a lower chlorophyll accumulation. In carotenoid metabolism, genes encoding zeaxanthin epoxidase (ZEP) and 9-cis-epoxy carotenoid dioxygenase (NCED) showed significantly different expression levels in the WT and YL. Moreover, the weighted gene co-expression network analysis (WGCNA) suggested that the most associated transcriptional factor, which belongs to the AP2/ERF-ERF family, was engaged in regulating pigment metabolism. Furthermore, quantitative experiments validated the above results. Conclusions: By comparing the golden-leaf cultivar and the wide type of ginkgo across three seasons, this study not only confirm the vital role of chlorophyll in leaf coloration of YL but also provided new insights into the seasonal transcriptome landscape and co-expression network. Our novel results pinpoint candidate genes for further wet-bench experiments in tree species. [ABSTRACT FROM AUTHOR]

Background: The effects of seasonal factors on sensitive skin (SS) have been reported intensively, but the mechanisms still remain poorly understood. Objective: To investigate the effects of seasonal factors on SS, by comparing the physiological changes in the healthy skin and different subgroups of SS with seasonal variation. Methods: Through a questionnaire survey, lactic acid sting test, and capsaicin test, qualified subjects were classified into four groups: healthy skin, only lactic acid sting test positive (LAST(+)/CAT(−)), only capsaicin test positive (LAST(−)/CAT(+)), and both positive (LAST(+)/CAT(+)). Skin physiological parameters were measured in winter and summer. Results: A total of 140 subjects completed the study. Significant differences were found in transepidermal water loss (TEWL), skin pH, and cutaneous blood flow (CBF) between winter and summer in the four groups. There were significant differences in stratum corneum hydration (SCH) of the LAST(+)/CAP(−) and LAST(+)/CAP(+) groups, current perception threshold (CPT) at 250 Hz of the LAST(+)/CAT(+) group, and epidermal density of the healthy skin group between the two seasons. Sum of the scores of sting (SSS) showed a close correlation with TEWL, SCH, pH, CPT at 250 Hz, and epidermal density. Sum of the scores of burning (SSB) showed a strong correlation with TEWL, pH, CPT at 250 Hz and 5 Hz, and epidermal density. Conclusions: Seasonal variation influences the skin barrier function of different types of sensitive skin at different levels. We therefore strongly suggest that, with seasonal variation, different treatments will be undertaken for different subgroups of sensitive skin. [ABSTRACT FROM AUTHOR]

Anxiety induction is widely used in the investigations of the mechanism and treatment of state anxiety. State anxiety is accompanied by immediate psychological and physiological responses. However, the existing state anxiety measurement, such as the commonly used state anxiety subscale of the State-Trait Anxiety Inventory, mainly relies on questionnaires with low temporal resolution. This study aims to develop a tracking model of state anxiety with high temporal resolution. To capture the dynamic changes of state anxiety levels, we induced the participants' state anxiety through exposure to aversive pictures or the risk of electric shocks and simultaneously recorded multi-modal data, including dimensional emotion ratings, electrocardiogram, and galvanic skin response. Using the paired self-reported state anxiety levels and multi-modal measures, we trained and validated machine learning models to predict state anxiety based on psychological and physiological features extracted from the multi-modal data. The prediction model achieved a high correlation between the predicted and self-reported state anxiety levels. This quantitative model provides fine-grained and sensitive measures of state anxiety levels for future affective brain-computer interaction and anxiety modulation studies.

Affect recognition is an interdisciplinary research field bringing together researchers from natural and social sciences. Affect recognition research aims to detect the affective state of a person based on observables, with the goal to, for example, provide reasoning for the person’s decision making or to support mental wellbeing (e.g., stress monitoring). Recently, beside of approaches based on audio, visual or text information, solutions relying on wearable sensors as observables, recording mainly physiological and inertial parameters, have received increasing attention. Wearable systems enable an ideal platform for long-term affect recognition applications due to their rich functionality and form factor, while providing valuable insights during everyday life through integrated sensors. However, existing literature surveys lack a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods and best practices of wearable affect and stress recognition. Following a summary of different psychological models, we detail the influence of affective states on the human physiology and the sensors commonly employed to measure physiological changes. Then, we outline lab protocols eliciting affective states and provide guidelines for ground truth generation in field studies. We also describe the standard data processing chain and review common approaches related to the preprocessing, feature extraction and classification steps. By providing a comprehensive summary of the state-of-the-art and guidelines to various aspects, we would like to enable other researchers in the field to conduct and evaluate user studies and develop wearable systems.

The concept of biometric identification technology (BIT) was described, and several typical BITs were introduced such as identification technologies for hand, face, physiological features and behavioral trait. The application of BIT was analyzed in military field of foreign countries and China. It's pointed out BIT's future involved in bioassay, integration with wireless video identification technique and etc, comprehensive identification technique with high throughput, high precision and multi mechanism as well as enhanced safety. [ABSTRACT FROM AUTHOR]

Physiology characteristics and agronomic trait of peanut during its growing season were studied in both lime concretion black soil and sandy loam soil. Results showed that the chlorophyll content of leaf, SOD, POD and CAT activities, soluble protein content and leaf area index (LAI) in peanut growing season presented single -peak curves. The peak values appeared during 60 to 75 days after seedling emergence. Chlorophyll, soluble protein contents and LAI of peanut in black soil were higher than those in sandy soil. The anti - aging enzymes activities and MDA content of peanut in black soil were lower. The peak value and duration of peak value of LAI and dry matter accumulation rate of peanut in black soil were significantly higher and longer than those in sandy soil. The height of main stem, length of lateral branch, number of pods and other main agronomic characteristics of peanut in black soil were significantly better than that of peanut in sandy soil ( p < 0. 05 ) . Biological yield and pod yield of peanut in black soil were 30% and 42.9% higher compared to that of peanut in sandy soil. [ABSTRACT FROM AUTHOR]

This study concerns security issues of the emerging Wireless Body Sensor Network (WBSN) formed by biomedical sensors worn on or implanted in the human body for mobile healthcare applications. A novel authenticated symmetric-key establishment scheme is proposed for WBSN, which fully exploits the physiological features obtained by network entities via the body channel available in WBSN but not other wireless networks. The self-defined Intrinsic Shared Secret (ISS) is used to replace the pre-deployment of secrets among network entities, which thus eliminates centralized services or authorities essential in existing protocols, and resolves the key transport problem in the pure symmetric-key cryptosystem for WBSN as well. The security properties of the proposed scheme are demonstrated in terms of its attack complexity and the types of attacks it can resist. Besides, the scheme can be implemented under a light-weight way in WBSN systems. Due to the importance of the ISS concept, the analysis on using false acceptance/false rejection method to evaluate the performance of ISS for its usage in the scheme is also demonstrated. [ABSTRACT FROM AUTHOR]