Nanoparticle Exposure and Hormetic Dose–Responses: An Update

The concept of hormesis, as an adaptive response of biological systems to moderate environmental challenges, has raised considerable nano-toxicological interests in view of the rapid pace of production and application of even more innovative nanomaterials and the expected increasing likelihood of environmental and human exposure to low-dose concentrations. Therefore, the aim of this review is to provide an update of the current knowledge concerning the biphasic dose–responses induced by nanoparticle exposure. The evidence presented confirmed and extended our previous findings, showing that hormesis is a generalized adaptive response which may be further generalized to nanoscale xenobiotic challenges. Nanoparticle physico-chemical properties emerged as possible features affecting biphasic relationships, although the molecular mechanisms underlining such influences remain to be fully understood, especially in experimental settings resembling long-term and low-dose realistic environmental exposure scenarios. Further investigation is necessary to achieve helpful information for a suitable assessment of nanomaterial risks at the low-dose range for both the ecosystem function and the human health

Psychotic symptoms in post traumatic stress disorder: a case illustration and literature review

Posttraumatic stress disorder (PTSD) is a condition being increasingly recognized. The diagnosis is based on the re-experiencing of a traumatic event. There have been reports of the presence of psychotic symptoms in some cases of PTSD. This may represent increased severity or a different diagnostic clinical entity. It has also been suggested that psychotic symptoms may be over-represented in the Hispanic population. In this manuscript, we describe a case to illustrate this relationship and we review the current literature on the relationship of psychotic symptoms among PTSD patients. The implications regarding diagnosis, treatment, and prognosis are discussed. Keywords: Psychosis; PTSD; Trauma; Hallucinations; Delusions; Posttraumatic stress disorderSA Psych Rev 2003;6: 21-2

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

Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows

Feeding behaviour is an important parameter of animal performance, health and welfare, as well as reflecting levels and quality of feed available. Previously, sensors were only used for measuring animal feeding behaviour in indoor housing systems. However, sensors such as the RumiWatchSystem can also monitor such behaviour continuously in pasture-based environments. Therefore, the aim of this study was to validate the RumiWatchSystem to record cow activity and feeding behaviour in a pasture-based system. The RumiWatchSystem was evaluated against visual observation across two different experiments. The time duration per hour at grazing, rumination, walking, standing and lying recorded by the RumiWatchSystem was compared to the visual observation data in Experiment 1. Concordance Correlation Coefficient (CCC) values of CCC = 0.96 for grazing, CCC = 0.99 for rumination, CCC = 1.00 for standing and lying and CCC = 0.92 for walking were obtained. The number of grazing and rumination bouts within one hour were also analysed resulting in Cohen's Kappa (κ) = 0.62 and κ = 0.86 for grazing and rumination bouts, respectively. Experiment 2 focused on the validation of grazing bites and rumination chews. The accordance between visual observation and automated measurement by the RumiWatchSystem was high with CCC = 0.78 and CCC = 0.94 for grazing bites and rumination chews, respectively. These results indicate that the RumiWatchSystem is a reliable sensor technology for observing cow activity and feeding behaviour in a pasture based milk production system, and may be used for research purposes in a grazing environment

Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows

peer-reviewedFeeding behaviour is an important parameter of animal performance, health and welfare, as well as reflecting levels and quality of feed available. Previously, sensors were only used for measuring animal feeding behaviour in indoor housing systems. However, sensors such as the RumiWatchSystem can also monitor such behaviour continuously in pasture-based environments. Therefore, the aim of this study was to validate the RumiWatchSystem to record cow activity and feeding behaviour in a pasture-based system. The RumiWatchSystem was evaluated against visual observation across two different experiments. The time duration per hour at grazing, rumination, walking, standing and lying recorded by the RumiWatchSystem was compared to the visual observation data in Experiment 1. Concordance Correlation Coefficient (CCC) values of CCC = 0.96 for grazing, CCC = 0.99 for rumination, CCC = 1.00 for standing and lying and CCC = 0.92 for walking were obtained. The number of grazing and rumination bouts within one hour were also analysed resulting in Cohen‘s Kappa (κ) = 0.62 and κ = 0.86 for grazing and rumination bouts, respectively. Experiment 2 focused on the validation of grazing bites and rumination chews. The accordance between visual observation and automated measurement by the RumiWatchSystem was high with CCC = 0.78 and CCC = 0.94 for grazing bites and rumination chews, respectively. These results indicate that the RumiWatchSystem is a reliable sensor technology for observing cow activity and feeding behaviour in a pasture based milk production system, and may be used for research purposes in a grazing environment