Fusarium head blight (FHB), caused by Fusarium graminearum, not only leads to severe yield losses but also poses a threat to food safety due to the mycotoxins produced by the pathogen. Since this disease is preventable but not curable, the current control mainly relies on chemical fungicides, the long-term use of which may lead to pathogen resistance and environmental pollution. To develop green control methods, we screened 13 biocontrol strains from the rhizosphere soil of wheat, among which strain No. 12 (identified as Pythium aphanidermatum) showed significant antifungal effects. In the plate confrontation test, this strain reduced the colony diameter of the pathogen by 69.2% (1.47 mm vs. 4.78 mm in the control group), with an inhibition rate of 77% (P < 0.01). Microscopic observation revealed obvious deformations in the pathogen hyphae, suggesting a lysing effect. The coleoptile experiment further confirmed that the pre-treatment with this strain reduced the incidence rate to 0. These findings provide new candidate strains for the biocontrol of FHB and offer a scientific basis for reducing the use of chemical fungicides and promoting sustainable agricultural development.
Triticum/growth & development* ; Fusarium/growth & development* ; Plant Diseases/prevention & control* ; Soil Microbiology ; Pest Control, Biological/methods* ; Pythium/physiology* ; Biological Control Agents ; Rhizosphere ; Fungicides, Industrial

The aim of this study was to promote fucoxanthin accumulation in Phaeodactylum tricornutum by photo-fermentation through optimizing the mode of multiple nitrogen supplementation and blue light enhancement. The results showed that the mixed nitrogen source (tryptone: urea=1:1, N mol/N mol; total nitrogen concentration at 0.02 mol/L) added to the culture system by six times was the best mode in shake flasks. Two-phase culture with light adjustment was then carried out in 5 L photo-fermenter with an enhanced blue light (R: G: B=67.1:16.7:16.3) in the second phase, leading to improved cell density (1.12×10⁸ cells/mL), biomass productivity (330 mg/(d·L)), fucoxanthin content (19.62 mg/g), titer (69.71 mg/L) and productivity (6.97 mg/(d·L)). Compared with one-phase culture under red/blue (R: G: B=70.9:18.3:10.9) light and six-times nitrogen supplementation, the fucoxanthin content was significantly increased by 7.68% (P < 0.05) but the productivity did not change significantly (P > 0.05). Compared with one-phase culture under red/blue (R: G: B=70.9:18.3:10.9) light and one-time nitrogen supplementation, the content and productivity of fucoxanthin were significantly increased by 45.98% and 48.30% (P < 0.05), respectively. This study developed a two-phase culture mode with multiple nitrogen supplementation and blue light enhancement, which effectively promoted the accumulation of fucoxanthin and improved the efficiency of nitrogen source utilization, thus providing a new approach for fucoxanthin accumulation in P. tricornutum by photo-fermentation.
Nitrogen ; Light ; Xanthophylls ; Diatoms ; Dietary Supplements

新冠病毒感染疫情严重威胁着世界各国人民的生命健康。目前，对病毒感染的防治研究主要集中在抑制病毒与分子受体的结合上。AXL作为新发现的严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）受体，在协助病毒感染人体呼吸系统中发挥着重要作用，是未来临床干预的潜在靶点。本研究对已发表的单细胞测序数据进行整理和分析，发现AXL在年轻人肺细胞中的表达水平明显高于老年人。人胚肺二倍体成纤维细胞（2BS）是衰老研究的公认细胞株。本文采用2BS细胞构建复制性细胞衰老模型，发现年轻细胞中AXL的蛋白水平明显高于衰老细胞，据此推测年轻人感染的风险可能更高，需要注意防护。我们发现一种羊栖菜褐藻多糖硫酸酯组分（SFW-3）可显著下调年轻2BS细胞中AXL的表达水平，表明SFW-3具有一定的抗SARS-CoV-2感染的研究价值，同时表明2BS细胞株也可作为潜在的SARS-CoV-2体外感染模型。
Humans ; SARS-CoV-2 ; Sargassum/metabolism* ; Diploidy ; Sulfates/metabolism* ; COVID-19 ; Polysaccharides/pharmacology* ; Lung

The aim of this study was to develop a technical system for high-efficient production of fucoxanthin by photo-fermentation of Phaeodactylum tricornutum. In a 5 L photo-fermentation tank, the effects of initial light intensity, nitrogen source and concentration as well as light quality on biomass concentration and fucoxanthin accumulation in P. tricornutum were investigated systematically under mixotrophic condition. The results showed that the biomass concentration, fucoxanthin content and productivity reached the highest level of 3.80 g/L, 13.44 mg/g and 4.70 mg/(L·d) under the optimal conditions of initial light intensity of 100 μmol/(m²·s), 0.02 mol TN/L of tryptone: urea (1:1, N mol/N mol) as mixed nitrogen source, and a mixed red/blue (R: B=6:1) light, 1.41, 1.33 and 2.05-fold higher than that before optimization, respectively. This study developed a key technology for enhancing the production of fucoxanthin by photo-fermentation of P. tricornutum, facilitating the development of marine natural products.
Fermentation ; Xanthophylls ; Light ; Diatoms ; Nitrogen

Drowning is the death caused by asphyxiation due to fluid blocking the airway. In the practice of forensic medicine, it is the key to determine whether the corpse was drowned or entered the water after death. At the same time, the drowning site inference and postmortem submersion interval (PMSI) play an important role in the investigating the identity of the deceased, narrowing the investigation scope, and solving the case. Based on diatoms testing, molecular biology, imaging and artificial intelligence and other technologies, domestic and foreign forensic scientists have done relative research in the identification of the cause of death, drowning site inference and PMSI, and achieved certain results in forensic medicine application. In order to provide a reference for future study of bodies in the water, this paper summarizes the above research contents.
Artificial Intelligence ; Diatoms ; Drowning/diagnosis* ; Forensic Pathology ; Humans ; Lung ; Water

Diatom test is the main laboratory test method in the diagnosis of drowning in forensic medicine. It plays an important role in differentiating the antemortem drowning from the postmortem drowning and inferring drowning site. Artificial intelligence (AI) automatic diatom test is a technological innovation in forensic drowning diagnosis which is based on morphological characteristics of diatom, the application of AI algorithm to automatic identification and classification of diatom in tissues and organs. This paper discusses the morphological diatom test methods and reviews the research progress of automatic diatom recognition and classification involving AI algorithms. AI deep learning algorithm can assist diatom testing to obtain objective, accurate, and efficient qualitative and quantitative analysis results, which is expected to become a new direction of diatom testing research in the drowning of forensic medicine in the future.
Artificial Intelligence ; Autopsy ; Diatoms ; Drowning/diagnosis* ; Humans ; Lung

Diatom detection is an important method for identifying drowning and throwing corpses after death and inferring the drowning sites in forensic examination of corpses in water. In recent years,high-throughput sequencing technology has achieved rapid development and has been widely used in research related to diatom taxonomic investigations. This paper reviews the research status and prospects of high-throughput sequencing technology and its application in forensic diatom detection.
Cadaver ; Diatoms/genetics* ; Drowning/diagnosis* ; Forensic Pathology/methods* ; High-Throughput Nucleotide Sequencing ; Humans ; Lung ; Technology

OBJECTIVES: To select four algorithms with relatively balanced complexity and accuracy among deep learning image classification algorithms for automatic diatom recognition, and to explore the most suitable classification algorithm for diatom recognition to provide data reference for automatic diatom testing research in forensic medicine. METHODS: The "diatom" and "background" small sample size data set (20 000 images) of digestive fluid smear of corpse lung tissue in water were built to train, validate and test four convolutional neural network (CNN) models, including VGG16, ResNet50, InceptionV3 and Inception-ResNet-V2. The receiver operating characteristic curve (ROC) of subjects and confusion matrixes were drawn, recall rate, precision rate, specificity, accuracy rate and F1 score were calculated, and the performance of each model was systematically evaluated. RESULTS: The InceptionV3 model achieved much better results than the other three models with a balanced recall rate of 89.80%, a precision rate of 92.58%. The VGG16 and Inception-ResNet-V2 had similar diatom recognition performance. Although the performance of diatom recall and precision detection could not be balanced, the recognition ability was acceptable. ResNet50 had the lowest diatom recognition performance, with a recall rate of 55.35%. In terms of feature extraction, the four models all extracted the features of diatom and background and mainly focused on diatom region as the main identification basis. CONCLUSIONS Including the Inception-dependent model, which has stronger directivity and targeting in feature extraction of diatom. The InceptionV3 achieved the best performance on diatom identification and feature extraction compared to the other three models. The InceptionV3 is more suitable for daily forensic diatom examination.
Algorithms ; Deep Learning ; Diatoms ; Humans ; Neural Networks, Computer ; ROC Curve

OBJECTIVES: To explore the application values of diatom artificial intelligence (AI) search system in the diagnosis of drowning. METHODS: The liver and kidney tissues of 12 drowned corpses were taken and were performed with the diatom test, the view images were obtained by scanning electron microscopy (SEM). Diatom detection and forensic expert manual identification were carried out under the thresholds of 0.5, 0.7 and 0.9 of the diatom AI search system, respectively. Diatom recall rate, precision rate and image exclusion rate were used to detect and compare the efficiency of diatom AI search system. RESULTS: There was no statistical difference between the number of diatoms detected in the target marked by the diatom AI search system and the number of diatoms identified manually (P>0.05); the recall rates of the diatom AI search system were statistically different under different thresholds (P<0.05); the precision rates of the diatom AI system were statistically different under different thresholds(P<0.05), and the highest precision rate was 53.15%; the image exclusion rates of the diatom AI search system were statistically different under different thresholds (P<0.05), and the highest image exclusion rate was 99.72%. For the same sample, the time taken by the diatom AI search system to identify diatoms was only 1/7 of that of manual identification. CONCLUSIONS Diatom AI search system has a good application prospect in drowning cases. Its automatic diatom search ability is equal to that of experienced forensic experts, and it can greatly reduce the workload of manual observation of images.
Artificial Intelligence ; Diatoms ; Drowning/diagnosis* ; Humans ; Liver ; Lung ; Microscopy, Electron, Scanning

OBJECTIVES: To construct a YOLOv3-based model for diatom identification in scanning electron microscope images, explore the application performance in practical cases and discuss the advantages of this model. METHODS: A total of 25 000 scanning electron microscopy images were collected at 1 500× as an initial image set, and input into the YOLOv3 network to train the identification model after experts' annotation and image processing. Diatom scanning electron microscopy images of lung, liver and kidney tissues taken from 8 drowning cases were identified by this model under the threshold of 0.4, 0.6 and 0.8 respectively, and were also identified by experts manually. The application performance of this model was evaluated through the recognition speed, recall rate and precision rate. RESULTS: The mean average precision of the model in the validation set and test set was 94.8% and 94.3%, respectively, and the average recall rate was 81.2% and 81.5%, respectively. The recognition speed of the model is more than 9 times faster than that of manual recognition. Under the threshold of 0.4, the mean recall rate and precision rate of diatoms in lung tissues were 89.6% and 87.8%, respectively. The overall recall rate in liver and kidney tissues was 100% and the precision rate was less than 5%. As the threshold increased, the recall rate in all tissues decreased and the precision rate increased. The F1 score of the model in lung tissues decreased with the increase of threshold, while the F1 score in liver and kidney tissues with the increase of threshold. CONCLUSIONS The YOLOv3-based diatom electron microscope images automatic identification model works at a rapid speed and shows high recall rates in all tissues and high precision rates in lung tissues under an appropriate threshold. The identification model greatly reduces the workload of manual recognition, and has a good application prospect.
Diatoms ; Drowning/diagnosis* ; Humans ; Liver/diagnostic imaging* ; Lung/diagnostic imaging* ; Microscopy, Electron, Scanning