Survey of the fatty acid composition of Canadian beef: Backfat and longissimus lumborum muscle

[EN]A survey of Canadian retail beef was undertaken with emphasis on the trans fatty acid (TFA) and conjugated linoleic acid (CLA) isomers, and compared with current health recommendations. Thirty striploin steaks were collected in the winter and summer from major grocery stores in Calgary (Alberta, Canada). Steak fatty acid compositions (backfat and longissimus lumborum muscle analysed separately) showed minor seasonal differences with lower total saturates (PB0.05) and higher total monounsaturates (PB 0.01) in winter, but no differences in total polyunsaturated fatty acids. The ratio of n-6 and n-3 polyunsaturated fatty acid in longissimus lumborum averaged 5.8. The average TFA content in longissimus lumborum was 0.128 g 100 g_1 serving size, and 10t-18:1 was found to be the predominant isomer (32% of total trans), while vaccenic acid was second most abundant (15% of total trans). The CLA content in longissimus lumborum was similar to that of backfat, ranging from 0.43 to 0.60% of total fatty acids and rumenic acid represented 60% of total isomers. Overall, there is still room for improvement in the saturated, mono- and polyunsaturated fatty acid composition of Canadian beef to meet general dietary guidelines for human consumption and additional targets should include reducing 10t-18:1 while increasing both rumenic and vaccenic acids.

Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.

Creación de neologismos en euskera: Análisis descriptivo de la terminología de la odontología en lengua vasca y propuestas para la formación de términos en los ámbitos de especialidad.

450 p.