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.

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.

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.

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.

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.

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.

Case report: Adrenal myelolipoma resected by laparoscopic surgery.

Introduction: Adrenal myelolipomas are benign tumors composed mainly of lipomatous elements with myeloid cells. With the development of medical imaging technology, the detection rate has gradually increased. We report a case of adrenal myelolipoma successfully excised through the laparoscope and reviewed existing literature in recent ten years to summarize the feasibility of the laparoscopic approach for this tumor. Case presentation: Herein, we described a case of adrenal myelolipoma resected by laparoscope in a 63-year-old male patient. He did not have any other symptoms except the incidental finding of a left adrenal mass. An abdominal CT examination revealed a mixed-density lesion containing some amount of adipose tissue. In conjunction with the patient's willingness, we performed a laparoscopic operation to remove the lump. The definite diagnosis was confirmed as an adrenal myelolipoma according to the pathology. The patient recovered well postoperatively and without signs of recurrence at a 5-month follow-up. Conclusion: Adrenal myelolipoma is commonly benign, asymptomatic, and hormonal inactivity. A surgical strategy is suggested for high-complication-risk patients. The laparoscopic approach is safe and effective with an obvious advantage over open procedures.

LncRNA CEBPA-DT promotes liver cancer metastasis through DDR2/ß-catenin activation via interacting with hnRNPC.

BACKGROUND: Hepatocellular carcinoma (HCC) is the world's third leading cause of cancer-related death; due to the fast growth and high prevalence of tumor recurrence, the prognosis of HCC patients remains dismal. Long non-coding RNA CEBPA-DT, a divergent transcript of the CCAAT Enhancer Binding Protein Alpha (CEBPA) gene, has been shown to participate in multiple tumor progression. However, no research has established its cancer-promoting mechanism in HCC yet. METHODS: CEBPA-DT was identified in human HCC tissues through RNA sequencing. The expression level of CEBPA-DT was assessed by quantitative real-time PCR. The biological effects of CEBPA-DT were evaluated in vitro and in vivo through gain or loss of function experiments. RNA fluorescence in situ hybridization (FISH), RNA immunoprecipitation (RIP) and RNA pull-down assays were applied to investigate the downstream target of CEBPA-DT. Immunofluorescence, subcellular protein fractionation, western blot, and co-immunoprecipitation were performed to analyze the subcellular location of ß-catenin and its interaction with Discoidin domain-containing receptor 2 (DDR2). RESULTS: CEBPA-DT was upregulated in human HCC tissues with postoperative distant metastasis and intimately related to the worse prognosis of HCC patients. Silencing of CEBPA-DT inhibited the growth, migration and invasion of hepatoma cells in vitro and in vivo, while enhancement of CEBPA-DT played a contrasting role. Mechanistic investigations demonstrated that CEBPA-DT could bind to heterogeneous nuclear ribonucleoprotein C (hnRNPC), which facilitated cytoplasmic translocation of hnRNPC, enhanced the interaction between hnRNPC and DDR2 mRNA, subsequently promoted the expression of DDR2. Meanwhile, CEBPA-DT induced epithelial-mesenchymal transition (EMT) process through upregulation of Snail1 via facilitating nuclear translocation of ß-catenin. Using DDR2 inhibitor, we revealed that the CEBPA-DT induced the interaction between DDR2 and ß-catenin, thus promoting the nuclear translocation of ß-catenin to activate transcription of Snail1, contributing to EMT and HCC metastasis. CONCLUSIONS: Our results suggested that CEBPA-DT promoted HCC metastasis through DDR2/ß-catenin mediated activation of Snail1 via interaction with hnRNPC, indicating that the CEBPA-DT-hnRNPC-DDR2/ß-catenin axis may be used as a potential therapeutic target for HCC treatment.

Case report: A mesocolic lymphangioma in a 14-year-old child resected by laparoscopic surgery.

Introduction: Cystic lymphangioma is a benign malformation tumor of the lymphatic system. Its location is variable, and mesocolic localization remains extremely rare. Case presentation: We report a case of right mesocolon giant cystic lymphangioma in a previously healthy 14-year-old boy who was successfully managed through a minimally invasive laparoscopic excision. The patient presented with 8 months of dull abdominal pain, sporadic, located on the peri-umbilicus, exacerbated for a month. An abdominal computed tomography (CT) revealed a large, multiseptated cystic mass on the right mesocolon. Right mesocolic excision using a laparoscope was performed on this patient. He was discharged on the fifth day without complications. Recurrence was not detected in three months of follow-up. Conclusion: Cystic lymphangiomas in the mesocolon are rare benign neoplasms that pose diagnostic challenges. Complete resection is the optimal option for diagnostic confirmation and recurrence prevention. Laparoscopic surgery is feasible for children with mesocolic lymphangioma.

Evaluation of rice bacterial blight severity from lab to field with hyperspectral imaging technique.

Hyperspectral imaging technique combined with machine learning is a powerful tool for the evaluation of disease phenotype in rice disease-resistant breeding. However, the current studies are almost carried out in the lab environment, which is difficult to apply to the field environment. In this paper, we used visible/near-infrared hyperspectral images to analysis the severity of rice bacterial blight (BB) and proposed a novel disease index construction strategy (NDSCI) for field application. A designed long short-term memory network with attention mechanism could evaluate the BB severity robustly, and the attention block could filter important wavelengths. Best results were obtained based on the fusion of important wavelengths and color features with an accuracy of 0.94. Then, NSDCI was constructed based on the important wavelength and color feature related to BB severity. The correlation coefficient of NDSCI extended to the field data reached -0.84, showing good scalability. This work overcomes the limitations of environmental conditions and sheds new light on the rapid measurement of phenotype in disease-resistant breeding.

FLASH radiotherapy: A promising new method for radiotherapy.

Among the treatments for malignant tumors, radiotherapy is of great significance both as a main treatment and as an adjuvant treatment. Radiation therapy damages cancer cells with ionizing radiation, leading to their death. However, radiation-induced toxicity limits the dose delivered to the tumor, thereby constraining the control effect of radiotherapy on tumor growth. In addition, the delayed toxicity caused by radiotherapy significantly harms the physical and mental health of patients. FLASH-RT, an emerging class of radiotherapy, causes a phenomenon known as the 'FLASH effect', which delivers radiotherapy at an ultra-high dose rate with lower toxicity to normal tissue than conventional radiotherapy to achieve local tumor control. Although its mechanism remains to be fully elucidated, this modality constitutes a potential new approach to treating malignant tumors. In the present review, the current research progress of FLASH-RT and its various particular effects are described, including the status of research on FLASH-RT and its influencing factors. The hypothetic mechanism of action of FLASH-RT is also summarized, providing insight into future tumor treatments.

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification.

Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other's advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.

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.

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning.

BACKGROUND: Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. RESULTS: In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS-NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. CONCLUSIONS: This study illustrated that informative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.