Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm

The development of multi-source remote sensing technologies is helpful for geologists to obtain more comprehensive and complete lithological maps. In recent years, establishing automatic classification models based on Machine Learning (ML) algorithms has become an important approach to identify various lithologies supported by remote sensing data. Aiming at the specific geological and geographical conditions in a semi-arid area, Duolun County, Inner Mongolia Autonomous Region, China, this paper integrated GaoFen-2 (GF-2), Sentinel-2A, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and GaoFen-3 (GF-3) remote sensing data, and used Support Vector Machine (SVM) classifier on the basis of Particle Swarm Optimization (PSO) to carry out the lithology classification. Firstly, on the basis of removing the interference of vegetation information from the acquired remote sensing data, a 63-dimensional candidate feature sequence was constructed by extracting spectral, backscattering, polarization and texture features. Secondly, an improved PSO algorithm with the Inertia Factor changing with the S-curve Decreasing function (SDIF-PSO) was proposed, and on this basis, a feature selection and lithology classification algorithm using SVM classifier based on two-layer SDIF-PSO was designed. Finally, the iterative optimization process of multiple optimization algorithms for SVM model parameters and the lithology classification accuracy before and after feature selection were compared. The experimental results showed that the proposed SDIF-PSO algorithm had the best optimization capability, with the highest cross-validation accuracy of 90.90%, which was improved by 3.85% than that of Grid-Search Optimization (GSO) algorithm, and 0.15% than that of the improved PSO algorithm with the Inertia Factor changing with the Linear Decreasing function (LDIF-PSO) and the improved PSO algorithm with the Inertia Factor Decreasing with the Concave function (CDIF-PSO). The dimension of the best feature combination was reduced to 35 through feature selection, and the convergence cross-validation accuracy reaches 92.14%, which was improved by 1.24% than that of all 63-dimensional candidate features in the same optimization process using SDIF-PSO algorithm

Differential crustal deformation across the Cona-Oiga rift, southern Tibetan Plateau

The Cona-Oiga rift (COR) is the easternmost member of a series of ∼N-S trending Cenozoic rifts in the southern Tibetan Plateau. The Yarlung River flows from west to east, across the COR and exhibits diverse river morphology along strike. We analyze tributary fluvial longitudinal profiles in the Yarlung drainage and the published strain rate field on both sides of the rift in order to gain knowledge about its development and crustal deformation patterns within the plateau interior. Tributaries on the western side of the rift have relatively low normalized steepness index (k ). Concave curves in χ-elevation plots and cross-divide contrasts in χ, mean relief, and mean gradient, indicate ongoing river reorganization in this region. In contrast, high topographic relief and steep stream channels developed on the eastern side. In addition, spatial variations in negative dilatational strain rates indicate diverse crustal shortening and regional uplift across the COR. We suggest that the Cona-Oiga rift plays an important role in accommodating deformation associated with the continuing indentation of the eastern Himalayan syntaxis. Tectonic forcing might be the dominant factor controlling landscape evolution across the COR

Prognostic significance of preoperative serum tumor markers in hepatoid adenocarcinoma of stomach (HAS)

Abstract Background The role of preoperative serum tumor markers in HAS patients was vague, we designed the study to explore the effect of preoperative serum tumor markers on predicting the prognosis of HAS patients. Methods A total of 139 patients were included according to the different tumor makers. X-tile tool was employed to identify the optimal cut-off values of respective tumor makers. Multivariate analyses were conducted to determine independent risk factors. Results The optimal cut-off value of alpha-fetoprotein (AFP) for 3-years overall survival (OS) and recurrence-free survival (RFS) was 516 ng/mL. Patients with high-level AFP values assumed significantly worse OS and RFS than those with low-level AFP values (P = 0.028 and P = 0.011, respectively). The optimal cut-off value of Carbohydrate antigen (CA)19–9 for OS and RFS was 51.3 U/mL. And the survival results were similar with AFP in the aspects of OS and RFS (P = 0.009 and P < 0.001, respectively). Multivariate analyses showed that high serum AFP was an independent risk factor for OS and RFS of HAS patients (HR7.264; 95% CI 1.328–39.738; P = 0.022 and HR 2.688; 95% CI 0.922–7.836; P = 0.070, respectively). CA19–9 could perform as a fair substitute to predict the HAS patients’ OS and RFS when the preoperative serum AFP was unavailable (HR 7.816; 95% CI 2.084–29.308; P = 0.002 and HR 4.386; 95% CI 1.824–10.547; P = 0.001, respectively). Other tumor markers didn’t present significant influences. Conclusions Applying preoperative serum AFP level to predict the HAS patients’ prognosis is feasible and preoperative serum high-AFP is an independent risk factor for OS and RFS of HAS patients. Preoperative serum CA19–9 could be an alternative choice when AFP was absent

Construction of lncRNA-m6A gene-mRNA regulatory network to identify m6A-related lncRNAs associated with the progression of lung adenocarcinoma

Abstract Background We evaluated the prognostic value of m6A-related long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD). Methods The expression levels of lncRNAs and mRNAs in LUAD and normal adjacent tissues from The Cancer Genome Atlas dataset were analyzed using the limma package. m6A enzyme-related differentially expressed lncRNAs and mRNAs were identified and used to construct a regulatory network. Survival analysis was performed and the correlation between lncRNAs, m6A regulators, and mRNAs was analyzed; followed by functional enrichment analysis. Results A comparison of LUAD samples and normal tissues identified numerous differentially expressed lncRNAs and mRNAs, demonstrating that a comprehensive network was established. Two lncRNAs and six mRNAs were selected as prognosis related factors including SH3PXD2A-AS1, MAD2L1, CCNA2, and CDC25C. The pathological stage and recurrence status were identified as independent clinical factors (P < 0.05). The expression levels of these RNAs in the different clinical groups were consistent with those in the different risk groups. The interactions of m6A proteins, two lncRNAs, and six mRNAs were predicted, and functional analysis showed that m6A target mRNAs were involved in the cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis pathways. Conclusions These m6A target lncRNAs and mRNAs may be promising biomarkers for predicting clinical prognosis, and the lncRNA-m6A regulator-mRNA regulatory network could improve our understanding of m6A modification in LUAD progression

Simultaneous detection of 15 antibiotic growth promoters in bovine muscle, blood and urine by UPLC-MS/MS

<p>An analytical method was established for the rapid detection of antibiotic growth promoters (AGPs) in bovine muscle, and bovine blood and bovine urine, using ultra high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). After the addition of an aqueous solution of EDTA-Na₂, the pH of bovine urine samples was directly adjusted to 5.2 by acetic acid-ammonium acetate and purified by HLB solid-phase extraction cartridge; bovine muscle and bovine blood samples processing were extracted with acetonitrile (ACN) and ACNwater (90:10; v/v) without any purification step. The samples were then centrifuged, concentrated and analysed by UPLC-MS/MS on an ACQUITY UPLC® BEH C18 column using gradient elution. The developed method was validated and mean recovery percentages at three spiked levels were 74–119%, 76–115% and 76–119%, respectively, in bovine muscle, bovine blood, and bovine urine. The relative standard deviation (RSD) ranged from 1.0% to 14.7% in spiked bovine muscle, bovine blood and bovine urine. The limits of detection (LOD) of all analytes were in the ranges 0.11–3.82 µg kg⁻¹, 0.10–2.49 µg kg⁻¹ and 0.06–4.53 µg kg⁻¹ in bovine muscle, bovine blood, and bovine urine, respectively. The method was sensitive, accurate and was applied to monitor real samples. To the best of our knowledge, this is first method available for simultaneous determination of several classes of APGs in bovine muscle, and bovine blood and bovine urine.</p