Carvacrol, &al and geraniol showed potent antibacterial activity against Salmonella typhimurium and its rifampicin-resistant (Rip) strain as determined in txyptic soy broth and by zone of inhibition on agar-based medium. Carvacrol had the most potent bactericidal activity, with minimum inhibitory and bactericidal concentrations (MIC and MBC) of 250 pg/mL for both tester strains. When tested at 0.5, 1.5 and 3.0% in 1%Tween 20 for bactericidal activity against RifR-S. typhimurium inoculated on fish cubes, carvacrol at 3.0% completely killed the inoculated bacteria, while geraniol killed most of the bacteria, and citral killed the least. Carvacrol and geraniol showed potent antibacterial activity at 1.5%. Bactericidal activity became more evident as storage of fish cubes at 4°C lengthened. The comparable inhibition of these strains of SaZmon@la and species of Gram-negative bacteria by carvacrol and geraniol support their application as potential antibacterial agents in food systems. CarvacrolCitral a (cis) Citral b (trans) Geraniol Fig. l-Structures of carvacrol, citral, and geraniol.

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Tannic acid, propyl gallate, gallic acid and ellagic acid were tested for their inhibitory effects on selected food‐borne bacteria by the well assay technique. Tannic acid and propyl gallate were inhibitory whereas gallic acid and ellagic acid were not.

In this paper, we focus on category-level 6D pose and size estimation from a monocular RGB-D image. Previous methods suffer from inefficient category-level pose feature extraction, which leads to low accuracy and inference speed. To tackle this problem, we propose a fast shapebased network (FS-Net) with efficient category-level feature extraction for 6D pose estimation. First, we design an orientation aware autoencoder with 3D graph convolution for latent feature extraction. Thanks to the shift and scaleinvariance properties of 3D graph convolution, the learned latent feature is insensitive to point shift and object size. Then, to efficiently decode category-level rotation information from the latent feature, we propose a novel decoupled rotation mechanism that employs two decoders to complementarily access the rotation information. For translation and size, we estimate them by two residuals: the difference between the mean of object points and ground truth translation, and the difference between the mean size of the category and ground truth size, respectively. Finally, to increase the generalization ability of the FS-Net, we propose an online box-cage based 3D deformation mechanism to augment the training data. Extensive experiments on two benchmark datasets show that the proposed method achieves state-ofthe-art performance in both category-and instance-level 6D object pose estimation. Especially in category-level pose estimation, without extra synthetic data, our method outperforms existing methods by 6.3% on the NOCS-REAL dataset 1 .

Higher dimensional data such as video and 3D are the leading edge of multimedia retrieval and computer vision research. In this survey, we give a comprehensive overview and key insights into the state of the art of higher dimensional features from deep learning and also traditional approaches. Current approaches are frequently using 3D information from the sensor or are using 3D in modeling and understanding the 3D world. With the growth of prevalent application areas such as 3D games, self-driving automobiles, health monitoring and sports activity training, a wide variety of new sensors have allowed researchers to develop feature description models beyond 2D. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms. Due to the major importance of the evaluation process, we also present an overview of the current datasets and benchmarks. Moreover, based on more than 330 papers from this study, we present the major challenges and future directions.

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