Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages

As a quarantine disease with a rapid spread tendency in the context of climate change, accurate detection and location of pine wilt disease (PWD) at different infection stages is critical for maintaining forest health and being highly productivity. In recent years, unmanned aerial vehicle (UAV)-based optical remote-sensing images have provided new instruments for timely and accurate PWD monitoring. Numerous corresponding analysis algorithms have been proposed for UAV-based image classification, but their applicability of detecting different PWD infection stages has not yet been evaluated under a uniform conditions and criteria. This research aims to systematically assess the performance of multi-source images for detecting different PWD infection stages, analyze effective classification algorithms, and further analyze the validity of thermal images for early detection of PWD. In this study, PWD infection was divided into four stages: healthy, chlorosis, red and gray, and UAV-based hyperspectral (HSI), multispectral (MSI), and MSI with a thermal band (MSI&TIR) datasets were used as the data sources. Spectral analysis, support vector machine (SVM), random forest (RF), two- and three-dimensional convolutional network (2D- and 3D-CNN) algorithms were applied to these datasets to compare their classification abilities. The results were as follows: (I) The classification accuracy of the healthy, red, and gray stages using the MSI dataset was close to that obtained when using the MSI&TIR dataset with the same algorithms, whereas the HSI dataset displayed no obvious advantages. (II) The RF and 3D-CNN algorithms were the most accurate for all datasets (RF: overall accuracy = 94.26%, 3D-CNN: overall accuracy = 93.31%), while the spectral analysis method is also valid for the MSI&TIR dataset. (III) Thermal band displayed significant potential in detection of the chlorosis stage, and the MSI&TIR dataset displayed the best performance for detection of all infection stages. Considering this, we suggest that the MSI&TIR dataset can essentially satisfy PWD identification requirements at various stages, and the RF algorithm provides the best choice, especially in actual forest investigations. In addition, the performance of thermal imaging in the early monitoring of PWD is worthy of further investigation. These findings are expected to provide insight into future research and actual surveys regarding the selection of both remote sensing datasets and data analysis algorithms for detection requirements of different PWD infection stages to detect the disease earlier and prevent losses

A simple and efficient method for extraction of Taq DNA polymerase

Background: Thermostable DNA polymerase (Taq Pol \u399) from Thermus aquaticus has beenwidely used in PCR, which was usually extracted with Pluthero's method. Themethod used ammonium sulfate to precipitate the enzyme, and it saved effort and money but not time. Moreover, we found that 30\u201340% activity of Taq Pol I was lost at the ammonium sulfate precipitation step, and the product contained a small amount of DNA. Results: We provided a novel, simplified and low-costmethod to purify the Taq Pol \u399 after overproduction of the enzyme in Escherichia coli , which used ethanol instead of ammonium sulfate to precipitate the enzyme. The precipitate can be directly dissolved in the storage buffer without dialysis. In addition, DNA and RNA contamination was removed with DNase I and RNase A before precipitation, and the extraction procedure was optimized. Our improvements increase recovery rate and specific activity of the enzyme, and save labor, time, and cost. Conclusions: Our method uses ethanol, DNase I, and RNase A to purify the Taq Pol \u399, and simplifies the operation, and increases the enzyme recovery rate and quality

Over-Expression of a Maize N-Acetylglutamate Kinase Gene (ZmNAGK) Improves Drought Tolerance in Tobacco

Water deficit is a key limiting factor that affects the growth, development and productivity of crops. It is vital to understand the mechanisms by which plants respond to drought stress. Here an N-acetylglutamate kinase gene, ZmNAGK, was cloned from maize (Zea mays). ZmNAGK was expressed at high levels in maize leaves and at lower levels in root, stem, female flower and male flower. The expression of ZmNAGK was significantly induced by PEG, NaCl, ABA, brassinosteroid and H2O2. The ectopic expression of ZmNAGK in tobacco resulted in higher tolerance to drought compared to plants transformed with empty vector. Further physiological analysis revealed that overexpression of ZmNAGK could enhance the activities of antioxidant defense enzymes, and decrease malondialdehyde content and leakage of electrolyte in tobacco under drought stress. Moreover, the ZmNAGK transgenic tobacco accumulated more arginine and nitric oxide (NO) than control plants under drought stress. In addition, the ZmNAGK transgenic tobaccos activated drought responses faster than vector-transformed plants. These results indicate that ZmNAGK can play a vital role in enhancing drought tolerance by likely affecting the arginine and NO accumulation, and ZmNAGK could be involved in different strategies in response to drought stress

Elevated limb-bud and heart development (LBH) expression indicates poor prognosis and promotes gastric cancer cell proliferation and invasion via upregulating Integrin/FAK/Akt pathway

The limb-bud and heart development (LBH) gene is a highly conserved, tissue-specific transcription cofactor in vertebrates that regulates multiple key genes in embryonic development. The role of LBH in various cancer types is still controversial, and its specific role and molecular mechanism in the oncogenesis of gastric cancer (GC) remains largely unexplored. In the present study, the prognostic significance and clinicopathological characteristics of LBH in GC was determined. The LBH mRNA expression was first investigated in four independent public datasets (TCGA-STAD, GSE15459, GSE29272, and GSE62254) and then validated with our samples at the protein level. LBH was overexpressed at both the mRNA and protein levels in cancer compared with normal tissues. High LBH expression was correlated with advanced T, N, and M stages. Kaplan–Meier analysis and log-rank test indicated that higher LBH expression was statistically correlated with shorter overall survival (OS) in the public datasets and our study samples. Univariate and multivariate Cox regression analysis showed that LBH was an independent prognostic biomarker for survival in TCGA-STAD, GSE15459, GSE62254 cohorts, and our GC patients. In vitro experiments showed that knockdown of LBH can significantly inhibit the proliferation and invasion of HGC-27 cells, while overexpression of LBH can significantly enhance the proliferation and invasion of BGC-823 cells. Gene Set Enrichment Analysis (GSEA), Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) indicated that high LBH expression is associated with the PI3K-Akt pathway, focal adhesion, and extracellular matrix (ECM)-receptor interaction. Western blot analysis showed that knockdown of LBH significantly inhibited the expression of integrin α5, integrin β1, p-FAK, and p-Akt. Therefore, results from the present study indicate that LBH is a potential independent prognostic biomarker and promotes proliferation and invasion of GC cells by activating the integrin/FAK/Akt pathway

Cadmium-induced genomic instability in Arabidopsis: molecular toxicological biomarkers for early diagnosis of cadmium stress

Microsatellite instability (MSI) analysis, random-amplified polymorphic DNA (RAPD), and methylation-sensitive arbitrarily primed PCR (MSAP-PCR) are methods to evaluate the toxicity of environmental pollutants in stress-treated plants and human cancer cells. Here, we evaluate these techniques to screen for genetic and epigenetic alterations of Arabidopsis plantlets exposed to 0–5.0 mg L−1 cadmium (Cd) for 15 d. There was a substantial increase in RAPD polymorphism of 24.5, and in genomic methylation polymorphism of 30.5–34.5 at CpG and of 14.5–20 at CHG sites under Cd stress of 5.0 mg L−1 by RAPD and of 0.25–5.0 mg L−1 by MSAP-PCR, respectively. However, only a tiny increase of 1.5 loci by RAPD occurred under Cd stress of 4.0 mg L−1, and an additional high dose (8.0 mg L−1) resulted in one repeat by MSI analysis. MSAP-PCR detected the most significant epigenetic modifications in plantlets exposed to Cd stress, and the patterns of hypermethylation and polymorphisms were consistent with inverted U-shaped dose responses. The presence of genomic methylation polymorphism in Cd-treated seedlings, prior to the onset of RAPD polymorphism, MSI and obvious growth effects, suggests that these altered DNA methylation loci are the most sensitive biomarkers for early diagnosis and risk assessment of genotoxic effects of Cd pollution in ecotoxicology

Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique

Objectives/Hypothesis: To develop a deep-learning–based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings. Study Design: Retrospective study. Methods: A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)-based classifier. A comparison between the proposed CNN-based classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted. Results: In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN-based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P <.001), polyps (91% vs. 86%, P <.001), leukoplakia (91% vs. 65%, P <.001), and malignancy (90% vs. 54%, P <.001). Conclusions: The CNN-based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions. Level of Evidence: NA Laryngoscope, 130:E686–E693, 2020

Genetic Diagnostic Evaluation of Trio-Based Whole Exome Sequencing Among Children With Diagnosed or Suspected Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a group of clinically and genetically heterogeneous neurodevelopmental disorders. Recent tremendous advances in the whole exome sequencing (WES) enable rapid identification of variants associated with ASD including single nucleotide variations (SNVs) and indels. To further explore genetic etiology of ASD in Chinese children with negative findings of copy number variants (CNVs), we applied WES in 80 simplex families with a single affected offspring with ASD or suspected ASD, and validated variations predicted to be damaging by Sanger sequencing. The results showed that an overall diagnostic yield of 8.8% (9.2% in the group of ASD and 6.7% in the group of suspected ASD) was observed in our cohort. Among patients with diagnosed ASD, developmental delay or intellectual disability (DD/ID) was the most common comorbidity with a diagnostic yield of 13.3%, followed by seizures (50.0%) and craniofacial anomalies (40.0%). All of identified de novo SNVs and indels among patients with ASD were loss of function (LOF) variations and were slightly more frequent among female (male vs. female: 7.3% vs. 8.5%). A total of seven presumed causative genes (CHD8, AFF2, ADNP, POGZ, SHANK3, IL1RAPL1, and PTEN) were identified in this study. In conclusion, WES is an efficient diagnostic tool for diagnosed ASD especially those with negative findings of CNVs and other neurological disorders in clinical practice, enabling early identification of disease related genes and contributing to precision and personalized medicine

A Recombinant Vaccine of H5N1 HA1 Fused with Foldon and Human IgG Fc Induced Complete Cross-Clade Protection against Divergent H5N1 Viruses

Development of effective vaccines to prevent influenza, particularly highly pathogenic avian influenza (HPAI) caused by influenza A virus (IAV) subtype H5N1, is a challenging goal. In this study, we designed and constructed two recombinant influenza vaccine candidates by fusing hemagglutinin 1 (HA1) fragment of A/Anhui/1/2005(H5N1) to either Fc of human IgG (HA1-Fc) or foldon plus Fc (HA1-Fdc), and evaluated their immune responses and cross-protection against divergent strains of H5N1 virus. Results showed that these two recombinant vaccines induced strong immune responses in the vaccinated mice, which specifically reacted with HA1 proteins and an inactivated heterologous H5N1 virus. Both proteins were able to cross-neutralize infections by one homologous strain (clade 2.3) and four heterologous strains belonging to clades 0, 1, and 2.2 of H5N1 pseudoviruses as well as three heterologous strains (clades 0, 1, and 2.3.4) of H5N1 live virus. Importantly, immunization with these two vaccine candidates, especially HA1-Fdc, provided complete cross-clade protection against high-dose lethal challenge of different strains of H5N1 virus covering clade 0, 1, and 2.3.4 in the tested mouse model. This study suggests that the recombinant fusion proteins, particularly HA1-Fdc, could be developed into an efficacious universal H5N1 influenza vaccine, providing cross-protection against infections by divergent strains of highly pathogenic H5N1 virus

Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees

Data accessibility statement: Full census data are available upon reasonable request from the ForestGEO data portal, http://ctfs.si.edu/datarequest/ We thank Margie Mayfield, three anonymous reviewers and Jacob Weiner for constructive comments on the manuscript. This study was financially supported by the National Key R&D Program of China (2017YFC0506100), the National Natural Science Foundation of China (31622014 and 31570426), and the Fundamental Research Funds for the Central Universities (17lgzd24) to CC. XW was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB3103). DS was supported by the Czech Science Foundation (grant no. 16-26369S). Yves Rosseel provided us valuable suggestions on using the lavaan package conducting SEM analyses. Funding and citation information for each forest plot is available in the Supplementary Information Text 1.Peer reviewedPostprin