Monitoring the dynamic changes in aroma during the whole processing of Qingzhuan tea at an industrial scale: From fresh leaves to finished tea.

Aroma is one of the most outstanding quality characteristics of Qingzhuan tea (QZT), but its formation is still unclear. Thus, the volatile organic compounds (VOCs) during the whole processing of QZT were investigated by headspace solid-phase microextraction/gas chromatography-mass spectrometry. Based on 144 identified VOCs, the results showed that de-enzyming, sun-drying, and piling fermentation were the key processes of QZT aroma formation. Furtherly, 42 differential VOCs (VIP > 1.0 and p < 0.05) and 16 key VOCs (rOAV > 1.0 and/or ROAV > 1.0) were screened. Especially, sulcatone and ß-ionone (rOAV > 100 and ROAV > 10) were considered the most important contributors to the aroma of QZT. The metabolisms of key VOCs were mainly involved in oxidative degradation of fatty acids, degradation of carotenoids, and methylation of gallic acid. This study could help to more comprehensively understand the aroma formation in QZT processing at an industrial scale.

Dynamic Analysis of Physicochemical Properties and Polysaccharide Composition during the Pile-Fermentation of Post-Fermented Tea.

Ultra-high performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS) was used to study the diversity of tea polysaccharides and the dynamic changes in the physicochemical indexes of tea samples. FT-IR spectra and the free radical scavenging ability of tea polysaccharides, during pile-fermentation of post-fermented tea, were analyzed. The results showed that 23 saccharide co mponents in tea polysaccharides were identified: these belonged to 11 monosaccharides, 5 oligosaccharides, and 6 derivatives of monosaccharides and oligosaccharides. The abundance of oligosaccharides decreased gradually, while monosaccharides, and derivatives of monosaccharides and oligosaccharides increased gradually with the development of pile-fermentation. According to the differences in polysaccharide composition and their abundance, the tea polysaccharide samples extracted from different pile-fermentation stages could be clearly classed into three groups, W-0, W-1~W-4 and W-5~C-1. The pile-fermentation process affected the yield, the content of each component, FT-IR spectra, and the DPPH free radical scavenging ability of tea polysaccharides. Correlation analysis showed that microorganisms were directly related to the changes in composition and the abundance of polysaccharides extracted from different pile-fermentation stages. The study will further help to reveal the function of tea polysaccharides and promote their practical application as a functional food.

Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection.

Indoor positioning applications are developing at a rapid pace; active visual positioning is one method that is applicable to mobile platforms. Other methods include Wi-Fi, CSI, and PDR approaches; however, their positioning accuracy usually cannot achieve the positioning performance of the active visual method. Active visual users, however, must take a photo to obtain location information, raising confidentiality and privacy issues. To address these concerns, we propose a solution for passive visual positioning based on pedestrian detection and projection transformation. This method consists of three steps: pretreatment, pedestrian detection, and pose estimation. Pretreatment includes camera calibration and camera installation. In pedestrian detection, features are extracted by deep convolutional neural networks using neighboring frame detection results and the map information as the region of interest attention model (RIAM). Pose estimation computes accurate localization results through projection transformation (PT). This system relies on security cameras installed in non-private areas so that pedestrians do not have to take photos. Experiments were conducted in a hall about 100 square meters in size, with 41 test-points for the localization experiment. The results show that the positioning error was 0.48 m (RMSE) and the 90% error was 0.73 m. Therefore, the proposed passive visual method delivers high positioning performance.

Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free.

Indoor localization is one of the fundamentals of location-based services (LBS) such as seamless indoor and outdoor navigation, location-based precision marketing, spatial cognition of robotics, etc. Visual features take up a dominant part of the information that helps human and robotics understand the environment, and many visual localization systems have been proposed. However, the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost. To better address this problem, a localization method based on image retrieval is proposed in this paper, which mainly consists of two parts. The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image. The second one is pose estimation phase that computes accurate localization result. Owing to the robust CNN feature extractor, our scheme is applicable to complex indoor environments and easily transplanted to outdoor environments. The pose estimation scheme was inspired by monocular visual odometer, therefore, only RGB images and poses of reference images are needed for accurate image geo-localization. Furthermore, our method attempts to use lightweight datum to present the scene. To evaluate the performance, experiments are conducted, and the result demonstrates that our scheme can efficiently result in high location accuracy as well as orientation estimation. Currently the positioning accuracy and usability enhanced compared with similar solutions. Furthermore, our idea has a good application foreground, because the algorithms of data acquisition and pose estimation are compatible with the current state of data expansion.

An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras.

The demand for location-based services (LBS) in large indoor spaces, such as airports, shopping malls, museums and libraries, has been increasing in recent years. However, there is still no fully applicable solution for indoor positioning and navigation like Global Navigation Satellite System (GNSS) solutions in outdoor environments. Positioning in indoor scenes by using smartphone cameras has its own advantages: no additional needed infrastructure, low cost and a large potential market due to the popularity of smartphones, etc. However, existing methods or systems based on smartphone cameras and visual algorithms have their own limitations when implemented in relatively large indoor spaces. To deal with this problem, we designed an indoor positioning system to locate users in large indoor scenes. The system uses common static objects as references, e.g., doors and windows, to locate users. By using smartphone cameras, our proposed system is able to detect static objects in large indoor spaces and then calculate the smartphones' position to locate users. The system integrates algorithms of deep learning and computer vision. Its cost is low because it does not require additional infrastructure. Experiments in an art museum with a complicated visual environment suggest that this method is able to achieve positioning accuracy within 1 m.

Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras.

Artificial Intelligence (AI) technologies and their related applications are now developing at a rapid pace. Indoor positioning will be one of the core technologies that enable AI applications because people spend 80% of their time indoors. Humans can locate themselves related to a visually well-defined object, e.g., a door, based on their visual observations. Can a smartphone camera do a similar job when it points to an object? In this paper, a visual positioning solution was developed based on a single image captured from a smartphone camera pointing to a well-defined object. The smartphone camera simulates the process of human eyes for the purpose of relatively locating themselves against a well-defined object. Extensive experiments were conducted with five types of smartphones on three different indoor settings, including a meeting room, a library, and a reading room. Experimental results shown that the average positioning accuracy of the solution based on five smartphone cameras is 30.6 cm, while that for the human-observed solution with 300 samples from 10 different people is 73.1 cm.

Arylurea Derivatives: A Class of Potential Cancer Targeting Agents.

Arylurea derivatives, an important class of small molecules, have received considerable attention in recent years due to their wide range of biological applications. Various molecular targeted agents with arylurea scaffold as potential enzyme/receptor inhibitors were constructed with the successful development of sorafenib and regorafenib. This review focuses on those arylureas possessing anti-cancer activities from 2010 to date. According to their different mechanisms of action, these arylureas are divided into the following six categories: (1) Ras/Raf/MEK/ERK signaling pathway inhibitors; (2) tumor angiogenesis inhibitors, their targets include Vascular Endothelial Growth Factor Receptors (VEGFRs), Fibroblast Growth Factor Receptors (FGFRs), Platelet-Derived Growth Factor Receptors (PDGFRs), Epidermal Growth Factor Receptors (EGFRs), Insulin-Like Growth Factor 1 Receptor (IGF-1R), Fmslike Tyrosine Kinase 3 (FLT3), c-Kit, MET, and Smoothened (Smo); (3) PI3K/AKT/mTOR signaling pathway inhibitors; (4) cell cycle inhibitors, their targets include Checkpoint Kinases (Chks), Cyclin- Dependent Kinases (CDKs), Aurora, SUMO activating enzyme 1 (SUMO E1), tubulin, and DNA; (5) tumor differentiation, migration, and invasion inhibitors, their targets include Matrix Metalloproteinases (MMPs), LIM kinase (Limk), Nicotinamide Phosphoribosyltransferase (Nampt), and Histone Deacetylase (HDAC); (6) arylureas from the rational modification of natural products. This review focuses on the Structure-Activity Relationships (SARs) of these arylureas. The structural evolution and current status of some typical anti-cancer agents used in clinic and/or in clinical trials are emphasized.

An auto-adaptive background subtraction method for Raman spectra.

Background subtraction is a crucial step in the preprocessing of Raman spectrum. Usually, parameter manipulating of the background subtraction method is necessary for the efficient removal of the background, which makes the quality of the spectrum empirically dependent. In order to avoid artificial bias, we proposed an auto-adaptive background subtraction method without parameter adjustment. The main procedure is: (1) select the local minima of spectrum while preserving major peaks, (2) apply an interpolation scheme to estimate background, (3) and design an iteration scheme to improve the adaptability of background subtraction. Both simulated data and Raman spectra have been used to evaluate the proposed method. By comparing the backgrounds obtained from three widely applied methods: the polynomial, the Baek's and the airPLS, the auto-adaptive method meets the demand of practical applications in terms of efficiency and accuracy.

Design, synthesis, and biological evaluation of novel quinazolinyl-diaryl urea derivatives as potential anticancer agents.

Through a structure-based molecular hybridization approach, a series of novel quinazolinyl-diaryl urea derivatives were designed, synthesized, and screened for their in vitro antiproliferative activities against three cancer cell lines (HepG2, MGC-803, and A549). Six compounds (7 g, 7 m, 7 o, 8 e, 8 g, and 8 m) showed stronger activity against a certain cell line compared with the positive reference drugs sorafenib and gefitinib. Among the six compounds, 8 g exhibited the strongest activity. In particular, compound 8 g induced A549 apoptosis, arrested cell cycle at the G0/G1 phase, elevated intracellular reactive oxygen species level, and decreased mitochondrial membrane potential. This compound can also effectively regulate the expression of apoptosis- and cell cycle-related proteins, and influence the Raf/MEK/ERK pathway. Molecular docking and structure-activity relationship analyses revealed that it can bind well to the active site of the receptor c-Raf, which was consistent with the biological data. Therefore, compound 8 g may be a potent antitumor agent, representing a promising lead for further optimization.

Synthesis, Fluorescence Properties, and Antiproliferative Potential of Several 3-Oxo-3H-benzo[f]chromene-2-carboxylic Acid Derivatives.

In this study, two series of 3-oxo-3H-benzo[f]chromene-2-carboxylic acid derivatives (compounds 5a-i and 6a-g) were synthesized. Their in vitro proliferation inhibitory activities against the A549 and NCI-H460 human non-small cell lung cancer (NSCLC) cell lines were evaluated. Their photophysical properties were measured. Among these target compounds, 5e exhibited the strongest antiproliferative activity by inducing apoptosis, arresting cell cycle, and elevating intracellular reactive oxygen species (ROS) level, suggesting that it may be a potent antitumor agent. In addition, compound 6g with very low cytotoxicity, demonstrated excellent fluorescence properties, which could be used as an effective fluorescence probe for biological imaging.