筛选诱导烟草对烟草花叶病毒产生系统抗性的真菌

真菌产生的代谢产物可激活烟草体内抗病防御相兲酶的表达,诱导烟草产生系统抗性,增强烟草对病毒的抗性。本研究对38种植物病原真菌及45种分离自（恩施、襄樊）土壤、烟叶中的真菌进行了过敏性反应实验和系统抗性实验。结果表明,筛选出了对烟草花叶病毒抗性较强的真菌。在烟草上产生过敏反应的菌株有32个;系统抗性实验中对烟草花叶病毒抑制率较高的菌株有16个,其中,油茶炭疽菌、棉花黄萎病菌、esf-13、esf-3、小麦赤霉、立枯丝核菌、E1、esf-6、xfpf-6等菌株的抗性较高,枯斑抑制率均大于70%,最高可达96.44%。抗性较高的真菌可开发为烟草病毒病诱抗剂。     

Short communication: Genetic characterization of digital cushion thickness

Dairy cow lameness is a serious animal welfare issue. It is also a significant cause of economic losses, reducing reproductive efficiency and milk production and increasing culling rates. The digital cushion is a complex structure composed mostly of adipose tissue located underneath the distal phalanx and has recently been phenotypically associated with incidence of claw horn disruption lesions (CHDL); namely, sole ulcers and white line disease. The objective of this study was to characterize digital cushion thickness genetically and to investigate its association with body condition score (BCS), locomotion score (LOCO), CHDL, and milk production. Data were collected from 1 large closely monitored commercial dairy farm located in upstate New York; 923 dairy cows were used. Before trimming, the following data were collected by a member of the research team: BCS, cow height measurement, and LOCO. Presence or not of CHDL (sole ulcer or white line disease, or both) was recorded at trimming. Immediately after the cows were hoof trimmed, they underwent digital sonographic B-mode examination for the measurement of digital cushion thickness. Factors such as parity number, stage of lactation, calving date, mature-equivalent 305-d milk yield (ME305MY), and pedigree information were obtained from the farm’s dairy management software (DairyCOMP 305; Valley Agricultural Software, Tulare, CA). Univariate animal models were used to obtain variance component estimations for each studied trait (CHDL, BCS, digital cushion thickness average, LOCO, height, and ME305MY) and a 6-variate analysis was conducted to estimate the genetic, residual, and phenotypic correlations between the studied traits. The heritability estimate of DCTA was 0.33 ± 0.09, whereas a statistically significant genetic correlation was estimated between DCTA and CHDL (−0.60 ± 0.29). Of the other genetic correlations, significant estimates were derived for BCS with LOCO (−0.49 ± 0.19) and ME305MY (−0.48 ± 0.20). Digital cushion thickness is moderately heritable and genetically strongly correlated with CHDL.     

Measures of weight distribution of dairy cows to detect lameness and the presence of hoof lesions

There is increasing interest in automated methods of detecting lame cows. Hoof lesion data and measures of weight distribution from 61 lactating cows were examined in this study. Lame cows were identified with different numerical rating scores (NRS) used as thresholds (NRS >3 and NRS ≥3.5) for lameness. The ratio of weight applied to a pair of legs (LWR) when the cow was standing was calculated using a special weigh scale, and the cows were gait scored using a 1 to 5 NRS. Hoof lesions were scored and the cows placed into 1 of 4 mutually exclusive categories of hoof lesion: a) no lesions, b) moderate or severe hemorrhages, c) digital dermatitis, and d) sole ulcers. Regression analysis and receiver operating characteristic (ROC) curves were used to analyze the relation between hoof lesions and LWR. A clear relationship was found between NRS and LWR for the cows with sole ulcers (R² = 0.79). The LWR could differentiate cows with sole ulcers from sound cows with no hoof lesions [area under the curve (AUC) = 0.87] and lame cows from nonlame cows with lameness thresholds NRS >3 (AUC = 0.71) and NRS ≥3.5 (AUC = 0.88). There was no relationship between LWR and NRS for cows with digital dermatitis. Measurement of how cows distribute their weight when standing holds promise as a method of automated detection of lameness.     

Sampling strategies for assessing lameness,injuries, and body condition score on dairy farms

Our objective was to evaluate how sampling strategies (i.e., how many cows to sample and which animals to include) used in 4 dairy cattle welfare assessment programs affect the classification of dairy farms relative to thresholds of acceptability for animal-based measures. We predicted that classification performance would improve when more cows were sampled and when selecting from all lactating cows versus when some pens were excluded. On 38 freestall farms, we assessed all 12,375 cows for lameness, injuries on the tarsal (hock) and carpal joints, and body condition score and calculated the farm-level prevalence for each measure. Based on approaches used in the industry, we evaluated 6 sampling strategies generated using formulas with precision (d) of 15, 10, or 5% applied to either a single high-producing pen or all lactating cows; an additional sample was included with d = 10% applied to the entire herd, selecting lactating cows in proportion to their representation in the herd. For each sampling strategy, cow records were selected randomly (in 10,000 replicates) to calculate prevalence. The strategy of assessing all cows in the high-producing pen was also compared. Farms were classified as meeting (below) or failing to meet (above) thresholds of ≤15% moderate lameness; ≤20% moderate carpal or hock injuries; <10, <5, and ≤1% severe lameness; or injuries on the carpus or hock; and <5, <3, <1, or 0% thin cows. For each measure and threshold, we calculated pooled percent agreement, kappa, sensitivity, specificity, and positive and negative predictive value for each sampling strategy using true prevalence as the gold standard for herd classification. Across measures and thresholds, classification performance increased with the number of cows sampled [i.e., when narrower precision values (d = 5 vs. 10 vs. 15%) were used in the sample size calculation]. Because narrower precision values can dramatically increase sample size, assessment programs may need to consider both feasibility and the degree of misclassification they will accept. Applying the formula directly to lactating cows performed better than applying it to the entire herd and then selecting lactating cows in proportion to their representation in the herd. Farm classifications were similar whether cows in the hospital pen were included or excluded from the sample. Selecting all cows from the high-producing pen resulted in classifications similar to when including all lactating cows, suggesting that assessing cows from the high-producing pen may serve as an acceptable proxy for all lactating cows on the farm.     

Comparison of models to identify lame cows based on gait and lesion scores, and limb movement variables

Bovine lameness results in pain and suffering in cattle and economic loss for producers. A system for automatically detecting lame cows was developed recently that measures vertical force components attributable to individual limbs. These measurements can be used to calculate a number of limb movement variables. The objective of this investigation was to explore whether gait scores, lesion scores, or combined gait and lesion scores were more effectively captured by a set of 5 limb movement variables. A set of 700 hind limb examinations was used to create gait-based, lesion-based, and combined (gait- and lesion-based) models. Logistic regression models were constructed using 1, 2, or 3 d of measurements. Resulting models were tested on cows not used in modeling. The accuracy of lesion-score models was superior to that of gait-score models; lesion-based models generated greater values of areas under the receiving operating characteristic curves (range 0.75 to 0.84) and lower mean-squared errors (0.13 to 0.16) compared with corresponding values for the gait-based models (0.63 to 0.73 and 0.26 to 0.31 for receiving operating characteristic and mean-squared errors, respectively). These results indicate that further model development and investigation could generate automated and objective methods of lameness detection in dairy cattle.     

Cerebral Hyperperfusion after Revascularization Inhibits Development of Cerebral Ischemic Lesions Due to Artery-to-Artery Emboli during Carotid Exposure in Endarterectomy for Patients with Preoperative Cerebral Hemodynamic Insufficiency: Revisiting the “Impaired Clearance of Emboli” Concept

The purpose of the present study was to determine whether cerebral hyperperfusion after revascularization inhibits development of cerebral ischemic lesions due to artery-to-artery emboli during exposure of the carotid arteries in carotid endarterectomy (CEA). In patients undergoing CEA for internal carotid artery stenosis (≥70%), cerebral blood flow (CBF) was measured using single-photon emission computed tomography (SPECT) before and immediately after CEA. Microembolic signals (MES) were identified using transcranial Doppler during carotid exposure. Diffusion-weighted magnetic resonance imaging (DWI) was performed within 24 h after surgery. Of 32 patients with a combination of reduced cerebrovascular reactivity to acetazolamide on preoperative brain perfusion SPECT and MES during carotid exposure, 14 (44%) showed cerebral hyperperfusion (defined as postoperative CBF increase ≥100% compared with preoperative values), and 16 (50%) developed DWI-characterized postoperative cerebral ischemic lesions. Postoperative cerebral hyperperfusion was significantly associated with the absence of DWI-characterized postoperative cerebral ischemic lesions (95% confidence interval, 0.001–0.179; p = 0.0009). These data suggest that cerebral hyperperfusion after revascularization inhibits development of cerebral ischemic lesions due to artery-to-artery emboli during carotid exposure in CEA, supporting the “impaired clearance of emboli” concept. Blood pressure elevation following carotid declamping would be effective when embolism not accompanied by cerebral hyperperfusion occurs during CEA.     

边缘增强结合多尺度信息融合的皮肤病变分割

针对皮肤病灶大小不一、与周围皮肤对比度低、边界模糊不规则、存在伪影及毛发干扰等问题, 本文提出一种边缘增强结合多尺度信息融合的皮肤病变分割算法, 该算法由编码器、多尺度感知模块、边缘增强模块、轻量化解码器组成. 首先在编码器中构建Transformer模块以提取全局信息, 使用卷积操作以提取局部信息; 其次设计多尺度感知模块, 采用密集连接结构的门控空洞卷积金字塔模块来融合多尺度特征; 接着构建边缘增强模块, 利用深层特征促进对边缘特征的探索以更好的保留细节和边缘信息; 最后设计轻量化解码器, 采用CARAFE轻量化算子进行上采样, 在使用相对较少参数的情况下保持较高的分割精度. 在公开数据集ISIC2016和ISIC2018上做对比实验, 结果表明, 本文算法分割精度高于其他流行算法.     

大相对孔径宽光谱星敏感器光学镜头设计

为满足大口径长焦距星敏感器的需求,对一种大口径热不敏星敏感器进行光机设计,根据指标要求,对热不敏系统进行光机热集成分析。使用MSC.Patran软件对主次镜结构施加温度载荷,计算出主次镜结构的热弹性变形,先利用MSC.Nastran软件计算热变形后节点的刚体位移,再利用Sigft光机接口软件分析得到变形后主次镜表面的Zernike多项式系数。将结果导入Zemax中,预判镜片面型变化以及刚体位移对弥散斑、光轴漂移量及波像差的影响。最后通过在20℃±5℃的温度范围内装调测试验证了系统性能满足指标要求以及光机热集成分析的准确性,提供了一套准确快捷的光机热集成分析流程。     

脑白质损伤分割方法比较和策略改进

当前对白质损伤分割研究的缺陷就是不能真正用于实时分割任务,其主要原因是所用特征的表现性能较低.针对此问题,提出了新的数据处理方法和损伤分割特征.方法通过划分数据减少处理数据量的同时采用分治策略,区分处理白质灰质区域,从而改善了整体白质损伤分割的性能.在ACCORD-MIND MRI数据集上,使用SVM分类器将该新特征和传统特征的分割性能作了比较.实验结果表明,该特征在白质损伤分割中优于传统的特征,并能获得较好的分割结果和较快的分割速度.