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Purpose Timely and accurate monitoring of soil salinity content (SSC) is essential for precise irrigation management of large-scale farmland. Uncrewed aerial vehicle (UAV) low-altitude remote sensing with high spatial and temporal resolution provides a scientific and effective technical means for SSC monitoring. Many existing soil salinity inversion models have only been tested by a single variable selection method or machine learning algorithm, and the influence of variable selection method combined with machine learning algorithm on the accuracy of soil salinity inversion remain further studied. Methods Firstly, based on UAV multispectral remote sensing data, by extracting the spectral reflectance of each sampling point to construct 30 spectral indexes, and using the pearson correlation coefficient (PCC), gray relational analysis (GRA), variable projection importance (VIP), and support vector machine-recursive feature elimination (SVM-RFE) to screen spectral index and realize the selection of sensitive variables. Subsequently, screened and unscreened variables as model input independent variables, constructed 20 soil salinity inversion models based on the support vector machine regression (SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and random forest (RF) machine learning algorithms, the aim is to explore the feasibility of different variable selection methods combined with machine learning algorithms in SSC inversion of crop-covered farmland. To evaluate the performance of the soil salinity inversion model, the determination coefficient (R2), root mean square error (RMSE) and performance deviation ratio (RPD) were used to evaluate the model performance, and determined the best variable selection method and soil salinity inversion model by taking alfalfa covered farmland in arid oasis irrigation areas of China as the research object. Results The variable selection combined with machine learning algorithm can significantly improve the accuracy of remote sensing inversion of soil salinity. The performance of the models has been improved markedly using the four variable selection methods, and the applicability varied among the four methods, the GRA variable selection method is suitable for SVM, BPNN, and ELM modeling, while the PCC method is suitable for RF modeling. The GRA-SVM is the best soil salinity inversion model in alfalfa cover farmland, with Rv2 of 0.8888, RMSEv of 0.1780, and RPD of 1.8115 based on the model verification dataset, and the spatial distribution map of soil salinity can truly reflect the degree of soil salinization in the study area. Conclusion Based on our findings, the variable selection combined with machine learning algorithm is an effective method to improve the accuracy of soil salinity remote sensing inversion, which provides a new approach for timely and accurate acquisition of crops covered farmland soil salinity information.

Abstract For comprehensive studies of the brain structure and function, fluorescence imaging of the whole brain is essential. It requires large-scale volumetric imaging in cellular or molecular resolution, which could be quite challenging. Recent advances in tissue clearing technology (e.g. CLARITY, PACT) provide new solutions by homogenizing the refractive index of the samples to create transparency. However, it has been difficult to acquire high quality results through immunofluorescence (IF) staining on the cleared samples. To address this issue, we developed TSA-PACT, a method combining tyramide signal amplification (TSA) and PACT, to transform samples into hydrogel polymerization frameworks with covalent fluorescent biomarkers assembled. We show that TSA-PACT is able to reduce the opacity of the zebrafish brain by more than 90% with well-preserved structure. Compared to traditional method, TSA-PACT achieves approximately tenfold signal amplification and twofold improvement in signal-to-noise ratio (SNR). Moreover, both the structure and the fluorescent signal persist for at least 16 months with excellent signal retention ratio. Overall, this method improves immunofluorescence signal sensitivity, specificity and stability in the whole brain of juvenile and adult zebrafish, which is applicable for fine structural analysis, neural circuit mapping and three-dimensional cell counting.

Substance-related disorders are a group of medical conditions that affect a person's brain and behavior and lead to an inability to control the use of legal or illegal drug(s) or medication. Substance-related disorder is a serious public health and society problem worldwide. Genetic factors have been proven to have an important role. Researchers have carried out a lot of work in this field, and a large number of research results have been published in academic journals around the world. However, there are few overviews of research progress, presentation, and development trends in this field. In this study, a total of 636 articles related to genetic factors of substance-related disorders were retrieved from the Web of Science (WoS) database from 1997 to 2018, and the scientific literatures were analyzed by bibliometrics. The study found that the United States (US) has maintained a leading position in the field of research, with many core institutions and plenty of high-quality research results. Alcohol use disorder is still the most concerning issue in this field. Over the past 20 years, new techniques such as genome-wide association study (GWAS) based on high-throughput sequencing technology have replaced family studies, twin studies, and retrospective studies in this field. We believe that it is urgent to study the genetic factors of substance-related disorders, which can greatly deepen the understanding of the pathogenesis of substance-related disorders and may provide potential targets for precise treatment of such diseases.

Malignant pleural mesothelioma (MPM) is a highly aggressive cancer with short survival time. Unbalanced competing endogenous RNAs (ceRNAs) have been shown to participate in the tumor pathogenesis and served as biomarkers for the clinical prognosis. However, the comprehensive analyses of the ceRNA network in the prognosis of MPM are still rarely reported. In this study, we obtained the transcriptome data of the MPM and the normal samples from TCGA, EGA, and GEO databases and identified the differentially expressed (DE) mRNAs, lncRNAs, and miRNAs. The functions of the prognostic genes and the overlapped DEmRNAs were further annotated by the multiple enrichment analyses. Then, the targeting relationships among lncRNA–miRNA and miRNA–mRNA were predicted and calculated, and a prognostic ceRNA regulatory network was established. We included the prognostic 73 mRNAs and 13 miRNAs and 26 lncRNAs into the ceRNA network. Moreover, 33 mRNAs, three miRNAs, and seven lncRNAs were finally associated with prognosis, and a model including seven mRNAs, two lincRNAs, and some clinical factors was finally established and validated by two independent cohorts, where CDK6 and SGMS1-AS1 were significant to be independent prognostic factors. In addition, the identified co-expressed modules associated with the prognosis were overrepresented in the ceRNA network. Multiple enrichment analyses showed the important roles of the extracellular matrix components and cell division dysfunction in the invasion of MPM potentially. In summary, the prognostic ceRNA network of MPM was established and analyzed for the first time and these findings shed light on the function of ceRNAs and revealed the potential prognostic and therapeutic biomarkers of MPM.

BackgroundPrevious studies of the functions of IL15Rα have been limited to immune activities and skeletal muscle development. Immunological factors have been identified as one of the multiple causes of psychosis, and neurological symptoms have been described in IL15Rα knockout (KO) mice. Seeking to explore possible mechanisms for this in the IL15Rα–/– mouse brain, we analyzed gene expression patterns in the cortex and hippocampus using the RNA-seq technique.MethodsIL15Rα KO mice were generated and littermate wildtype (WT) mice were used as a control group. A Y-maze was used to assess behavior differences between the two groups. The cortex and hippocampus of 3-month-old male mice were prepared and RNA-seq and transcriptome analysis were performed by gene set enrichment analysis (GSEA).ResultsCompared with the WT group, IL15Rα KO animals showed higher speed in the novel arm and more entrance frequency in the old arm in the Y-maze experiment. GSEA indicated that 18 pathways were downregulated and 13 pathways upregulated in both cortex and hippocampus from the GO, KEGG, and Hallmark gene sets. The downregulated pathways formed three clusters: respiratory chain and electron transport, regulation of steroid process, and skeletal muscle development.ConclusionIL15Rα KO mice exhibit altered expression of multiple pathways, which could affect many functions of the brain. Lipid biosynthesis and metabolism in the central nervous system (CNS) should be investigated to provide insights into the effect of IL15Rα on psychosis in this murine model.

Abstract Estimation of postmortem interval (PMI) is a central role in medico-legal identification. Analysis of vitreous potassium ions (K+) concentration is frequently used by forensic workers to estimate PMI. This paper describes interdisciplinary research to introduce fluorescence sensing techniques into forensic medicine. On the basis of silver nanoclusters (AgNCs) probe stabilized by DNA, a simple and highly sensitive fluorescence aptasensor has been proposed to selectively detect K+ ions. The linear range for K+ ions was found to be 0.1 nM-1 mM, with limit of detection of 0.06 nM. Moreover, 63 vitreous humour cases within 36 h after death were further studied to verify the utility of K+ ions in estimating the PMI. By the fluorescence aptasensor method, a new formula was built to determine the postmortem interval based on K+ ions concentration: PMI(h) = −0.55 + 1.66 × CK +(r = 0.791). And the real significance of this research was demonstrated by additional 6 cases with known PMIs. In comparison with the conventional method, the presented aptasensor strategy is cost-effective and easy in measuring vitreous K+, which may be potentially a better way for estimation of PMI in medico-legal practice.

Autism spectrum disorder (ASD) cases have increased rapidly in recent decades, which is associated with various genetic abnormalities. To provide a better understanding of the genetic factors in ASD, we assessed the global scientific output of the related studies. A total of 2944 studies published between 1997 and 2018 were included by systematic retrieval from the Web of Science (WoS) database, whose scientific landscapes were drawn and the tendencies and research frontiers were explored through bibliometric methods. The United States has been acting as a leading explorer of the field worldwide in recent years. The rapid development of high-throughput technologies and bioinformatics transferred the research method from the traditional classic method to a big data-based pipeline. As a consequence, the focused research area and tendency were also changed, as the contribution of de novo mutations in ASD has been a research hotspot in the past several years and probably will remain one into the near future, which is consistent with the current opinions of the major etiology of ASD. Therefore, more attention and financial support should be paid to the deciphering of the de novo mutations in ASD. Meanwhile, the effective cooperation of multi-research centers and scientists in different fields should be advocated in the next step of scientific research undertaken.

The molecular mechanisms underlying the neurodevelopmental disorders (NDDs) caused by DDX3X variants remain poorly understood. In this study, we validated that de novo DDX3X variants are enriched in female developmental delay (DD) patients and mainly affect the evolutionarily conserved amino acids based on a meta-analysis of 46,612 NDD trios. We generated a ddx3x deficient zebrafish allele, which exhibited reduced survival rate, DD, microcephaly, adaptation defects, anxiolytic behaviors, social interaction deficits, and impaired spatial recognitive memory. As revealed by single-nucleus RNA sequencing and biological validations, ddx3x deficiency leads to reduced neural stem cell pool, decreased total neuron number, and imbalanced differentiation of excitatory and inhibitory neurons, which are responsible for the behavioral defects. Indeed, the supplementation of L-glutamate or glutamate receptor agonist ly404039 could partly rescue the adaptation and social deficits. Mechanistically, we reveal that the ddx3x deficiency attenuates the stability of the crebbp mRNA, which in turn causes downregulation of Notch signaling and defects in neurogenesis. Our study sheds light on the molecular pathology underlying the abnormal neurodevelopment and behavior of NDD patients with DDX3X mutations, as well as providing potential therapeutic targets for the precision treatment. [ABSTRACT FROM AUTHOR]

Backgroud: Pre-eclampsia (PE) is a common pregnancy-induced hypertension disease. Some case--control studies reported the association between vascular endothelial growth factor (VEGF) gene polymorphisms (rs3025039, rs2010963) and PE risk. However, these associations were inconsistent in several studies. Therefore, we conducted this meta-analysis to assess the role of VEGF gene polymorphisms in PE more precisely. Methods: Eligible studies were searched in PubMed, Embase, Web of Science and Chinese (Chinese National Knowledge Infrastructure (CNKI) and WanFang) databases. Statistical analyses were performed by Stata 12.0 software. Odds ratio (OR) and 95% confidence interval (CI) were used to assess the strength of the association. In addition, subgroup analyses, sensitive analyses and publication bias analyses were performed to further assess this meta-analysis. Results: In total, 21 studies were included in the meta-analysis covering 2018 cases and 2632 controls. There were significant associations between VEGF polymorphisms (rs3025039, rs2010963) and PE risk in the overall populations. In the subgroup analyses, we found that rs3025039 polymorphism was associated with the increased risk of PE among Chinese. As for rs2010963 polymorphism, a significant association was observed in subgroup of Caucasian. Conclusion: The present study suggested that the two VEGF gene polymorphisms (rs3025039, rs2010963) are associated with increased risk of PE in different ethnic groups, which means that the targets may be useful genetic markers for early prediction of PE. [ABSTRACT FROM AUTHOR]