Rapid detection of hepatitis B virus DNA level based on interval-point data fusion of infrared spectra.

Hepatitis B is an infectious disease cause by the hepatitis B virus (HBV). In recent years, HBV-DNA level clinically gets more attention for its detailed information than other serological markers. Unfortunately, common clinical method for HBV-DNA level detection is limited for its hours consuming. This study combined infrared spectroscopy with machine learning to investigate the feasibility of near-infrared (NIR) and mid-infrared (MIR) spectra for rapid detection of HBV-DNA level. Based on partial least squares-discriminant analysis (PLS-DA) modeling method, the optimal NIR and MIR models and traditional data fusion models were constructed, respectively. Considering inequal weight between interval and point data in machine learning, interval-point data fusion method was used to compare with other traditional date fusion methods. The results of the study illustrate that interval-point data fusion of NIR and MIR spectra combined with PLS-DA modeling can provide a rapid method for HBV-DNA level detection.

Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging.

This research explored the feasibility of early warning and diagnostic visualization of Sclerotinia infected tomato by using hyperspectral imaging technology. Healthy tomato plants and tomato plants with Sclerotinia sclerotiorum were cultivated, and hyperspectral images at 400-1000 nm were collected from healthy and infected tomato leaves at 1, 3, 5, and 7 days of incubation. After preprocessing the spectra with first derivative (FD), second derivative (SD), standard normal variant (SNV), and multiplicative scatter correction (MSC) partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to construct tomato sclerotinia identification model and select the best preprocessing method. On this basis, two band screening methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were introduced to reduce data redundancy and improve the model's prediction accuracy. The results showed that the accuracy of the validation sets and operation speed of the CARS-PLS and CARS-SVM models were 87.88% and 1.8 s, and 87.95% and 1.78 s, respectively. The experiment was based on the SNV-CARS-SVM prediction model combined with image processing, spectral extraction, and visualization analysis methods to create diagnostic visualization software, which opens a new avenue to the implementation of online monitoring and early warning system for sclerotinia infected tomato.

Mitochondrial Porin Is Involved in Development, Virulence, and Autophagy in Fusarium graminearum.

Mitochondrial porin, the voltage-dependent anion-selective channel (VDAC), is the most abundant protein in the outer membrane, and is critical for the exchange of metabolites and phospholipids in yeast and mammals. However, the functions of porin in phytopathogenic fungi are not known. In this study, we characterized a yeast porin orthologue, Fgporin, in Fusarium graminearum. The deletion of Fgporin resulted in defects in hyphal growth, conidiation, and perithecia development. The Fgporin deletion mutant showed reduced virulence, deoxynivalenol production, and lipid droplet accumulation. In addition, the Fgporin deletion mutant exhibited morphological changes and the dysfunction of mitochondria, and also displayed impaired autophagy in the non-nitrogen medium compared to the wild type. Yeast two-hybrid and bimolecular fluorescence complementation assays indicated that Fgporin interacted with FgUps1/2, but not with FgMdm35. Taken together, these results suggest that Fgporin is involved in hyphal growth, asexual and sexual reproduction, virulence, and autophagy in F. graminearum.

Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy.

To monitor environmental water pollution effectively and meet human water needs, it is crucial to develop a fast, simple, and accurate method for monitoring chemical oxygen demand (COD) in various water systems. In this study, COD prediction models for different water systems were developed by combining near-infrared (NIR) spectroscopy with partial least squares regression (PLSR). Samples of wastewater, surface water, and seawater were collected from Guangzhou, Guangdong Province, China. Three pretreatment methods were used to preprocess the spectra in order to improve the accuracy and minimalism of the model. We investigate the performance of two variable selection algorithms, namely, binary gray wolf optimization (BGWO) and competitive adaptive reweighting sampling (CARS). The results show that both BGWO and CARS improved the performance of the model in terms of higher accuracy and less wavelength input; both of the combined model performances were better than that of PLSR alone, and CARS-PLSR achieved the best results. Using CARS-PLSR, surface water, wastewater, and seawater model inputs were reduced by 96 %, 96 %, and 82 % as compared to the PLSR results, respectively, and the testing sets R2 reached 0.860, 0.815, and 0.692, respectively. The spectral variable selection algorithm could identify the important spectral variables between COD content and NIR spectra in three water systems, thereby improving the accuracy and simplicity of the PLSR model for COD prediction. Our results have important practical value for predicting COD content in different water systems by NIR spectroscopy.

Genetic variation and phylogenetic relationship analysis of Jatropha curcas L. inferred from nrDNA ITS sequences.

Genetic variation and phylogenetic relationships among 102 Jatropha curcas accessions from Asia, Africa, and the Americas were assessed using the internal transcribed spacer region of nuclear ribosomal DNA (nrDNA ITS). The average G+C content (65.04%) was considerably higher than the A+T (34.96%) content. The estimated genetic diversity revealed moderate genetic variation. The pairwise genetic divergences (GD) between haplotypes were evaluated and ranged from 0.000 to 0.017, suggesting a higher level of genetic differentiation in Mexican accessions than those of other regions. Phylogenetic relationships and intraspecific divergence were inferred by Bayesian inference (BI), maximum parsimony (MP), and median joining (MJ) network analysis and were generally resolved. The J. curcas accessions were consistently divided into three lineages, groups A, B, and C, which demonstrated distant geographical isolation and genetic divergence between American accessions and those from other regions. The MJ network analysis confirmed that Central America was the possible center of origin. The putative migration route suggested that J. curcas was distributed from Mexico or Brazil, via Cape Verde and then split into two routes. One route was dispersed to Spain, then migrated to China, eventually spreading to southeastern Asia, while the other route was dispersed to Africa, via Madagascar and migrated to China, later spreading to southeastern Asia.