Expression Patterns and Molecular Mechanisms Regulating Drought Tolerance of Soybean [Glycine max (L.) Merr.] Conferred by Transcription Factor Gene GmNAC19.

NAC transcription factors are commonly involved in the plant response to drought stress. A transcriptome analysis of root samples of the soybean variety 'Jiyu47' under drought stress revealed the evidently up-regulated expression of GmNAC19, consistent with the expression pattern revealed by quantitative real-time PCR analysis. The overexpression of GmNAC19 enhanced drought tolerance in Saccharomyces cerevisiae INVSc1. The seed germination percentage and root growth of transgenic Arabidopsis thaliana were improved in comparison with those of the wild type, while the transgenic soybean composite line showed improved chlorophyll content. The altered contents of physiological and biochemical indices (i.e., soluble protein, soluble sugar, proline, and malondialdehyde) related to drought stress and the activities of three antioxidant enzymes (i.e., superoxide dismutase, peroxidase, and catalase) revealed enhanced drought tolerance in both transgenic Arabidopsis and soybean. The expressions of three genes (i.e., P5CS, OAT, and P5CR) involved in proline synthesis were decreased in the transgenic soybean hairy roots, while the expression of ProDH involved in the breakdown of proline was increased. This study revealed the molecular mechanisms underlying drought tolerance enhanced by GmNAC19 via regulation of the contents of soluble protein and soluble sugar and the activities of antioxidant enzymes, providing a candidate gene for the molecular breeding of drought-tolerant crop plants.

Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning.

One of the primary challenges in Electroencephalogram (EEG) emotion recognition lies in developing models that can effectively generalize to new unseen subjects, considering the significant variability in EEG signals across individuals. To address the issue of subject-specific features, a suitable approach is to employ projection dictionary learning, which enables the identification of emotion-relevant features across different subjects. To accomplish the objective of pattern representation and discrimination for subject-independent EEG emotion recognition, we utilized the fast and efficient projection dictionary pair learning (PDPL) technique. PDPL involves the joint use of a synthesis dictionary and an analysis dictionary to enhance the representation of features. Additionally, to optimize the parameters of PDPL, which depend on experience, we applied the genetic algorithm (GA) to obtain the optimal solution for the model. We validated the effectiveness of our algorithm using leave-one-subject-out cross validation on three EEG emotion databases: SEED, MPED, and GAMEEMO. Our approach outperformed traditional machine learning methods, achieving an average accuracy of 69.89% on the SEED database, 24.11% on the MPED database, 64.34% for the two-class GAMEEMO, and 49.01% for the four-class GAMEEMO. These results highlight the potential of subject-independent EEG emotion recognition algorithms in the development of intelligent systems capable of recognizing and responding to human emotions in real-world scenarios.

EEG-Based Parkinson's Disease Recognition via Attention-Based Sparse Graph Convolutional Neural Network.

Parkinson's disease (PD) is a complicated neurological ailment that affects both the physical and mental wellness of elderly individuals which makes it problematic to diagnose in its initial stages. Electroencephalogram (EEG) promises to be an efficient and cost-effective method for promptly detecting cognitive impairment in PD. Nevertheless, prevailing diagnostic practices utilizing EEG features have failed to examine the functional connectivity among EEG channels and the response of associated brain areas causing an unsatisfactory level of precision. Here, we construct an attention-based sparse graph convolutional neural network (ASGCNN) for diagnosing PD. Our ASGCNN model uses a graph structure to represent channel relationships, the attention mechanism for selecting channels, and the L1 norm to capture channel sparsity. We conduct extensive experiments on the publicly available PD auditory oddball dataset, which consists of 24 PD patients (under ON/OFF drug status) and 24 matched controls, to validate the effectiveness of our method. Our results show that the proposed method provides better results compared to the publicly available baselines. The achieved scores for Recall, Precision, F1-score, Accuracy and Kappa measures are 90.36%, 88.43%, 88.41%, 87.67%, and 75.24%, respectively. Our study reveals that the frontal and temporal lobes show significant differences between PD patients and healthy individuals. In addition, EEG features extracted by ASGCNN demonstrate significant asymmetry in the frontal lobe among PD patients. These findings can offer a basis for the establishment of a clinical system for intelligent diagnosis of PD by using auditory cognitive impairment features.

EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search.

Objective. Major depressive disorder (MDD) is a prevalent psychiatric disorder whose diagnosis relies on experienced psychiatrists, resulting in a low diagnosis rate. As a typical physiological signal, electroencephalography (EEG) has indicated a strong association with human beings' mental activities and can be served as an objective biomarker for diagnosing MDD.Approach. The basic idea of the proposed method fully considers all the channel information in EEG-based MDD recognition and designs a stochastic search algorithm to select the best discriminative features for describing the individual channels.Main results. To evaluate the proposed method, we conducted extensive experiments on the MODMA dataset (including dot-probe tasks and resting state), a 128-electrode public EEG-based MDD dataset including 24 patients with depressive disorder and 29 healthy controls. Under the leave-one-subject-out cross-validation protocol, the proposed method achieved an average accuracy of 99.53% in the fear-neutral face pairs cued experiment and 99.32% in the resting state, outperforming state-of-the-art MDD recognition methods. Moreover, our experimental results also indicated that negative emotional stimuli could induce depressive states, and high-frequency EEG features contributed significantly to distinguishing between normal and depressive patients, which can be served as a marker for MDD recognition.Significance. The proposed method provided a possible solution to an intelligent diagnosis of MDD and can be used to develop a computer-aided diagnostic tool to aid clinicians in early diagnosis for clinical purposes.

Progressive distribution adapted neural networks for cross-corpus speech emotion recognition.

In this paper, we investigate a challenging but interesting task in the research of speech emotion recognition (SER), i.e., cross-corpus SER. Unlike the conventional SER, the training (source) and testing (target) samples in cross-corpus SER come from different speech corpora, which results in a feature distribution mismatch between them. Hence, the performance of most existing SER methods may sharply decrease. To cope with this problem, we propose a simple yet effective deep transfer learning method called progressive distribution adapted neural networks (PDAN). PDAN employs convolutional neural networks (CNN) as the backbone and the speech spectrum as the inputs to achieve an end-to-end learning framework. More importantly, its basic idea for solving cross-corpus SER is very straightforward, i.e., enhancing the backbone's corpus invariant feature learning ability by incorporating a progressive distribution adapted regularization term into the original loss function to guide the network training. To evaluate the proposed PDAN, extensive cross-corpus SER experiments on speech emotion corpora including EmoDB, eNTERFACE, and CASIA are conducted. Experimental results showed that the proposed PDAN outperforms most well-performing deep and subspace transfer learning methods in dealing with the cross-corpus SER tasks.

Adapting Multiple Distributions for Bridging Emotions from Different Speech Corpora.

In this paper, we focus on a challenging, but interesting, task in speech emotion recognition (SER), i.e., cross-corpus SER. Unlike conventional SER, a feature distribution mismatch may exist between the labeled source (training) and target (testing) speech samples in cross-corpus SER because they come from different speech emotion corpora, which degrades the performance of most well-performing SER methods. To address this issue, we propose a novel transfer subspace learning method called multiple distribution-adapted regression (MDAR) to bridge the gap between speech samples from different corpora. Specifically, MDAR aims to learn a projection matrix to build the relationship between the source speech features and emotion labels. A novel regularization term called multiple distribution adaption (MDA), consisting of a marginal and two conditional distribution-adapted operations, is designed to collaboratively enable such a discriminative projection matrix to be applicable to the target speech samples, regardless of speech corpus variance. Consequently, by resorting to the learned projection matrix, we are able to predict the emotion labels of target speech samples when only the source label information is given. To evaluate the proposed MDAR method, extensive cross-corpus SER tasks based on three different speech emotion corpora, i.e., EmoDB, eNTERFACE, and CASIA, were designed. Experimental results showed that the proposed MDAR outperformed most recent state-of-the-art transfer subspace learning methods and even performed better than several well-performing deep transfer learning methods in dealing with cross-corpus SER tasks.

E2ENNet: An end-to-end neural network for emotional brain-computer interface.

Objectve: Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet. Methods: Baseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions. Results: Extensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset. Conclusion: Experimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals. Significance: This study provides a methodology for implementing a plug-and-play emotional brain-computer interface system.

An End-to-End Depression Recognition Method Based on EEGNet.

Major depressive disorder (MDD) is a common and highly debilitating condition that threatens the health of millions of people. However, current diagnosis of depression relies on questionnaires that are highly correlated with physician experience and hence not completely objective. Electroencephalography (EEG) signals combined with deep learning techniques may be an objective approach to effective diagnosis of MDD. This study proposes an end-to-end deep learning framework for MDD diagnosis based on EEG signals. We used EEG signals from 29 healthy subjects and 24 patients with severe depression to calculate Accuracy, Precision, Recall, F1-Score, and Kappa coefficient, which were 90.98%, 91.27%, 90.59%, and 81.68%, respectively. In addition, we found that these values were highest when happy-neutral face pairs were used as stimuli for detecting depression. Compared with exiting methods for EEG-based MDD classification, ours can maintain stable model performance without re-calibration. The present results suggest that the method is highly accurate for diagnosis of MDD and can be used to develop an automatic plug-and-play EEG-based system for diagnosing depression.

Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network.

The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals. First, we use filtering and Euclidean alignment for data preprocessing. In the feature extraction, we use short-time Fourier transform and Hilbert-Huang transform to extract time-frequency features, and convolutional neural networks to extract spatial features. Finally, bi-directional long short-term memory explored the timing relationship. Before performing the convolution operation, according to the unique topology of the EEG channel, the EEG features are converted into 3D tensors. This study has achieved good results on two emotion databases: SEED and Emotional BCI of 2020 WORLD ROBOT COMPETITION. We applied this method to the recognition of depression based on EEG and achieved a recognition rate of more than 70% under the five-fold cross-validation. In addition, the subject-independent protocol on SEED data has achieved a state-of-the-art recognition rate, which exceeds the existing research methods. We propose a novel EEG emotion recognition framework for depression detection, which provides a robust algorithm for real-time clinical depression detection based on EEG.

Temporal Distinction between Male and Female Floral Organ Development in Nicotiana tabacum cv. Xanthi (Solanaceae).

Early floral developmental investigations provide crucial evidence for phylogenetic and molecular studies of plants. The developmental and evolutionary mechanisms underlying the variations in floral organs are critical for a thorough understanding of the diversification of flowers. Ontogenetic comparisons between anthers and pistil within single flowers were characterized over time in Nicotiana tabacum cv. Xanthi. The ages of 42 tobacco flower or flower primordia were estimated using corolla growth analysis. Results showed that the protodermal layer in carpel primordia contributes to carpel development by both anticlinal and periclinal divisions. Periclinal divisions in the hypodermal layer of the placenta were observed around 4.8 ± 1.3 days after the formation of early carpel primordia (ECP) and ovule initiation occurred 10.0 ± 0.5 days after ECP. Meiosis in anthers and ovules began about 8.9 ± 1.1 days and 14.4 ± 1.3 days after ECP, respectively. Results showed an evident temporal distinction between megasporogenesis and microsporogenesis. Flower ages spanned a 17-day interval, starting with flower primordia containing the ECP and anther primordia to the tetrad stage of meiosis in megasporocytes and the bicellular stage in pollen grains. These results establish a solid foundation for future studies in order to identify the developmental and molecular mechanisms responsible for the mating system in tobacco.

Floral Organogenesis in Three Members of the Tribe Delphinieae (Ranunculaceae).

Three species (Aconitum taipeicum, Delphinium giraldii, and Consolida ajacis) of the tribe Delphinieae (Ranunculaceae) were examined using scanning electron microscopy and histological methods. The results showed that members of Delphinieae differ from their polysymmetrical relatives by four unique features: (1) a spiral phyllotaxis of their perianth and stamens, and a series of carpels, which initiated superficially in a whorl-liked arrangement; (2) sepal 2 being the largest one among the five sepals and becoming helmet-shaped or having a spur; (3) petals 2 and 5 initiated adaxially of sepal 2 and also becoming spurred; and (4) the monosymmetry of the first flower becoming established when sepal 2 becomes the largest. Major differences among the species include the timing of development of the second series; the fusion of two petals into a single one in C. ajacis; and, during early developmental stages, the two young spurred petals giving rise to a stalk and two bulges in A. taipeicum, a single bulge in D. giraldii, or an arch blade in C. ajacis. The unequal growth of the perianth, together with the reduction and the rearrangement of the carpels, are critical in inducing the symmetrical transformation of the flowers.

Electrophysiological and Neurochemical Considerations of Distinct Neuronal Populations in the Rat Pedunculopontine Nucleus and Their Responsiveness Following 6-Hydroxydopamine Lesions.

The pedunculopontine nucleus (PPN) is composed of a morphologically and neurochemically heterogeneous population of neurons, which is severely affected by Parkinson's disease (PD). However, the role of each subtype of neurons within the PPN in the pathophysiology of PD has not been completely elucidated. In this study, we present the discharge profiles of three classified subtypes of PPN neurons and their alterations after 6-hydroxydopamine (6-OHDA) lesion. Following 6-OHDA lesion, the spike timing of the Type II (GABAergic) and Type III (glutamatergic) neurons had phase-lock with the oscillations in the delta and beta band frequency range in the PPN, respectively. Morphological evidence has shown distinct alteration in three kinds of neurons after 6-OHDA lesion. These findings revealed that the changes in the firing characteristics of neurons in PPN in hemi-parkinsonism rats are closely associated with damaged neuronal morphology, which would make contributions to the divergence of dysfunctions in Parkinsonism.

Altered Local Field Potential Relationship Between the Parafascicular Thalamic Nucleus and Dorsal Striatum in Hemiparkinsonian Rats.

The thalamostriatal pathway is implicated in Parkinson's disease (PD); however, PD-related changes in the relationship between oscillatory activity in the centromedian-parafascicular complex (CM/Pf, or the Pf in rodents) and the dorsal striatum (DS) remain unclear. Therefore, we simultaneously recorded local field potentials (LFPs) in both the Pf and DS of hemiparkinsonian and control rats during epochs of rest or treadmill walking. The dopamine-lesioned rats showed increased LFP power in the beta band (12 Hz-35 Hz) in the Pf and DS during both epochs, but decreased LFP power in the delta (0.5 Hz-3 Hz) band in the Pf during rest epochs and in the DS during both epochs, compared to control rats. In addition, exaggerated low gamma (35 Hz-70 Hz) oscillations after dopamine loss were restricted to the Pf regardless of the behavioral state. Furthermore, enhanced synchronization of LFP oscillations was found between the Pf and DS after the dopamine lesion. Significant increases occurred in the mean coherence in both theta (3 Hz-7 Hz) and beta bands, and a significant increase was also noted in the phase coherence in the beta band between the Pf and DS during rest epochs. During the treadmill walking epochs, significant increases were found in both the alpha (7 Hz-12 Hz) and beta bands for two coherence measures. Collectively, dramatic changes in the relative LFP power and coherence in the thalamostriatal pathway may underlie the dysfunction of the basal ganglia-thalamocortical network circuits in PD, contributing to some of the motor and non-motor symptoms of the disease.

Genetic-based feature selection for efficient motion imaging of a brain-computer interface framework.

OBJECTIVE: A brain-computer interface (BCI) equips humans with the ability to control computers and technical devices mentally. However, the enormous data and the existing irrelevant features of the electrocorticogram signal limit the performance of the classifier. To address these problems, a novel signal processing framework for a binary motor imagery-based BCI system (MI-BCI) is proposed in this paper. APPROACH: Stockwell transform and Bayesian linear discriminant analysis were applied to feature extraction and classification, respectively, and a genetic algorithm (GA) was used in the process of feature selection to extract the most relevant features for classification. The superiority of the algorithm is demonstrated through test results based on the BCI Competition III dataset I. MAIN RESULTS: By comparing the processes with or without feature selection, the performance of the classification was proven to improve using the GA. By adjusting the parameters of the GA, the best feature set (selected 48.6% features) was selected to achieve classification sensitivity, specificity, precision, and accuracy of 94%, 98%, 97.9%, and 96%, respectively, exceeding the results of the existing state-of-the art algorithms. SIGNIFICANCE: As the proposed method can reduce the number of features and select the best feature set, its classification performance was improved and the classification time was shortened; thus, it can be applied to various BCI systems.

Effects of Salt-Controlled Self-Assembly of Triblock Copolymers F68 on Interaction Forces between Oil Drops in Aqueous Solution.

Nonionic triblock copolymers, surfactant Pluronic F68 (PEO76-PPO29-PEO76), are widely used in industrial processes, such as foaming, emulsification, and stabilization. The behaviors of triblock copolymers such as the salt-dependent self-assembly in bulk solution and the irreversible adsorption at the oil/water interface are mainly focused to explore their effects on the interaction forces between nano-spacing interfaces of oil droplets. In this study, the atomic force microscopy (AFM) technique was employed to measure the drop interaction forces with different F68 bulk concentrations. All selected bulk concentrations (≥100 µM) of copolymers can ensure the formation of a stable layer structure of stretched polymer chains ("brush") at the oil/water interface, which behaved as a mechanical barrier at the interface. This study quantified the forces caused by the space hindrance of F68 copolymers both in the bulk phase and at the interface of oil/F68 aqueous solution during drop interaction. The effects of monovalent electrolyte (NaCl)-induced self-assembly behavior of triblock copolymers F68 in bulk solution on drop interaction forces were measured through the AFM technique.

The mating system of the Sichuan snub-nosed monkey (Rhinopithecus roxellana).

This article reports the first genetic study of the mating system of the Sichuan snub-nosed monkey (Rhinopithecus roxellana), an endemic and endangered species in China. The investigation was carried out in a population (WRT) in the Qinling Mountains using data from both field observation and paternity analysis through microsatellite DNA profiling. During a mating season, a male on an average copulated with 5.7 females. Approximately 18% of the females were observed to copulate with more than one male over the study period. The majority of copulations (94.5%) were initiated by females. Twenty-eight of 430 observed matings were extra-unit copulations. Eight polymorphic microsatellite loci were used for paternity analysis. The number of alleles at each locus ranged from 3 to 7 (mean=4.3). Observed heterozygosity ranged between 0.32 and 0.79. None of the loci showed significant deviation from Hardy-Weinberg equilibrium. Results from paternity exclusion showed that 12 of 21 (57.1%) immature individuals were sired by extra-unit males. Although the basic social unit of snub-nosed monkeys is consistent with a polygynous mating system, both field observation and genetic data suggests that their mating system is polygamous. Infanticide and inbreeding avoidance are the most likely explanations for the promiscuity of female snub-nosed monkeys.