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Ferrocenyl conjugated oxazepine/quinoline derivatives were presented through the reaction of hexadehydro-Diels–Alder (HDDA) generated arynes with ferrocenyl oxazolines under mild conditions via ring-expanding or rearrangement processes. Water molecule participated in this unexpected rearrangement process to produce quinoline skeletons, and DFT calculations supported a ring-expanding and intramolecular hydrogen migration process for the formation of oxazepine derivatives. Two variants of this chemistry, expanded the reactivity between ferrocenyl conjugated substances and arynes, further providing an innovative approach for the synthesis of ferrocene derivatives.

Abstract Purpose Previous reports argue that preoperative sleep conditions of patients can influence the dosage of general anaesthesia drugs. Therefore, we aimed to investigate the dose-effect relationship of preoperative sleep disorders on the induction of general anaesthesia with remimazolam tosilate and calculate the Median effective (ED50) and 95% effective (ED95) dosages. Methods Included in our study were 56 patients who underwent laparoscopic cholecystectomy at our hospital. A separate group of 27 patients with sleep disorders (SD group) and 29 patients without sleep disorders (NSD group) using the Pittsburgh Sleep Quality Index (PSQI) were also included. According to the Dixon ‘up-and-down’ design, patients received remimazolam at preselected concentrations starting at 0.2 mg/kg. After the administration of remimazolam, loss of consciousness was observed. By observing whether consciousness disappeared within a minute, we adjusted the dose of remimazolam by 0.1 mg/kg (up and down) in the following patient. The Median effective dose (ED50), 95% effective dose (ED95), and 95% confidence interval (CI) of remimazolam for effective sedation were calculated. Results The ED50 of remimazolam was 0.226 mg/kg (95%CI 0.221–0.232 mg/kg) in the SD group and 0.191 mg/kg (95%CI, 0.183–0.199 mg/kg) in the NSD group. The ED95 of remimazolam was 0.237 mg/kg (95%CI 0.231–0.262 mg/kg) in the SD group and 0.209 mg/kg (95%CI 0.200–0.254 mg/kg) in the NSD group. Conclusions In the SD group, the ED50 and ED95 of remimazolam during anaesthesia induction were 0.226 and 0.237 mg/kg, respectively. The induction dose of remimazolam in the SD group was significantly higher than that in the NSD group.

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.

For the problem that the traceability parameters of sudden water pollution are difficult to determine, a fast traceability model based on a simplified mechanistic model coupled with an optimization algorithm is proposed to improve the accuracy of sudden water pollution traceability. In this paper, according to the diffusion law of pollutants, a quantitative formula of pollutant diffusion is proposed, and the differential calculation process of the pollutant convection equation is optimized. The Dynamic Programming and Beetle Antennae Search algorithm (DP-BAS) with dynamic step size is used in the reverse optimization process, which can avoid the problem of entering the local optimal solution in the calculation process. The DP-BAS is used to inverse solve the quantization equation to realize the decoupling of pollutant traceability parameters, transforming the multi-parameter coupled solution into a single-parameter solution, reducing the solution dimension, and optimizing the difficulty and solution complexity of pollutant traceability. The proposed traceability model is applied to the simulation case, the results show that the mean square errors of pollutant placement mass, location, and time are 2.39, 1.16, and 1.19 percent, respectively. To further verify the model reliability, the Differential Evolution and Markov Chain Monte Carlo simulation method (DE-MCMC) as well as Genetic Algorithms (GA) were introduced to compare with the proposed model to prove that the model has certain reliability and accuracy.

BackgroundThe efficacy of induction chemotherapy (IC) for acute myeloid leukemia (AML) has improved significantly with the application of targeting drugs. Our previous study showed that a 4-day IC regimen of cyclophosphamide (CTX) and Ara-C [CA (4 + 3)] achieved similar complete remission (CR) rate (80%) compared with the traditional 7-day regimen, and the survival rate appeared to be better.MethodsIn this pilot study, we further shortened the CA regimen to 3 days, added low-dose venetoclax (VEN, 200 mg/day) (VCA), and reported the efficacy and safety here.ResultsTwenty-five newly diagnosed adult AML patients were enrolled in this study and evaluated for the remission rate after one cycle of the VCA regimen. The CR/Cri was 92%, and all these patients had undetectable minimal residual disease (MRD−). The estimated overall survival at 12 months was 79.3%. The median time for both platelet recovery and absolute neutrophil count recovery was 16 days, faster than that of traditional IC. Compared with the previous CA (4 + 3) regimen, a higher CR rate (92% vs. 80%, P < 0.01) and a deeper degree of remission (CRMRD− rate, 92% vs. 45%, P < 0.01) were found in the VCA group.ConclusionsThis study showed that the 3-day CTX and Ara-C regimen is highly effective in newly diagnosed AML patients, and the addition of VEN to the CA regimen achieves higher and deeper one-course remission.

The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accuracy and a lightweight framework. Furthermore, there is a lack of accurate recognition methods for non-large-scale crops. In this paper, we propose an improved encoder-decoder framework based on DeepLab v3+ to accurately identify crops with different planting patterns. The network employs ShuffleNet v2 as the backbone to extract features at multiple levels. The decoder module integrates a convolutional block attention mechanism that combines both channel and spatial attention mechanisms to fuse attention features across the channel and spatial dimensions. We establish two datasets, DS1 and DS2, where DS1 is obtained from areas with large-scale crop planting, and DS2 is obtained from areas with scattered crop planting. On DS1, the improved network achieves a mean intersection over union (mIoU) of 0.972, overall accuracy (OA) of 0.981, and recall of 0.980, indicating a significant improvement of 7.0%, 5.0%, and 5.7%, respectively, compared to the original DeepLab v3+. On DS2, the improved network improves the mIoU, OA, and recall by 5.4%, 3.9%, and 4.4%, respectively. Notably, the number of parameters and giga floating-point operations (GFLOPs) required by the proposed Deep-agriNet is significantly smaller than that of DeepLab v3+ and other classic networks. Our findings demonstrate that Deep-agriNet performs better in identifying crops with different planting scales, and can serve as an effective tool for crop identification in various regions and countries.

Achieving global food security requires an understanding of the current status and the future trends in the yield gap for cropping systems worldwide. The aim of this study was to scientifically understand the existing yield gap research, recognize the knowledge base and influential articles, and uncover key research themes and how these have evolved over the past three decades. Bibliometric methods were used to analyze articles related to the yield gap available in the largest scientific database, the Web of Science. A total of 6049 relevant articles published from 1993 to 2023 were numerically analyzed for patterns, trends, and clusters. The findings identified a few primary authors of widely cited publications. Geographically, the United States and China were the two major contributors to the publication pool, with articles from China mostly affiliated with the Chinese Academy of Sciences and China Agricultural University, while the United States had a more scattered distribution of affiliated institutions. The research on yield gaps primarily focused on biological factors, such as the effects of crop varieties, agronomic management, climate change, and soil conditions, with a limited exploration of social and economic factors. Within the auspices of the current food issues worldwide, this study provides a thorough view of the progress and key topics in crop yield gap research, contributing to the existing body of knowledge and providing guidance for researchers, policymakers, and stakeholders involved in agricultural productivity enhancement and sustainable food production. Amid the increasing trend in hunger worldwide over the past decade, we thus concluded that, by establishing appropriate benchmarks, re-prioritizing research needs, and focusing on transforming natural resources rather than inputs, the crop yield gap approach can be useful in terms of the clear inclusion of local contexts and socioeconomic constraints.

Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A feasible method to address the challenges is to segment strawberry lesions from the background and learn fine-grained features of the lesions. Following this idea, we present a novel Class-Attention-based Lesion Proposal Convolutional Neural Network (CALP-CNN), which utilizes a class response map to locate the main lesion object and propose discriminative lesion details. Specifically, the CALP-CNN firstly locates the main lesion object from the complex background through a class object location module (COLM) and then applies a lesion part proposal module (LPPM) to propose the discriminative lesion details. With a cascade architecture, the CALP-CNN can simultaneously address the interference from the complex background and the misclassification of similar diseases. A series of experiments on a self-built dataset of field strawberry diseases is conducted to testify the effectiveness of the proposed CALP-CNN. The classification results of the CALP-CNN are 92.56%, 92.55%, 91.80% and 91.96% on the metrics of accuracy, precision, recall and F1-score, respectively. Compared with six state-of-the-art attention-based fine-grained image recognition methods, the CALP-CNN achieves 6.52% higher (on F1-score) than the sub-optimal baseline MMAL-Net, suggesting that the proposed methods are effective in identifying strawberry diseases in the field.

BackgroundRenal cell carcinoma (RCC) is one out of the most universal malignant tumors globally, and its incidence is increasing annually. MicroRNA (miRNA) in serum could be considered as a non-invasive detecting biomarker for RCC diagnosis.MethodA total of 224 participants (112 RCC patients (RCCs) and 112 normal controls (NCs)) were enrolled in the three-phrase study. Reverse transcription quantitative PCR (RT-qPCR) was applied to reveal the miRNA expression levels in RCCs and NCs. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to predict the diagnostic ability of serum miRNAs for RCC. Bioinformatic analysis and survival analysis were also included in our study.ResultsCompared to NCs, the expression degree of miR-155-5p, miR-224-5p in serum was significantly upregulated in RCC patients, and miR-1-3p, miR-124-3p, miR-129-5p, and miR-200b-3p were downregulated. A four-miRNA panel was construed, and the AUC of the panel was 0.903 (95% CI: 0.847–0.944; p < 0.001; sensitivity = 75.61%, specificity = 93.67%). Results from GEPIA database indicated that CHL1, MPP5, and SORT1 could be seen as promising target genes of the four-miRNA panel. Survival analysis of candidate miRNAs manifested that miR-155-5p was associated with the survival rate of RCC significantly.ConclusionsThe four-miRNA panel in serum has a great potential to be non-invasive biomarkers for RCC sift to check.

Background: Renal cell carcinoma (RCC) has been a major health problem and is one of the most malignant tumors around the world. Serum microRNA (miRNA) profiles previously have been reported as non-invasive biomarkers in cancer screening. The aim of this study was to explore serum miRNAs as potential biomarkers for screening RCC.Methods: A three-phase study was conducted to explore serum miRNAs as potential biomarkers for screening RCC. In the screening phase, 12 candidate miRNAs related to RCC were selected for further study by the ENCORI database with 517 RCC patients and 71 NCs. A total of 220 participants [108 RCC patients and 112 normal controls (NCs)] were enrolled for training and validation. The dysregulated candidate miRNAs were further confirmed with 30 RCC patients and 30 NCs in the training phase and with 78 RCC patients and 82 NCs in the validation phase. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used for assessing the diagnostic value of miRNAs. Bioinformatic analysis and survival analysis were also included in our study.Results: Compared to NCs, six miRNAs (miR-18a-5p, miR-138-5p, miR-141-3p, miR-181b-5p, miR-200a-3p, and miR-363-3p) in serum were significantly dysregulated in RCC patients. A four-miRNA panel was built by combining these candidate miRNAs to improve the diagnostic value with AUC = 0.908. ABCG1 and RNASET2, considered potential target genes of the four-miRNA panel, may play a significant role in the development of RCC.Conclusion: A four-miRNA panel in serum was identified for RCC screening in our study. The four-–miRNA panel has a great potential to be a non-invasive biomarker for RCC screening.

ObjectiveHere, we investigated the relationship between social avoidance and depression in college students, explored the mediating roles of loneliness and trust, and the regulatory role of self-esteem, to provide a theoretical intervention approach based on internal mechanisms.MethodsUsing a simple random overall sampling method, 1,021 college students were investigated using self-rating depression, social avoidance and distress, loneliness, interpersonal trust and self-esteem scales.ResultsThere was a significant positive correlation between social avoidance and depression. Loneliness and interpersonal trust played chain-mediating roles between social avoidance and depression. The influence of social avoidance on interpersonal trust was regulated by self-esteem. Specifically, the social avoidance level of the low self-esteem group was more likely to be affected by interpersonal trust issues.ConclusionSocial avoidance not only directly affects college students’ depression, it also has indirect effects through interpersonal trust and loneliness. Thus, interpersonal trust and loneliness have chain-mediating effects between social avoidance and depression in college students, and self-esteem regulates the mediation process.

Piezoelectric micromechanical ultrasonic transducers (pMUTs) are new types of distance sensors with great potential for applications in automotive, unmanned aerial vehicle, robotics, and smart homes. However, previously reported pMUTs are limited by a short sensing distance due to lower output sound pressure. In this work, a pMUT with a special dual-ring structure based on scandium-doped aluminum nitride (ScAlN) is proposed. The combination of a dual-ring structure with pinned boundary conditions and a high piezoelectric performance ScAlN film allows the pMUT to achieve a large dynamic displacement of 2.87 μm/V and a high electromechanical coupling coefficient (kt2) of 8.92%. The results of ranging experiments show that a single pMUT achieves a distance sensing of 6 m at a resonant frequency of 91 kHz, the farthest distance sensing registered to date. This pMUT provides surprisingly fertile ground for various distance sensing applications.

Science and technology innovation are crucial components underpinning agriculture. We constructed an evaluation framework including 4 pillars and 21 indicators, taking 31 provinces in China as examples to examine the level of agriculture science and technology development from a regional perspective. We found that there is an obvious gap between east and west nationwide, and that only about half of the provinces have reached the high and medium levels. It was worth noting that the innovation conditions in Shanghai and Beijing presented huge advantages, of vital importance to a first-class talent team, a complete innovation system, a stable and prosperous market, and active exchanges and cooperation. In addition, to maximize the transformation of agricultural science and technology achievements into real productivity, local government should also strengthen the construction of agricultural research and innovation platforms, technology transfer, and transformation of results. The findings advance understanding of the strengths and weaknesses of the evaluation subjects’ agricultural science and technology development from a regional perspective and are expected to provide some basis for the government and stakeholders to make relevant decisions.

Abstract Optimizing the induction therapy of acute myeloid leukemia (AML) may improve the remission rate and reduce the risk of relapse, thereby extend survival. Cyclophosphamide (CTX) shows benefit in treating relapsed and refractory AML patients, but it has not been reported in first‐line induction regimens. To assess the efficacy and safety of CTX and moderate‐dosage cytarabine (Ara‐C) as induction chemotherapy in newly diagnosed adult AML, 40 patients were enrolled to receive CTX (20 mg/kg/d) for 4 consecutive days and Ara‐C for 3 (1 g/m2 q12h, CA4+3) or 5 (1 g/m2 qd, CA4+5) days. With one course of induction chemotherapy, the overall response rate and the complete remission rate (CR) was 82.5% (33/40) and 77.5% (31/40), respectively. The expected 5 years overall survival and relapse‐free survival was 64% in patients experienced CR and fulfilled consolidation therapy. The neutrophil and platelet recovery time were 17 (range, 10–20) days and 16.5 (range, 12–30) days in the CA4+3 group, faster than that of 20 (16‐36) days and 20 (14‐36) days in the CA4+5 group (P = .006 and P = .006). The cyclophosphamide and cytarabine (CA) regimen was generally safe and had reversible adverse effects. The patients who failed to respond to the CA regimen did not benefit from a second course of other traditional induction chemotherapy either. In conclusion, the combined regimen of CTX and Ara‐C represents a promising therapeutic approach to induce the first CR of newly diagnosed adult AML.

Developing multicomponent crystal forms, especially cocrystals and salts, is becoming a promising pathway to improve the solubility and bioavailability of drugs. Herein, new multicomponent crystals of SHR0302, a new generation of Janus Kinase (JAK) inhibitor that suffers from poor solubility, were developed based on a cooperative approach of computational and experimental coformer screenings. Virtual screening methods, including the conductor-like screening model for realistic solvents (COSMO-RS) and molecular complementary (MC) analysis, were employed to predict the binding affinity between SHR0302 and selected coformers. The developed screening method was capable of reducing the screening database to 30 coformers from a total of 42 proposed coformers. The proof-of-concept experimental screening study was performed to demonstrate the efficiency of computational screening, wherein three new multicomponent crystalline forms were found and fully characterized by powder X-ray diffraction, thermal analysis, and IR and 1H-NMR spectroscopy. Further, the measurements of the solubility property of these new multicomponent crystal forms reveal an apparent promotion compared with the drug alone. Finally, the receiver operator characteristic (ROC) curve was used to assess the prediction performance of the COSMO-RS model. It was found that the established screening model can effectively shorten the experimental screening time and efforts.

The novel coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global public health emergency. As the number of confirmed cases increases, cardiovascular complications, such as myocardial injury and cardiac dysfunction, are evidenced. Takotsubo syndrome (TTS), which is common in the intensive care unit, is diagnosed among COVID-19 patients. There have been 68 more cases reports with over 119 patients since a COVID-19 patient with TTS was first reported on April 14, 2020. Angiotensin-converting enzyme 2 (ACE2), which is widely expressed in the lungs and heart, is the virus receptor. Nevertheless, randomized studies on COVID-19 related TTS are lacking, and the pathogenesis and pathophysiology are still unclear. Therefore, this review provides an overview of the potential pathogenesis, pathophysiology, clinical manifestations, diagnosis, and treatment strategy for TTS in the COVID-19 era based on current practices.

Background: Renal cell carcinoma (RCC) is a most common kidney malignancy, with atypical symptoms in the early stage and poor outcome in the late stage. Recently, emerging evidence revealed that some miRNAs play an essential role in the tumorigenesis and progression of RCC. Therefore, the aim of this study is that understand the detailed molecular mechanism of miR-23a-3p in RCC and identify its potential clinical value. Methods: In this study, RT-qPCR, wound scratch assay, cell proliferation assay, transwell assay and flow cytometry assay were performed to detect miR-23a-3p expression and its proliferation, migration and apoptosis in RCC. The bioinformatics analysis, RT-qPCR, western blot and luciferase reporter assay were performed to discern and examine the relationship between miR-23a-3p and its potential targets. Moreover, we analyzed the relationship between miR-23a-3p expression and clinicopathological variables or overall survival (OS) from 118 formalin-fixed paraffin-embedded RCC samples. Results: miR-23a-3p is significantly up-regulated in RCC tissue samples, RCC cell lines and the TCGA database. Upregulating miR-23a-3p enhances, while silencing miR-23a-3p suppresses cell viability, proliferation and mobility in ACHN and 786-O cell lines. Besides, overexpression of miR-23a-3p inhibits the cell apoptosis. Then our study further reveals that miR-23a-3p regulates tumorigenesis by targeting Proline-Rich Nuclear Receptor Coactivator 2 (PNRC2). Also, the cox proportional hazard regression analysis indicates that low expression of miR-23a-3p patients has a remarkable longer OS. Conclusions: Our results reveals that miR-23a-3p may not only serve as a new biomarker for prognosis but also serve as a new therapeutic strategy in the RCC treatment.

With the development of oriented object detection technology, especially in the area of remote sensing, significant progress has been made, and multiple excellent detection architectures have emerged. Oriented detection architectures can be broadly divided into five-parameter systems and eight-parameter systems that encounter the periodicity problem of angle regression and the discontinuous problem of vertex regression during training, respectively. Therefore, we propose a new multi-branch anchor-free one-stage model that can effectively alleviate the corner case when representing rotating objects, called Surround-Net. The creative contribution submitted in this paper mainly includes three aspects. Firstly, a multi-branch strategy is adopted to make the detector choose the best regression path adaptively for the discontinuity problem. Secondly, to address the inconsistency between classification and quality estimation (location), a modified high-dimensional Focal Loss and a new Surround IoU Loss are proposed to enhance the unity ability of the features. Thirdly, in the refined process after backbone feature extraction, a center vertex attention mechanism is adopted to deal with the environmental noise introduced in the remote sensing images. This type of auxiliary module is able to focus the model’s attention on the boundary of the bounding box. Finally, extensive experiments were carried out on the DOTA dataset, and the results demonstrate that Surround-Net can solve regression boundary problems and can achieve a more competitive performance (e.g., 75.875 mAP) than other anchor-free one-stage detectors with higher speeds.

Context: Since December 2019, more than 80,000 patients have been diagnosed with coronavirus disease 2019 (COVID-19) in China. Social support status of COVID-19 patients, especially the impact of social support on their psychological status and quality of life, needs to be addressed with increasing concern.Objectives: In this study, we used social support rating scale (SSRS) to investigate the social support in COVID-19 patients and nurses.Methods: The present study included 186 COVID-19 patients at a Wuhan mobile cabin hospital and 234 nurses at a Wuhan COVID-19 control center. Responses to a mobile phone app-based questionnaire about social support, anxiety, depression, and quality of life were recorded and evaluated.Results: COVID-19 patients scored significantly lower than nurses did on the Social Support Rating Scale (SSRS). Among these patients, 33.9% had anxiety symptoms, while 23.7% had depression symptoms. Overall SSRS, subjective social support scores and objective support scores of patients with anxiety were lower than those of patients without anxiety. This result was also found in depression. In addition, all dimensions of social support were positively correlated with quality of life. Interestingly, in all dimensions of social support, subjective support was found to be an independent predictive factor for anxiety, depression, and quality of life, whereas objective support was a predictive factor for quality of life, but not for anxiety and depression via regression analysis.Conclusion: Medical staffs should pay attention to the subjective feelings of patients and make COVID-19 patients feel respected, supported, and understood from the perspective of subjective support, which may greatly benefit patients, alleviate their anxiety and depression, and improve their quality of life.

PDK1 (3-Phosphoinositide dependent protein kinase-1) is a member in the PI3K (phosphatidylinositol 3 kinase) pathway and is implicated in neurodevelopmental disease with microcephaly. Although the role of PDK1 in neurogenesis has been broadly studied, it remains unknown how PDK1 may regulate oligogenesis in the central nervous system (CNS). To address this question, we generated oligodendrocyte (OL) lineage cells specific PDK1 conditional knockout (cKO) mice. We find that PDK1 cKOs display abnormal white matter (WM), massive loss of mature OLs and severe defect in myelination in the CNS. In contrast, these mutants exhibit normal neuronal development and unchanged apoptosis in the CNS. We demonstrate that deletion of PDK1 severely impairs OL differentiation. We show that genetic or pharmacological inhibition of PDK1 causes deficit in the mammalian target of rapamycin (mTor) signaling and down-regulation of Sox10. Together, these results highlight a critical role of PDK1 in OL differentiation during postnatal CNS development.

Some emergencies have produced enormous changes in market demand in recent years as the economy has progressed. The supply chain’s capacity to adapt to this market demand has a substantial impact on the company’s future growth. As the frequency of emergencies rises, emergency management is becoming increasingly critical for enterprises. This article examines the responsiveness and profit status of the supply chain under the three conditions of total complete information symmetry, incomplete information symmetry, and information asymmetry based on the information sharing degree of supply chain firms. The research offers a novel approach to emergency management. It makes recommendations for how the company may share information to improve its emergency management capabilities. Finally, the feasibility of the model is verified and analyzed by a case.

At present, there are seven known types of human coronaviruses (HCoVs), which can be further divided into two categories: low pathogenic and highly pathogenic. The low pathogenic HCoVs infect the upper respiratory tract, mainly causing mild, cold-like respiratory diseases. By contrast, highly pathogenic HCoVs mainly infect the lower respiratory tract and cause fatal types of pneumonia, which include severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), as well as the recent outbreak of coronavirus disease 2019 (COVID-19). Highly pathogenic HCoV infection has a high morbidity and mortality, which is usually related to the strong immune response induced by highly proinflammatory cytokines, which is also known as “cytokine storm”. Therefore, it is particularly important to explore the role of cytokine storm in the process of highly pathogenic HCoV infection. We review the epidemiological and clinical manifestations of highly pathogenic HCoV infection, and reveal the pathology of cytokine storm and its role in the process of highly pathogenic HCoV infection.

Celtis sinensis Pers. is a popular garden landscape tree in riparian areas and a valuable resistant tree in many extreme environments. Here, we determined the first complete chloroplast genome of C. sinensis using high-throughput sequencing technology. Our results showed the chloroplast genome of C.sinensis was 159,092 bp long and displayed a typical quadripartite structure consisting of a pair of inverted repeats with a length of 26,895 bp and separating by two single-copy regions (LSC, 86,085 bp and SSC, 19,217 bp). Besides, the chloroplast genome of C. sinensis totally contained 131 genes, including 87 protein coding genes, 36 transfer RNAs and eight ribosomal RNAs. Additionally, a maximum likelihood phylogenetic analysis based on the 19 chloroplast genomes demonstrated the monophyly of Cannabaceae and C. sinensis formed a sister clade to Celtis biondii.