Personalizing medicine in silico and in socio

Proponents of the emerging field of P4 medicine (defined as personalized, predictive, preventive and participatory) argue that computational integration and analysis of patient-specific “big data” will revolutionize our health care systems, in particular primary care-based disease prevention. While many ambitions remain visionary, steps to personalize medicine are already taken via personalized genomics, mobile health technologies and pilot projects. An important aim of P4 medicine is to enable disease prevention among healthy persons through detection of risk factors. In this paper, we examine the current status of P4 medicine in light of historical and current challenges to predictive and preventive medicine, including overdiagnosis and overtreatment. Moreover, we ask whether it is likely that in silico integration of patient-specific data will be able to better deal such challenges and to turn risk predictions into disease-preventive actions in a wider social context. Given the lack of evidence that P4 medicine can tip the balance between benefits and harms in preventive medicine, we raise concerns about the current promotion of P4 medicine as a solution to the current challenges in public health

Personalizing medicine in silico and in socio

Proponents of the emerging field of P4 medicine (defined as personalized, predictive, preventive and participatory) argue that computational integration and analysis of patient-specific “big data” will revolutionize our health care systems, in particular primary care-based disease prevention. While many ambitions remain visionary, steps to personalize medicine are already taken via personalized genomics, mobile health technologies and pilot projects. An important aim of P4 medicine is to enable disease prevention among healthy persons through detection of risk factors. In this paper, we examine the current status of P4 medicine in light of historical and current challenges to predictive and preventive medicine, including overdiagnosis and overtreatment. Moreover, we ask whether it is likely that in silico integration of patient-specific data will be able to better deal such challenges and to turn risk predictions into disease-preventive actions in a wider social context. Given the lack of evidence that P4 medicine can tip the balance between benefits and harms in preventive medicine, we raise concerns about the current promotion of P4 medicine as a solution to the current challenges in public health

The effect of feed composition on the sensory quality of organic rainbow trout during ice storage

The aim of this work was to study whether the type of protein and lipid source in feed for organic Rainbow trout (Onchorhynchus mykiss) influenced the sensory quality. The protein sources were fishmeal and a matrix of organic vegetable plant mealsproteins, while the lipid sources were fish oil and organic oils of linseed, sunflower, rapeseed and grape seed, respectively. Sensory profiling was performed after 3, 5, 7 and 14 days of storage in ice. Besides sensory analysis also lipid profiles weare measured. The resultsed showed that the lipid type in the feed aeffected the sensory characteristics after 3 days of storage. Especially the trout that had grape seed oil in the feed had a different sensory profile than the trout that had fish oil in the feed. These differences could be explained by the lipid profiles in the fillets. Also after 7 days of ice storage differences in the sensory profile wereas observed again as a result of the used lipid types used. After 7 days of ice storage ThusHere the trout that had grape seed oil orand rapeseed oil for instance had a more neutral flavor and odor profile compared to the other trout fed on linseed or sunflower oil´s. After 14 days of storage no effect of lipid type in the feed was observed, but the trout which had fish meal as protein source had higher intensity of several negative sensory descriptors compared to trout that had the vegetable protein matrix. Overall the results showed that the dietaryfeeds content of protein and lipid aeffecteds the sensory characteristics of the trout in different ways during the ice storage period

The effects of feed composition on the sensory quality of organic rainbow trout during ice storage

The focus of this work was to study which effects the type of protein and lipid source in the feed for organic Rainbow trout influences had on the sensory quality of final product. Two and four different protein and lipid sources were used in the experiment respectively. The protein sources were fishmeal and a mixture of protein from organic vegetable, while the lipid sources were fish oil and organic oil from linseed, sunflower, rapeseed and grape seed. Sensory analysis was performed after 3, 5, 7 and 14 days of storage in ice. The results showed that both protein and lipid source in the feed can influence sensory characteristics of the trout. After 3 and 7 days of storage in ice differences in the sensory characteristics between rainbow trout’s which have had different lipid sources in the feed were observed. While a difference between the trout fed with different protein sources were observed after 14 days of storage, indicating that vegetable protein in the feed increases the self-life of organic rainbow trout

The effect of protein and lipid source in organic feed for organic trout on sensory quality

The aim of this work was to study whether the type of protein and lipid source in feed for organic Rainbow trout (Onchorhynchus mykiss) influenced the sensory quality. The protein sources were fishmeal and a matrix of organic vegetable plant mealsproteins, while the lipid sources were fish oil and organic oils of linseed, sunflower, rapeseed and grape seed, respectively. Sensory profiling was performed after 3, 5, 7 and 14 days of storage in ice. Besides sensory analysis also lipid profiles weare measured. The resultsed showed that the lipid type in the feed aeffected the sensory characteristics after 3 days of storage. Especially the trout that had grape seed oil in the feed had a different sensory profile than the trout that had fish oil in the feed. These differences could be explained by the lipid profiles in the fillets. Also after 7 days of ice storage differences in the sensory profile wereas observed again as a result of the used lipid types used. After 7 days of ice storage ThusHere the trout that had grape seed oil orand rapeseed oil for instance had a more neutral flavor and odor profile compared to the other trout fed on linseed or sunflower oil´s. After 14 days of storage no effect of lipid type in the feed was observed, but the trout which had fish meal as protein source had higher intensity of several negative sensory descriptors compared to trout that had the vegetable protein matrix. Overall the results showed that the dietaryfeeds content of protein and lipid aeffecteds the sensory characteristics of the trout in different ways during the ice storage period

Load forecasting of supermarket refrigeration

This paper presents a study of models for forecasting the electrical load for supermarket refrigeration. The data used for building the models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village in Denmark. Every hour the hourly electrical load for refrigeration is forecasted for the fol-lowing 42 hours. The forecast models are adaptive linear time series models. The model has two regimes; one for opening hours and one for closing hours, this is modelled by a regime switching model and two different methods for predicting the regimes are tested. The dynamic relation between the weather and the load is modelled by simple transfer functions and the non-linearities are described using spline functions. The results are thoroughly evaluated and it is shown that the spline functions are suitable for handling the non-linear relations and that after applying an auto-regressive noise model the one-step ahead residuals do not contain further significant information

FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360-degree camera, lidar, and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.Comment: Submitted to special issue of MDPI Sensors: Sensors in Agricultur

Recognising the small Ree groups in their natural representations

We present Las Vegas algorithms for constructive recognition and constructive membership testing of the Ree groups 2G_2(q) = Ree(q), where q = 3^{2m + 1} for some m > 0, in their natural representations of degree 7. The input is a generating set X. The constructive recognition algorithm is polynomial time given a discrete logarithm oracle. The constructive membership testing consists of a pre-processing step, that only needs to be executed once for a given X, and a main step. The latter is polynomial time, and the former is polynomial time given a discrete logarithm oracle. Implementations of the algorithms are available for the computer algebra system MAGMA