Food safety knowledge, practice and training were examined among 689 food workers in Ireland. Parameters such as role, years worked, level of food safety training acquired, and establishment were all found to have a significant effect (p-values <0.01) on knowledge score. It is notable that 28% of all respondents claimed 'never' to have received food safety training, suggesting insufficient compliance with this legislative requirement. Notably, absence of training only accounted for 1% (n=1) of all canteen workers surveyed. In addition, individuals working in canteens were found to have the highest knowledge score (81%) and the highest percentage of level 3 training (60%). Respondents were asked a series of questions relating to operational prerequisite hygiene requirements such as working while unwell, critical limits, food allergens and hand hygiene. This study highlights the value of food safety training and elucidates potential areas for improvement. 1. Introduction Globalisation, coupled with the demand for increased product shelf-life, has led to longer and intrinsically more complex supply chains than ever before (Copenhagen, 2015; Walsh & Leva, 2018). This creates many challenges for the food sector in delivering safe food produce to customers; particularly in light of the current consumer demand for minimally processed food (De Corato, 2019). The global burden of foodborne disease has been reported to be comparable to major infectious diseases, HIV/AIDS, malaria and tuberculosis; with 1 in 10 individuals falling ill and 420,000 associated fatalities annually (Havelaar et al., 2015; WHO, 2015). In regional terms, European figures suggest that 23 million individuals became ill from foodborne disease, with an estimated 5,000 fatalities, reported in the EU every year (WHO, 2015). Interestingly, 61% of all foodborne outbreaks (including waterborne cases), reported in Europe (EFSA, 2018) and 78% in the USA (CDC, 2018), have been attributed to food from the food service sector. Similarly, approximately 50% of foodborne illness (Bolton, Meally, Blair, McDowell, & Cowan, 2008), has been previously reported to be associated with catering establishments and restaurants in Ireland, respectively. These figures combined with several recent studies documenting insufficient levels of knowledge, negative attitudes and optimistic bias among food handlers (

Biomolecular changes associated with cancer progression can be identified using Raman spectroscopy, allowing for this technique to be utilized as a non-invasive tool for the diagnosis of bladder cancer. Applications of Raman spectroscopy for diagnostics in real-time have consistently produced higher sensitivities and specificities than current clinical methods. This technique can be applied

BackgroundBrain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke.Methods32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data.ResultsStroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset.ConclusionsThis exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.

The persistence of sperm using confirmatory microscopic analysis, the persistence of sperm with tails, time since intercourse (TSI) analysis, and results from the acid phosphatase (AP) reaction from approximately 5581 swabs taken from circa 1450 sexual assault cases are presented. The observed proportions of sperm in the vagina and anus declines significantly after 48 h TSI, and sperm on oral swabs were observed up to 15 h TSI. The AP reaction as a predictor of sperm on intimate swabs is questioned. All AP reaction times gave a low true positive rate; 23% of sperm-positive swabs gave a negative AP reaction time. We show the AP reaction is an unsafe and an unreliable predictor of sperm on intimate swabs. We propose that TSI not AP informs precase assessment and the evaluative approach for sexual assault cases. To help inform an evaluative approach, TSI guidelines are presented.

We study the predictive capabilities of magnetic-feature properties (MF) generated by the Solar Monitor Active Region Tracker (SMART: Higgins et al. in Adv. Space Res. 47, 2105, 2011) for solar-flare forecasting from two datasets: the full dataset of SMART detections from 1996 to 2010 which has been previously studied by Ahmed et al. (Solar Phys. 283, 157, 2013) and a subset of that dataset that only includes detections that are NOAA active regions (ARs). The main contributions of this work are: we use marginal relevance as a filter feature selection method to identify the most useful SMART MF properties for separating flaring from non-flaring detections and logistic regression to derive classification rules to predict future observations. For comparison, we employ a Random Forest, Support Vector Machine, and a set of Deep Neural Network models, as well as lasso for feature selection. Using the linear model with three features we obtain significantly better results (True Skill Score: TSS = 0.84) than those reported by Ahmed et al. (Solar Phys. 283, 157, 2013) for the full dataset of SMART detections. The same model produced competitive results (TSS = 0.67) for the dataset of SMART detections that are NOAA ARs, which can be compared to a broader section of flare-forecasting literature. We show that more complex models are not required for this data.