Machine learning based canine posture estimation using inertial data

The aim of this study was to design a new canine posture estimation system specifically for working dogs. The system was composed of Inertial Measurement Units (IMUs) that are commercially available, and a supervised learning algorithm which was developed for different behaviours. Three IMUs, each containing a 3-axis accelerometer, gyroscope, and magnetometer, were attached to the dogs’ chest, back, and neck. To build and test the model, data were collected during a video-recorded behaviour test where the trainee assistance dogs performed static postures (standing, sitting, lying down) and dynamic activities (walking, body shake). Advanced feature extraction techniques were employed for the first time in this field, including statistical, temporal, and spectral methods. The most important features for posture prediction were chosen using Select K Best with ANOVA F-value. The individual contributions of each IMU, sensor, and feature type were analysed using Select K Best scores and Random Forest feature importance. Results showed that the back and chest IMUs were more important than the neck IMU, and the accelerometers were more important than the gyroscopes. The addition of IMUs to the chest and back of dog harnesses is recommended to improve performance. Additionally, statistical and temporal feature domains were more important than spectral feature domains. Three novel cascade arrangements of Random Forest and Isolation Forest were fitted to the dataset. The best classifier achieved an f1-macro of 0.83 and an f1-weighted of 0.90 for the prediction of the five postures, demonstrating a better performance than previous studies. These results were attributed to the data collection methodology (number of subjects and observations, multiple IMUs, use of common working dog breeds) and novel machine learning techniques (advanced feature extraction, feature selection and modelling arrangements) employed. The dataset and code used are publicly available on Mendeley Data and GitHub, respectively

Probing the Soybean Bowman–Birk Inhibitor Using Recombinant Antibody Fragments

The nutritional and health benefits of soy protein have been extensively studied over recent decades. The Bowman–Birk inhibitor (BBI), derived from soybeans, is a double-headed inhibitor of chymotrypsin and trypsin with anticarcinogenic and anti-inflammatory properties, which have been demonstrated in vitro and in vivo. However, the lack of analytical and purification methodologies complicates its potential for further functional and clinical investigations. This paper reports the construction of anti-BBI antibody fragments based on the principle of protein design. Recombinant antibody (scFv and diabody) molecules targeting soybean BBI were produced and characterized in vitro (<i>K</i>_D ∼ 1.10^–9 M), and the antibody-binding site (epitope) was identified as part of the trypsin-specific reactive loop. Finally, an extremely fast purification strategy for BBI from soybean extracts, based on superparamagnetic particles coated with antibody fragments, was developed. To the best of the authors' knowledge, this is the first report on the design and characterization of recombinant anti-BBI antibodies and their potential application in soybean processing

Opinion on the application of the Irish Constitution and EU General Data Protection Regulation to the Adoption (Information and Tracing) Bill 2016 and the Government’s ‘Options for Consideration’

[No abstract available]non-peer-reviewe