Occupational Risk Factors and Hypertensive Disorders in Pregnancy: A Systematic Review

Hypertensive disorders in pregnancy (HDP), including gestational hypertension (GH) and preeclampsia (PE), characterize a major cause of maternal and prenatal morbidity and mortality. In this systematic review, we tested the hypothesis that occupational factors would impact the risk for HDP in pregnant workers. MEDLINE, Scopus, and Web of Knowledge databases were searched for studies published between database inception and 1 April 2021. All observational studies enrolling > 10 pregnant workers and published in English were included. Un-experimental, non-occupational human studies were excluded. Evidence was synthesized according to the risk for HDP development in employed women, eventually exposed to chemical, physical, biological and organizational risk factors. The evidence quality was assessed through the Newcastle–Ottawa scale. Out of 745 records identified, 27 were eligible. No definite conclusions could be extrapolated for the majority of the examined risk factors, while more homogenous data supported positive associations between job-strain and HDP risk. Limitations due to the lack of suitable characterizations of workplace exposure (i.e., doses, length, co-exposures) and possible interplay with personal issues should be deeply addressed. This may be helpful to better assess occupational risks for pregnant women and plan adequate measures of control to protect their health and that of their childre

Machine learning based prediction of insufficient herbage allowance with automated feeding behaviour and activity data

peer-reviewedSensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support farmers in many decision-making processes. This article thus considers the performance of a set of RumiWatchSystem recorded variables in the prediction of insufficient herbage allowance for spring calving dairy cows. Several commonly used models in machine learning (ML) were applied to the binary classification problem, i.e., sufficient or insufficient herbage allowance, and the predictive performance was compared based on the classification evaluation metrics. Most of the ML models and generalised linear model (GLM) performed similarly in leave-out-one-animal (LOOA) approach to validation studies. However, cross validation (CV) studies, where a portion of features in the test and training data resulted from the same cows, revealed that support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) performed relatively better than other candidate models. In general, these ML models attained 88% AUC (area under receiver operating characteristic curve) and around 80% sensitivity, specificity, accuracy, precision and F-score. This study further identified that number of rumination chews per day and grazing bites per minute were the most important predictors and examined the marginal effects of the variables on model prediction towards a decision support system

Identification of possible cow grazing behaviour indicators for restricted grass availability in a pasture-based spring calving dairy system

Precision livestock farming uses biosensors to measure different parameters of individual animals to support farmers in the decision making process. Although sensor development is advanced, there is still little implementation of sensor-based solutions on commercial farms. Especially on pasture-based dairy systems, the grazing management of cows is largely not supported by technology. A key factor in pasture-based milk production is the correct grass allocation to maximize the grass utilization per cow, while optimizing cow performance. Currently, grass allocation is mostly based on subjective eye measurements or calculations per herd. The aim of this study was to identify possible indicators of insufficient or sufficient grass allocation in the cow grazing behaviour measures. A total number of 30 cows were allocated a restricted pasture allowance of 60% of their intake capacity. Their behavioural characteristics were compared to those of 10 cows (control group) with pasture allowance of 100% of their intake capacity. Grazing behaviour and activity of cows were measured using the RumiWatchSystem for a complete experimental period of 10 weeks. The results demonstrated that the parameter of bite frequency was significantly different between the restricted and the control groups. There were also consistent differences observed between the groups for rumination time per day, rumination chews per bolus and frequency of cows standing or lying