Objectives
To investigate the diagnostic performance of the coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) for detecting COVID-19.
Methods
We searched PubMed, EMBASE, ...MEDLINE, Web of Science, Cochrane Library, and Scopus database until September 21, 2021. Statistical analysis included data pooling, forest plot construction, heterogeneity testing, meta-regression, and subgroup analyses.
Results
We included 24 studies with 8382 patients. The pooled sensitivity and specificity and the area under the curve (AUC) of CO-RADS ≥ 3 for detecting COVID-19 were 0.89 (95% confidence interval (CI) 0.85–0.93), 0.68 (95% CI 0.60–0.75), and 0.87 (95% CI 0.84–0.90), respectively. The pooled sensitivity and specificity and AUC of CO-RADS ≥ 4 were 0.83 (95% CI 0.79–0.87), 0.84 (95% CI 0.78–0.88), and 0.90 (95% CI 0.87–0.92), respectively. Cochran’s
Q
test (
p
< 0.01) and Higgins
I
2
heterogeneity index revealed considerable heterogeneity. Studies with both symptomatic and asymptomatic patients had higher specificity than those with only symptomatic patients using CO-RADS ≥ 3 and CO-RADS ≥ 4. Using CO-RADS ≥ 4, studies with participants aged < 60 years had higher sensitivity (0.88 vs. 0.80,
p
= 0.02) and lower specificity (0.77 vs. 0.87,
p
= 0.01) than studies with participants aged > 60 years.
Conclusions
CO-RADS has favorable performance in detecting COVID-19. CO-RADS ≥ 3/4 might be applied as cutoff values given their high sensitivity and specificity. However, there is a need for more well-designed studies on CO-RADS.
Key Points
•
CO-RADS shows a favorable performance in detecting COVID-19.
•
CO-RADS ≥ 3 had a high sensitivity 0.89 (95% CI 0.85–0.93), and it may prove advantageous in screening the potentially infected people to prevent the spread of COVID-19.
•
CO-RADS ≥ 4 had high specificity 0.84 (95% CI 0.78–0.88) and may be more suitable for definite diagnosis of COVID-19.
The train timetable and station operation plan play a critical role in the high‐speed railway (HSR) planning and management. The existing hierarchical optimization methods for the planning process of ...the HSR would affect the efficiency of train schedules and are often difficult to achieve an optimized scheme. This paper proposes a position‐track‐time three‐dimensional network, which describes the process of train operations in sections and stations at a macroscopic scale, while the track infrastructure including the position of insulation joints in stations are modeled microscopically. The modeled train running times and dwell times are based on standard timetable design values given in full minutes by the China Railway Corporation, while the interlocking times and minimum headway times are not specified explicitly. The problem is formulated as a large‐scale 0–1 linear integer programming model, which is solved using an extended branch‐and‐price algorithm. The effectiveness and precision of the model are verified through a real‐world case study on the Beijing–Shanghai HSR line. The results indicate that the proposed model can effectively improve the line capacity by 17.2% while ensuring that there is no conflict between train operations in sections and stations.
In recent decades, frequent compound dry and hot events have posed a great threat to humans and the ecological environment, especially in Inner Mongolia, which has arid and semi-arid characteristics. ...In this study, monthly temperature and precipitation data from 115 meteorological stations in Inner Mongolia from 1982 to 2020 were used to establish a standardized dry and hot index (SDHI). Theil–Sen median trend analysis, Mann–Kendall test, partial correlation analysis, and stepwise multiple regression models were used to characterize the changes in compound dry and hot events and the normalized difference vegetation index (NDVI) from 1982 to 2020, and the relationship between the SDHI and NDVI was quantitatively evaluated. The results showed that the overall SDHI values in Inner Mongolia showed a significant decrease at a rate of 0.03/year from 1982 to 2020, indicating an increase in the severity of compound dry and hot events. NDVI values showed a significant increasing trend and NDVI showed mutated 2001. Among the grassland vegetation types, SDHI and NDVI trends were more significant in forests, and meadow steppe, desert steppe, and desert were more susceptible to compound dry and hot events, and forests had the greatest severity of compound dry and hot events. The results of the partial correlation analysis showed that the average value of the partial correlation coefficient between the SDHI and NDVI was 0.68, and the area of positive correlation was 84.13%. Spatially, it showed strong response characteristics in the middle and gradual weakening towards the east and west sides. The correlation between NDVI and climatic conditions varied greatly in different vegetation areas. The forest area is most sensitive to the influence of temperature, and the desert steppe area is most affected by compound dry and hot events. The overall vegetation growth in Inner Mongolia was most affected by temperature conditions, followed by compound dry and hot conditions, and the influence of drought conditions was the least significant. The results of the relative importance analysis confirmed this. The research results provide a more detailed understanding of compound dry and hot events in arid and semi-arid regions and useful insights and support for ecological protection.
•The spectral characteristics of aquatic vegetations and yellow algae were analyzed.•Several spectral indices were proposed to distinguish aquatic vegetations and yellow algae.•Seasonal and inter ...annual dynamics of aquatic vegetations and yellow algae were investigated.•The responses of aquatic vegetations and yellow algae to air temperature were discussed.•The MAI concept is considered to be extendable and applicable.
Global lakes have suffered from algae blooms and loss or expansion of aquatic vegetations in recent decades. Remote sensing is considered as an effective approach to monitor aquatic vegetations and algae blooms. However, individual spectral index is unable to separate them due to the similarity in spectral features. In this paper, spectral characteristics analyses were conducted to find suitable spectral indices for distinguishing emergent vegetation, submerged aquatic vegetation and floating yellow algae in a complex aquatic environment, Ulansuhai Lake, China. It was found that near infrared band was appropriate to extract open water, and short-wave infrared band was suitable for extracting emergent vegetation, whereas the green and red bands were the characteristic spectra for distinguishing submerged aquatic vegetation and yellow algae. Hence, we firstly used the normalized difference vegetation index (NDVI) to extract open water, and the emergent vegetation spectral index (EVSI) to extract emergent vegetation. Then, a new developed macroalgae index (MAI) was used for distinguishing submerged aquatic vegetation and yellow algae. The applicability of the spectral indices was tested against both in situ measurements and Landsat-8 Operational Land Imager (OLI) data. The results indicated that the combination of these spectral indices was an effective method to separate aquatic vegetations and yellow algae. The proposed method was then applied to time-series Landsat images for investigating the seasonal and inter annual dynamics of aquatic vegetations and yellow algae and their responses to air temperature in the Ulansuhai Lake. The results show that emergent vegetation area increased from May to its maximum in July. The submerged aquatic vegetation area gradually increased from May to its maximum coverage in August, and decreased from late September. The yellow algae appeared in late May, and reached its maximum area in June or July, and disappeared in October. The long-term variation analyses showed that emergent vegetation area increased from 1986 to 2014, and was decreasing from 2014. The area of submerged aquatic vegetation increased during 1986–2008, and sharply decreased from 2009 to 2013, followed by a significant increasing from 2014. The yellow algae bloom mainly outbroke during the period of 1998 to 2010. We also found that the yellow algae area was more sensitive to short term mean temperature, while the area of emergent vegetation was sensitive to longer timescale of temperature. The submerged aquatic vegetation area had no significant correlation with air temperature. Besides, our study also indicated that the MAI concept can be extendable and applicable to other high-resolution satellite sensors (e.g., GF-2 PMS) and other regions with different algae blooms (e.g., Yellow Sea).
Excessive discharges of nitrogen and phosphorus nutrients lead to eutrophication in coastal waters. Optical remote sensing retrieval of the key eutrophication indicators, namely dissolved inorganic ...nitrogen concentration (DIN), soluble reactive phosphate concentration (SRP), and chemical oxygen demand (COD), remains challenging due to lack of distinct spectral features. Although machine learning (ML) has shown the potential, the retrieval accuracy is limited, and the interpretability is insufficient in terms of the black-box characteristics. To address these limitations, based on robust and explainable ML algorithms, we constructed models for retrieving DIN, SRP, and COD over coastal waters of Northern South China Sea (NSCS), which is experiencing prominent eutrophication. Retrieval models based on classification and regression trees (CART) ML algorithms were developed using 4038 groups of in situ observations and quasi-synchronous satellite images. A comparison of CART algorithms, including Random Forest, Gradient Boosting Decision Tree, and eXtreme Gradient Boosting (XGBoost), indicated the highest retrieval accuracy of XGBoost for DIN (R2 = 0.88, MRE = 24.39 %), SRP (R2 = 0.92, MRE = 33.27 %), and COD (R2 = 0.75, MRE = 18.58 %) for validation dataset. On the basis of spectral remote sensing reflectance, further inputs of ocean physio-chemical properties, spatio-temporal information, and inherent optical properties may reduce retrieval errors by 30.16 %, 19.85 %, and 3.95 %, respectively, and their combined use reduced errors by 54.71 %. Besides, explainable ML analysis characterized the contribution of input features and enhanced the transparency of ML black-box models. Based on the proposed models, 27,278 satellite images and spatio-temporal reconstruction method, 1-km resolution gap-free daily DIN, SRP, and COD products were constructed from 2002 to 2022 for the coastal waters of NSCS. Under the influence of urbanization and river discharge, nitrogen and phosphorus concentrations in this area were found to have increased by 6.09 % and 11.04 %, respectively, over the past 21 years, with the fastest rise in the Pearl River Estuary, where the eutrophic water area had shown an increase rate of approximately 112.66 km2/yr. The proposed robust and explainable ML retrieval models may support ocean environment management and water quality monitoring by providing key eutrophication indicators products over coastal waters.
Homogeneous precipitation process can effectively ensure a uniform and sufficient coverage of CdS on CdTe NRs array film.
Display omitted
High-density CdTe nanorod arrays are successfully embedded in ...a uniform and compact CdS layer, forming a novel three-dimensional (3D) CdTe NRs/CdS heterojunction structure. The CdS layer is prepared by homogeneous precipitation (HP) method using decomposition of urea. The effects of temperature and concentration of reactants on the growth and composition of CdS film are investigated in detail. The results demonstrate that the temperature affects the thermal decomposition of urea significantly, and the concentration of CdCl2 and CS (NH2)2 plays an essential role in the compositional ratio of CdS film. Further investigations reveal that, in comparison with the traditional precipitation method, a better coverage of CdS on the surface of CdTe NRs can be obtained by HP method due to the slow and even hydrolysis of urea. Moreover, photovoltaic performance of the novel CdTe NRs/CdS 3D photovoltaic device is also investigated. This study demonstrates that the 3D heterostructure has potential application in thin film solar cells, and the successful deposition of CdS layer on the surface of CdTe NRs by HP method suggests a promising technique for large-scale fabrication of these solar cells.
Abstract
This paper studies the text classification based on deep learning. Aiming at the problem of over fitting and training time consuming of CNN text classification model, a SDCNN model is ...constructed based on sparse dropout convolutional neural network. Experimental results show that, compared with CNN, SDCNN further improves the classification performance of the model, and its classification accuracy and precision can reach 98.96% and 85.61%, respectively, indicating that SDCNN has more advantages in text classification problems.
•Actinomorphic flower-like ZnO/ZnFe2O4 core-shell composite.•The formation of p-n heterojunction at the interface of ZnO/ZnFe2O4 composite.•Highly improved NO2 gas-sensing performances.
Actinomorphic ...flower-like ZnO/ZnFe2O4 composite has been successfully synthesized by a facile two-step synthesis method. The as-prepared samples have been characterized by XRD, SEM and TEM. Compared with pure-ZnO sensor, the ZnO/ZnFe2O4-based sensor exhibited remarkably higher response, shorter response-recovery time and more superior selective to low-concentration NO2 at low operating temperature of 200 °C. The mechanism of enhanced gas-sensing performance of the ZnO/ZnFe2O4 composite with p-n heterostructure has been investigated. It indicates that the as-prepared ZnO/ZnFe2O4 composite is promising for NO2 gas sensor.
The tea plant
Camellia sinensis
(L.) O. Kuntze is one of the most important leaf crops, and it is widely used for the production of non-alcoholic beverages worldwide. Tea also has a long history of ...medicinal use.
Colletotrichum camelliae
Massee is one of the dominant fungal pathogens that infects tea leaves and causes severe tea anthracnose disease. To analyze the molecular biology of
C. camelliae
, the quantification of pathogen gene expression by the RT-qPCR method is necessary. Reliable RT-qPCR results require the use of stable reference genes for data normalization. However, suitable reference genes have not been reported in
C. camelliae
thus far. In this study, 12 candidate genes (i.e.,
CcSPAC6B12.04c
,
CcWDR83
,
Cchp11
,
Ccnew1
,
CcHplo
,
CcRNF5
,
CcHpcob
,
CcfaeB-2
,
CcYER010C
,
CcRNM1
,
CcUP18
, and
CcACT
) were isolated from
C. camelliae
and assessed as potential reference genes. The expression stability of these genes in
C. camelliae
during spore germination and mycelial growth and interaction with host plants was first evaluated using several statistical algorithms, such as geNorm, NormFinder, and Bestkeeper. A web-based analysis program, Refinder, was then used to find the most suitable reference genes. Our results indicated that
Cenew1
,
CcHplo
, and
CcSPAC6B12.04c
were the most stable reference genes in
C. camelliae
under all conditions. Our work provided the most suitable reference genes for future studies performed to quantify the target gene expression levels of
C. camelliae.
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