近年来,集体食品安全事件频频发生,公众对食品安全的关注度持续升高。营区食堂作为负责部队后勤保障和营养供给的内部机构,维系着军人的体质健康和营养均衡,因此保障营区食堂的食品安全显得尤为重要和迫切。本文通过在营区食堂建立和实...近年来,集体食品安全事件频频发生,公众对食品安全的关注度持续升高。营区食堂作为负责部队后勤保障和营养供给的内部机构,维系着军人的体质健康和营养均衡,因此保障营区食堂的食品安全显得尤为重要和迫切。本文通过在营区食堂建立和实施危害分析与关键控制点(Hazard Analysis and Critical Control Point,HACCP)体系,分析营区食堂餐食生产各工序中的食品安全危害,确定关键控制点和关键限值,并建立HACCP计划表,以期为营区食堂的食品安全管理提供参考。此外,HACCP计划的实施离不开日常监督检查,完善其检查机制将会对食品安全管理产生积极的效应,具有一定的借鉴意义。展开更多
目的分析达格列净对糖尿病肾病(DN)患者肾脏功能改善作用及机制。方法选取2021年10月至2022年9月该院收治的518例DN患者作为研究对象,根据随机数字表法分为治疗组与对照组,每组259例。对照组采用常规治疗,治疗组在对照组基础上结合达格...目的分析达格列净对糖尿病肾病(DN)患者肾脏功能改善作用及机制。方法选取2021年10月至2022年9月该院收治的518例DN患者作为研究对象,根据随机数字表法分为治疗组与对照组,每组259例。对照组采用常规治疗,治疗组在对照组基础上结合达格列净治疗。比较两组疗效及治疗前后糖代谢指标[空腹血糖(FPG)、糖化血红蛋白(HbA1c)和餐后2 h血糖(2 h PG)]、肾脏功能指标[尿素氮(BUN)、血肌酐(Scr)、尿白蛋白排泄率(UAER)]、细胞因子指标[白细胞介素(IL)-6、肿瘤坏死因子(TNF)-α和C反应蛋白(CRP)],以及血清胱抑素(Cys)C、转化生长因子(TGF)-β1和胰岛素样生长因子(IGF)-1水平。结果治疗组总有效率高于对照组(P<0.05)。两组治疗后UAER、FPG、HbA1c、2 h PG、BUN、Scr、IL-6、CRP、TNF-α、CysC、TGF-β1、IGF-1水平低于治疗前,差异均有统计学意义(P<0.05)。治疗组治疗后UAER、FPG、HbA1c、2 h PG、BUN、Scr、IL-6、CRP、TNF-α、CysC、TGF-β1、IGF-1水平低于对照组,差异均有统计学意义(P<0.05)。结论达格列净对DN患者疗效显著,改善肾脏功能,其机制可能与减轻炎症反应及降低血清CysC、TGF-β1和IGF-1水平有关。展开更多
Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often...Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.展开更多
文摘近年来,集体食品安全事件频频发生,公众对食品安全的关注度持续升高。营区食堂作为负责部队后勤保障和营养供给的内部机构,维系着军人的体质健康和营养均衡,因此保障营区食堂的食品安全显得尤为重要和迫切。本文通过在营区食堂建立和实施危害分析与关键控制点(Hazard Analysis and Critical Control Point,HACCP)体系,分析营区食堂餐食生产各工序中的食品安全危害,确定关键控制点和关键限值,并建立HACCP计划表,以期为营区食堂的食品安全管理提供参考。此外,HACCP计划的实施离不开日常监督检查,完善其检查机制将会对食品安全管理产生积极的效应,具有一定的借鉴意义。
文摘目的分析达格列净对糖尿病肾病(DN)患者肾脏功能改善作用及机制。方法选取2021年10月至2022年9月该院收治的518例DN患者作为研究对象,根据随机数字表法分为治疗组与对照组,每组259例。对照组采用常规治疗,治疗组在对照组基础上结合达格列净治疗。比较两组疗效及治疗前后糖代谢指标[空腹血糖(FPG)、糖化血红蛋白(HbA1c)和餐后2 h血糖(2 h PG)]、肾脏功能指标[尿素氮(BUN)、血肌酐(Scr)、尿白蛋白排泄率(UAER)]、细胞因子指标[白细胞介素(IL)-6、肿瘤坏死因子(TNF)-α和C反应蛋白(CRP)],以及血清胱抑素(Cys)C、转化生长因子(TGF)-β1和胰岛素样生长因子(IGF)-1水平。结果治疗组总有效率高于对照组(P<0.05)。两组治疗后UAER、FPG、HbA1c、2 h PG、BUN、Scr、IL-6、CRP、TNF-α、CysC、TGF-β1、IGF-1水平低于治疗前,差异均有统计学意义(P<0.05)。治疗组治疗后UAER、FPG、HbA1c、2 h PG、BUN、Scr、IL-6、CRP、TNF-α、CysC、TGF-β1、IGF-1水平低于对照组,差异均有统计学意义(P<0.05)。结论达格列净对DN患者疗效显著,改善肾脏功能,其机制可能与减轻炎症反应及降低血清CysC、TGF-β1和IGF-1水平有关。
基金This research was supported by the National Natural Science Foundation of China No.62276086the National Key R&D Program of China No.2022YFD2000100Zhejiang Provincial Natural Science Foundation of China under Grant No.LTGN23D010002.
文摘Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.
