Studies of Diaporthe (Diaporthaceae, Diaporthales) species associated with plant cankers in Beijing, China, with three new species described.

The genus Diaporthe (Diaporthaceae, Diaporthales) comprises endophytes, pathogens and saprophytes, inhabiting a wide range of woody hosts and resulting in serious canker disease. To determine the diversity of Diaporthe species associated with canker disease of host plants in Beijing, China, a total of 35 representative strains were isolated from 18 host genera. Three novel species (D.changpingensis, D.diospyrina and D.ulmina) and four known species (D.corylicola, D.donglingensis, D.eres and D.rostrata) were identified, based on morphological comparison and phylogenetic analyses using partial ITS, cal, his3, tef1-α and tub2 loci. These results provide an understanding of the taxonomy of Diaporthe species associated with canker diseases in Beijing, China.

Simultaneous identification of groundwater pollution source and important hydrogeological parameters considering the noise uncertainty of observational data.

Groundwater pollution identification is an inverse problem. When solving the inverse problem using regular methods such as simulation-optimization or stochastic statistical approaches, requires repeatedly calling the simulation model for forward calculations, which is a time-consuming process. Currently, the problem is often solved by building a surrogate model for the simulation model. However, the surrogate model is only an intermediate step in regular methods, such as the simulation-optimization method that also require the creation and solution of an optimization model with the minimum objective function, which adds complexity and time to the inversion task and presents an obstacle to achieving fast inversion. In the present study, the extreme gradient boosting (XGBoost) method and the back propagation neural network (BPNN) method were used to directly establish the mapping relationships between the output and input of the simulation model, which could directly obtain the inversion results of the variables to be identified (pollution sources release histories and hydraulic conductivities) based on actual observational data for fast inversion. In addition, to consider the uncertainty of observation data noise, the inversion accuracy of the two machine learning methods was compared, and the method with higher precision was selected for the uncertainty analysis. The results indicated that both the BPNN and XGBoost methods could perform inversion tasks well, with a mean absolute percentage error (MAPE) of 4.15% and 1.39%, respectively. Using the BPNN, with better accuracy for uncertainty analysis, when the maximum probabilistic density value was selected as the inversion result, the MAPE was 2.13%. We obtained the inversion results under different confidence levels and decision makers of groundwater pollution prevention and control can choose different inversion results according to their needs.

Morphology and molecular analyses reveal three new species of Botryosphaeriales isolated from diseased plant branches in China.

The Botryosphaeriales represents an ecologically diverse group of fungi, comprising endophytes, saprobes, and plant pathogens. In this study, taxonomic analyses were conducted based on morphological characteristics and phylogenetic analyses of multi-gene sequence data from four loci (ITS, LSU, tef1-α, and tub2). Thirteen isolates obtained from Beijing and Yunnan Province were identified as seven species of Botryosphaeriales, including Aplosporellajaveedii, Dothiorellaalpina, Phaeobotryonaplosporum and Ph.rhois, and three previously undescribed species, namely Aplosporellayanqingensis, Dothiorellabaihuashanensis, and Phaeobotryonplatycladi. Additionally, the new records of Dothiorellaalpina from the host species Populusszechuanica, Phaeobotryonaplosporum from Juglansmandshurica, and Phaeobotryonrhois from Populusalbavar.pyramidalis are included.

Diagnostic value of HA, PC-III, IV-C, and LN in infants with congenital biliary atresia.

Hyaluronic acid (HA), type III procollagen III (PC-III), type IV collagen IV (IV-C), and laminin (LN) have certain diagnostic value for hepatobiliary diseases. No published studies have compared the diagnostic accuracy of these 4 indicators for the diagnosis of congenital biliary atresia (CBA) in infants. This study aimed to investigate the diagnostic value of HA, PC-III, IV-C, and LN in infants with CBA. From January 2017 to December 2020, 185 infants with nonphysiological jaundice in the Second Department of General Surgery at the Children's Hospital of Hebei were enrolled in this study. Forty-six infants with CBA (CBA group) and 139 infants without CBA (noncongenital biliary atresia group) were diagnosed using ultrasonography, magnetic resonance imaging, intraoperative exploration, and intraoperative cholangiography. The levels of HA, PC-III, IV-C, and LN in the 2 groups were statistically analyzed. The diagnostic accuracy was determined using receiver operating characteristic curves and by calculating the area under the curve. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors. Compared to the noncongenital biliary atresia group, the levels of HA, PC-III, IV-C, and LN were significantly increased in the CBA group (P <.05). The receiver operating characteristic analysis showed the optimal cutoff values for HA, PC-III, IV-C, and LN were 162.7, 42.5, 199.7, and 101.2 ng/mL, and the area under the curves were 0.892, 0.762, 0.804, and 0.768, respectively. The sensitivity values for the diagnosis of CBA were 76.82%, 71.61%, 70.32%, and 72.28%, and the specificity values for the diagnosis of biliary atresia were 70.22%, 70.44%, 66.34%, and 68.71%, respectively. In the multivariate model, HA ≥162.7 ng/mL (odds ratio [OR] = 5.28, 95% confidence interval [CI]: 3.15-8.37), PC-III ≥42.5 ng/mL (OR = 4.61, 95% CI: 2.54-7.16), IV-C ≥199.7 ng/mL (OR = 5.02, 95% CI: 2.98-7.64), and LN ≥101.2 ng/mL (OR = 6.25, 95% CI: 2.41-10.07) remained associated with the occurrence of CBA. HA, PC-III, IV-C, and LN have high accuracy for the diagnosis of CBA in infants, and these factors are potential diagnostic biomarkers for CBA.

Recognition of a linear source contamination based on a mixed-integer stacked chaos gate recurrent unit neural network-hybrid sparrow search algorithm.

Groundwater contamination source recognition involves the recovery of contamination source time series release histories from observation data. In the present study, a linear source contamination recognition task was addressed. When using a simulation-optimization inverse framework to solve the recognition task, high calculated expense and high dimensional search space always hinder the task efficiency. Moreover, traditional surrogate methods face obstacle of handling with time-sequence data. Therefore, a novel stacked chaos gate recurrent unit (SCGRU) neural network was proposed as a surrogate model to precisely emulate the sequence to sequence mapping relationship of a high computational running simulation model. To address the challenge of high dimensional search, a mixed-integer programming strategy was employed to reduce the dimension of unknown variables. Furthermore, a hybrid sparrow search algorithm (HSSA) was implemented to alleviate being trapped into local optimum. In particular, the proposed SCGRU-HSSA framework was utilized to determine the length and release intensities during the stress period of a linear source. Based on the results obtained, the following conclusions were derived: (1) SCGRU can replace the origin simulation model with high accuracy and fast running speed; (2) when using chaos sine mapping and a Cauchy mutation strategy, the SSA escaped from the local optimum, improving the search efficiency of the recognition task; and (3) SCGRU-HSSA methodology is stable and reliable in recognizing features of linear source contamination.

Groundwater contamination source identification using improved differential evolution Markov chain algorithm.

The groundwater contamination source identification (GCSI) can provide important bases for the design of pollution remediation plans. The Bayesian theory is commonly used in the GCSI problem. Usually, we use the Markov chain Monte Carlo (MCMC) method to realize the Bayesian framework. However, due to the ill-posed nature of the GCSI and the system model's complexity, the conventional MCMC algorithm is time-consuming and has low accuracy. In this study, we proposed an adaptive mutation differential evolution Markov chain (AM-DEMC) algorithm. In this algorithm, the Kent mapping chaotic sequence method, combined with differential evolution (DE) algorithm, was used to generate the initial population. In the iteration process, we introduced a hybrid mutation strategy to generate the candidate vectors. Moreover, we adaptively adjust the essential parameter F of the AM-DEMC algorithm according to the individual fitness value. For further improving the efficiency of solving the GCSI problem, the Kriging method was used to establish a surrogate model to avoid the enormous computational load associated with the numerical simulation model. Finally, a hypothetical groundwater contamination case was given to verify the effectiveness of the AM-DEMC algorithm. The results indicated that the proposed AM-DEMC algorithm successfully identified the contamination sources' characteristics and simulation model's parameters. It also exhibited stronger search-ability and higher accuracy than the MCMC and DE-MC algorithms.

Effect of manipulative reduction combined with air enema on intestinal mucosal immune function in children with intussusception.

OBJECTIVE: To explore the effect of manipulative reduction combined with air enema on intestinal mucosal immune function in children with intussusception. METHODS: This is a prospective randomized controlled study in which 60 children with primary intussusception admitted to Hebei Children's Hospital from October 2018 to October 2019 were selected for this study. They were randomly divided into two groups. The 30 patients in the experimental group underwent manipulative reduction and air enema reduction, and 30 patients in the control group underwent only air enema reduction. Pain scores and pressure during enema were recorded and analyzed. Fasting blood of children in the experimental group were drawn to test the serum T lymphocyte subsets CD3+, CD4+, CD8+ levels, B lymphocyte subsets CD19+ level, and NK cell subsets CD56+ levels before reduction. Among them, fasting blood of 28 children with successful reduction were drawn again in the morning after reduction, and the indicators of each immune cell subgroup before and after reduction were analyzed. Two children with unsuccessful reduction were no longer tested for these indicators. RESULTS: Twenty-Eight children in the experimental group had successful reduction, and two children with unsuccessful reduction were changed to open surgery (28/30). Twenty five Children in the control group had successful reduction, and five were changed to open surgery (25/30). There was no significant difference in the success rate of reduction between two groups (p>0.05). Close observation for 12~24h after reduction found that none of the children had signs of peritonitis. The pain score and reduction pressure of the observation group were lower than those of the control group, and the difference was statistically significant (p<0.05). The levels of serum CD3+, CD4+, and CD8+ after reduction in the experimental group were significantly higher than before reduction, and the difference was statistically significant (p<0.05). CD19+ level was significantly lower than before reduction, and the difference was statistically significant (p<0.05). There was no significant difference in changes of other indicators. CONCLUSIONS: Manipulative reduction combined with air enema reduction can relieve pain and air injection pressure during enema, reduce reperfusion injury caused by intestinal ischemia, and protect intestinal mucosal immune function, which is a favored treatment.

Groundwater contamination sources identification based on kernel extreme learning machine and its effect due to wavelet denoising technique.

Measurements of contaminant concentrations inevitably contain noise because of accidental and systematic errors. However, groundwater contamination sources identification (GCSI) is highly dependent on the data measurements, which directly affect the accuracy of the identification results. Thus, in the present study, the wavelet hierarchical threshold denoising method was employed to denoise concentration measurements and the denoised measurements were then used for GCSI. A 0-1 mixed-integer nonlinear programming optimization model (0-1 MINLP) based on a kernel extreme learning machine (KELM) was applied to identify the location and release history of a contamination source. The results showed the following. (1) The wavelet hierarchical threshold denoising method was not very effective when applied to concentration measurements observed every 2 months (the number of measurements is small and relatively discrete) compared with those obtained every 2 days (the number of measurements is large and relatively continuous). (2) When the concentration measurements containing noise were employed for GCSI, the identifications results were further from the true values when the measurements contained more noise. The approximation of the identification results to the true values improved when the denoised concentration measurements were employed for GCSI. (3) The 0-1 MINLP based on the surrogate KELM model could simultaneously identify the location and release history of contamination sources, as well reducing the computational load and decreasing the calculation time by 96.5% when solving the 0-1 MINLP.

[Effects of postconditioning on autophagy of lung ischemic reperfusion injury in rats].

OBJECTIVE: To explore the effects of postconditioning on autophagy of lung injury in situ during lung ischemic reperfusion. METHODS: Twenty-four male Sprague-Dawley rats were randomly divided into 3 groups of sham-operated (S), ischemic-reperfusion (I/R) and ischemic postconditioning (IpostC) (n = 8 each). All underwent left thoracotomy after anesthesia. In the S group, a line was only placed around left hilum but not fastened. In the I/R group, a line was fastened to block the blood flow of left lung for 30 min and then loosened for reperfusion for 120 min. In the IpostC group, after blocking the blood flow of left lung for 30 min, left hilum was fastened for 30 sec and loosened for 30 sec. Lung tissues were measured by Western blot. Histopathological changes of lung tissues were observed, lung injury scores calculated and autophagic vacuoles determined by electron microscope. RESULTS: The relative expression levels of mammalian target of rapamycin (mTOR) and phosphorylated mTOR (p-mTOR) in group I/R (0.40 ± 0.03, 0.33 ± 0.10) were not different with those of group S (0.37 ± 0.07, 0.31 ± 0.10) (both P > 0.05). However, both significantly increased in group IpostC (0.46 ± 0.09, 0.55 ± 0.07) (both P < 0.05). As compared with group S, the relative expression level of LC3-IIand lung injury score significantly increased in groups I/R and IpostC (0.53 ± 0.08, 0.38 ± 0.03 vs 0.25 ± 0.06; 15.79 ± 1.33, 11.67 ± 1.55 vs 5.58 ± 0.39) while obviously declined in group IpostC versus group I/R (all P < 0.05). In group I/R, neutrophil infiltration, interstitial edema, atelectasis and hyaline membrane formation were observed microscopically in lung tissues and the formation of autophagic vacuoles was evident under electron microscope. The changes of group IpostC were milder than those of group I/R. CONCLUSIONS: Ischemic postconditioning has protective effects on lung ischemic reperfusion injury by attenuating autophagy. It may be related with strengthening mTOR.