A longitudinal prospective cohort study investigating the association of premilking stimulation and teat-end shape on milking characteristics and teat tissue condition in dairy cows

Abstract Background Premilking udder preparation is essential for harvesting high-quality milk as gently, completely, and quickly as possible. The associations between characteristics such as teat-end shape and premilking stimulation on milking characteristics and machine milking-induced changes to the teat tissue condition have not been rigorously investigated. The primary objective was to investigate the interactive effects of manual premilking stimulation (i.e., preparation lag time) and teat-end shape on total milk yield, two-minute milk yield, milking unit-on time, and time in low milk flow rate. Our secondary objective was to study the association of manual premilking stimulation and changes to the teat tissue condition after machine milking (i.e., short-term changes). In a longitudinal prospective cohort study, 384 milking observations from 129 cows were analysed. Holstein cows were housed in sand-bedded free-stall pens, fed a total mixed ration, and milked 3 times a day. Cows were classified by teat-end shape into 1 of 3 categories: pointed, flat, or round. Individual cow milking characteristics were recorded with electronic on-farm milk meters. The duration of manual stimulation, preparation lag time, and presence of short-term changes were documented for each milking observation. General linear mixed models were used to study the interactive effects of preparation lag time and teat-end shape on milking characteristics. Results There was an interaction between preparation lag time and teat-end shape for two-minute milk yield and time in low milk flow rate. The preparation lag time effect was modified by teat-end shape, while no interaction was observed for total milk yield or milking unit-on time. A generalized linear mixed model revealed that preparation lag time was associated with short-term changes in teat tissue condition, where the odds of short-term changes decreased as preparation lag time increased. Conclusions In summary, cows with different teat-end shapes may require different premilking stimulation regimens. Increasing preparation lag time benefits teat tissue condition during machine milking. Further research is warranted to optimize individual premilking stimulation in dairy cows

Advancing clinical cohort selection with genomics analysis on a distributed platform.

The affordability of next-generation genomic sequencing and the improvement of medical data management have contributed largely to the evolution of biological analysis from both a clinical and research perspective. Precision medicine is a response to these advancements that places individuals into better-defined subsets based on shared clinical and genetic features. The identification of personalized diagnosis and treatment options is dependent on the ability to draw insights from large-scale, multi-modal analysis of biomedical datasets. Driven by a real use case, we premise that platforms that support precision medicine analysis should maintain data in their optimal data stores, should support distributed storage and query mechanisms, and should scale as more samples are added to the system. We extended a genomics-based columnar data store, GenomicsDB, for ease of use within a distributed analytics platform for clinical and genomic data integration, known as the ODA framework. The framework supports interaction from an i2b2 plugin as well as a notebook environment. We show that the ODA framework exhibits worst-case linear scaling for array size (storage), import time (data construction), and query time for an increasing number of samples. We go on to show worst-case linear time for both import of clinical data and aggregate query execution time within a distributed environment. This work highlights the integration of a distributed genomic database with a distributed compute environment to support scalable and efficient precision medicine queries from a HIPAA-compliant, cohort system in a real-world setting. The ODA framework is currently deployed in production to support precision medicine exploration and analysis from clinicians and researchers at UCLA David Geffen School of Medicine