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1.
The objective of this study was to determine if a 3-dimensional computer vision automatic locomotion scoring (3D-ALS) method was able to outperform human observers for classifying cows as lame or nonlame and for detecting cows affected and nonaffected by specific type(s) of hoof lesion. Data collection was carried out in 2 experimental sessions (5 mo apart). In every session all cows were assessed for (1) locomotion by 2 observers (Obs1 and Obs2) and by a 3D-ALS; and (2) identification of different types of hoof lesions during hoof trimming (i.e., skin and horn lesions and combinations of skin/horn lesions and skin/hyperplasia). Performances of observers and 3D-ALS for classifying cows as lame or nonlame and for detecting cows affected or nonaffected by types of lesion were estimated using the percentage of agreement (PA), kappa coefficient (κ), sensitivity (SEN), and specificity (SPE). Observers and 3D-ALS showed similar SENlame values for classifying lame cows as lame (SENlame comparison Obs1-Obs2 = 74.2%; comparison observers-3D-ALS = 73.9–71.8%). Specificity values for classifying nonlame cows as nonlame were lower for 3D-ALS when compared with observers (SPEnonlame comparison Obs1-Obs2 = 88.5%; comparison observers-3D-ALS = 65.3–67.8%). Accordingly, overall performance of 3D-ALS for classifying cows as lame and nonlame was lower than observers (Obs1-Obs2 comparison PAlame/nonlame = 84.2% and κlame/nonlame = 0.63; observers-3D-ALS comparisons PAlame/nonlame = 67.7–69.2% and κlame/nonlame = 0.33–0.36). Similarly, observers and 3D-ALS had comparable and moderate SENlesion values for detecting horn (SENlesion Obs1 = 68.6%; Obs2 = 71.4%; 3D-ALS = 75.0%) and combinations of skin/horn lesions (SENlesion Obs1 = 51.1%; Obs2 = 64.5%; 3D-ALS = 53.3%). The SPEnonlesion values for detecting cows without lesions when classified as nonlame were lower for 3D-ALS than for observers (SPEnonlesion Obs1 = 83.9%; Obs2 = 80.2%; 3D-ALS = 60.2%). This was translated into a poor overall performance of 3D-ALS for detecting cows affected and nonaffected by horn lesions (PAlesion/nonlesion Obs1 = 80.6%; Obs2 = 78.3%; 3D-ALS = 63.5% and κlesion/nonlesion Obs1 = 0.48; Obs2 = 0.44; 3D-ALS = 0.25) and skin/horn lesions (PAlesion/nonlesion Obs1 = 75.1%; Obs2 = 75.9%; 3D-ALS = 58.6% and κlesion/nonlesion Obs1 = 0.35; Obs2 = 0.42; 3D-ALS = 0.10), when compared with observers. Performance of observers and 3D-ALS for detecting skin lesions was poor (SENlesion for Obs1, Obs2, and 3D-ALS <40%). Comparable SENlame and SENlesion values for observers and 3D-ALS are explained by an overestimation of lameness by 3D-ALS when compared with observers. Thus, comparable SENlame and SENlesion were reached at the expense high number of false positives and low SPEnonlame and SPEnonlesion. Considering that observers and 3D-ALS showed similar performance for classifying cows as lame and for detecting horn and combinations of skin/horn lesions, the 3D-ALS could be a useful tool for supporting dairy farmers in their hoof health management.  相似文献   
2.
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.  相似文献   
3.
A national genetic evaluation program for hoof health could be achieved by using hoof lesion data collected directly by hoof trimmers. However, not all cows in the herds during the trimming period are always presented to the hoof trimmer. This preselection process may not be completely random, leading to erroneous estimations of the prevalence of hoof lesions in the herd and inaccuracies in the genetic evaluation. The main objective of this study was to estimate genetic parameters for individual hoof lesions in Canadian Holsteins by using an alternative cohort to consider all cows in the herd during the period of the hoof trimming sessions, including those that were not examined by the trimmer over the entire lactation. A second objective was to compare the estimated heritabilities and breeding values for resistance to hoof lesions obtained with threshold and linear models. Data were recorded by 23 hoof trimmers serving 521 herds located in Alberta, British Columbia, and Ontario. A total of 73,559 hoof-trimming records from 53,654 cows were collected between 2009 and 2012. Hoof lesions included in the analysis were digital dermatitis, interdigital dermatitis, interdigital hyperplasia, sole hemorrhage, sole ulcer, toe ulcer, and white line disease. All variables were analyzed as binary traits, as the presence or the absence of the lesions, using a threshold and a linear animal model. Two different cohorts were created: Cohort 1, which included only cows presented to hoof trimmers, and Cohort 2, which included all cows present in the herd at the time of hoof trimmer visit. Using a threshold model, heritabilities on the observed scale ranged from 0.01 to 0.08 for Cohort 1 and from 0.01 to 0.06 for Cohort 2. Heritabilities estimated with the linear model ranged from 0.01 to 0.07 for Cohort 1 and from 0.01 to 0.05 for Cohort 2. Despite a low heritability, the distribution of the sire breeding values showed large and exploitable variation among sires. Higher breeding values for hoof lesion resistance corresponded to sires with a higher prevalence of healthy daughters. The rank correlations between estimated breeding values ranged from 0.96 to 0.99 when predicted using either one of the 2 cohorts and from 0.94 to 0.99 when predicted using either a threshold or a linear model.  相似文献   
4.
5.
The principle restorative step in the treatment of ischemic stroke depends on how fast the lesion is delineated from the Magnetic Resonance Imaging (MRI) images. This will serve as a vital aid to estimate the extent of damage caused to the brain cells. However, manual delineation of the lesion is time-consuming and it is subjected to intra-observer and inter-observer variability. Most of the existing methods for ischemic lesion segmentation rely on extracting handcrafted features followed by application of a machine learning algorithm. Identifying such features demand multi-domain expertise in Neuro-radiology as well as Image processing. This can be accomplished by learning the features automatically using Convolutional Neural Network (CNN). To perform segmentation, the spatial arrangement of pixel needs to be preserved in addition to learning local features of an image. Hence, a deep supervised Fully Convolutional Network (FCN) is presented in this work to segment the ischemic lesion. The highlight of this research is the application of Leaky Rectified Linear Unit activation in the last two layers of the network for a precise reconstruction of the ischemic lesion. By doing so, the network was able to learn additional features which are not considered in the existing U-Net architecture. Also, an extensive analysis was conducted in this research to select optimal hyper-parameters for training the FCN. A mean segmentation accuracy of 0.70 has been achieved from the experiments conducted on ISLES 2015 dataset. Experimental observations show that our proposed FCN method is 10% better than the existing works in terms of Dice Coefficient.  相似文献   
6.
《Planning》2019,(2)
2017年,美国心脏病学会和美国心脏协会提出将高血压诊断标准降为130/80 mm Hg (1 mm Hg=0. 133 kPa),这与其他指南有所不同。降压治疗的核心目标在于靶器官的保护,近年来降压治疗与脑小血管病预防的相关问题日益受到临床重视。目前研究结果表明,降压治疗对腔隙性卒中患者的二级预防以及白质病变进展的预防可能有一定积极意义,但确切的降压目标值并未确定。血压与临床结局可能呈现J型关系,血压过低或过高可能均有害,而取得最大获益的降压目标有待进一步探索。  相似文献   
7.
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.  相似文献   
8.
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。  相似文献   
9.
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.  相似文献   
10.
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