Opportunities exist for automated animal health monitoring and early detection of diseases such as mastitis with greater on-farm adoption of precision technologies. Our objective was to evaluate time series changes in individual milk component or behavioral variables for all clinical mastitis (CM) cases (ACM), for CM caused by gram-negative (GN) or gram-positive (GP) pathogens, or CM cases in which no pathogen was isolated (NPI). We developed algorithms using a combination of milk and activity parameters for predicting each of these infection types. Milk and activity data were collated for the 14 d preceding a CM event (n = 170) and for controls (n = 166) matched for breed, parity, and days in milk. Explanatory variables in the univariate and multiple regression models were the slope change in milk (milk yield, conductivity, somatic cell count, lactose percentage, protein percentage, and fat percentage) and activity parameters (steps, lying time, lying bout duration, and number of lying bouts) over 7 d. Slopes were estimated using linear regression between d -7 and -5, d -7 and -4, d -7 and -3, d -7 and -2, and d -7 and -1 relative to CM detection for all parameters. Univariate analyses determined significant slope ranges for explanatory variables against the 4 responses: ACM, GN, GP, and NPI. Next, all slope ranges were offered into the multivariate models for the same 4 responses using 3 baselines: d -10, -7, and -3 relative to CM detection. In the univariate analysis, no explanatory variables were significant indicators of ACM, whereas at least 1 parameter was significant for each of GN, GP, and NPI models. Superior sensitivity (Se) and specificity (Sp) estimates were observed for the best GP (Se = 82%, Sp = 87%) and NPI (Se = 80%, Sp = 94%) multiple regression models compared with the best ACM (Se = 73%, Sp = 75%) and GN (Se = 71%, Sp = 74%) models. Sensitivity for the GN model was greater at the baseline closest to the day of CM detection (d -3), whereas the opposite was observed for the GP and NPI model as Se was maximized at the d -10 baseline. Based on this screening of relationships, milk and activity sensor data could be used in CM detection systems.

中文翻译：

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使用每日牛奶成分和行为传感器数据识别革兰氏阴性和革兰氏阳性临床乳腺炎。

通过在农场上大量采用精密技术，存在自动动物健康监测和早期发现疾病（例如乳腺炎）的机会。我们的目标是评估所有临床乳腺炎（CM）病例（ACM），革兰氏阴性（GN）或革兰氏阳性（GP）病原体引起的CM或在没有分离出病原体（NPI）。我们使用牛奶和活性参数的组合开发了算法来预测这些感染类型中的每一种。对照CM事件前14天（n = 170）和对照（n = 166）的牛奶和活性数据进行比较，以匹配牛奶的品种，均价和天数。单变量和多元回归模型中的解释变量是牛奶的斜率变化（牛奶产量，电导率，体细胞计数，7 d内的乳糖百分比，蛋白质百分比和脂肪百分比）和活动参数（步数，卧床时间，卧床持续时间和卧床次数）。对于所有参数，使用d -7和-5，d -7和-4，d -7和-3，d -7和-2以及d -7和-1之间的线性回归估计斜率。单变量分析确定了针对4种响应的解释变量的显着斜率范围：ACM，GN，GP和NPI。接下来，使用3个基线（相对于CM检测的d -10，-7和-3）将相同的4个响应的所有斜率范围提供给多元模型。在单变量分析中，没有解释变量是ACM的显着指标，而对于GN，GP和NPI模型中的每一个，至少有1个参数是显着的。相对于最佳ACM（最好的GP（Se = 82％，Sp = 87％）和NPI（Se = 80％，Sp = 94％）多元回归模型，观察到了较高的灵敏度（Se）和特异性（Sp）估计值。 Se = 73％，Sp = 75％）和GN（Se = 71％，Sp = 74％）模型。在最接近CM检测日的基线（d -3），GN模型的灵敏度更高，而GP和NPI模型则相反，因为Se在d -10基线达到最大。基于这种关系筛选，可以在CM检测系统中使用牛奶和活动传感器数据。而GP和NPI模型则相反，因为Se在d -10基线达到最大。基于这种关系筛选，可以在CM检测系统中使用牛奶和活动传感器数据。而GP和NPI模型则相反，因为Se在d -10基线达到最大。基于这种关系筛选，可以在CM检测系统中使用牛奶和活动传感器数据。