Search

The global repercussions of the COVID-19 pandemic on economies and public health worldwide have been profound. This study aims to examine the developmental trends of the COVID-19 pandemic, establish predictive models, and provide insights for effective control measures against potential future disease outbreaks. Considering the coexistence of both linear and nonlinear factors in COVID-19 data, conventional single-machine learning and traditional forecasting models encounter challenges in accurately predicting pandemic trends. To enhance the precision of COVID-19 pandemic predictions by integrating linear and nonlinear factors, this study proposes three combined forecasting models: CNN-LSTM-ARIMA, TCN-LSTM-ARIMA, and SSA-LSTM-ARIMA. These models leverage the strengths of deep learning in capturing nonlinear factors and the capabilities of the traditional ARIMA model in handling linear factors. Initially, LSTM and ARIMA models are used to model and predict the COVID-19 pandemic in Quebec, Canada. Subsequently, CNN models, TCN models, and the Sparrow Search Algorithm are employed to integrate predictions from the LSTM and ARIMA models. Comparative analyses of the three combined models, it was found that the CNN-LSTM-ARIMA model exhibits the highest predictive accuracy, with an MSE of 7048.26, RMSE of 83.95, MAE of 61.18, MAPE of 0.16, and $R^{2}$ of 0.95. To validate the applicability and stability of the CNN-LSTM-ARIMA model in predicting COVID-19 pandemics, Italian COVID-19 pandemic data was employed. The three combined forecasting models are established and evaluated using model evaluation metrics. The results affirm that the CNN-LSTM-ARIMA model remains the optimal choice, underscoring its high stability and suitability for COVID-19 pandemic forecasting endeavors.

COVID-19 has developed into a global public health emergency and has led to restrictions in numerous nations. Thousands of deaths have resulted from the infection of millions of individuals globally. Additionally, COVID-19 has had a significant impact on social and economic activity around the world. The elderly and those with existing medical issues, however, are particularly vulnerable to the effects of COVID-19. Pneumonia, acute respiratory distress syndrome, organ failure, death, etc. are all possible outcomes in severe cases…Traditional prediction approaches like the ARIMA model and multiple linear regression model to handle the linear prediction problem because the new crown virus is in the process of continual mutation. Deep learning models that can take into account nonlinear elements include BP neural network prediction and LSTM neural network prediction. To combine the benefits of traditional and deep learning predictive models and create superior predictive models, we can blend traditional and deep learning predictive models. When the MSE, RMSE, and MAE of these three combined models, PSO-LSTM-ARIMA, MLR-LSTM-ARIMA, and BPNN-LSTM-ARIMA, are compared. We discovered that the third model, which included MSE, RMSE, and MAE, had the best prediction accuracy. The LSTM model and the ARIMA model were selected for this investigation. To begin, it employed a single model to forecast pandemic data in Germany. The BP neural network, particle swarm method, and multiple linear regression were then utilized to merge it. To corroborate this finding, we re-predicted the epidemic data from Japan and retrieved the MSE, RMSE, and MAE values of the BPNN-LSTM-ARIMA model, which were 6141895.956, 2478.285 and 1249.832. The most accurate model is still this integrated model. The BP neural network coupled LSTM model and ARIMA model offers the highest accurate prediction effect, according to our research. Combinatorial models anticipate outbreak data through our study, which can aid governments and public health authorities in improving their responses and educating the public about pandemic trends and potential future directions. As a result, industries and enterprises may make better risk-management decisions to protect the health and safety of their operations and personnel. It also helps healthcare facilities better prepare and deploy medical resources to better meet the demands from the pandemic.

Summary: Boswellia sacra Flueck (family Burseraceae) tree is wounded to produce frankincense. We report its de novo assembled genome (667.8 Mb) comprising 18,564 high-confidence protein-encoding genes. Comparing conserved single-copy genes across eudicots suggest >97% gene space assembly of B. sacra genome. Evolutionary history shows B. sacra gene-duplications derived from recent paralogous events and retained from ancient hexaploidy shared with other eudicots. The genome indicated a major expansion of Gypsy retroelements in last 2 million years. The B. sacra genetic diversity showed four clades intermixed with a primary genotype—dominating most resin-productive trees. Further, the stem transcriptome revealed that wounding concurrently activates phytohormones signaling, cell wall fortification, and resin terpenoid biosynthesis pathways leading to the synthesis of boswellic acid—a key chemotaxonomic marker of Boswellia. The sequence datasets reported here will serve as a foundation to investigate the genetic determinants of frankincense and other resin-producing species in Burseraceae.

Heavy metal pollution is a global ecological safety issue, especially in crops, where it directly threatens regional ecological security and human health. Selecting scientific evaluation methods is an important prerequisite for understanding the distribution of heavy metals in a region. To evaluate the distribution characteristics of arsenic (As), cadmium (Cd), copper (Cu), and zinc (Zn) in farmland soil–rice system in Doumen District of Zhuhai City, Pearl River Delta, we analyzed the high geological background area and heavy metal contents in soil by inverse distance–weighted interpolation and single-factor pollution index. Bioconcentration factor (BCF) was used to study the migration and accumulation characteristics of heavy metals. Then, the soil–rice system was evaluated comprehensively with a novel evaluation method, i.e., the influence index of comprehensive quality (IICQ). Results showed that As, Cd, Cu, and Zn in the soil of the study area followed normal distribution. Cd and Cu were the main pollutants whose point contamination rates were 50% and 22.86%, respectively. A total of 2.86% of the soil were contaminated by As, and no Zn contamination was observed in the soil. At the same time, As and Cd in rice were partially polluted, and the Cu and Zn were not polluted. The order of bioaccumulation coefficient was Cd > Zn > Cu > As, and no evident enrichment was observed. According to the impact index of IICQ to evaluate the pollution of heavy metals in the soil–rice system, 96.98% of the soil in study area was in a state of moderate, heavy, and extreme pollution, which were concentrated in the northern and central parts of the study area. The soil–rice system in the high geological background area was in a subhealthy state. A total of 90.69% of the soil were polluted, but the rice met the national food safety standards. [ABSTRACT FROM AUTHOR]

Summary: As the largest cultivated fiber crop in the world, cotton (Gossypium hirsutum) is often exposed to various biotic stresses during its growth periods. Verticillium wilt caused by Verticillium dahliae is a severe disease in cotton, and the molecular mechanism of cotton resistance for Verticillium wilt needs to be further investigated. Here, we revealed that the cotton genome contains nine types of GST genes. An evolutionary analysis showed that a newly identified cluster (including Gh_A09G1508, Gh_A09G1509 and Gh_A09G1510) located on chromosome 09 of the A‐subgenome was under positive selection pressure during the formation of an allotetraploid. Transcriptome analysis showed that this cluster participates in Verticillium wilt resistance. Because the Gh_A09G1509 gene showed the greatest differential expression in the resistant cultivar under V. dahliae stress, we overexpressed this gene in tobacco and found that its overexpression resulted in enhanced Verticillium wilt resistance. Suppression of the gene cluster via virus‐induced gene silencing made cotton plants of the resistant cultivar Nongda601 significantly susceptible. These results demonstrated that the GST cluster played an important role in Verticillium wilt resistance. Further investigation showed that the encoded enzymes of the cluster were essential for the delicate equilibrium between the production and scavenging of H2O2 during V. dahliae stress. Significance statement: Evolution of the glutathione S‐transferases gene family was clarified in the Gossypium genus. A newly identified tau cluster in the A‐subgenome was shown to have undergone adaptive evolution, and provided Verticillium wilt resistance in cotton. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Modern Biomedicine is the property of Publishing House of Progress in Modern Biomedicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Resources & Environment / Nongye Ziyuan yu Huanjing Xuebao is the property of Journal of Agricultural Resources & Environment Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)