Parsing is All You Need for Accurate Gait Recognition in the Wild

Binary silhouettes and keypoint-based skeletons have dominated human gait recognition studies for decades since they are easy to extract from video frames. Despite their success in gait recognition for in-the-lab environments, they usually fail in real-world scenarios due to their low information entropy for gait representations. To achieve accurate gait recognition in the wild, this paper presents a novel gait representation, named Gait Parsing Sequence (GPS). GPSs are sequences of fine-grained human segmentation, i.e., human parsing, extracted from video frames, so they have much higher information entropy to encode the shapes and dynamics of fine-grained human parts during walking. Moreover, to effectively explore the capability of the GPS representation, we propose a novel human parsing-based gait recognition framework, named ParsingGait. ParsingGait contains a Convolutional Neural Network (CNN)-based backbone and two light-weighted heads. The first head extracts global semantic features from GPSs, while the other one learns mutual information of part-level features through Graph Convolutional Networks to model the detailed dynamics of human walking. Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset. Based on Gait3D-Parsing, we comprehensively evaluate our method and existing gait recognition methods. The experimental results show a significant improvement in accuracy brought by the GPS representation and the superiority of ParsingGait. The code and dataset are available at https://gait3d.github.io/gait3d-parsing-hp .Comment: 16 pages, 14 figures, ACM MM 2023 accepted, project page: https://gait3d.github.io/gait3d-parsing-h

Infrared Drying as a Quick Preparation Method for Dried Tangerine Peel

To establish the most convenient and effective method to dry tangerine peels, different methods (sun drying, hot-air drying, freeze drying, vacuum drying, and medium- and short-wave infrared drying) were exploited. Our results indicated that medium- and short-wave infrared drying was the best method to preserve nutraceutical components; for example, vitamin C was raised to 6.77 mg/g (D.W.) from 3.39 mg/g (sun drying). Moreover, the drying time can be shortened above 96% compared with sun drying. Importantly, the efficiency of DPPH radical scavenging was enhanced from 26.66% to 55.92%. These findings would provide a reliable and time-saving methodology to produce high-quality dried tangerine peels

Using the Intrinsic Fluorescence of DNA to Characterize Aptamer Binding

The reliable, readily accessible and label-free measurement of aptamer binding remains a challenge in the field. Recent reports have shown large changes in the intrinsic fluorescence of DNA upon the formation of G-quadruplex and i-motif structures. In this work, we examined whether DNA intrinsic fluorescence can be used for studying aptamer binding. First, DNA hybridization resulted in a drop in the fluorescence, which was observed for A30/T30 and a 24-mer random DNA sequence. Next, a series of DNA aptamers were studied. Cortisol and Hg2+ induced fluorescence increases for their respective aptamers. For the cortisol aptamer, the length of the terminal stem needs to be short to produce a fluorescence change. However, caffeine and adenosine failed to produce a fluorescence change, regardless of the stem length. Overall, using the intrinsic fluorescence of DNA may be a reliable and accessible method to study a limited number of aptamers that can produce fluorescence changes

Soybean Seedling Root Segmentation Using Improved U-Net Network

Soybean seedling root morphology is important to genetic breeding. Root segmentation is a key technique for identifying root morphological characteristics. This paper proposed a semantic segmentation model of soybean seedling root images based on an improved U-Net network to address the problems of the over-segmentation phenomenon, unsmooth root edges and root disconnection, which are easily caused by background interference such as water stains and noise, as well as inconspicuous contrast in soybean seedling images. Soybean seedling root images in the hydroponic environment were collected for annotation and augmentation. A double attention mechanism was introduced in the downsampling process, and an Attention Gate mechanism was added in the skip connection part to enhance the weight of the root region and suppress the interference of background and noise. Then, the model prediction process was visually interpreted using feature maps and class activation mapping maps. The remaining background noise was removed by connected component analysis. The experimental results showed that the Accuracy, Precision, Recall, F1-Score and Intersection over Union of the model were 0.9962, 0.9883, 0.9794, 0.9837 and 0.9683, respectively. The processing time of an individual image was 0.153 s. A segmentation experiment on soybean root images was performed in the soil-culturing environment. The results showed that this proposed model could extract more complete detail information and had strong generalization ability. It can achieve accurate root segmentation in soybean seedlings and provide a theoretical basis and technical support for the quantitative evaluation of the root morphological characteristics in soybean seedlings

Effects of Anaerobic Fermentation on Black Garlic Extract by Lactobacillus: Changes in Flavor and Functional Components

The purpose of this study is to investigate the potential application of probiotics in the development of novel functional foods based on black garlic. The single-factor analysis (extraction temperatures, solid-to-liquid ratios, and extraction times) and the response surface methodology were firstly used to optimize hot water extraction of soluble solids from black garlic. The optimal extraction conditions were temperature 99.96°C, solid-to-liquid ratio 1:4.38 g/ml, and extracting 2.72 h. The effects of Lactobacillus (Lactobacillus plantarum, Lactobacillus rhamnosus, and co-culture of them) fermentation on the physicochemical properties of black garlic extract broth were studied for the first time. Artificial and electronic sensory evaluations demonstrated that fermentation significantly influenced the sensory characteristics. The variations of metabolites in different broth samples (S1, unfermented; S2, 1-day fermentation by L. plantarum; S3, 2-day fermentation by L. rhamnosus; and S4, 1-day fermentation by co-cultured Lactobacillus) were further investigated by gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry/mass spectrometry analysis. As a result, Lactobacillus fermentation significantly reduced the pH; increased the contents of the total acid, amino nitrogen, total polyphenol, and total flavonoid; and reduced the content of 5-hydroxymethylfurfural (a carcinogenic component) by 25.10–40.81% in the black garlic extract. The contents of several components with unpleasant baking flavors (e.g., furfural, 2-acetylfuran, and 5-methyl furfural) were reduced, whereas the contents of components with green grass, floral, and fruit aromas were increased. More importantly, the contents of several functional components including lactic acid, Gly-Pro-Glu, sorbose, and α-CEHC (3,4-dihydro-6-hydroxy-2,5,7,8-tetramethyl-2H-1-benzopyran-2-propanoic acid) were increased after Lactobacillus fermentation. The results demonstrated the potential of probiotic fermentation to improve the quality of black garlic. This work will provide an insight into the strategic design of novel black garlic products and facilitate the application of black garlic in functional foods

Bio-specific Au/Fe3+ porous spongy nanoclusters for sensitive SERS detection of Escherichia coli O157:H7

Abstract: For sensitive and fast detection of Escherichia coli O157:H7, organic and inorganic hybrid Au/Fe3+ nanoclusters (NCs) were synthesized for the first time using gold nanoparticles (GNPs), bovine serum albumin, ferric chloride, phosphate-buffered saline, and antibodies. The Au/Fe3+ porous spongy NCs with large surface area showed excellent bio-specific capability for E. coli O157:H7. GNPs in Au/Fe3+ NCs functioned as signal enhancers, significantly increasing the Raman signal via the metathesis reaction product of Prussian blue and obviously improving the detection sensitivity. We combined the novel Au/Fe3+ NCs with antibody-modified magnetic nanoparticles to create a biosensor capable of sensitive detection of E. coli O157:H7, which showed a good linear response (101 to 106 cfu/mL), high detection sensitivity (2 cfu/mL), and good recovery rate (93.60–97.50%) in spiked food samples. These results make the biosensor well-suited for food safety monitoring. This strategy achieves the goal of sensitive and quantitative detection of E. coli O157:H7

CFHBA-PID Algorithm: Dual-Loop PID Balancing Robot Attitude Control Algorithm Based on Complementary Factor and Honey Badger Algorithm

The PID control algorithm for balancing robot attitude control suffers from the problem of difficult parameter tuning. Previous studies have proposed using metaheuristic algorithms to tune the PID parameters. However, traditional metaheuristic algorithms are subject to the criticism of premature convergence and the possibility of falling into local optimum solutions. Therefore, the present paper proposes a CFHBA-PID algorithm for balancing robot Dual-loop PID attitude control based on Honey Badger Algorithm (HBA) and CF-ITAE. On the one hand, HBA maintains a sufficiently large population diversity throughout the search process and employs a dynamic search strategy for balanced exploration and exploitation, effectively avoiding the problems of classical intelligent optimization algorithms and serving as a global search. On the other hand, a novel complementary factor (CF) is proposed to complement integrated time absolute error (ITAE) with the overshoot amount, resulting in a new rectification indicator CF-ITAE, which balances the overshoot amount and the response time during parameter tuning. Using balancing robot as the experimental object, HBA-PID is compared with AOA-PID, WOA-PID, and PSO-PID, and the results demonstrate that HBA-PID outperforms the other three algorithms in terms of overshoot amount, stabilization time, ITAE, and convergence speed, proving that the algorithm combining HBA with PID is better than the existing mainstream algorithms. The comparative experiments using CF prove that CFHBA-PID is able to effectively control the overshoot amount in attitude control. In conclusion, the CFHBA-PID algorithm has great control and significant results when applied to the balancing robot