EEG microstate analysis reveals large-scale brain network alterations in depressed adolescents with suicidal ideation.

BACKGROUND: Accumulating evidence showed abnormalities in brain network connectivity in depressive individuals with suicidal ideation (SI). We aimed to investigate the large-scale brain network dynamics in adolescents with SI and major depressive disorder (MDD). METHODS: We recruited 47 first-episode drug-naïve adolescents with MDD and SI, 26 depressed adolescents without SI (noSI), and 26 age-matched healthy controls (HC). The Columbia Suicidal Ideation Severity Scale (C-SSRS) was utilized to assess suicide ideation. We acquired 64-channel resting-state EEG recordings from all subjects and used microstate analysis to investigate the large-scale brain network dynamics. RESULTS: We observed a significant reduction in the occurrence and coverage of microstate B within the SI group when contrasted with the noSI group. Conversely, there was a significant increase in the occurrence and coverage of microstate A in the SI group as compared to the HC group. Additionally, we observed heightened transition probabilities from microstates D and C to microstate A in the SI group; meanwhile, transitions from microstate D to B were more prevalent in the noSI group. Furthermore, the noSI group exhibited a significant decline in the transition probabilities from microstate D to microstate C. LIMITATIONS: The cross-sectional nature limits the capacity to determine whether microstate dynamics have prognostic significance for SI. CONCLUSION: We provided evidence that depressed adolescents with SI have a distinct pattern in microstate dynamics compared to those without SI. These findings suggest that microstate dynamics might serve as a potential neurobiomarker for identifying SI in depressed adolescents.

Identification of Radix Bupleuri From Different Geographic Origins Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry and Support Vector Machine Algorithm.

BACKGROUND: The geographic origin of Radix bupleuri is an important factor affecting its efficacy, which needs to be effectively identified. OBJECTIVE: The goal is to enrich and develop the intelligent recognition technology applicable to the identification of the origin of traditional Chinese medicine. METHOD: This article establishes an identification method of Radix bupleuri geographic origin based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and support vector machine (SVM) algorithm. The Euclidean distance method is used to measure the similarity between Radix bupleuri samples, and the quality control chart method is applied to quantitatively describe their quality fluctuation. RESULTS: It is found that the samples from the same origin are relatively similar and mainly fluctuate within the control limit, but the fluctuation range is large, and it is impossible to distinguish the samples from different origins. The SVM algorithm can effectively eliminate the impact of intensity fluctuations and huge data dimensions by combining the normalization of MALDI-TOF MS data and the dimensionality reduction of principal components, and finally achieve efficient identification of the origin of Radix bupleuri, with an average recognition rate of 98.5%. CONCLUSIONS: This newly established approach for identification of the geographic origin of Radix bupleuri has been realized, and it has the advantages of objectivity and intelligence, which can be used as a reference for other medical and food-related research. HIGHLIGHTS: A new intelligent recognition method of medicinal material origin based on MALDI-TOF MS and SVM has been established.

Bayesian Discriminant Analysis of Yogurt Products Based on Raman Spectroscopy.

BACKGROUND: The quality discrimination of dairy products is an important basis on which to achieve quality assurance. OBJECTIVE: Taking the discriminant analysis of brand yogurt products as an example, a new rapid discriminant method can be constructed. METHOD: The first three principal components were selected as the pattern vectors of the samples. Then, at random, 75% of the samples were collected as a training set, and their mean values and covariance matrices were calculated to construct a Gauss Bayesian discriminant model. The remaining 25% of samples were employed as a test set, and the pattern vectors of each sample were input into the above model. Next, the posterior probability of each sample in relation to each category could be obtained. Results: The category corresponding to the maximum posterior probability as the brand classification of each sample was defined. CONCLUSIONS: We constructed a Gauss Bayesian discriminant model to discriminate these different yogurt products after the principal component feature extraction of Raman properties. The results indicate the rationality and wide application prospects of this approach. HIGHLIGHTS: A fast dairy product discriminant method based on Gauss Bayesian model and Raman spectroscopy was established.

Red Fluorescent Carbon Dot Powder for Accurate Latent Fingerprint Identification using an Artificial Intelligence Program.

Development and comparison of the latent fingerprints (LFPs) are two major studies in detection and identification of LFPs, respectively. However, integrated research studies on both fluorescent materials for LFP development and digital-processing programs for LFP comparison are scarcely seen in the literature. In this work, highly efficient red-emissive carbon dots (R-CDs) are synthesized in one pot and mixed with starch to form R-CDs/starch phosphors. Such phosphors are comparable with various substrates and suitable for the typical powder dusting method to develop LFPs. The fluorescence images of the developed LFPs are handled with an artificial intelligence program. For the optimal sample, this program presents an excellent matching score of 93%, indicating that the developed sample has very high similarity with the standard control. Our results are significantly better than the benchmark obtained by the traditional method, and thus, both the R-CDs/starch phosphors and the digital processing program fit well for the practical applications.

The statistical fusion identification of dairy products based on extracted Raman spectroscopy.

At present, practical and rapid identification techniques for dairy products are still scarce. Taking different brands of pasteurized milk as an example, they are all milky white in appearance, and their Raman spectra are very similar, so it is not feasible to identify them directly using the naked eye. In the current work, a clear feature extraction and fusion strategy based on a combination of Raman spectroscopy and a support vector machine (SVM) algorithm was demonstrated. The results showed a 58% average recognition accuracy rate for dairy products as based on the original Raman full spectral data and up to nearly 70% based on a single spectral interval. Data normalization processing effectively improved the recognition accuracy rate. The average recognition accuracy rate of dairy products reached 91% based on the normalized Raman full spectral data or nearly 85% based on a normalized single spectral interval. The fusion of multispectral feature regions yielded high accuracy and operation efficiency. After screening and optimizing based on SVM algorithm, the best spectral feature intervals were determined to be 335-354 cm-1, 435-454 cm-1, 485-540 cm-1, 820-915 cm-1, 1155-1185 cm-1, 1300-1414 cm-1, and 1415-1520 cm-1 under the experimental conditions, and the average identification accuracy rate here reached 93%. The developed scheme has the advantages of clear feature extraction and fusion, and short identification time, and it provides a technical reference for food quality control.

Raman chemical feature extraction for quality control of dairy products.

As a fast information acquisition technique, Raman spectroscopy can be used to control the quality of dairy products. Feature extraction is a necessary processing step to improve the efficiency of Raman spectral data. Principal component analysis is a traditional method that can effectively extract the features and reduce the dimension of spectral data. However, it is difficult to analyze the chemical information of the extracted feature, thus limiting its practical application. In this work, Raman spectral chemical feature extraction was carried out. The quality control of Dingxin dairy products (a famous dairy brand in China; purchased from Heilongjiang Zhaodong Tianlong Dairy Co. Ltd., Heilongjiang, China) was used as an example. Raman peak intensity, peak area, and peak ratio were extracted as chemical features and further investigated using Euclidean distance and the quality fluctuation control chart. The potential quality discrimination ability of the Raman feature extraction methods was demonstrated. The results showed that the Puzhen dairy products (purchased from Inner Mongolia Yinuo Halal Food Co. Ltd., Inner Mongolia, China) and Xueyuan dairy products (used as a control; purchased from Inner Mongolia Wulanchabu City Jining Xueyuan Dairy Co. Ltd., Inner Mongolia, China) could be distinguished from Dingxin dairy products when the Raman chemical features special peak intensity, peak area, and peak ratio were used, and their discriminatory ability increased in sequence. Hence, it was shown that Raman chemical feature extraction can achieve quality control and discriminant analysis of dairy products and that the spectral information is clear.

[Application of Raman Spectroscopy and Pattern Recognition Methods for Determining the Authenticity and Detecting the Adulteration of Milk Powder].

The authenticity and adulteration of dairy products are attracting broad attention in recent years. There is a need to develop rapid, simple and accurate analytical methods for the detection of authenticity and adulteration of dairy products. To discriminate between milk powder samples, Raman spectra of FIRMUS, Nestlé and Being Mate milk powder were collected. The nearest neighbor algorithm (NN)combined with the characteristic peaks were employed for the design of a model. On the basis of 10 cross validation, the average recognition rate was 99.56%. In order to achieve the analysis of the adulteration of milk powder, FIRMUS milk powder was mixed with Nestlé milk powder according to the mass ratio 0 :1, 1 : 3, 1 : 1, 3 : 1 and 1 : 0 to get five kinds of the adulterated milk powder samples. Then, fat was extracted from the adulterated milk powder samples. Raman spectra of the fat were collected, then two methods were employed for the design of models. One was the nearest neighbor algorithm combined with the characteristic peaks, another was the kernel principal component analysis (KPCA) combined with NN. On the basis of 10 cross validation, the average recognition rate reached 93.33% and 98.89%, the average operation time was 0.085 and 0.104 s. The results of this work showed that the nearest neighbor algorithm combined with the characteristic peaks can be applied for the determination of the authenticity of milk powder while Raman-KPCA-NN model can provide a simple, accurate and rapid method to investigate the adulteration of milk power.

[Study on the Recognition of Liquor Age of Gujing Based on Raman Spectra and Support Vector Regression].

It is an important and difficult research point to recognize the age of Chinese liquor rapidly and exactly in the field of liquor analyzing, which is also of great significance to the healthy development of the liquor industry and protection of the legitimate rights and interests of consumers. Spectroscopy together with the pattern recognition technology is a preferred method of achieving rapid identification of wine quality, in which the Raman Spectroscopy is promising because of its little affection of water and little or free of sample pretreatment. So, in this paper, Raman spectra and support vector regression (SVR) are used to recognize different ages and different storing time of the liquor of the same age. The innovation of this paper is mainly reflected in the following three aspects. First, the application of Raman in the area of liquor analysis is rarely reported till now. Second, the concentration of studying the recognition of wine age, while most studies focus on studying specific components of liquor and studies together with the pattern recognition method focus more on the identification of brands or different types of base wine. The third one is the application of regression analysis framework, which cannot be only used to identify different years of liquor, but also can be used to analyze different storing time, which has theoretical and practical significance to the research and quality control of liquor. Three kinds of experiments are conducted in this paper. Firstly, SVR is used to recognize different ages of 5, 8, 16 and 26 years of the Gujing Liquor; secondly, SVR is also used to classify the storing time of the 8-years liquor; thirdly, certain group of train data is deleted form the train set and put into the test set to simulate the actual situation of liquor age recognition. Results show that the SVR model has good train and predict performance in these experiments, and it has better performance than other non-liner regression method such as the Partial Least Squares Regression (PLS) method, and can also be applied in the practice of liquor analysis.

Comparison study of two diagnostic and grading systems for conjunctivochalasis.

BACKGROUND: Different diagnostic and grading systems of conjunctivochalasis have resulted in apparent disparity between the prevalence rates of recent population-based studies. This study aimed to investigate the disparity between 4-level system cited from Meller and Tseng in 1998 (abbreviated here as Meller's system) and 5-level system modified from Meller's system cited from Zhang and associates (abbreviated here as Zhang's system) regarding the diagnosis and the patients' preferences for the treatment of conjunctivochalasis in the general population. METHODS: A total of 546 senile residents living in the Guiyangyuan community of Shanghai, China, participated in the study. The diagnostic criteria for conjunctivochalasis were based on two diagnostic grading systems: Meller's system and Zhang's system, which was modified from Meller's system. The participants' preference regarding medical treatment for conjunctivochalasis was determined according to the response to a question. One year later, a follow-up interview determines whether the patient had undergone surgery for conjunctivochalasis. RESULTS: With Meller's system, 398 participants were confirmed as having conjunctivochalasis, and the prevalence rate was 72.89%. According to Zhang's system, only 213 participants were diagnosed as having conjunctivochalasis, and the prevalence rate was 39.01%. A total of 109 eyes underwent medical treatment or surgery for conjunctivochalasis in the following year, including eight eyes that were diagnosed as grade II and 101 eyes that were diagnosed as grade III according to Meller's system and five eyes that were diagnosed as grade I, 55 eyes that were diagnosed as grade II, 31 eyes that were diagnosed as grade III, and 18 eyes that were diagnosed as grade IV according to Zhang' system. CONCLUSION: Diagnoses of conjunctivochalasis using Zhang's system are more consistent with patient requests and the medical treatment strategies used than diagnoses made using Meller's system.

One-pot synthesis of water-dispersible Ag2S quantum dots with bright fluorescent emission in the second near-infrared window.

The second near-infrared window (NIR-II, wavelength of 1.0-1.4 µm) is optimal for the bioimaging of live animals due to their low albedo and endogenous autofluorescence. Herein, we report a facile and one-pot biomimetic synthesis approach to prepare water-dispersible NIR-II-emitting ultrasmall Ag(2)S quantum dots (QDs). Photoluminescence spectra showed that the emission peaks could be tuned from 1294 to 1050 nm as the size of the Ag(2)S QDs varied from 6.8 to 1.6 nm. The x-ray diffraction patterns and x-ray photoelectron spectra confirmed that the products were monoclinic α-Ag(2)S. Fourier transform infrared spectrograph analysis indicated that the products were protein-conjugated Ag(2)S QDs. Examination of cytotoxicity and the hemolysis test showed that the obtained Ag(2)S QDs had good biocompatibility, indicating that such a nanomaterial could be a new kind of fluorescent label for in vivo imaging.