Ecdysteroids: possible candidates for the hormone which triggers salivary gland degeneration in the ixodid tickAmblyomma hebraeum

Summary Following engorgement, female ixodid ticks secrete a tick salivary gland degeneration factor (TSGDF) into the hemolymph. Here we show that the arthropod ecdysteroid hormones, ecdysome and 20-hydroxyecdysone, induce degeneration of tick salivary glands maintained in organ culture. The effective dose range in vitro is 30–300 ng/ml, a range reported to be physiological for this species following repletion. In addition, infusion of 20-hydroxyecdysome in vivo induces salivary gland degeneration. We therefore propose that TSGDF may be an ecdysteroid.Acknowledgments. Some of the data reported here were presented to the annual meeting of the Canadian Society of Zoologists, 15–18 May 1983; Program of abstracts, page 53. Financial support of the Canadian National Sportsmen's Fund and NSERC Canada to W.R.K. is gratefully acknowledged.     

Adrenocortical metabolism and plasma corticosterone in soman intoxicated rabbits

Summary The organophosphate neurotoxin soman produced impairments in adrenocortical RNA and protein metabolism. Fasciculate and reticular cell RNA and protein contents were supporessed with sublethal to acutely lethal dosages (20, 30 and 40 g/kg, s.c.) during the acute excitatory phase of intoxication and at 6–8 h post injection. All three dosages produced ca 90% inactivation of plasma cholinesterase. A transient elevation of plasma corticosterone occurred with 20 g/kg soman whereas there was a protracted increase with 30 g/kg. Corticosterone was not significantly elevated with 40 g/kg, but death occurred at 13±4 min. Thus, the magnitude and/or nature of soman-induced metabolic impairments does not appear to prevent adrenal activation.Supported by US Army Medical Research and Development Command Contract DAMD 17-81-C-1202.     

The Young-Householder algorithm and the least squares multidimensional scaling of squared distances

It is shown that replacement of the zero diagonal elements of the symmetric data matrix of approximate squared distances by certain other quantities in the Young-Householder algorithm will yield a least squares fit to squared distances instead of to scalar products. Iterative algorithms for obtaining these replacement diagonal elements are described and relationships with the ELEGANT algorithm (de Leeuw 1975; Takane 1977) are discussed. In large residual situations a penalty function approach, motivated by the ELEGANT algorithm, is adopted. Empirical comparisons of the algorithms are given.An early version of this paper was presented at the Multidimensional Data Analysis Workshop, Pembroke College, Cambridge, July 1985. I want to thank Jan de Leeuw and Yoshio Takane for bringing the ELEGANT algorithm to my attention and for clarifying its rationale and notation. My thanks go also to Stephen du Toit for help with the ALSCAL computations reported in Section 7.     

用爆炸波起爆癸烷云雾的两种模式

用垂直激波管研究了400μm癸烷液滴与空气混合物的爆炸波起爆。每种混合物有两个极限起爆能值,即高值Ecu,和低值Ecl。当起爆能的值高于Ecu时,观察到100%发生爆轰;而当其低于Ecl时,就不会发生爆轰。有趣的是,当起爆能水平在Ecu和Ecl之间时观察到对应于不同的起爆能水平有不同的起爆行为。如实验结果所表明的,爆轰的激发不仅是由于直接起爆,而且也由于“爆燃”到爆轰的转变。     

关于我国铁路组织按图行车现状和问题的研究

组织“按图行车”是实现铁路运输管理现代化的重要方面。本文根据调查资料,研究了当前我国铁路实现“按图行车”的现状、货物列车晚点的一般规律,并据以提出了进一步提高我国铁路组织“按图行车”水平的意见。     

基于自注意力机制的新生儿胆道闭锁识别

新生儿胆道闭锁是新生儿常见的致命疾病之一,并且该病在亚洲的发病率高于世界其他地区.新生儿胆道闭锁需要及时发现及时治疗,然而由于缺少专业的儿科医生和辅助诊疗手段,新生儿父母往往不能及时发现而错过了最佳治疗时间.因此,本文开发了一个具有实际应用价值的预诊算法,通过新生儿粪便图片预测新生儿是否患有新生儿胆道闭锁,并提醒新生儿父母及时就诊.为了让算法在应用场景下识别率更高,本文的算法基于一个真实场景下拍摄的新生儿粪图片数据集开发.首先我们设计了一个自注意力网络模型BANet(Biliary Atresia Network),将图片的浅层特征和深层特征相结合,可以得到更好的分类效果.由于拍摄自应用场景下的图片存在过暗和过曝等问题.通过分析数据集的亮度分布,我们设计了一个自动亮度调节算法解决.此外,图片中的阴影也会对识别结果造成干扰,因此我们在训练阶段增加了一种阴影数据增强方式来缓解这一问题.为验证本文提出算法的有效性,本文设计了一个和医生的对比试验.结果证明BANet在四分类的识别率、二分类的识别率、特异性和敏感性等客观评价指标上占有明显优势.本文提出的BANet能够有效利用图片中的颜色、异常点...     

地方高校高等数学课程学习成绩影响因素的统计分析——以滁州学院为例

为了进一步改善高等数学课程的教学效果,提高学生的数学素养,以地方应用型高校滁州学院为例,2018—2021年连续4年从学生、教师和学校3个维度基于高等数学课程的学习效果开展问卷调查;基于收集的样本数据,围绕学生学习的内生动力、外部因素、学校管理等多个维度,运用因子分析和线性回归分析方法,对学生学习高等数学课程成绩的影响因素进行统计分析,探索出了提高大学生高等数学课程学习成绩的有效方案。这为地方高校提高高等数学课程教学效果提供了一种途径和方法,可为相关的教学改革研究提供参考。     

基于改进I-Attention U-Net的锌浮选泡沫图像分割算法

针对泡沫图像的高度复杂性导致其难以被准确分割的难题,本文提出了一种新的I-Attention U-Net网络用于泡沫图像分割.该算法以U-Net网络作为主干网络,使用Inception模块替换第一卷积池化层来提取泡沫图像的多尺度、多层次浅层特征信息;引入金字塔池化模块,通过对不同尺度的特征图求和来提升分割效果;并对自注意力门控单元进行改进,使注意力单元更适合于浮选泡沫图像的分割,强化深层特征的重要性并对不同尺寸的泡沫边界进行强化学习.研究结果表明：本文所提出算法的Jaccard系数为91.73%,Dice系数为95.66%.与同类其他分割算法结果相比,Jaccard系数及Dice系数分别提高了1.59%、0.88%.该模型能够较好地对锌浮选泡沫图像进行分割,解决欠分割与过分割的问题,为后续的泡沫特征提取奠定基础.此外,该方法检测时间和模型参数少,具备可以部署在工业现场计算机的能力,有一定的实际应用价值.