ABSTRACT
Objectives: Coronavirus disease-19 (COVID-19)/influenza poses unprecedented challenges to the global economy and healthcare services. Numerous studies have described alterations in the microbiome of COVID-19/influenza patients, but further investigation is needed to understand the relationship between the microbiome and these diseases. Herein, through systematic comparison between COVID-19 patients, long COVID-19 patients, influenza patients, no COVID-19/influenza controls and no COVID-19/influenza patients, we conducted a comprehensive review to describe the microbial change of respiratory tract/digestive tract in COVID-19/influenza patients. Methods: We systematically reviewed relevant literature by searching the PubMed, Embase, and Cochrane Library databases from inception to August 12, 2023. We conducted a comprehensive review to explore microbial alterations in patients with COVID-19/influenza. In addition, the data on α-diversity were summarized and analyzed by meta-analysis. Results: A total of 134 studies comparing COVID-19 patients with controls and 18 studies comparing influenza patients with controls were included. The Shannon indices of the gut and respiratory tract microbiome were slightly decreased in COVID-19/influenza patients compared to no COVID-19/influenza controls. Meanwhile, COVID-19 patients with more severe symptoms also exhibited a lower Shannon index versus COVID-19 patients with milder symptoms. The intestinal microbiome of COVID-19 patients was characterized by elevated opportunistic pathogens along with reduced short-chain fatty acid (SCFAs)-producing microbiota. Moreover, Enterobacteriaceae (including Escherichia and Enterococcus) and Lactococcus, were enriched in the gut and respiratory tract of COVID-19 patients. Conversely, Haemophilus and Neisseria showed reduced abundance in the respiratory tract of both COVID-19 and influenza patients. Conclusion: In this systematic review, we identified the microbiome in COVID-19/influenza patients in comparison with controls. The microbial changes in influenza and COVID-19 are partly similar.
ABSTRACT
Numerous vehicle detection methods have been proposed to obtain trustworthy traffic data for the development of intelligent traffic systems. Most of these methods perform sufficiently well under common scenarios, such as sunny or cloudy days; however, the detection accuracy drastically decreases under various bad weather conditions, such as rainy days or days with glare, which normally happens during sunset. This study proposes a vehicle detection system with a visibility complementation module that improves detection accuracy under various bad weather conditions. Furthermore, the proposed system can be implemented without retraining the deep learning models for object detection under different weather conditions. The complementation of the visibility was obtained through the use of a dark channel prior and a convolutional encoder-decoder deep learning network with dual residual blocks to resolve different effects from different bad weather conditions. We validated our system on multiple surveillance videos by detecting vehicles with the You Only Look Once (YOLOv3) deep learning model and demonstrated that the computational time of our system could reach 30 fps on average; moreover, the accuracy increased not only by nearly 5% under low-contrast scene conditions but also 50% under rainy scene conditions. The results of our demonstrations indicate that our approach is able to detect vehicles under various bad weather conditions without the need to retrain a new model.
ABSTRACT
Dynamic voltage and frequency scaling (DVFS) is a well-known method for saving energy consumption. Several DVFS studies have applied learning-based methods to implement the DVFS prediction model instead of complicated mathematical models. This paper proposes a lightweight learning-directed DVFS method that involves using counter propagation networks to sense and classify the task behavior and predict the best voltage/frequency setting for the system. An intelligent adjustment mechanism for performance is also provided to users under various performance requirements. The comparative experimental results of the proposed algorithms and other competitive techniques are evaluated on the NVIDIA JETSON Tegra K1 multicore platform and Intel PXA270 embedded platforms. The results demonstrate that the learning-directed DVFS method can accurately predict the suitable central processing unit (CPU) frequency, given the runtime statistical information of a running program, and achieve an energy savings rate up to 42%. Through this method, users can easily achieve effective energy consumption and performance by specifying the factors of performance loss.
ABSTRACT
The main objectives of the research described in the present paper are to develop a semi-analysis model of water clarity for case 2 waters without inputting the absorption and scattering coefficient, which are not easy to be obtained for offshore marine areas so far. Based on the Zsd (Secchi depth)inversion theory, a simple semi-analysis spectra model was established for offshore seawater clarity by analyzing the relationship between vertical diffuse attenuation coefficient K(d) (490) and the beam attenuation coefficient c(490) with remote sensing reflectance. This semi-analysis spectra model needed two band reflectance ratios on- ly, while tidal correction was produced for this model to improve the precision of the retrieving results. The semi-analysis spectra model was applied to ASD hyperspectral reflectance data measured in the Pearl River Estuary Ecological Zone (October 21, 23, 2012, November 2, 2012; N=20) and the Xuwen Coral Reef Protection Zone (January 13, 14, 2013, N=25) which covered different water body of tidal times and different pollution sources. The results indicated that the changing tendency of predicted values was consistent with the synchronous measurement values after comparing them. However, water clarity calculated by the ASD hyperspectral reflectance measured in spring tidal time, generated 0. 4 m deviation compared with in-situ water clarity, while water clarity calculated by the ASD hyperspectral reflectance measured in neap tidal time is close to the in-situ water clarity. So the tidal correction coefficient of 0.4 was further applied for the model. After modification, the coefficient of determination between the inversed and measured water clarity was 0. 663, the average absolute error was 0. 14 m and the average relative error was 19.5%. Research demonstrated that this semi-analysis inversion algorithm just needs two band reflectance ratio to complete the inversion of water clarity, which is simple and works relatively well for lower clarity (less than 2 meters) waters compared to He' (2004) and Doron' (2011) algorithms.
ABSTRACT
By the methods of field survey and laboratory analysis, an investigation was conducted on the seasonal dynamics of biomass and carbon storage of Phragmites australis and Spartina alterniflora dominated vegetation belts in the Jiuduan Shoal Wetland of Yangtze Estuary, East China in 2010-2012. The organic carbon storage of the biomass (including aboveground part, underground part, and standing litter) of the two plants was the highest in autumn and the lowest in spring. The average carbon storage of the biomass of S. alterniflora per unit area (445.81 g x m(-2)) was much higher than that of P. australis (285.52 g x m(-2)), and the average carbon storage of the standing litter of S. alterniflora (315.28 g x m(-2)) was also higher than that of P. australia (203.15 g x m(-2)). However, the organic carbon storage in the surface soil (0-30 cm) under P. australis community (1048.62 g x m(-2)) was almost as twice times as that under S. alterniflora community (583.33 g x m(-2)). Overall, the carbon accumulation ability of P. australis community (3212.96 g x m(-2)) was stronger than that of the S. alterniflora community (2730.42 g x m(-2)). Therefore, it is of significance to protect the P. australis community in terms of carbon sequestration at the salt marsh.
