Flow-induced release of adenosine 5′-triphosphate from endothelial cells of the rat mesenteric arterial bed

Adenosine 5-triphosphate (ATP) was released into the perfusate of rat isolated mesenteric arterial beds during each of two consecutive increases in flow. There was no significant difference between the amounts of ATP released on each occasion. Substance P was also released into the perfusate by increased flow, although its release was more variable. Removal of the endothelium of the mesenteric vessels with sodium deoxycholate led to a significant reduction (74%) in the amount of ATP released compared with the release before the endothelium had been removed. This suggests that the ATP released into the mesenteric arterial perfusate during increased flow arises from endothelial cells.     

Determination of erythrocyte pyrimidine 5′-nucleotidase activity by31P nuclear magnetic resonance: Comparison of normal controls and multiple sclerosis patients

Summary A technique to assay erythrocyte pyrimidine 5-nucleotidase activity in situ using³¹P nuclear magnetic resonance spectroscopy is presented. The assay is chemically specific, simple and applicable to untreated lysates. A comparison of enzyme levels in normal controls and in multiple sclerosis patients employing the assay yielded no significant differences between both groups. Difficulties encountered in the quantitative analysis of the assay using¹H-NMR spectroscopy are briefly discussed.     

基于轻量化深度学习网络的数字仪表识别

数字式仪表常用于变电站、工厂等生产环境,是一种直观的设备监测仪器。然而当前数字式仪表的读取方式还依赖于人工巡检,手动记录等,这些传统的巡检方式来监测设备的运行状态大大降低了巡检效率。为了实现传统行业的数字化转型,本文提出基于轻量化深度学习的数字仪表识别方法,通过改进的YOLOv5的目标检测框架,针对数字仪表目标区域在整张图片大小不一致的情况,提出对于感兴趣区域（ROI）的迭代目标检测方法,首次检测将感兴趣区域进行检测并切割统一到相同的尺度,随后迭代检测网络针对感兴趣区域内的字符进行检测并分类,以达到精确读数的目的。为提升多尺度检测性能,本文采用Res2Net模块主干网络中的的残差模块。采用GIoU取代通用的IoU作为位置损失函数加速模型训练效果的收敛。实验表明,改进后的框架实现了99.62%的准确率和99.55%的召回率,相比基线网络分别提升了12.72%和5.85%。通过将框架在边缘计算平台上的终端部署,在实际生产中取代了人工巡检,实现了商业化运行。     

鄂尔多斯盆地长7储层致密油水平井CO2补充能量与吞吐参数优化

长7储层是鄂尔多斯盆地主要的致密区块,近一半的致密油分布其中。针对长7储层开采渗透率低、开采难度大、依靠弹性开采产能较低、能量衰竭较快等问题,通过数值模拟的方法,构建水平井分段压裂数值模拟模型,首先对比不同吞吐方式对油田产能的影响,优选出CO₂吞吐为首选的补充能量方式。其次对井网形式、井网参数及吞吐参数进行优化研究。最终优选出合适的吞吐方案,达到提高生产效率的目的。结果表明,该储层的最优注采吞吐参数为：周期注入量为2 673 t、注入压力为21 MPa、注入速度为90 t/d、焖井时间为10 d。研究对鄂尔多斯盆地长7储层提高采收率具有一定的参考意义。     

基于改进YOLOv5的遥感图像飞机目标检测

针对遥感图像中飞机检测尺寸大小不一、背景复杂导致的难以识别问题,提出一种基于YOLOv5网络模型的改进方法。首先,在YOLOv5网络模型中融入Swin-Transformer模块,使网络全局建模并使全维度信息交互,以提升网络的特征提取能力;其次,对损失函数进行优化,引入SIOU损失函数以考虑真实框和预测框之间的向量角度问题。对比实验表明,优化前后网络模型检测精度均为95.3%。在检测精度相同的情况下,改进后的网络模型召回率为91.2%,比改进前提升0.6个百分点;改进后平均检测精度mAP_0.5为95.7%,比改进前提升0.2个百分点。结果表明,改进后的YOLOv5网络模型能在一定程度上提升遥感图像中飞机目标检测性能。     

高岭土负载绿色合成纳米铁的制备及其对孔雀石绿和铅离子的去除性能研究

红背桂树叶提取物绿色合成铁纳米颗粒（Fe NPs）在水环境修复领域具有很高的应用潜力。但由于Fe NPs存在团聚、易氧化等不稳定因素,在去除污染物时抑制了反应活性。为了解决这一问题,使用一步法制备了高岭土负载Fe NPs （K-Fe NPs）,并系统地检测了其对孔雀石绿和Pb²⁺混合污染物的去除反应活性。采用X射线衍射（XRD）和傅里叶红外光谱（FTIR）对Fe NPs、高岭土和K-Fe NPs进行表征和分析。3种材料对比实验结果表明,K-Fe NPs对单独的孔雀石绿和Pb²⁺的去除效率（99.10%和93.41%）优于Fe NPs（93.67%和85.33%）和高岭土（32.54%和12.50%）。此外,K-Fe NPs经过4次重复循环对孔雀石绿和Pb²⁺的去除率仍分别为74.02%和55.48%。结果表明,K-Fe NPs在染料和重金属离子复合污染修复领域具有一定应用前景。     

基于掺铕钨酸盐荧光强度比的非接触温度传感

为了突破传统测温技术应用的局限性,利用NaY （WO₄）₂：Eu³⁺玻璃陶瓷（glass ceramics,GC）实现了具有非接触、实时响应、自校准等优势的双模荧光强度比（luminescence intensity ratio,LIR）测温。采用高温熔融淬灭法制备出含NaY （WO₄）₂：Eu³⁺纳米晶的透明GC样品,并进行系列光谱测量和热敏性能分析。结果表明,样品中Eu³⁺的激发态能级⁵D₁和⁵D₀和基态能级⁷F₂和⁷F₀为两对独立的热耦合能级,可分别基于这两对热耦合能级实现性能优异的双模LIR温度传感。该双模LIR测温技术数据可靠、测温范围广、灵敏度高,再结合GC材料优势,是可用于光纤温度传感器的核心技术材料。     

超高压高CO2气藏偏差系数图版扩展与应用

Standing-Katz图版法确定天然气偏差系数是目前最常用、最基础的方法之一,但存在人工读值误差大、在超高压（拟对比压力大于15）和高CO₂含量天然气使用受限等问题。设计单组分偏差系数测定实验,结合Standing-Katz图版曲线变化规律,将图版拟对比压力扩展到30,拟对比温度扩展到3.6,并将图版数字化;基于不同CO₂含量偏差系数测定实验结果,推导了考虑不同CO₂含量的天然气拟临界压力、拟临界温度计算新模型,与常规非烃校正模型相比,新模型计算精度更高,适用范围更广。进一步将新临界参数校正模型嵌入到偏差系数扩展图版中,得到不同CO₂含量的偏差系数扩展图版,该图版在南海西部超高温高压、高CO₂含量天然气偏差系数计算中误差较小（不超过3%）,应用效果良好。     

Metabolism of 3β-hydroxycholest-5-en-26-oic acid in hamsters

Summary Metabolism of 26-hydroxycholesterol to 3-hydroxychol-5-en-24-oic acid and other C24-bile acids has been expected to occur by way of 3-hydroxycholest-5-en-26-oic acid in studies in vitro. 3-Hydroxycholest-5-en-26-oic acid was infused intravenously into bile fistula hamsters and the following C24-bile acids were identified: 3-hydroxychol-5-en-24-oic acid, lithocholic acid, chenodeoxycholic acid, and a small amount of cholic acid.