Variable selection for monitoring sickness behavior in lactating dairy cattle with the application of control charts

The present observational study investigated the application of multivariate cumulative sum (MCUSUM) control charts by including variables selected by principal component analysis and partial least squares (PLS) regression to identify sickness behavior in dairy cattle. Therefore, sensor information (24 variables) was collected from 480 milking cows on a German dairy farm between September 2018 and December 2019. These variables were gathered in potentially different scenarios on farm. In total, data from 749 animals were available for evaluation. Variables were chosen based on the information of 499 cows (62 healthy; 437 sick) with 93,598 observations. The available diagnoses were collected together to form 1,025 sickness events. Hence, the different numbers of selected variables were included into the MCUSUM control charts. The performance of the MCUSUM control charts was evaluated by a 10-fold cross validation; hence, 90% of the original data set (749 cows) represented the training data, and the remaining 10% was used to test the training results. On average, the 10 training data sets included 124,871 observations with 1,392 sickness events, and the 10 testing data sets included, on average, 13,704 observations with 153 sickness events. The MCUSUM generated from the variables selected by principal component analysis showed comparable results in training and testing in all scenarios; therefore, 70.0 to 97.4% of the sickness events were detected. The false-positive rates ranged from 8.5 to 29.6%, and thus they created at least 2.6 false-positive alerts per day in testing. The variables selected by the PLS regression approach showed comparable sickness detection rates (70.0-99.9%) as well as false-positive rates (8.2-62.8%) in most scenarios. The best performing scenario produced 2.5 false-positive alerts in testing. Summarizing, both approaches showed potential for practical implementation; however, the PLS variable selection approach showed fewer false positives. Therefore, the PLS regression approach could generate a more reliable sickness detection algorithm, if combined with MCUSUM control charts, and considered for practical implementation.

Alterations in sick dairy cows' daily behavioural patterns

The recent development of dairy production is characterised by increasing herd sizes and therefore increasingly complicated visual observation of cow behaviour, which is traditionally the basis for diagnoses of production diseases. The limitation of the direct visual behavioural observation due to the increasing number of individual cows implies a growing need for an automated detection of changes within behavioural patterns to identify cows that show sickness behaviour. Sensor systems can be used to measure behavioural patterns such as activity, resting, feeding and rumination. Behavioural patterns change with the occurrence of sickness but also interact with external factors. Changes such as prolonged lying duration or shortened feeding duration caused by metabolic disorders or infections, respectively, then serve as a detection tool for sick individuals. The aim of the present review is to outline the impact of production diseases on the daily behavioural patterns of dairy cows by referring to sickness behaviour.

Somatostatin receptor scintigraphy in small-cell lung cancer: results of a multicenter study

The aims of this study were to determine the accuracy of somatostatin receptor scintigraphy in the detection of the primary tumor and its metastases in small-cell lung cancer (SCLC) in a large patient population, and to investigate the course of somatostatin uptake in primary tumors during therapy.

METHODS

In a total of 100 patients, 134 examinations were performed. Twenty-seven of the patients were examined before and after chemotherapy. Planar whole-body images were acquired 4 hr and 24 hr after injection of approximately 200 MBq (111)In-pentetreotide. SPECT of the thorax was performed after 24 hr. Tumor-to-background (T/B) ratios for the primary tumor were averaged from anterior and posterior projections.

RESULTS

Compared to conventional investigations, somatostatin receptor scintigraphy (SRS) visualized the primary tumor with varying degrees of uptake in 96% of the examinations. Regional metastases and distant metastases were detected in 60% and 45% of the examinations, respectively. The uptake of the somatostatin analog by the primary tumor was significantly lower in the patients examined during chemotherapy as compared to those examined before treatment (T/B ratio = 1.94+/-0.79 versus 2.35+/-0.9, p < 0.005). A decrease in T/B ratio was noted in patients with remission at the time of SRS (from 2.40+/-1.56 to 1.63+/-0.72, p < 0.05). No difference in the pretreatment uptake of octreotide by the primary tumor was identified between patients with tumor progression and those with partial or complete remission.

CONCLUSION

Somatostatin receptor scintigraphy has a high sensitivity in the detection of the primary tumor in SCLC but fails in the detection of metastases. Thus, SRS does not provide useful information for staging of SCLC. Since somatostatin uptake by the primary tumor is affected by chemotherapy, it may be used to follow up on the course of SCLC.