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Abstract Freshwater fishery resources in Chinese rivers have been markedly impaired as the results of overfishing, damming, and watershed development. However, little is known about trophic ecology of fish assemblages in the rivers of China. In this study, fishes were collected from an upstream, midstream, and downstream site of the Moyangjiang River, Southern China, to investigate trophic niche structure using stable isotope analysis. We calculated several trophic niche metrics using δ13C and δ15N ratios of the fish assemblages at each study site. Results showed no significant differences in the δ13C ratios between the pelagic and benthic fish assemblages at each study site. This suggests a homogenous dietary base shared by all fish at each site, likely as the result of continuous water column mixing in this shallow and fast‐moving river. The upstream fish assemblage had the greatest species abundance and trophic diversity. The fish assemblage at the midstream site displayed the smallest trophic niche space (total area of the δ13C and δ15N bi‐plot space) and trophic diversity. The midstream site also showed the most depleted 13C, suggesting a more degraded habitat compared with the other two study sites. The fish assemblages at the downstream site displayed the greatest basal resource diversity, largest CR, and trophic niche space and trophic diversity. However, the fish assemblage at the downstream site also displayed the lowest species abundance, redundancy, and evenness of trophic spacing. These findings suggest that the trophic niche of the fish assemblage at the downstream site, which received resources from upstream flow, experienced greater watershed development and greater in situ production, demonstrating positive and negative impacts by anthropogenic activities. Future studies should also gather information on fish production, watershed development, and water quality, to aid in the interpretation of the stable isotope analysis of fish trophic niches.

Abstract Objective To investigate how the ulcerative colitis (UC) be treated with Chinese herbal medicines (CHM), using Chinese medicine (CM) pattern (zheng) identification, in the current clinical practice. Methods A total of 7 electronic databases were systematically searched for UC clinical studies with CHM interventions (including single herbs and CHM formulas) published in English and Chinese from the date of their inception to November 25, 2020. Descriptive statistics were adopted to demonstrate the characteristics of study design, and to collate the commonly CM patterns of UC and frequently used CHM herbs and formulas. Further, IBM SPSS Modeler 18.0 and Cytoscape 3.7.1 software were used to analyze and visualize the associations between different categories of CHM and their zheng indications. Results A total of 2311 articles were included in this study, of which most (> 90%) were RCTs with CHM formulas. The most common zheng of UC was Large intestine dampness-heat, while the basic type of CM patten was Spleen deficiency. The most frequently used classical formula was Bai-Tou-Weng-Tang, followed by Shen-Ling-Bai-Zhu-San, and the commonly used proprietary CHM was Xi-Lei-San (enema). Sulfasalazine and Mesalazine are commonly used as concomitant western medicines. The most frequently used single medicinals were Huang Lian and Bai Zhu, which also identified as the core herbs for different CM patterns. Conclusion This study examined the application of CHM interventions for UC and summarized their characteristics in clinical practice. These data indicated there were limited information about the safety assessment of CHM formulas and further RCTs including CM pattern(s) with strict design are necessary.

Abstract Infectious diarrhea in China showed a significant pattern. Many researchers have tried to reveal the drivers, yet usually only meteorological factors were taken into consideration. Furthermore, the diarrheal data they analyzed were incomplete and the algorithms they exploited were inefficient of adapting realistic relationships. Here, we investigate the impacts of meteorological and social factors on the number of infectious diarrhea cases in China. A machine learning algorithm called the Random Forest is utilized. Our results demonstrate that nearly half of infectious diarrhea occurred among children under 5 years old. Generally speaking, increasing temperature or relative humidity leads to increased cases of infectious diarrhea in China. Nevertheless, people from different age groups or different regions own different sensitivities to meteorological factors. The weight of feces that are harmfully treated could be a possible reason for infectious diarrhea of the elderly as well as children under 5 years old. These findings indicate that infectious diarrhea prevention for children under 5 years old remains a primary task in China. Personalized prevention countermeasures ought to be provided to different age groups and different regions. It is essential to bring the weight of feces that are harmfully treated to the forefront when considering infectious diarrhea prevention.

Abstract Objective To assess the reporting quality of randomised controlled trials (RCTs) of massage, particularly whether necessary elements related to massage interventions were adequately reported. Methods A total of 8 electronic databases were systematically searched for massage RCTs published in English and Chinese from the date of their inception to June 22, 2020. Quality assessment was performed using three instruments, namely the CONSORT (Consolidated Standards of Reporting Trials) 2010 Checklist (37 items), the CONSORT Extension for NPT (Nonpharmacologic Treatments) 2017 checklist (18 items), and a self-designed massage-specific checklist (16 items) which included massage rationale, intervention and control group details. Descriptive statistics were additionally used to analyse the baseline characteristics of included trials. Results A total of 2,447 massage RCTs were identified, of which most (96.8%) were distributed in China. For the completeness of CONSORT, NPT Extension, and massage-specific checklists, the average reporting percentages were 50%, 10% and 45%, respectively. Of 68 assessed items in total (exclusion of 3 repeated items on intervention), 42 were poorly presented, including 18 CONSORT items, 15 NPT items, and 9 massage-specific items. Although the overall quality of reporting showed slightly improvement in articles published after 2010, the international (English) journals presented a higher score of the CONSORT and NPT items, while the Chinese journals were associated with the increased score of massage-specific items. Conclusion The quality of reporting of published massage RCTs is variable and in need of improvement. Reporting guideline “CONSORT extension for massage” should be developed.

Objective: To investigate the effect of Fuhe decoction on the behavior and levels of monoamine neurotransmitters in different brain regions in a depression rat model induced by chronic unpredictable mild stimulation (CUMS) combined with social isolation. Methods: Fifty male SD rats were randomly divided into a blank group, model group, fluoxetine group, Chaiqinwendan decoction group, and Fuhe decoction group. Chronic unpredictable mild stimulation combined with a social isolation method was used to replicate the depression rat model. After 42 days of administration, a tail suspension test and high-performance liquid electrochemical detection (HPLC-ECD) were used to detect the behavioral changes and changes in the content of monoamine neurotransmitters norepinephrine (NE), dopamine (DA), 5-hydroxytrytamine (5-HT), and metabolites in different brain regions of rats in each group before and after treatment. Results: Compared with the model group, the epinephrine (E) content in the Fuhe decoction group was highly significantly increased (P

Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software has automated the process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by the photogrammetric research community. In this regard, this paper presents a transparent framework for the automated aerial triangulation of UAV images. The proposed framework is conducted in three steps. In the first step, two approaches, which take advantage of prior information regarding the flight trajectory, are implemented for reliable relative orientation recovery. Then, initial recovery of image exterior orientation parameters (EOPs) is achieved through either an incremental or global approach. Finally, a global bundle adjustment involving Ground Control Points (GCPs) and check points is carried out to refine all estimated parameters in the defined mapping coordinate system. Four real image datasets, which are acquired by two different UAV platforms, have been utilized to evaluate the feasibility of the proposed framework. In addition, a comparative analysis between the proposed framework and the existing commercial software is performed. The derived experimental results demonstrate the superior performance of the proposed framework in providing an accurate 3D model, especially when dealing with acquired UAV images containing repetitive pattern and significant image distortions.

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude. [ABSTRACT FROM AUTHOR]