Objective To assess the influence of infant rearing on the behavior of depressed adult female Macaca fascicularis and the influence of depressed infant-rearing adult female Macaca fascicularis on their infants in a free enclosure environment. Methods Here, 20 depressed subjects and then 20 healthy subjects were randomly selected from a total population of 1007 adult female Macaca fascicularis subjects. Four depressed subjects and eight healthy subjects were rearing infants. By focal observation, three trained observers video-recorded the selected subjects over a total observational period of 560 hours. The video footage was analyzed by qualified blinded analysts that coded the raw footage into quantitative behavioral data (i.e., durations of 53 pre-defined behavioral items across 12 behavioral categories) for statistical analysis. Results Between infant-rearing and non-rearing healthy subjects, ten differential behaviors distributed across five behavioral categories were identified. Between infant-rearing and non-rearing depressed subjects, nine behaviors distributed across five behavioral categories were identified. Between infant-rearing healthy and infant-rearing depressed subjects, fifteen behaviors distributed across six behavioral categories were identified. Conclusion Infant-rearing depressed adult female Macaca fascicularis subjects may have a worse psychological status as compared to non-rearing depressed counterparts. Infant rearing may negatively influence depressed Macaca fascicularis mothers. Infant-rearing depressed subjects were less adequate at raising infants as compared to infant-rearing healthy subjects. Thus, maternal depression in this macaque species may negatively impact infatile development, which is consistent with previous findings in humans.
The method of ICP-OES for the direct determination of high content of rubidium in rubidium chloride solutions was studied through mass dilution method and optimizing parameters of the instrument in the present paper. It can reduce the times of dilution and the error introduced by the dilution, and improve the accuracy of determination results of rubidium. Through analyzing the sensitivity of the three detection spectral lines for rubidium ion, linearly dependent coefficient and the relative errors of the determination results, the spectral line of Rb 780. 023 nm was chosen as the most suitable wavelength to measure the high content of rubidium in the rubidium chloride solutions. It was found that the instrument parameters of ICP-OES such as the atomizer flow, the pump speed and the high-frequency power are the major factors for the determination of rubidium ion in the rubidium chloride solutions. As we know instrument parameters of ICP-OES have an important influence on the atomization efficiency as well as the emissive power of the spectral lines of rubidium, they are considered as the significant factors for the determination of rubidium. The optimization parameters of the instrument were obtained by orthogonal experiments and further single factor experiment, which are 0. 60 L . min-1 of atomizer flow, 60 r . min-1 of pump speed, and 1 150 W of high-frequency power. The same experiments were repeated a week later with the optimization parameters of the instrument, and the relative errors of the determination results are less than 0. 5% when the concentration of rubidium chloride ranged from 0. 09% to 0. 18%. As the concentration of rubidium chloride is 0. 06%, the relative errors of the determination results are -1. 7%. The determination of lithium chloride and potassium chloride in the high concentration of the aqueous solutions was studied under the condition of similar instrument parameters. It was found by comparison that the determination results of lithium chloride are better than that of potassium chloride and rubidium chloride. The method of ICP-OES used for determination of high content of rubidium is fast and simple for operation, and the results are accurate. It is suitable for studying the equilibrium in the salt-water system containing rubidium and for analysis of products of rubidium with high content.
Objective To investigate the characteristics of hydrogen proton magnetic resonance spectroscopy (H-MRS) in posterior portion cingulate gyrus and the correlations thereof with the results of mini-mental state examination (MMSE), Alzheimer disease assessment scale-cognitive (ADAS-cog) in patients with mild-moderate Alzheimer's disease (AD). Methods H-MRS in posterior portion cingulate gyms was conducted in gender, age, and educational background-matched 24 patients with AD, 8 patients with vascular dementia (VD) ,and 11 normal controls (NC group) to measure the values of NAA(N-acetyl aspartate)myo-inositol (mI),and creatine and phosphocreatine (Cr). All the4 subjects underwent MMSE and assessment with ADAS-cog as well. The correlations among these results were assessed. Results The NAA/Cr ratios in posterior portion cingnlate gyrus of the AD and VD groups were (1.24±0.12) and (1.25 ±0.15) respectively, both significantly lower than that of the NC group [(1.46±0.19), P = 0.003, P =0.017] without significant difference between the AD and VD groups (P =0.800). The ml/Cr value of the AD group was (0.74±0.15), not significantly different from that of the VD group [(0.65±0.15), P = 0.153], and significantly higher than that of the NC group [(0.62±0.09), P= 0.007], however, the mI/Cr value of the VD group was not significantly different from that of the NC group (P = 0.662). If the NAA/Cr ratio < 1.31 was used as criteria of AD,the positive predictive value of AD was 73% and negative predictive value was 71% . The NAA/Cr ratio was positively correlated with the MMSE score (r =0.731 ,P = 0.000), and negatively correlated with the ADAS-cog score (r = - 0.541, P = 0.011). Conclusions The NAA/Cr ratio decreases in the posterior portion cingulate gyrus of AD patients, and has statistical significant correlations with the score of MMSE and ADAS-cog. The H-MRS characteristics of posterior portion cingnlate gyrus cannot effectively differentiate the early AD from early VD.
Key words:
Alzheimer disease; Dementia,vascular; Magnetic resonance spectroscopy; Cognition
Emotion, as an advanced function of the human brain, affects kinds of human behaviors. Electroencephalographs (EEG) are widely used in the field of emotion classification owing to their low cost and portability. In this work, we study the effects of a non-linear EEG feature and a channel selection method on emotion recognition. First, the fractal dimension(FD) which could reflect the state of the brain is extracted with a sliding window. The top seven channels are screened out by calculating the F-score from the whole samples. Then, based on the signals from forehead channels, filtered channels and associated channels, emotions on valence and arousal are classified by Support Vector Machine(SVM) and K Nearest Neighbours(KNN). The result shows that the forehead channels Fp2, AF8, Fpz play an important role in valence classification. When combining the forehead channels with other channels that have higher F-score, the SVM classifier has a better accuracy on the whole set with 89.37% on valence and 87.07% on arousal. Besides, the overall accuracy calculated on each participants with associated channels get significant improvement. Especially, the KNN classifier has a much better result on every subject. This phenomenon indicates that by combining the higher F-score channels with the forehead channels, the associated channels can not only take advantage of the forehead channels' ability to categorize emotions but also consider individual differences.
Diagnosis of major depressive disorder (MDD) using resting-state functional connectivity (rs-FC) data faces many challenges, such as the high dimensionality, small samples, and individual difference. To assess the clinical value of rs-FC in MDD and identify the potential rs-FC machine learning (ML) model for the individualized diagnosis of MDD, based on the rs-FC data, a progressive three-step ML analysis was performed, including six different ML algorithms and two dimension reduction methods, to investigate the classification performance of ML model in a multicentral, large sample dataset [1021 MDD patients and 1100 normal controls (NCs)]. Furthermore, the linear least-squares fitted regression model was used to assess the relationships between rs-FC features and the severity of clinical symptoms in MDD patients. Among used ML methods, the rs-FC model constructed by the eXtreme Gradient Boosting (XGBoost) method showed the optimal classification performance for distinguishing MDD patients from NCs at the individual level (accuracy = 0.728, sensitivity = 0.720, specificity = 0.739, area under the curve = 0.831). Meanwhile, identified rs-FCs by the XGBoost model were primarily distributed within and between the default mode network, limbic network, and visual network. More importantly, the 17 item individual Hamilton Depression Scale scores of MDD patients can be accurately predicted using rs-FC features identified by the XGBoost model (adjusted R2 = 0.180, root mean squared error = 0.946). The XGBoost model using rs-FCs showed the optimal classification performance between MDD patients and HCs, with the good generalization and neuroscientifical interpretability.
This paper presents a proposal for a person authentication system, which localizes facial landmarks and extracts biometrical features for face authentication. An efficient algorithm for eye localization and biometrical feature extraction and person identification is developed by using Gabor filters. We build artificial average eye models for eye location. Databases of biometrical features around the eye area of clients are constructed. For authentication, Schwartz inequality and the sum square error (SSE) are used. And experimental results on the proposed system are presented.
In recent years, an increasing number of university students are found to be at high risk of depression. Through a large scale depression screening, this paper finds that around 6.5% of the university postgraduate students in China experience depression. We then investigate whether the gait patterns of these individuals have already changed as depression is suggested to associate with gait abnormality. Significant differences are found in several spatiotemporal, kinematic and postural gait parameters such as walking speed, stride length, head movement, vertical head posture, arm swing, and body sway, between the depressed and non-depressed groups. Applying these features to classifiers with different machine learning algorithms, we examine whether natural gait analysis may serve as a convenient and objective tool to assist in depression recognition. The results show that when using a random forest classifier, the two groups can be classified automatically with a maximum accuracy of 91.58%. Furthermore, a reasonable accuracy can already be achieved by using parameters from the upper body alone, indicating that upper body postures and movements can effectively contribute to depression analysis.
The rapidly growing number of depressed people increases the burden of clinical diagnosis. Due to the abnormal speech signal of depressed patients, automatic audio-based depression recognition has the potential to become a complementary method for diagnosing. However, recognition performance varies largely with different speech acquisition tasks and classifiers, making results not comparable, and the performance requires further improvement before clinical application. This work extracted high-level statistical acoustic features (prosodic, voice-quality, and spectral features) of 23 depressed patients and 29 healthy subjects under spontaneous pronunciation tasks (interview and picture description) and mechanical pronunciation tasks (story reading and word reading), then applied principal component analysis (PCA) to reduce features dimensions, finally employed multilayer perceptron (MLP) to establish the classification model and compared with traditional classifiers (logistic regression, support vector machine, decision tree, and naive Bayes). The results showed that spontaneous pronunciation induced more significantly discriminative acoustic features and achieved better recognition performance accordingly. And the PCA retained 90% useful information with 50% features. Furthermore, MLP achieved the best performance with the accuracy 0.875 and average F1 score 0.855 under the picture description task. This study provides support for task design and classifier building for audio-based depression recognition, which could assist in mass screening for depression.
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