基于突变级数法的天然草地分类

本研究在综合分析天然草地的生态状况、可再生产能力和经济条件3方面因素的基础上,建立了天然草地分类评价的递阶层次指标体系;在相关研究成果的基础上,将天然草地分为温性荒漠类、温性草原类和草甸类3个类别,并选用新疆准噶尔盆地西部地区的18个天然草地样本作为训练样本,建立了突变级数判据;选用另外13个天然草地样本作为判别样本,以检验该突变级数判据的可行性和可靠性。结果表明,该突变级数判据对判别样本的分类结果合理,突变级数法可以很好地反映不同类型天然草地间的资源属性,且分类结果与灰色关联度分析法、投影寻踪法、投影寻踪动态聚类法、集对分析法和自组织特征映射(SOFM)神经网络法的分类结果基本一致。     

天然草地分类的Bayes判别分析法

基于多元统计分析理论,综合考虑天然草地的生态状况、可再生产能力和经济条件等因素,选择植被覆盖度、可食风干牧草产量、牧草利用率、草地可利用面积系数、草群中优良牧草比率和羊单位需草地面积6个参数作为判别因子,建立了天然草地分类的Bayes判别分析法。将天然草地分为温性荒漠类、温性草原类和低山地草甸类3个类别,并作为Bayes判别分析的3个正态总体,以新疆准噶尔盆地西部地区的31个天然草地为样本(其中18个为训练样本),以Bayes线性函数值和后验概率对样本所归属的总体进行识别,将建立的模型对训练样本进行回判,以回代误判率对模型进行检验。研究表明,Bayes判别分析模型可以很好地反映不同类型天然草地间的资源属性,对训练样本的回代误判率为0;对另外13个天然草地样本的分类结果与灰色关联度分析法、投影寻踪法、投影寻踪动态聚类法、集对分析法、SOFM神经网络法以及突变级数法等的分类结果基本一致。     

基于PCA方法以牧草营养成分评判青海省海南州天然草地类型质量初探

为了科学评价天然草地各草地类型中牧草饲料品质的质量,在常规分析的基础上,采用主成分分析法对青海省海南州天然草地各草地类型中的牧草饲料进行了综合质量评定和量化评估,通过对天然草地各草地类型牧草饲料品质主成分分析的综合得分进行对比,较为全面客观地评价了天然草地各草地类型的优劣。结果表明,天然草地各草地类型中高寒草原类牧草饲料品质最好,其次是低平地草甸类、高寒草甸类、温性荒漠类、温性荒漠草原类、温性草原类。     

基于PCA方法以牧草营养成分评判青海省海南州天然草地类型质量初探

为了科学评价天然草地类型及牧草饲料的质量,应用主成分分析法对青海省海南州天然草地各草地类型牧草饲料,在常规分析的基础上进行了综合质量评定和量化评估,通过对各天然草地类型牧草饲料品质主成分分析的综合得分进行对比,较为全面客观地评价天然草地各草地类型的优劣。结果表明：天然草地各类型中高寒草原类牧草饲料品质最好,其次是低平地草甸类、高寒草甸类、温性荒漠类、温性荒漠草原类、温性草原类。     

用模糊概率综合比较法对海南州天然草地类型饲草营养成分品质的评定

为了科学评价天然草地类型饲草饲料品质的质量,采用模糊概率综合质量比较法对海南州天然草地各草地类型饲草饲料,在常规分析的基础上进行了综合质量评定,结果表明:天然草地各草地类型中温性草原类饲草饲料品质最好,其次是高寒草原类、高寒草甸类、低平地草甸类、温性荒漠草原类、温性荒漠类饲草饲料,分别居第3、4、5、6位。     

用模糊概率法评定各类型天然草地饲草品质初探

为了科学评价天然草地各类型饲草的质量,采用模糊概率综合质量比较法对海南州天然草地各类型饲草,在常规分析的基础上进行了综合质量评定,结果表明:天然草地各类型中温性草原类饲草品质最好,其次是高寒草原类,高寒草甸类、低平地草甸类、温性荒漠草原类、温性荒漠类饲草分别居第3、4、5、6位。     

用模糊概率法评定各类型天然草地饲草品质初探

为了科学评价天然草地各类型饲草的质量，采用模糊概率综合质量比较法对海南州天然草地各类型饲草，在常规分析的基础上进行了综合质量评定，结果表明：天然草地各类型中温性草原类饲草品质最好，其次是高寒草原类，高寒草句类、低平地草甸类、温性荒漠草原类、温性荒漠类饲草分别居第3、4、5、6位。     

青海省共和县天然草地牧草产量结构特征分析

为了研究青海省共和县天然草地牧草产量结构特征,对其经济类群产量进行了分析和相关性计算。结果表明,共和县天然草产量最高的为高寒草甸类天然草地,优势种植物多为莎草科植物;除温性荒漠类、低地草甸类天然草地外,其他天然草地类优良牧草产量占总产量比重较大,这说明共和县天然草地整体退化程度较轻,可以通过生态工程进行修复。     

青海省主要类型天然草地EVI动态变化

利用2002—2008年MODIS/EVI卫星遥感数据,对青海省高寒草甸类、高寒草原类、温性草原类3种天然草地类型植被指数动态进行了分析,重点研究增强型植被指数(EVI)在各生长阶段的变化,并利用波动系数对同一草地类型内部的差异进行了探讨。研究结果表明,青海省3种天然草地类型的EVI反映了草地生长的动态变化,显示出显著的周期性,与草地生长规律相符,高寒草甸类最为明显,而波动系数的变化表明同一草地类型在不同年份的相同时段内的生长状况具有一定的差异性。     

青海湖流域天然草地资源年际动态分析

通过对青海湖流域天然草地生产力2010～2019年10a监测结果显示:草地生态环境恶化局面得到了初步改善。草层高度不同程度上升,植被覆盖度增加,青海湖流域自然区域各草地类型生产力与2010年相比,温性草原类、高寒草原类、高寒草甸类3种草地类型均呈现不同程度的降低。而温性荒漠草原类和高寒草甸草原类草地产量分别增长44.55%、 14.41%。     

五台山亚高山草甸的人工神经网络分类与排序

人工神经网络理论是较新的数学分支学科,神经网络分类和排序是刚引入植物生态学的分析方法。本研究应用这2种方法研究了五台山亚高山高寒草甸。结果SOFM聚类将78个样方分为8个植物群落类型,基本上代表了本地区高寒草甸的群丛类型,具有明确的生态意义;SOFM排序反映了明显的生态梯度,表明海拔是影响草甸植被生长和分布的最主要因子,坡向和坡度也有一定的作用;SOFM聚类和排序方法分析应用效果好,2种方法结合使用更好;五台山草甸需要进一步加强保护。     

Deep learning-based diagnosis of stifle joint diseases in dogs

In this retrospective, analytical study, we developed a deep learning-based diagnostic model that can be applied to canine stifle joint diseases and compared its accuracy with that achieved by veterinarians to verify its potential as a reliable diagnostic method. A total of 2382 radiographs of the canine stifle joint from cooperative animal hospitals were included in a dataset. Stifle joint regions were extracted from the original images using the faster region-based convolutional neural network (R-CNN) model, and the object detection accuracy was evaluated. Four radiographic findings: patellar deviation, drawer sign, osteophyte formation, and joint effusion, were observed in the stifle joint and used to train a residual network (ResNet) classification model. Implant and growth plate groups were analyzed to compare the classification accuracy against the total dataset. All deep learning-based classification models achieved target accuracies exceeding 80%, which is comparable to or slightly less than those achieved by veterinarians. However, in the case of drawer signs, further research is necessary to improve the low sensitivity of the model. When the implant group was excluded, the classification accuracy significantly improved, indicating that the implant acted as a distraction. These results indicate that deep learning-based diagnoses can be expected to become useful diagnostic models in veterinary medicine.     

USING MACHINE LEARNING TO CLASSIFY IMAGE FEATURES FROM CANINE PELVIC RADIOGRAPHS: EVALUATION OF PARTIAL LEAST SQUARES DISCRIMINANT ANALYSIS AND ARTIFICIAL NEURAL NETWORK MODELS

As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer‐assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine hip joint on ventrodorsal pelvis radiographs. A training set of images (120 of the hip and 80 from other regions) was used to train a linear partial least squares discriminant analysis (PLS‐DA) model and a nonlinear artificial neural network (ANN) model to classify hip images. Performance of the models was assessed using a separate test image set (36 containing hips and 20 from other areas). Partial least squares discriminant analysis model achieved a classification error, sensitivity, and specificity of 6.7%, 100%, and 89%, respectively. The corresponding values for the ANN model were 8.9%, 86%, and 100%. Findings indicated that statistical classification of veterinary images is feasible and has the potential for grouping and classifying images or image features, especially when a large number of well‐classified images are available for model training.     

脉冲场凝胶电泳技术在猪链球菌分型中的应用

脉冲场凝胶电泳是一种可用于分离20 kb至10 Mb大分子质量DNA的新型凝胶电泳技术。该法应用于猪链球菌的分子分型,通过观察电泳条带的差异,为确定菌株之间的亲缘关系,分析基因型和临床症状之间的关系等方面提供了可靠的技术手段。文章介绍了脉冲场凝胶电泳技术的应用状况及猪链球菌的分型现状,并阐述了该技术在猪链球菌分子分型研究方面的应用及发展前景。     

扁穗牛鞭草种质遗传多样性的ISSR分析

用ISSR标记对来自中国西南地区(四川、重庆、贵州)的28份扁穗牛鞭草材料的遗传多样性进行了检测。从96个ISSR引物中共筛选出13个多态性明显、反应稳定的引物。28份材料的DNA共扩增出129条谱带,平均每个引物扩增出9.9条带,多态性条带比率达84.2%。材料间遗传相似系数为0.466~0.980,表现出丰富的遗传多样性。通过聚类分析和主成分分析,将28份扁穗牛鞭草分为两大类,同一地区的扁穗牛鞭草品种(系)基本聚在同一类,呈现出一定的地域性分布规律。     

分子分类结合形态学观察鉴定重庆地区片形吸虫种类

目的明确重庆地区片形吸虫的种类,并为重庆地区片形吸虫的分类研究提供科学的参考依据。方法在对重庆地区黄牛、水牛肝脏上寄生的片形吸虫形态结构进行观察后,根据片形吸虫第一内转录间隔区(ITS-1)和第二内转录间隔区(ITS-2)基因设计特异性引物,运用多聚酶链式反应(PCR)技术,以法国肝片吸虫gDNA为对照,对所采样品gDNA用大片吸虫和肝片吸虫的ITS-1和ITS-2特异性引物进行扩增。结果形态学鉴定均为“不规则”片形吸虫,电泳结果显示均分别扩增出特异性ITS-1和ITS-2条带。结论综合形态学和PCR鉴定结果,初步认为重庆地区存在着大片吸虫和肝片吸虫的“中间型”。     

Computerized detection of supporting forelimb lameness in the horse using an artificial neural network.

The purpose of this study was to investigate whether artificial neural networks could be used to determine equine lameness by computational means only. The integral parts of our approach were the combination of automated signal tracking of horses on a treadmill and the computational power of artificial neural networks (ANN). The motion of 175 horses trotting on a treadmill was recorded using the SELSPOT II system for motion analysis. Two cameras traced infrared (IR) markers on the head and on the left forehoof. The motion of the head was Fourier-transformed and further processed by a multilayer feedforward ANN, which was trained to distinguish healthy from pathological gaits and to quantify the lameness. The classification was correct in 78.6% of cases. In 12% of cases the network gave contradictory results, in 5.9% the network found no answers, and in 3.5% the answers were wrong. However after proper training, it is proposed that neural networks are potentially capable of making a non-human diagnosis of equine lameness.     

A comparison of the cytologic and histologic features of meningiomas in four dogs

Abstract: The cytologic and histologic features of 2 intracranial and 2 spinal (extramedullary cervical) canine meningiomas were compared. Cerebrospinal fluid analysis in 2 cases revealed mild, mixed cell pleocytosis, primarily composed of small lymphocytes and monocytoid cells, with a moderate increase in total protein concentration. Cytologic features suggestive of meningioma included cells with both epithelial and mes-enchymal characteristics and a tendency towards cell clustering. Tumor location also was useful in making a diagnosis. The 4 meningiomas differed histologically from one another, and included angioblastic, psam-momatous, meningotheliomatous, and microcystic anaplastic types, which conformed to a classification scheme for human meningiomas. The classification scheme could not be applied to cytologic specimens.     

Detection of lameness and determination of the affected forelimb in horses by use of continuous wavelet transformation and neural network classification of kinematic data

OBJECTIVE: To investigate continuous wavelet transformation and neural network classification of gait data for detecting forelimb lameness in horses. ANIMALS: 12 adult horses with mild forelimb lameness. PROCEDURE: Position of the head and right forelimb foot, metacarpophalangeal (ie, fetlock), carpal, and elbow joints was determined by use of kinematic analysis before and after palmar digital nerve blocks. We obtained 8 recordings from horses without lameness, 8 with right forelimb lameness, and 8 with left forelimb lameness. Vertical and horizontal position of the head and vertical position of the foot, fetlock, carpal, and elbow joints were processed by continuous wavelet transformation. Feature vectors were created from the transformed signals and a neural network trained with data from 6 horses, which was then tested on the remaining 2 horses for each category until each horse was used twice for training and testing. Correct classification percentage (CCP) was calculated for each combination of gait signals tested. RESULTS: Wavelet-transformed vertical position of the head and right forelimb foot had greater CCP (85%) than untransformed data (21%). Adding data from the fetlock, carpal, or elbow joints did not improve CCP over that for the head and foot alone. CONCLUSIONS AND CLINICAL RELEVANCE: Wavelet transformation of gait data extracts information that is important for the detection and differentiation of forelimb lameness of horses. All of the necessary information to detect lameness and differentiate the side of lameness can be obtained by observation of vertical head movement in concert with movement of the foot of 1 forelimb.     

基于MATLAB的苜蓿草品质分级系统

为了客观评估苜蓿（Medicago sativa L.）草品质的等级,采用MATLAB中BP人工神经网络,利用苜蓿粗蛋白（CP）、中性洗涤纤维（NDF）、酸性洗涤纤维（ADF）、随意可消化量（DDM）、采食量（DMI）参数建立BP神经网络模型。通过200个苜蓿样本进行网络训练,并采用不同的BP神经网络隐含层的传递函数和隐含层神经元数量,获得最优BP神经网络模型。结果表明：在5个特征参数指标下,仿真评价苜蓿草品质等级的准确率达到99.6%,与人工评估结果相比,仿真结果更符合苜蓿草品质的客观现实。在此基础上,介绍已经开发建立的我国首个苜蓿草品质分级系统,有助于未来在苜蓿草市场中发挥其等级评定的应用潜力。