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The frequent occurrence of various sudden natural disasters in the world has caused heavy losses to human beings. It is very important to forecast the demand of emergency materials in order to protect people's safety and property. The purpose of this study is to use IACO-BP algorithm to forecast the demand of emergency supplies in the case of flood disaster. In this study, the flood disaster situation in recent years published by the Ministry of Water Resources of China is selected as the experimental data set. The data are sorted and analyzed by a variety of theoretical comprehensive operation, qualitative and quantitative research method, analytic hierarchy process and system dynamics method. The improved ant colony optimization algorithm is used to model the emergency material demand, and the population, flood level, flood level of the disaster area are analyzed. As the network input, the material situation outputs the material demand, so as to forecast. The results show that the iteration times of IACO-BP algorithm are 11 and the running time is 3S. The fluctuation of IACO-BP algorithm is the least and the most stable among the three algorithms. The material satisfaction degree predicted by IACO-BP algorithm is improved by 15% from the original 80.9%. It is concluded that this algorithm is very accurate and efficient in the prediction of emergency material demand, which can better assist the disaster situation. This study contributes to the prediction of emergency material demand for emergency disaster. [ABSTRACT FROM AUTHOR]

Background and Objectives: Advanced esophageal squamous cell carcinoma (ESCC) is a prevalent and highly malignant tumor with a poor prognosis. Recently, the RATIONALE-306 trial demonstrated that tislelizumab combined with chemotherapy provided overall survival benefits for these patients. This study aimed to assess the cost-effectiveness of this treatment approach in Chinese patients with advanced ESCC from the perspective of healthcare system. Methods: A Markov model was constructed to assess the economic and health benefits associated with tislelizumab plus chemotherapy over a 10-year lifetime horizon, utilizing data from the RATIONALE-306 trial. The analysis encompassed the calculation of several key parameters, including the incremental cost-effectiveness ratio (ICER), total cost, incremental cost, total effectiveness, and incremental effectiveness. Tislelizumab was considered cost-effective if the ICER obtained was below the willingness-to-pay (WTP) threshold of US$38,223 per quality-adjusted life-year (QALY); otherwise, it would be deemed not cost-effective. To ensure the robustness of the findings, the results were subjected to one-way sensitivity analysis and probabilistic sensitivity analysis (PSA). Results: In the base-case analysis, the incremental effectiveness and cost associated with tislelizumab plus chemotherapy, compared to chemotherapy alone, were determined to be 0.40 QALY and US$7604, respectively. This resulted in an ICER of US$18,846 per QALY, which is below the WTP threshold of US$38,223 per QALY. Furthermore, the results from the one-way sensitivity analysis and PSA indicated robustness of the findings. Conclusion: Our lifetime simulation study demonstrated that, in the case of advanced ESCC, the combination of tislelizumab and chemotherapy offers increased effectiveness compared to chemotherapy alone, albeit at a higher cost. Moreover, considering the current WTP threshold in China, the addition of tislelizumab to chemotherapy is considered a cost-effective approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • Different window sizes will affect the monitoring accuracy of soil moisture; • It is feasible to select the optimal window size with the local variogram; • The window size of 13 * 13 is the optimal window size applicable in the study area. Soil moisture content monitoring with UAV remote sensing always involves the selection of an appropriate window size for spectral information extraction, but research on the effect of window size on the accuracy of soil moisture content monitoring models and the selection of the optimal window size has been rarely reported. To solve these problems, an experiment was conducted on three typical bare plots in Shahaoqu Experimental Station at Hetao Irrigation District, Inner Mongolia, China. First, remote sensing images were obtained from the three bare plots from April 15 through 17, 2019, with a six-rotor UAV equipped with a six-channel multispectral camera. Synchronously, the moisture content at 0–10 cm of the surface soil was measured using the drying method. Then, the spectral information was extracted through windows of 16 different sizes (ranging from 1 * 1 to 31 * 31). Followed was the construction of thirty spectral indices using the ratio and normalized ratio methods, and the processing of the constructed indices using principal component analysis. The principal components accounting for 95% of the cumulative contribution rate were selected as the input variables for the construction of the monitoring models based on BP neural network. Finally, the model accuracy was tested using ANOVA, and the local variogram of the spectrum was used to explore the optimal window size selection. The results demonstrated: (1) There are differences in the spectral information extracted from different sizes of windows, which affects the accuracy of soil moisture monitoring model; (2) The spatial autocorrelation threshold of the plots at the local variogram was 13 * 13, resembling the window size with the highest accuracy, so it is feasible to select the optimal window size with the local variogram; (3) As the window size of spectral information increased, R2 first increased and then decreased, reaching the maximum value of 0.261 at the size of 13 * 13, and RMSE first decreased and then increased, reaching the minimum value of 0.017 when at the size of 7 * 7. These results can provide some reference for window size selection in spectral information extraction to monitor soil moisture content. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the main problems hindering the agricultural development in the arid and semi-arid regions. However, many previous studies of soil salinization monitoring failed to consider the impact of irrigation on soil salinity. To explore such impact, we used a UAV with a portable spectrometer to monitor the dynamic change of soil salinity before and after spring irrigation. In this study, 120 soil samples at the surface (0–10 cm) were taken in Shahaoqu Irrigation Area, Inner Mongolia, China, in mid-April (before spring irrigation) and mid-June (after spring irrigation), and the images of the four study zones A, B, C and D in this area were obtained from the UAV system. Based on these images, 25 spectral covariates (6 spectral bands, 16 spectral indices, and 3 two-dimensional indices) were calculated. Then, the sensitive spectral covariates were selected with such different variable selection methods as variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and genetic algorithm (GA). Finally, Multiple Linear Regression (MLR) and BP neural network algorithm (BPNN) was used to establish models for soil salinity inversion, based on which salinity maps of the study area were plotted. The results showed that the soil salinity of the study zone A (covered with vegetation) increased while that of the other three zones decreased after the spring irrigation. The general tendency was: the higher the original soil salinity was, the more obvious decrease the soil salinity had after the irrigation. All the three variable selection methods improved the inversion model accuracy, but the GA-BPNN model had the best performance (RP2 = 0.78, RMSEP = 0.16, and RPD = 2.13 before spring irrigation; and RP2 = 0.80, RMSEP = 0.14, and RPD = 2.26 after spring irrigation). It indicated that relatively accurate regional salinity maps could be plotted based on the spectral indices selected by GA and the salinity inversion model built on BPNN. These results have certain reference for soil salinization monitoring and farmland irrigation using UAV multispectral remote sensing. [ABSTRACT FROM AUTHOR]

Background: An unexpected dengue outbreak occurred in Hunan Province in 2018. This was the first dengue outbreak in this area of inland China, and 172 cases were reported.Methods: To verify the causative agent of this outbreak and characterise the viral genes, the genes encoding the structural proteins C/prM/E of viruses isolated from local residents were sequenced followed by mutation and phylogenetic analysis. Recombination, selection pressure, potential secondary structure and three-dimensional structure analyses were also performed.Results: Phylogenetic analysis revealed that all epidemic strains were of the cosmopolitan DENV-2 genotype and were most closely related to the Zhejiang strain (MH010629, 2017) and then the Malaysia strain (KJ806803, 2013). Compared with the sequence of DENV-2SS, 151 base substitutions were found in the sequences of 89 isolates; these substitutions resulted in 20 non-synonymous mutations, of which 17 mutations existed in all samples (two in the capsid protein, six in the prM/M proteins, and nine in the envelope proteins). Moreover, amino acid substitutions at the 602nd (E322:Q → H) and 670th (E390: N → S) amino acids may have enhanced the virulence of the epidemic strains. One new DNA binding site and five new protein binding sites were observed. Two polynucleotide binding sites and seven protein binding sites were lost in the epidemic strains compared with DENV-2SS. Meanwhile, five changes were found in helical regions. Minor changes were observed in helical transmembrane and disordered regions. The 429th amino acid of the E protein switched from a histamine (positively charged) to an asparagine (neutral) in all 89 isolated strains. No recombination events or positive selection pressure sites were observed. To our knowledge, this study is the first to analyse the genetic characteristics of epidemic strains in the first dengue outbreak in Hunan Province in inland China.Conclusions: The causative agent is likely to come from Zhejiang Province, a neighbouring province where dengue fever broke out in 2017. This study may help clarify the intrinsic geographical relatedness of DENV-2 and contribute to further research on pathogenicity and vaccine development. [ABSTRACT FROM AUTHOR]

• A large-scale dengue fever outbreak occurred in the China, Myanmar, and Laos border area in 2019. • Three dengue virus (DENV) serotypes were found to be co-circulating, which is very rare in this area. • DENV-1 and DENV-2 were the main pathogenic agents of this outbreak. • The co-circulation may have led to more serious clinical symptoms and a larger scale epidemic. Dengue fever was included in the top 10 global health threats announced by the World Health Organization (WHO) in early 2019. In some southern provinces of China, autochthonous outbreaks have also been reported over the last decade. An unexpected large outbreak of dengue fever was reported in Xishuangbanna, a border area of China, Myanmar, and Laos, in 2019. Among the 226 hospitalized cases, 90 were diagnosed as severe dengue according to the 2009 WHO guidelines. Serotyping and phylogenetic analyses of envelope gene sequences from 246 randomly selected samples showed that three serotypes of dengue virus were co-circulating in this outbreak, which is very rare in this area. Dengue virus serotype 1 (DENV-1, genotype I) and serotype 2 (DENV-2, Cosmopolitan genotype and Asian genotype) were the main pathogenic agents of this outbreak. Dengue virus serotype 3 (DENV-3) epidemic strains were classified as genotype III and formed a close cluster with the Thailand 2015 epidemic strain. The co-circulation may have led to more serious clinical symptoms and a larger scale epidemic. This finding is of great importance in understanding the circulation of DENV and to strengthen the detection and management of dengue fever in border areas. [ABSTRACT FROM AUTHOR]

Highlights • The first DENV-1 outbreak occurred in Xishuangbanna, a border area of China, Burma and Laos in 2017. • Dengue isolated strains show diversification between Burma and China. • Dengue fever has become a disease circulating in the interior of China. • Amino acid substitution (I440T) may lead to weakened virulence of the epidemic strains. • The diversity of global DENV-1 highly increased in 1972 and then slightly decreased after that until 2017. Abstract Background Xishuangbanna, a border area of China, Burma and Laos, had its first major DENV-1 outbreak in 2017. This study aims to explore the genetic characterization, potential source and evolution of the viruses in outbreak. Methods The structural protein C/prM/E genes of viruses isolated from local residents or Burmese travelers were sequenced followed by mutation, phylogenetic, homologous recombination, molecular clock and demographic reconstruction analysis. Results Phylogenetic analysis revealed that all of the strains were classified as three cluster of DENV-1. Cluster 1, 2 and 3 were most similar to China Guangzhou 2011, China Hubei 2014 and Laos 2008 strain, respectively. Among 236 base mutations, 31 caused nonsynonymous mutations when compared with the DENV-1SS. No homologous recombination signal was discovered. The structural protein of these strains had similar three-dimensional structure. Only site 434 showed differences among five predicted protein binding sites. Molecular clock phylogenetic and demographic reconstruction analysis showed that DENV-1 became highly diversified in 1972 followed by a slightly decreased period until 2017. Conclusions Dengue isolated strains show diversification between Burma and China. Amino acid substitution (I440T) may lead to weakened virulence of the epidemic strains. DENV-1 became highly diversified in 1972 followed by a slightly decreased period. [ABSTRACT FROM AUTHOR]

In the past few decades, dengue has spread rapidly and is an emerging disease in China. An unexpected dengue outbreak occurred in Xishuangbanna, Yunnan, China, resulting in 1331 patients in 2013. In order to obtain the complete genome information and perform mutation and evolutionary analysis of causative agent related to this largest outbreak of dengue fever. The viruses were isolated by cell culture and evaluated by genome sequence analysis. Phylogenetic trees were then constructed by Neighbor-Joining methods (MEGA6.0), followed by analysis of nucleotide mutation and amino acid substitution. The analysis of the diversity of secondary structure for E and NS1 protein were also performed. Then selection pressures acting on the coding sequences were estimated by PAML software. The complete genome sequences of two isolated strains (YNSW1, YNSW2) were 10,710 and 10,702 nucleotides in length, respectively. Phylogenetic analysis revealed both strain were classified as genotype II of DENV-3. The results indicated that both isolated strains of Xishuangbanna in 2013 and Laos 2013 stains (KF816161.1, KF816158.1, LC147061.1, LC147059.1, KF816162.1) were most similar to Bangladesh (AY496873.2) in 2002. After comparing with the DENV-3SS (H87) 62 amino acid substitutions were identified in translated regions, and 38 amino acid substitutions were identified in translated regions compared with DENV-3 genotype II stains Bangladesh (AY496873.2). 27(YNSW1) or 28(YNSW2) single nucleotide changes were observed in structural protein sequences with 7(YNSW1) or 8(YNSW2) non-synonymous mutations compared with AY496873.2. Of them, 4 non-synonymous mutations were identified in E protein sequences with (2 in the β-sheet, 2 in the coil). Meanwhile, 117(YNSW1) or 115 (YNSW2) single nucleotide changes were observed in non-structural protein sequences with 31(YNSW1) or 30 (YNSW2) non-synonymous mutations. Particularly, 14 single nucleotide changes were observed in NS1 sequences with 4/14 non-synonymous substitutions (4 in the coil). Selection pressure analysis revealed no positive selection in the amino acid sites of the genes encoding for structural and non-structural proteins. This study may help understand the intrinsic geographical relatedness of dengue virus 3 and contributes further to research on their infectivity, pathogenicity and vaccine development. [ABSTRACT FROM AUTHOR]