Evaluation of a community group weight management scheme: Are outcomes influenced by who delivers it?

This dissertation aimed to investigate if clinical outcomes, attendance rates and the demographics of patients vary according to the type of health worker who delivered a primary care group weight management scheme. An observational, retrospective evaluation: a before and after analysis of twelve week group weight management programmes. A dataset containing 974 suitable entries was examined according to three cohorts of primary care health workers; administrative assistants (AA), health care assistants (HCA) and nurses. Comparisons between these worker groups included: differences in worker outcomes for weight, waist circumference, body mass index (BMI), and blood pressure after 12 weeks; percentage attendance at week 12; gender, age, ethnicity and the registered general practice (GP) indices of multiple deprivation score (IMD) of patients who enrolled. Patients seen by personnel in health assistant roles lost more weight (-1kg, p=0.008) lowered their BMI (- 0.3 kg/m², p=0.008) and decreased their waist circumference (-2cm, p<0.001) significantly more than those seen by nurses. Poor data entry for blood pressure recordings hampered analysis of this outcome. Attendance was 38% and not significantly different between any workers (p=0.444). Those who completed the programme were predominantly white (99.5%) women (82%), significantly older (median age 61years compared to 57 years, p<0.001) and from GP practices with significantly lower IMD scores (27.3 compared to 31.5, p<0.001). Patient characteristics were generally similar, irrespective of the deliverer. Health care workers in supporting clinical roles were able to help patients achieve greater weight loss outcomes than nurses. High levels of erroneous and missing clinical data were problematic for less qualified deliverers and indicative of a lack of more advanced clinical abilities. High attrition and limited appeal were difficulties for all three health worker groups and jeopardised the overall efficacy of the scheme

N released from organic amendments is affected by soil management history

A ryegrass bioassay was conducted to investigate the effect of soil management history on nitrogen mineralisation from composted manure and pelleted poultry manure. Soils were used from 2 field experiments comparing conventional and organic/low input management systems. When composted manure was added, soils which had received high rates of composted FYM under biodynamic management released a greater amount of nitrogen for plant uptake than those with a history of mineral or fresh manure fertilisation, suggesting that biological preconditioning may result in greater efficiency of composted FYM as a nitrogen source for plants. “Native” N mineralisation was found to be related to total soil N content

Effect of organic, low-input and conventional production systems on yield and diseases in winter barley

The effect of organic, low-input and conventional management practices on barley yield and disease incidence was assessed in field trials over two years. Conventional fertility management (based on mineral fertiliser applications) and conventional crop protection (based on chemosynthetic pesticides) significantly increased the yield of winter barley as compared to organic fertility and crop protection regimes. Severity of leaf blotch (Rhynchosporium secalis) was highest under organic fertility and crop protection management and was correlated inversely with yield. For mildew (Erysiphe graminis), an interaction between fertility management and crop protection was detected. Conventional crop protection reduced severity of the disease, only under conventional fertility management. Under organic fertility management, incidence of mildew was low and application of synthetic pesticides in “low input” production systems had no significant effect on disease severity

Movement patterns of cheetahs ( Acinonyx jubatus ) in farmlands in Botswana

Botswana has the second highest population of cheetah (Acinonyx jubatus) with most living outside protected areas. As a result, many cheetahs are found in farming areas which occasionally results in human-wildlife conflict. This study aimed to look at movement patterns of cheetahs in farming environments to determine whether cheetahs have adapted their movements in these human-dominated landscapes. We fitted high-time resolution GPS collars to cheetahs in the Ghanzi farmlands of Botswana. GPS locations were used to calculate home range sizes as well as number and duration of visits to landscape features using a time-based local convex hull method. Cheetahs had medium-sized home ranges compared to previously studied cheetah in similar farming environments. Results showed that cheetahs actively visited scent marking trees and avoided visiting homesteads. A slight preference for visiting game farms over cattle farms was found, but there was no difference in duration of visits between farm types. We conclude that cheetahs selected for areas that are important for their dietary and social needs and prefer to avoid human-occupied areas. Improved knowledge of how cheetahs use farmlands can allow farmers to make informed decisions when developing management practices and can be an important tool for reducing human-wildlife conflict

Control strategies for late blight in organic potato production

This report was presented at the UK Organic Research 2002 Conference. Protective copper fungicides are currently used to control late blight in most organic production systems, but approval for their use in organic farming will be revoked in 2002. Evidence suggests that organic potato production will not be reliably economic in the absence of Cu. Current controls for late blight are reviewed including: variety selection/breeding for blight resistance, diversification strategies, agronomic strategies for the management of late blight, and alternative treatments to Cu-fungicides

Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks

This paper proposes a novel system to estimate and track the 3D poses of multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D pose of each person is computed by a central node which receives the single-view outcomes from each camera of the network. Each single-view outcome is computed by using a CNN for 2D pose estimation and extending the resulting skeletons to 3D by means of the sensor depth. The proposed system is marker-less, multi-person, independent of background and does not make any assumption on people appearance and initial pose. The system provides real-time outcomes, thus being perfectly suited for applications requiring user interaction. Experimental results show the effectiveness of this work with respect to a baseline multi-view approach in different scenarios. To foster research and applications based on this work, we released the source code in OpenPTrack, an open source project for RGB-D people tracking.Comment: Submitted to the 2018 IEEE International Conference on Robotics and Automatio

Joint Learning of Intrinsic Images and Semantic Segmentation

Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task can be favorable. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Therefore, in this paper, the tasks of semantic segmentation and intrinsic image decomposition are considered as a combined process by exploring their mutual relationship in a joint fashion. To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation. We analyze the gains of addressing those two problems jointly. Moreover, new cascade CNN architectures for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as single tasks. Furthermore, a dataset of 35K synthetic images of natural environments is created with corresponding albedo and shading (intrinsics), as well as semantic labels (segmentation) assigned to each object/scene. The experiments show that joint learning of intrinsic image decomposition and semantic segmentation is beneficial for both tasks for natural scenes. Dataset and models are available at: https://ivi.fnwi.uva.nl/cv/intrinsegComment: ECCV 201

ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks

Hash codes are efficient data representations for coping with the ever growing amounts of data. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests, with near-optimal information-theoretic code aggregation among trees. We start with a simple hashing scheme, where random trees in a forest act as hashing functions by setting `1' for the visited tree leaf, and `0' for the rest. We show that traditional random forests fail to generate hashes that preserve the underlying similarity between the trees, rendering the random forests approach to hashing challenging. To address this, we propose to first randomly group arriving classes at each tree split node into two groups, obtaining a significantly simplified two-class classification problem, which can be handled using a light-weight CNN weak learner. Such random class grouping scheme enables code uniqueness by enforcing each class to share its code with different classes in different trees. A non-conventional low-rank loss is further adopted for the CNN weak learners to encourage code consistency by minimizing intra-class variations and maximizing inter-class distance for the two random class groups. Finally, we introduce an information-theoretic approach for aggregating codes of individual trees into a single hash code, producing a near-optimal unique hash for each class. The proposed approach significantly outperforms state-of-the-art hashing methods for image retrieval tasks on large-scale public datasets, while performing at the level of other state-of-the-art image classification techniques while utilizing a more compact and efficient scalable representation. This work proposes a principled and robust procedure to train and deploy in parallel an ensemble of light-weight CNNs, instead of simply going deeper.Comment: Accepted to ECCV 201