Automated Home-Cage Behavioural Phenotyping of Mice

Neurobehavioral analysis of mouse phenotypes requires the monitoring of mouse behavior over long periods of time. Here, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and an extensive manually annotated video database used for training and testing the system. Our system performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home-cage behaviors of two standard inbred and two non-standard mouse strains. From this data we were able to predict in a blind test the strain identity of individual animals with high accuracy. Our video-based software will complement existing sensor based automated approaches and enable an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of mouse behavior.McGovern Institute for Brain ResearchCalifornia Institute of Technology. Broad Fellows Program in Brain CircuitryNational Science Council (China) (TMS-094-1-A032

Sampling Strategy and Accuracy Assessment for Digital Terrain Modelling

In this thesis, investigations into some of the problems related to three of the main concerns (i. e. accuracy, cost and efficiency) of digital terrain modelling have been carried out. Special attention has been given to two main issues - the establishment of a family of mathematical models which is comprehensive in theory and reliable in practice, and the development of a procedure for the determination of an optimum sampling interval for a DTM project with a specified accuracy requirement. Concretely, the following discussions or investigations have been carried out:- i). First of all, a discussion of the theoretical background to digital terrain modelling has been conducted and an insight into the complex matter of digital terrain surface modelling has been obtained. ii). Some investigations into the improvement of the quality of DTM source data have been carried out. In this respect, algorithms for gross error detection have been developed and a procedure for random noise filtering implemented. iii). Experimental tests of the accuracy of DTMs derived from various data sources (i. e. aerial photography, space photography and existing contour maps) have been carried out. In the case of the DTMs derived from photogrammetrically measured data, the tests were designed deliberately to investigate the relationship between DTM accuracy and sampling interval, terrain slope and data pattern. In the case of DTMs derived from digital contour data, the tests were designed to investigate the relationship between DTM accuracy and contour interval, terrain slope and the characteristics of the data set. iv). The problems related to the reliability of the DTM accuracy figures obtained from the results of the experimental tests have also been investigated. Some criteria have also been set for the accuracy, number and distribution of check points. v). A family of mathematical models has been developed for the prediction of DTM accuracy. These models have been validated by experimental test data and evaluated from a theoretical standpoint. Some of the existing accuracy models have also been evaluated for comparison purposes. vi). A procedure for the determination of the optimum sampling interval for a DTM project with a specified accuracy requirement has also been proposed. Based on this procedure, a potential sampling strategy has also been investigated

Enhancing Farm-Level Decision Making through Innovation

New information and knowledge are important aspects of innovation in modern farming systems. There is currently an abundance of digital and data-driven solutions that can potentially transform our food systems. At a time when the general public has concerns about how food is produced and the impact of farm production systems on the environment, strategies to increase public acceptance and the sustainability of food production are required more than ever. New tools and technology can provide timely insights into aspects such as nutrient profiles, the tracking of animal or plant wellbeing, and land-use options to enhance inputs and outputs associated with the farm business. Such solutions have the ultimate aim of enhancing production efficiency and contributing to the process of learning about the advantages of the innovation, while ensuring more sustainable food supplies. At the farm level, any new information needs to be in a useful format and beneficial for management and farm decision-making. The papers in this Special Issue evaluate agri-business innovation that can enhance farm-level decision-making

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum