Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy

Visible and near-infrared (Vis–NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R2 = 0.02; P1 correction, validation R2 = 0.56; and P2 correction, validation R2 = 0.57. For MBL, the validation results were as follows: no correction, validation R2 = 0.06; P1 correction, validation R2 = 0.65; and P2 correction, validation R2 = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC

Fusion of visible-to-near-infrared and mid-infrared spectroscopy to estimate soil organic carbon

Spectral techniques such as visible-to-near-infrared (VIS-NIR) and mid-infrared (MIR) spectroscopies have been regarded as effective alternatives to laboratory-based methods for determining soil organic carbon (SOC). Research to explore the potential of the fusion of VIS-NIR and MIR absorbance for improving SOC prediction is needed, since each individual spectral range may not contain sufficient information to yield reasonable estimation accuracy. Here, we investigated two data fusion strategies that differed in input data, including direct concatenation of full-spectral absorbance and concatenation of selected predictors by optimal band combination (OBC) algorithm. Specifically, continuous wavelet transform (CWT) was adopted to optimize the spectral data before and after data fusion. Prediction models for SOC were developed using partial least squares regression. Results demonstrated that estimations for SOC using MIR absorbance (i.e., validation R-2 = 0.45-0.64) generally outperformed those using VIS-NIR (i.e., validation R-2 = 0.20-0.44). Compared to the raw absorbance counterparts, CWT decomposing could improve the prediction accuracy for SOC, for both the individual absorbance and the fusion of VIS-NIR and MIR absorbance. Among all the models investigated, the combinational use of VIS-NIR and MIR using OBC fusion at CWT scale of 1 yielded the optimal prediction, providing the highest validation R-2 of 0.66. This model with 10 selected spectral parameters as input is of small total data volume, large processing speed and efficiency, confirming the potential of OBC in fusing both types of spectral data. In summary, CWT decomposing and OBC strategy are powerful algorithms in analyzing the spectral data, and allow the VIS-NIR and MIR spectral fusion models to improve the SOC estimation

Transcriptome Analysis of Gene Expression Patterns Potentially Associated with Premature Senescence in <i>Nicotiana tabacum</i> L.

Senescence affects the remobilization of nutrients and adaption of the plant to the environment. Combined stresses can result in premature senescence in plants which exist in the field. In this study, transcriptomic analysis was performed on mature leaves and leaves in three stages of premature senescence to understand the molecular mechanism. With progressive premature senescence, a declining chlorophyll (chl) content and an increasing malonaldehyde (MDA) content were observed, while plasmolysis and cell nucleus pyknosis occurred, mitochondria melted, thylakoid lamellae were dilated, starch grains in chloroplast decreased, and osmiophilic granules increased gradually. Moreover, in total 69 common differentially expressed genes (DEGs) in three stages of premature senescing leaves were found, which were significantly enriched in summarized Gene Ontology (GO) terms of membrane-bounded organelle, regulation of cellular component synthesis and metabolic and biosynthetic processes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis suggested that the plant hormone signal transduction pathway was significantly enriched. The common DEGs and four senescence-related pathways, including plant hormone signal transduction, porphyrin and chlorophyll metabolism, carotenoid biosynthesis, and regulation of autophagy were selected to be discussed further. This work aimed to provide potential genes signaling and modulating premature senescence as well as the possible dynamic network of gene expression patterns for further study

A meta-analysis of the Timing of Chest Radiotherapy in Patients with Limited-stage Small Cell Lung Cancer

Background and objective Although evidence for a significant survival benefit of chest radiotherapy has been proven, no conclusion could be drawn regarding the optimal timing of chest radiation. The aim of this study is to explore whether the timing of chest radiation may influence the survival of the patients with limited-stage small-cell lung cancer (LSSCLC) by performing a literature-based meta-analysis. Methods By searching Medline, CENTRAL (the Cochrane central register of controlled trials), CBM, and CNKI, et al, we collected both domestic and overseas published documents about randomized trials comparing different timing chest radiotherapy in patients with LS-SCLC. Early chest radiation was regarded as beginning within 30 days after the start of chemotherapy. Random or fixed effect models were applied to conduct meta-analysis on the trials. The combined odds ratio (OR) and the 95% confidence interval (CI) were calculated to estimate the mortality in 2 or 3 years and toxicity of the two treatments. The statistical heterogeneity was determined by cochran’s Chi-square test (Q test). The Begg’ test was used to determine the publication bias. Results Six trials that included a total of 1 189 patients were analyzed in the meta-analysis 587 patients were in the early radiation group and 602 patients were in the late radiation group. Considering all 6 eligible trials, the overall survival at 2/3 years was not significantly different between early and late chest radiation (OR=0.78, 95%CI: 0.55-1.05, Z=1.68, P=0.093). For the toxicity, no obvious difference was observed for early chest radiotherapy compared with late irradiation in pneumonitis (OR=1.93, 95%CI: 0.97-3.86, P=0.797), esophagitis (OR=1.43, 95%CI: 0.95-2.13, P=0.572) and thrombocytopenia (OR=1.23, 95%CI: 0.88-1.77, P=0.746), respectively. Conclusion No statistical difference was observed in 2/3 years survival and toxicity, including pneumonitis, esophagitis and thrombocytopenia, between early radiation and late radiotherapy in LS-SCLC

The validity domain of sensor fusion in sensing soil quality indicators

Soil health has gained increasing attention under the rapid development of industrialization and the requirement for green agriculture. Therefore, up-to-date soil information related to soil health is urgently needed to ensure food security and biodiversity protection. Previous studies have shown the potential of proximal soil sensing in measuring soil information, while it remains challenging to get cost-efficient and robust estimates of multiple soil health indicators simultaneously via sensor fusion. In this study, we investigated the potential of visible near-infrared (vis-NIR), and mid-infrared (MIR) spectroscopy as well as three model averaging methods in predicting three soil health properties, including soil organic matter (SOM), pH, cation exchange capacity (CEC). The model averaging methods are not only used for model fusion but also for high-level sensor fusion, which include Granger-Ramanathan (GR), Bayesian Model Averaging and Spectral-Guided Ensemble Modelling (S-GEM). Here, S-GEM is a recently proposed algorithm that can improve soil spectroscopic prediction by including spectral information in ensemble modelling. Four widely used prediction models were evaluated, including partial least square regression, Cubist, memory based learning and convolutional neural network. For SOM, sensor fusion based on model averaging algorithms was comparable to that of Sensorsingle + Modelmultiple (MIR singly based on S-GEM) with R2 of 0.86. However, MIR only with S-GEM performed the best among all methods (LCCC of 0.92, RMSE of 3.66 g kg−1 and RPIQ of 3.68). The 10-fold cross-validation results indicated that Sensorsingle + Modelmultiple (MIR singly based on S-GEM) performed best among all methods for pH, with R2 of 0.84, LCCC of 0.90, RMSE of 0.45 and RPIQ of 3.65. For CEC, Sensormultiple + Modelmultiple based on GR performed best with R2 of 0.66, LCCC of 0.80, RMSE of 3.48 cmol + kg−1 and RPIQ was 2.22. Our results also showed that sensor fusion failed to improve spectral prediction of soil information when the performance among sensors differed a lot (△R2 > 0.2), and the use of a single best sensor is therefore suggested in this case. When the sensors have a close model performance (△R2 < 0.2), Sensormultiple + Modelmultiple based on GR was recommended. The outcome of this study can provide a reference for determining the validity domain of sensor fusion methods in improving the accuracy of soil health prediction

Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon : feature selection coupled with random forest

Rapid monitoring of soil organic carbon (SOC) with fine sampling resolution is vital for further understanding of the global carbon cycle and sustainable management of soil resources. Proximal visible and near-infrared (Vis-NIR) spectroscopy is an effective approach to analyze SOC. However, this technique can only be used for point-to-point monitoring and not for grid pixels evenly spread throughout the area. Airborne hyperspectral imagery with high-spectral- and spatial-resolution provides a promising tool for mapping topsoil SOC at a fine scale, but suffers from the interference of some external factors. Using 45 topsoil samples collected from an agricultural field in the United States, this study aimed to compare the potential of airborne hyperspectral image in estimating and mapping of bare topsoil SOC with that derived from proximal laboratory Vis-NIR spectral data. Random forest (RF) along with two advanced feature selection algorithms, namely, continuous wavelet transform (CWT) and competitive adaptive reweighted sampling (CARS), was applied to optimize the performance of the prediction models. Results showed that laboratory and airborne spectra presented similar spectral shapes and strengths, but laboratory spectral curves were smoother than airborne spectral curves, which were noisier. Laboratory spectra (R-2 = 0.79-0.87) performed better than airborne hyperspectral imagery (R-2 = 0.49-0.76) in cross-validation, regardless of feature selection algorithms. The CWT-RF models resulted in the highest cross-validation results for laboratory (R-2 = 0.87) and airborne (R-2 = 0.76) spectra, suggesting their robustness in SOC prediction. The SOC maps retrieved from full-spectrum-RF, CWT-RF, and CARS-RF models all exhibited similar spatial distribution patterns. With airborne hyperspectral imagery serving as a valuable data source at pixel level for digital soil mapping, the methodological framework proposed in this paper could improve the accuracy and reduce the prediction uncertainty of SOC maps by selecting and adopting the optimal subset of spectral variables

Combined mutations of the penA with ftsX genes contribute to ceftriaxone resistance in Neisseria gonorrhoeae and peptide nucleic acids targeting these genes reverse ceftriaxone resistance

ABSTRACT: Objectives: To investigate the gene mutations associated with ceftriaxone (CRO) resistance among gonococcal isolates, and to determine the effects of the mutated genes on CRO minimum inhibitory concentrations (MICs) with transformation assays and antisense peptide nucleic acids (asPNAs). Methods: Ceftriaxone-resistant (CROR) and ceftriaxone-susceptible (CROS) isolates were identified using EUCAST and paired according to similarity in their MICs to other antimicrobials. The two groups of gonococci were sequenced and analysed. Mutated genes that showed a statistical difference between the two groups were transformed into gonococcal reference strains to determine their functions. AsPNAs were designed and transformed into the former transformant to further confirm the effects of the mutated genes. Results: Twenty-two paired CROR and CROS isolates were obtained. The incidence of the penA-A501T and penA-G542S mutations individually, as well as combined mutations (penA-A501T and ftsX-R251H, penA-G542S and ftsX R251H), was statistically different between the two groups. The MIC of ATCC43069 (A43) increased 2 times following transformation with penA-A501T, and the MICs of A43 and ATCC49226 (A49) increased 32 times and 2 times following transformation with penA-A501T and ftsX-R251H, respectively. Antisense PNA-P3 reduced the MIC of the A43 transformant most significantly when transformed individually. PNA-P3 and PNA-F1 (asPNAs of the penA and ftsX) restored CRO susceptibility. Conclusions: PenA-A501T and penA-G542S mutations are important in CRO resistance among gonococci isolates. The ftsX-R251H mutation is also related to CRO resistance, and combined mutations of ftsX-R251H and penA-A501T comediate a significant reduction in CRO susceptibility. The combined application of PNA-P3 and PNA-F1 could effectively reverse the resistance to CRO in N. gonorrhoeae

Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy

Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas