Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.

Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.

Rice Cakes Containing Dietary Fiber Supplemented with or without Artemisia Annua and Gynura Procumbens Merr. Alleviated the Risk Factors of Metabolic Syndrome.

We investigated whether the consumption of Korean rice cakes enriched with dietary fiber with or without polyphenol rich plants might decrease the risk factors of metabolic syndrome (MetS). Rice cakes were manufactured using fructooligosaccharides, resistant starch, and psyllium as sources of dietary fibers with and without polyphenol rich Artemisia annua and Gynura procumbens Merr. (RC+FP and RC+F, respectively), and prepared in three forms (songpyeon, seolgidduk, and chaldduk). Ninety subjects with at least one MetS risk factor were recruited for 6 weeks of dietary intervention. Sixty subjects were finally included for the analysis. Compared to the initial values, RC+FP group had decreased levels of fasting blood glucose (FBG), HOMA-IR and blood pressure after 6 weeks, whereas RC+F group didn't have significant changes in them. Regarding the improvement of individual MetS risk factors, RC+FP group showed significant reduction in FBG and blood pressures but RC+F group only had reduction in systolic blood pressure. After the intervention, a reduction in the number of MetS risk factors was greatert in the RC+FP group than in the RC+F group. In conclusion, Dietary fiber enriched rice cakes with or without polyphenols decreased the number and/or the levels of MetS risk factors. Polyphenol rich plant components may provide additional health benefits in controlling FBG and blood pressure.

Au-Polypyrrole Framework Nanostructures for Improved Localized Surface Plasmon Resonance Volatile Organic Compounds Gas Sensing.

In this paper, we propose an Au-polypyrrole (Ppy) nanorod gas sensor for the detection of volatile organic compound (VOC) gases. This gas sensor operates on the principle of localized surface plasmon resonance (LSPR). The Au-Ppy nanorods used in this experiment were synthesized using an anodic aluminum oxide template by the electrochemical deposition method. Using field emission scanning electron microscopy, we confirmed that the Au-Ppy nanorod arrays were successfully fabricated with a uniform size. By depositing gold, the Au-Ppy nanorods exhibited both optical and LSPR interference. The gas sensing properties of the fabricated nanorods were tested for VOCs such as acetic acid, benzene, and toluene with a short response time (~1 min). Moreover, the proposed VOC gas sensing system was tested with three types of VOC gases over a wide concentration range from 10 to 100 ppm. Highest sensitivity was observed for acetic acid gas, which had a linear relation with the gas concentration, indicating that the system can be used as a gas sensor.

Response characterization of a fiber optic sensor array with dye-coated planar waveguide for detection of volatile organic compounds.

We have developed a multi-array side-polished optical-fiber gas sensor for the detection of volatile organic compound (VOC) gases. The side-polished optical-fiber coupled with a polymer planar waveguide (PWG) provides high sensitivity to alterations in refractive index. The PWG was fabricated by coating a solvatochromic dye with poly(vinylpyrrolidone). To confirm the effectiveness of the sensor, five different sensing membranes were fabricated by coating the side-polished optical-fiber using the solvatochromic dyes Reinhardt's dye, Nile red, 4-aminophthalimide, 4-amino-N-methylphthalimide, and 4-(dimethylamino)cinnamaldehyde, which have different polarities that cause changes in the effective refractive index of the sensing membrane owing to evanescent field coupling. The fabricated gas detection system was tested with five types of VOC gases, namely acetic acid, benzene, dimethylamine, ethanol, and toluene at concentrations of 1, 2,…,10 ppb. Second-regression and principal component analyses showed that the response properties of the proposed VOC gas sensor were linearly shifted bathochromically, and each gas showed different response characteristics.

Impacts of sensory attributes and emotional responses on the hedonic ratings of odors in dairy products.

Consumers often decide to purchase certain items as a result of prompt emotional responses evoked by the products; therefore, it is important to investigate the emotional responses stimulated by food samples, as well as the sensory attributes of the products. The aim of this study was to examine influences of sensory attribute and emotional response on olfactory hedonic ratings of dairy products. Additionally, we compared this effect between women and men subjects. Sensory attributes of six natural odors produced by commercial dairy products were evaluated by 9 trained panelists. In addition, 100 untrained panelists (50 women and 50 men) rated the emotional response stimulated by the 6 odors using 25 paired semantic differential scales and the hedonicity using a 9 cm line scale, respectively. Untrained panelists more liked "sweet aromatics," "sour aromatics," "fermented aromatics," and "rich aromatics" over the other sensory attributes of dairy products. The olfactory hedonic ratings were increased when the odor was more characterized as "fragrant," "attractive," "comfortable," "familiar," "faint," "natural," or "modern." Specific sensory attributes were found to be related to specific adjective pairs representing the emotional response. Moreover, the ratings of adjective pairs representing emotional response of odors and their influences on hedonic ratings differed significantly between sexes. In conclusion, our findings suggest that the emotional responses as well as the sensory attributes affect the hedonic ratings of odors in dairy products.