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为探究林麝（Moschus berezovskii）的死亡原因，无菌采集死亡林麝脏器样本，通过细菌分离纯化、生化鉴定、16S rDNA基因测序、纸片扩散法和耐药基因检测方法对细菌进行分离、鉴定及分析分离菌株的耐药性。结果表明：从死亡林麝脏器样本中分离得到的菌株为弗格森埃希菌（Escherichia fergusonii），该分离菌株对头孢噻肟、头孢噻吩和青霉素耐药，对氨基糖苷类、喹诺酮类及氨基青霉素类药物敏感；携带耐药基因bla-OXA1/4/30、bla-CTX-M和bla-SHV。研究结果可为林麝养殖过程中致病性弗格森埃希菌的预防和治疗提供参考。

【目的】研究诱导型一氧化氮合酶（iNOS）和环氧化酶-2（COX-2）在舌鳞癌原发灶及其颈淋巴结转移灶组织中的表达差异，探讨舌鳞癌转移的机制。【方法】采用SP免疫组化法检测83例舌部原发鳞癌和其中35例相应的颈淋巴结转灶石蜡包埋标本中iNOS、COX-2的表达。【结果】①舌鳞癌颈淋巴结转移灶iNOS（9．6±2．1）、COX-2（8．7±0．9），相应的原发灶iNOS（7．7±1．8），COX-2（6．1±1．3）。无颈淋巴结转移的原发灶iNOS（3．5±2．7）。COX-2（2．4±1．9）。无转移的颈淋巴iNOS（0．7±0．3），COX-2（0．5±0．6），舌正常组织iNOS（0．6±0．4），COX-2（0．4±0．3）。上述组别两两比较，iNOS、COX-2免疫组化评分差异均有统计学意义（P〈0．01。P〈0．05）。②在舌鳞癌颈淋巴结转移癌组织中。iNOS、COX-2表达呈正相关关系（r=0．5841，P〈0．01）。③在舌鳞癌颈淋巴结转移灶中。M组iNOS（10．5±0．7）、COX-2（9．6±1．1）分别与N2组iNOS（9．4±0．6）、COX-2（9．1±0．2）比较差异无统计学意义（P〉0．05）。N1组iNOS（6．1±0．4）、COX-2（5．5±0．4）分别同N0组iNOS（0．7±0．3）、COX-2（0．5±0．6）比较。差异有统计学意义（P〈0．01）。N2组iNOS、COX-2分别同N1组iNOS、COX-2比较，差异有统计学意义（P〈0．01）。【结论】iNOS、COX-2在舌鳞癌的转移及颈淋巴结转移灶的生长中起着重要的作用。

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Poplar tree is one of the most important tree species in wood production and protective forest construction. The poplar artificial forest cultivation is ranked first in the world. Current severe drought and typical abiotic stress under global warming can pose a great challenge to the production of wood raw materials and the protection of forest resources. A series of planting strategies (such as the identification of water-deficient plants) have been used to cultivate the poplar species of high drought tolerance against drought stress. These approaches are required for the rapid observation and analysis of the growth status of poplar saplings, in order to execute irrigation decisions or drought-resistant variety screening. Previous research has focused mainly on the detection of drought stress, where the macroscopic morphology of plants can be classified to predict the drought level. Only a few studies have been focused on the digital expression of water deficiency symptoms in plants. Alternatively, the water content of plants has been directly predicted using relatively complex and expensive sensing devices. In this article, a low-cost machine vision was adopted to realize the lant phenotyping of poplar saplings under drought stress. Firstly, an extraction framework of the poplar skeleton was proposed to identify the individual leaves and key nodes inside the plant using the YOLOv8-pose detection model, in order to extract the overall morphological and structural information of the plant. Secondly, the few-shot learning techniques were introduced, including the training dataset expansion (or considered as an automatic synthesis for datasets) using minimal manual annotation. The annotated leaves and stems were extracted from the images, and then added into a ‘component pool’. New images of the poplar plant were also selected randomly. All the individual leaves and the key points were detected (including the leaf tip, connection point between blade and petiole, and connection point between the petiole and the trunk). Among them, the few-shot learning was trained using 600 synthetic poplar images with only 10 manually annotated samples. Thirdly, the angles of the main vein and petiole in each leaf were calculated from the skeleton information of the poplar sapling. Furthermore, the frequency distribution vector was converted (ranges from -90° to 90°, with an interval of 10°) for the artificial intelligence-based modeling. Finally, partial least square discrimination analysis (PLSDA), support vector machine (SVM), multilayer perception (MLP), and convolutional neural network (CNN) classifiers were compared to evaluate the drought stress level. The optimal model was selected for the final drought-stress grading. The results showed that the YOLOv8-pose model with the few-shot learning performed the best in the leaf recognition and key point extraction tasks, where the mean average precision with 0.5 as the threshold for intersection over union (mAP50) values were 0.798 and 0.914, respectively. The effectiveness and reliability few-shot learning-based YOLOv8-pose model were also validated using an independent test dataset composed of real samples and accurate manual annotations. Meanwhile, the manual annotation cost was significantly reduced for YOLOv8-pose model training. Subsequently, the classification model with one-dimensional CNN and leaf angle frequency distribution was superior in the drought stress grading task, with the highest classification accuracy of 0.850. In addition, the improved models shared relatively small parameters (model size less than 7 MB) with high speed. Low-cost computing can be realized in cloud service platforms with limited sources or embedded systems (such as Nvidia Jetson series devices). The phenotyping can also provide new technical support to identify the water-deficient poplar saplings and screen the drought-resistant plants. [ABSTRACT FROM AUTHOR]

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HSV-1 is an important pathogen that can be carried and transmitted in various populations and can cause diseases including herpes labialis, capsulatus, keratitis and viral encephalitis. Although there are several types of HSV-1 vaccines in various stages of development, there is still no commercially available vaccine on the market. The specific anti-HSV-1 drugs used in clinical practice, such as acyclovir, valaciclovir and peniclovir, are also facing the serious threat of resistance. The development of new specific anti-HSV-1 drugs is one of the main tasks currently faced. siRNA is a double-stranded RNA with a length of 20-25 nucleotides that plays an interfering role by silencing gene expression at the post-transcriptional level. As a new and potential antiviral drug, siRNA has attracted much attention and developed rapidly. In this paper, we review the recent progress of siRNA in anti-HSV1 research, including the design, delivery and targeting strategies of siRNA targeting key HSV-1 genes and HSV-1 interacting host cell genes. [ABSTRACT FROM AUTHOR]

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Accurate short-term power load forecasting plays an important role in ensuring the safe and stable operation of the power grid, optimizing energy management, improving the utilization rate of power generation equipment and reducing operating costs. Aiming at the problem of regional short-term power load prediction in univariate scenarios, a prediction method based on MA (moving average) and CNN-LSTM (convolutional networks long short-term memory networks) was proposed, and self-attention mechanism was added. Firstly, multiple sliding average was used to decompose the raw load data into multiple stationary series to reduce the noise and complexity of the data. Secondly, the one-dimensional sequence data was transformed into a multidimensional structure, and the CNN was used to extract the internal relationship between multiple time points. Then, data was input into the LSTM model for training, and weighted fusion was performed using the Self-Attention mechanism to improve the prediction accuracy. Finally, the predicted values of each series are added together to obtain the final load prediction value. In order to verify the effectiveness of the method, a prediction experiment was carried out on the real load data of the power grid interval of fifteen minutes in a certain region of China, and the prediction results were compared with other common model prediction results. Experimental results show that the accuracy of this method is higher than that of other methods in univariate short-term power load forecasting problems. [ABSTRACT FROM AUTHOR]

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Objective: To study the effects of hyperbaric oxygen treatment on the expression of oxidative factors and nuclear factor E2 related factor 2 / heme oxygenase-1 signaling pathway in the brain of traumatic brain injury rats. Methods: 72 male SD rats were randomly divided into sham operation group, traumatic brain injury group and hyperbaric oxygen treatment group, with 24 rats in each group. The rats in each group were divided into 3 d and 7 d subgroups, with 12 rats in each group. The modified neurological severity score was performed on the 3 d and 7 d. The expression levels of superoxide dismutase, malondialdehyde and catalase in brain tissue were detected by enzyme activity kits. The expression levels of Nrf2 and HO-1 proteins in brain tissue were detected by Western blot. Results: The neurological function of rats in TBI group and HBO group decreased in varying degrees compared with rats in SHAM group at 3 d and 7 d after the experiment (P<0.05), and the neurological function of rats in HBO group was improved compared with TBI group (P<0.05). The expression levels of SOD and CAT of rats in TBI and HBO groups were lower than those in SHAM group (P<0.05), and the expression level of MDA was higher than that in SHAM group (P<0.05); At the same time, the expression levels of SOD and CAT of rats in HBO group were higher than those in TBI group (P<0.05), and the expression level of MDA was lower than that in TBI group (P<0.05). Western blot showed that three days after the experiment, the expression levels of Nrf2 and HO-1 protein of rats in TBI group and HBO group were higher than those in SHAM group (P<0.05), and the expression levels of Nrf2 and HO-1 protein in HBO group were higher than those in TBI group (P<0.05). However, on the 7th day after the experiment, the expression levels of Nrf2 and HO-1 protein of rats in TBI group were lower than those in SHAM group (P<0.05), and the expression levels of Nrf2 and HO-1 protein in HBO group were higher than those in SHAM group and TBI group (P<0.05). Conclusions: Rats have different degrees of neurological damage on the 3 d and 7 d after TBI. Hyperbaric oxygen treatment can improve neurological function by regulating Nrf2 / HO-1 signal pathway, which can regulate the expression levels of SOD, MDA, CAT and inhibit oxidative stress response. [ABSTRACT FROM AUTHOR]

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