Animal as the Solution II: Phenotyping for Low Milk Urea Nitrogen A1PF Dairy Cows.

The societal pressure on intensive pastoral dairying demands the search for strategies to reduce the amount of N flowing through and excreted by dairy cows. One of the strategies that is being currently explored focuses on the animal as a solution, as there are differences in N metabolism between cows even within the same herd. This work was conducted to explore such an approach in A1PF herds in New Zealand and the possibility of identifying A1PF cows that are divergent for milk urea nitrogen (MUN) concentration through phenotyping as a potential viable strategy to reduce N leaching and emissions from temperate dairy systems.

Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting.

Goal And Aims: One challenge using wearable sensors is nonwear time. Without a nonwear (e.g.

Enhancement on selenium volatilization for phytoremediation: role of plant and soil microbe interaction.

This study aimed at quantifying the potential effects of plant and soil microbial interaction on selenium (Se) volatilization, with the specific objectives of identifying soil bacteria associated with rabbitfoot grass () and demonstrating the enhancement of Se volatilization in the soil-Indian mustard () system through inoculation of the soil with the identified best Se-volatilizing bacterial strain. Soil bacteria were isolated from topsoil and rhizosphere soils of rabbitfoot grass, and the bacterial colonies were characterized via PCR-DGGE and DGGE band analysis prior to their identification using 16S rDNA sequencing technique. produced over 500-fold more volatile Se in a culture medium treated with 15 µg Se/mL (equal mixture of SeO , SeO and selenomethionine) than any of the other eight identified bacterial strains.

Selective learning for sensing using shift-invariant spectrally stable undersampled networks.

The amount of data collected for sensing tasks in scientific computing is based on the Shannon-Nyquist sampling theorem proposed in the 1940s. Sensor data generation will surpass 73 trillion GB by 2025 as we increase the high-fidelity digitization of the physical world. Skyrocketing data infrastructure costs and time to maintain and compute on all this data are increasingly common.