EEG analysis in patients with schizophrenia based on microstate semantic modeling method.

Introduction: Microstate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals. Methods: This study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences. Results: The SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects. Discussion: This research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.

Incorporating edible oil during cooking tailors the microstructure and quality features of brown rice following heat moisture treatment.

While brown rice (BR) has numerous nutritional properties, the consumption potential of which is seriously restricted since the poor cooking quality and undesirable flavor. Here, edible oils (pork lard and corn oil, 1-5 wt%) were incorporated during the cooking of BR following heat moisture treatment. Incorporating corn oil rather than lard significantly ameliorated the texture properties (e.g. hardness, cohesiveness, and chewiness) and sensory properties of cooked BR. Both lard- and corn oil-incorporated cooked BR showed obvious structural changes accompanied by the formation of amylose-lipid complexes during cooking. It was confirmed that the incorporation of lard and corn oil allowed a higher degree of short-range molecular order, more V-type starch crystallites, and elevated nano-structural arrangements. Additionally, a decreased hardness (from 559.04 g to 424.18 g and 385.91 g, respectively) and enriched resistant starch (RS) were also observed, the highest RS content (15.95 % and 16.32 %, respectively) was observed when 1 wt% of lard and corn oil were incorporated.

Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy.

Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice's value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample's elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92-100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.