Long noncoding RNA TLNC1 promotes the growth and metastasis of liver cancer via inhibition of p53 signaling.

BACKGROUND: Long non-coding RNAs (lncRNAs) have been demonstrated to play vital roles in cancer development and progression. However, their biological roles and function mechanisms in liver cancer remain largely unknown. METHODS: RNA-seq was performed with clinical hepatoma tissues and paired adjacent normal liver tissues to identify differentially expressed lncRNAs. qPCR was utilized to examine the expression levels of lncRNAs. We studied the function of TLNC1 in cell growth and metastasis of hepatoma with both cell and mouse models. RNA-seq, RNA pull-down coupled with mass spectrometry, RNA immunoprecipitation, dual luciferase reporter assay, and surface plasmon resonance analysis were used to analyze the functional mechanism of TLNC1. RESULTS: Based on the intersection of our own RNA-seq, TCGA RNA-seq, and TCGA survival analysis data, TLNC1 was identified as a potential tumorigenic lncRNA of liver cancer. TLNC1 significantly enhanced the growth and metastasis of hepatoma cells both in vitro and in vivo. TLNC1 exerted its tumorigenic function through interaction with TPR and inducing the TPR-mediated transportation of p53 from nucleus to cytoplasm, thus repressing the transcription of p53 target genes and finally contributing to the progression of liver cancer. CONCLUSIONS: TLNC1 is a promising prognostic factor of liver cancer, and the TLNC1-TPR-p53 axis can serve as a potential therapeutic target for hepatoma treatment.

CircNFIB inhibits tumor growth and metastasis through suppressing MEK1/ERK signaling in intrahepatic cholangiocarcinoma.

BACKGROUND: Considerable evidence shows that circular RNAs (circRNAs) play an important role in tumor development. However, their function in intrahepatic cholangiocarcinoma (ICC) metastasis and the underlying mechanisms are incompletely understood. METHODS: circNFIB (hsa_circ_0086376, termed as cNFIB hereafter) was identified in human ICC tissues through circRNAs sequencing. The biological role of cNFIB was determined in vitro and in vivo by gain or loss of functional experiments. Fluorescence in situ hybridization (FISH), RNA immunoprecipitation (RIP) and RNA pull-down assays were conducted to analyze the interaction of cNFIB with dual specificity mitogen-activated protein kinase kinase1 (MEK1). Duolink in situ proximity ligation assay (PLA) and coimmunoprecipitation (co-IP) assay were used to investigate the effects of cNFIB on the interaction between MEK1 and mitogen-activated protein kinase 2 (ERK2). Finally, a series of in vitro and in vivo experiments were performed to explore the influences of cNFIB on the anti-tumor activity of trametinib (a MEK inhibitor). RESULTS: cNFIB was significantly down-regulated in human ICC tissues with postoperative metastases. The loss of cNFIB was highly associated with aggressive characteristics and predicted unfavorable prognosis in ICC patients. Functional studies revealed that cNFIB inhibited the proliferation and metastasis of ICC cells in vitro and in vivo. Mechanistically, cNFIB competitively interacted with MEK1, which induced the dissociation between MEK1 and ERK2, thereby resulting in the suppression of ERK signaling and tumor metastasis. Moreover, we found that ICC cells with high levels of cNFIB held the potential to delay the trametinib resistance. Consistently, in vivo and in vitro studies demonstrated that cotreatment with trametinib and lentivirus vector encoding cNFIB showed greater inhibitory effect than isolated trametinib treatment. CONCLUSIONS: Our findings identified that cNFIB played a key role in ICC growth and metastasis by regulating MEK1/ERK signaling. Given the efficacy of cNFIB modulation on ICC suppression and trametinib sensitivity, cNFIB appears to be a potential therapeutic molecule for ICC treatment.

Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping.

The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high-throughput plant phenotyping technologies have been adopted that use plant morphological and physiological measurements in a non-destructive manner to accelerate plant breeding processes. Here, a hyperspectral imaging (HSI) technique was implemented to monitor the plant phenotypes of 13 okra (Abelmoschus esculentus L.) genotypes after 2 and 7 days of salt treatment. Physiological and biochemical traits, such as fresh weight, SPAD, elemental contents and photosynthesis-related parameters, which require laborious, time-consuming measurements, were also investigated. Traditional laboratory-based methods indicated the diverse performance levels of different okra genotypes in response to salinity stress. We introduced improved plant and leaf segmentation approaches to RGB images extracted from HSI imaging based on deep learning. The state-of-the-art performance of the deep-learning approach for segmentation resulted in an intersection over union score of 0.94 for plant segmentation and a symmetric best dice score of 85.4 for leaf segmentation. Moreover, deleterious effects of salinity affected the physiological and biochemical processes of okra, which resulted in substantial changes in the spectral information. Four sample predictions were constructed based on the spectral data, with correlation coefficients of 0.835, 0.704, 0.609 and 0.588 for SPAD, sodium concentration, photosynthetic rate and transpiration rate, respectively. The results confirmed the usefulness of high-throughput phenotyping for studying plant salinity stress using a combination of HSI and deep-learning approaches.

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning.

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L-1) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2-2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.

Rapid Quantitative Detection of Deltamethrin in Corydalis yanhusuo by SERS Coupled with Multi-Walled Carbon Nanotubes.

With the increase in demand, artificially planting Chinese medicinal materials (CHMs) has also increased, and the ensuing pesticide residue problems have attracted more and more attention. An optimized quick, easy, cheap, effective, rugged and safe (QuEChERS) method with multi-walled carbon nanotubes as dispersive solid-phase extraction sorbents coupled with surface-enhanced Raman spectroscopy (SERS) was first proposed for the detection of deltamethrin in complex matrix Corydalis yanhusuo. Our results demonstrate that using the optimized QuEChERS method could effectively extract the analyte and reduce background interference from Corydalis. Facile synthesized gold nanoparticles with a large diameter of 75 nm had a strong SERS enhancement for deltamethrin determination. The best prediction model was established with partial least squares regression of the SERS spectra ranges of 545~573 cm-1 and 987~1011 cm-1 with a coefficient of determination (R2) of 0.9306, a detection limit of 0.484 mg/L and a residual predictive deviation of 3.046. In summary, this article provides a new rapid and effective method for the detection of pesticide residues in CHMs.

Cell-surface translocation of annexin A2 contributes to bleomycin-induced pulmonary fibrosis by mediating inflammatory response in mice.

Bleomycin, a widely used anti-cancer drug, may give rise to pulmonary fibrosis, a serious side effect which is associated with significant morbidity and mortality. Despite the intensive efforts, the precise pathogenic mechanisms of pulmonary fibrosis still remain to be clarified. Our previous study showed that bleomycin bound directly to annexin A2 (ANXA2, or p36), leading to development of pulmonary fibrosis by impeding transcription factor EB (TFEB)-induced autophagic flux. Here, we demonstrated that ANXA2 also played a critical role in bleomycin-induced inflammation, which represents another major cause of bleomycin-induced pulmonary fibrosis. We found that bleomycin could induce the cell surface translocation of ANXA2 in lung epithelial cells through exosomal secretion, associated with enhanced interaction between ANXA2 and p11. Knockdown of ANXA2 or blocking membrane ANXA2 mitigated bleomycin-induced activation of nuclear factor (NF)-κB pathway and production of pro-inflammatory cytokine IL-6 in lung epithelial cells. ANXA2-deficient (ANXA2-/-) mice treated with bleomycin exhibit reduced pulmonary fibrosis along with decreased cytokine production compared with bleomycin-challenged wild-type mice. Further, the surface ANXA2 inhibitor TM601 could ameliorate fibrotic and inflammatory response in bleomycin-treated mice. Taken together, our results indicated that, in addition to disturbing autophagic flux, ANXA2 can contribute to bleomycin-induced pulmonary fibrosis by mediating inflammatory response.

Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder.

The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000-550 cm-1 were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging.

Seed vitality is one of the primary determinants of high yield that directly affects the performance of seedling emergence and plant growth. However, seed vitality may be lost during storage because of unfavorable conditions, such as high moisture content and temperatures. It is therefore vital for seed companies as well as farmers to test and determine seed vitality to avoid losses of any kind before sowing. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with multiple data preprocessing methods and classification models was applied to identify the vitality of rice seeds. A total of 2400 seeds of three different years: 2015, 2016 and 2017, were evaluated. The experimental results show that the NIR-HSI technique has great potential for identifying vitality and vigor of rice seeds. When detecting the seed vitality of the three different years, the extreme learning machine model with Savitzky-Golay preprocessing could achieve a high classification accuracy of 93.67% by spectral data from only eight wavebands (992, 1012, 1119, 1167, 1305, 1402, 1629 and 1649 nm), which could be developed for a fast and cost-effective seed-sorting system for industrial online application. When identifying non-viable seeds from viable seeds of different years, the least squares support vector machine model coupled with raw data and selected wavelengths of 968, 988, 1204, 1301, 1409, 1463, 1629, 1646 and 1659 nm achieved better classification performance (94.38% accuracy), and could be adopted as an optimal combination to identify non-viable seeds from viable seeds.

Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging.

Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874⁻1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine-SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.

Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis.

There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS-DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.

Identification and Expression Profiling of the Auxin Response Factors in Capsicum annuum L. under Abiotic Stress and Hormone Treatments.

Auxin response factors (ARFs) play important roles in regulating plant growth and development and response to environmental stress. An exhaustive analysis of the CaARF family was performed using the latest publicly available genome for pepper (Capsicum annuum L.). In total, 22 non-redundant CaARF gene family members in six classes were analyzed, including chromosome locations, gene structures, conserved motifs of proteins, phylogenetic relationships and Subcellular localization. Phylogenetic analysis of the ARFs from pepper (Capsicum annuum L.), tomato (Solanum lycopersicum L.), Arabidopsis and rice (Oryza sativa L.) revealed both similarity and divergence between the four ARF families, and aided in predicting biological functions of the CaARFs. Furthermore, expression profiling of CaARFs was obtained in various organs and tissues using quantitative real-time RT-PCR (qRT-PCR). Expression analysis of these genes was also conducted with various hormones and abiotic treatments using qRT-PCR. Most CaARF genes were regulated by exogenous hormone treatments at the transcriptional level, and many CaARF genes were altered by abiotic stress. Systematic analysis of CaARF genes is imperative to elucidate the roles of CaARF family members in mediating auxin signaling in the adaptation of pepper to a challenging environment.

Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping.

Currently, the presence of genetically modified (GM) organisms in agro-food markets is strictly regulated by enacted legislation worldwide. It is essential to ensure the traceability of these transgenic products for food safety, consumer choice, environmental monitoring, market integrity, and scientific research. However, detecting the existence of GM organisms involves a combination of complex, time-consuming, and labor-intensive techniques requiring high-level professional skills. In this paper, a concise and rapid pipeline method to identify transgenic rice seeds was proposed on the basis of spectral imaging technologies and the deep learning approach. The composition of metabolome across 3 rice seed lines containing the cry1Ab/cry1Ac gene was compared and studied, substantiating the intrinsic variability induced by these GM traits. Results showed that near-infrared and terahertz spectra from different genotypes could reveal the regularity of GM metabolic variation. The established cascade deep learning model divided GM discrimination into 2 phases including variety classification and GM status identification. It could be found that terahertz absorption spectra contained more valuable features and achieved the highest accuracy of 97.04% for variety classification and 99.71% for GM status identification. Moreover, a modified guided backpropagation algorithm was proposed to select the task-specific characteristic wavelengths for further reducing the redundancy of the original spectra. The experimental validation of the cascade discriminant method in conjunction with spectroscopy confirmed its viability, simplicity, and effectiveness as a valuable tool for the detection of GM rice seeds. This approach also demonstrated its great potential in distilling crucial features for expedited transgenic risk assessment.

Efficacy of LR-5 and LR-4/5 by Liver Imaging Reporting and Data System (MRI) for hepatocellular carcinoma: A meta-analysis.

To comprehensive investigate the diagnostic efficacy of LR-5 and LR-4/5 by MRI LI-RADS of suspected liver nodules. A comprehensive search of authenticated international databases including PubMed/Medline, Ovid, Embase, Web of Science as well as a series of nation-level databases, including China National Knowledge Infrastructure was carried out to look for related studies with respect to the diagnostic performance of MRI LR-5 or LR-4/5 for HCC. Subsequently, main data including the basic information of the articles incorporated as well as main outcomes, including diagnostic sensitivity, specificity, accuracy, or original data like true positive, false positive, true negative and false negative values were extracted. Next, forest plots were generated to reveal the pooled diagnostic sensitivity, specificity. The diagnostic sensitivity, specificity of LR-5 and LR-4/5 by LI-RADS were comparatively satisfactory. The pooled diagnostic sensitivity and specificity of MRI LR-5 with respect to pathologically diagnosed HCC were 0.73 [95% CI 0.7-0.75] and 0.88 [95% CI 0.86-0.90] respectively. The pooled sensitivity and specificity of MRI LR-4/5 were 0.77 [95% CI 0.75-0.80] and 0.82 [95% CI 0.79-0.85] respectively. Through this systematic review and meta-analysis, we found a promisingly satisfactory diagnostic efficacy of LR-5 and LR-4/5 by MRI LI-RADS of suspected malignant liver nodules, manifested by optimal diagnostic sensitivity, specificity, and accuracy.

A novel ratiometric fluorescent probe for sensitive detection of jasmonic acid in crops.

Herein, a novel ratiometric fluorescent probe was proposed for sensitive detection of jasmonic acid (JA) based on NCQDs@Co-MOFs@MIPs. The prepared NCQDs, with uniquely dual-emissive performance, are insensitive to JA due to electrostatic repulsion. Interestingly, the introduction of Co-MOFs not only avoided the self-aggregation of NCQDs, but changed the surface charge of NCQDs and triggered the response of NCQDs to JA. More importantly, the imprinted recognition sites from MIPs provided "key-lock" structures to specifically capture JA molecules, greatly improving the selectivity of the probe to JA. Under the synergistic actions of Co-MOFs and MIPs, JA can interact with NCQDs through photo-induced electron transfer (PET), resulting in the changes on emission intensity of the probe at Em = 367 nm and 442 nm. Based on the observations, the quantification of JA was realized in the range of 1-800 ng/mL with the limit of detection (LOD) of 0.35 ng/mL. In addition, the probe was used for detecting JA in rice with satisfactory analysis results, indicating the probe holds great potential for monitoring JA levels in crops. Overall, this strategy provides new insights into the construction of practical probes for sensitive detection of plant hormones in crops.

Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network.

BACKGROUND: Pumpkin seeds are major oil crops with high nutritional value and high oil content. The collection and identification of different pumpkin germplasm resources play a significant role in the realization of precision breeding and variety improvement. In this research, we collected 75 species of pumpkin from the Zhejiang Province of China. 35,927 near-infrared hyperspectral images of 75 types of pumpkin seeds were used as the research object. RESULTS: To realize the rapid classification of pumpkin seed varieties, position attention embedded three-dimensional convolutional neural network (PA-3DCNN) was designed based on hyperspectral image technology. The experimental results showed that PA-3DCNN had the best classification effect than other classical machine learning technology. The classification accuracy of 99.14% and 95.20% were severally reached on the training and test sets. We also demonstrated that the PA-3DCNN model performed well in next year's classification with fine-tuning and met with 94.8% accuracy. CONCLUSIONS: The model performance improved by introducing double convolution and pooling structure and position attention module. Meanwhile, the generalization performance of the model was verified, which can be adopted for the classification of pumpkin seeds in multiple years. This study provided a new strategy and a feasible technical approach for identifying germplasm resources of pumpkin seeds.

Development and validation of a new prognostic immune-inflammatory-nutritional score for predicting outcomes after curative resection for intrahepatic cholangiocarcinoma: A multicenter study.

Background: Immune function, nutrition status, and inflammation influence tumor initiation and progression. This was a retrospective multicenter cohort study that investigated the prognostic value and clinical relevance of immune-, inflammatory-, and nutritional-related biomarkers to develop a novel prognostic immune-inflammatory-nutritional score (PIIN score) for patients with intrahepatic cholangiocarcinoma (ICC). Methods: The clinical data of 571 patients (406 in the training set and 165 in the validation set) were collected from four large hepato-pancreatico-biliary centers of patients with ICC who underwent surgical resection between January 2011 and September 2017. Twelve blood biomarkers were collected to develop the PIIN score using the LASSO Cox regression model. The predictive value was further assessed using validation datasets. Afterward, nomograms combining the PIIN score and other clinicopathological parameters were developed and validated based on the calibration curve, time-dependent AUC curves, and decision curve analysis (DCA). The primary outcomes evaluated were overall survival (OS) and recurrence-free survival (RFS) from the day of primary resection of ICC. Results: Based on the albumin-bilirubin (ALBI) grade, neutrophil- to- lymphocyte ratio (NLR), prognostic nutritional index (PNI), and systemic immune- inflammation index (SII) biomarkers, the PIIN score that classified patients into high-risk and low-risk groups could be calculated. Patients with high-risk scores had shorter OS (training set, p < 0.001; validation set, p = 0.003) and RFS (training set, p < 0.001; validation set, p = 0.002) than patients with low-risk scores. The high PIIN score was also associated with larger tumors (≥5 cm), lymph node metastasis (N1 stage), multiple tumors, and high tumor grade or TNM (tumor (T), nodes (N), and metastases (M)) stage. Furthermore, the high PIIN score was a significant independent prognostic factor of OS and RFS in both the training (p < 0.001) and validation (p = 0.003) cohorts, respectively. A PIIN-nomogram for individualized prognostic prediction was constructed by integrating the PIIN score with the clinicopathological variables that yielded better predictive performance than the TNM stage. Conclusion: The PIIN score, a novel immune-inflammatory-nutritional-related prognostic biomarker, predicts the prognosis in patients with resected ICC and can be a reliable tool for ICC prognosis prediction after surgery. Our study findings provide novel insights into the role of cancer-related immune disorders, inflammation, and malnutrition.

Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data.

Bacterial blight poses a threat to rice production and food security, which can be controlled through large-scale breeding efforts toward resistant cultivars. Unmanned aerial vehicle (UAV) remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods. However, the quality of data acquired by UAV can be affected by several factors such as weather, crop growth period, and geographical location, which can limit their utility for the detection of crop disease and resistant phenotypes. Therefore, a more effective use of UAV data for crop disease phenotype analysis is required. In this paper, we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model. The best results obtained with the predictive model showed an R p 2 of 0.86 with an RMSEp of 0.65. Moreover, model updating strategy was used to explore the scalability of the established model in different geographical locations. Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites. In addition, the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci (QTL) analysis to identify resistance QTL in genetic populations at different growth stages. Three new QTLs were identified, and QTLs identified at different growth stages were inconsistent. QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.

Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy.

Accurate delineation of plant cell organelles from electron microscope images is essential for understanding subcellular behaviour and function. Here we develop a deep-learning pipeline, called the organelle segmentation network (OrgSegNet), for pixel-wise segmentation to identify chloroplasts, mitochondria, nuclei and vacuoles. OrgSegNet was evaluated on a large manually annotated dataset collected from 19 plant species and achieved state-of-the-art segmentation performance. We defined three digital traits (shape complexity, electron density and cross-sectional area) to track the quantitative features of individual organelles in 2D images and released an open-source web tool called Plantorganelle Hunter for quantitatively profiling subcellular morphology. In addition, the automatic segmentation method was successfully applied to a serial-sectioning scanning microscope technique to create a 3D cell model that offers unique views of the morphology and distribution of these organelles. The functionalities of Plantorganelle Hunter can be easily operated, which will increase efficiency and productivity for the plant science community, and enhance understanding of subcellular biology.

Rapid Estimation of Earthquake Fatalities in Mainland China Based on Physical Simulation and Empirical Statistics-A Case Study of the 2021 Yangbi Earthquake.

At present, earthquakes cannot be predicted. Scientific decision-making and rescue after an earthquake are the main means of mitigating the immediate consequences of earthquake disasters. If emergency response level and earthquake-related fatalities can be estimated rapidly and quantitatively, this estimation will provide timely, scientific guidance to government organizations and relevant institutions to make decisions on earthquake relief and resource allocation, thereby reducing potential losses. To achieve this goal, a rapid earthquake fatality estimation method for Mainland China is proposed herein, based on a combination of physical simulations and empirical statistics. The numerical approach was based on the three-dimensional (3-D) curved grid finite difference method (CG-FDM), implemented for graphics processing unit (GPU) architecture, to rapidly simulate the entire physical propagation of the seismic wavefield from the source to the surface for a large-scale natural earthquake over a 3-D undulating terrain. Simulated seismic intensity data were used as an input for the fatality estimation model to estimate the fatality and emergency response level. The estimation model was developed by regression analysis of the data on human loss, intensity distribution, and population exposure from the Mainland China Composite Damaging Earthquake Catalog (MCCDE-CAT). We used the 2021 Ms 6.4 Yangbi earthquake as a study case to provide estimated results within 1 h after the earthquake. The number of fatalities estimated by the model was in the range of 0-10 (five expected fatalities). Therefore, Level IV earthquake emergency response plan should have been activated (the government actually overestimated the damage and activated a Level II emergency response plan). The local government finally reported three deaths during this earthquake, which is consistent with the model predictions. We also conducted a case study on a 2013 Ms7.0 earthquake in the discussion, which further proved the effectiveness of the method. The proposed method will play an important role in post-earthquake emergency response and disaster assessment in Mainland China. It can assist decision-makers to undertake scientifically-based actions to mitigate the consequences of earthquakes and could be used as a reference approach for any country or region.