Matching new entrants and retiring farmers through farm joint ventures: Insights from the Fresh Start Initiative in Cornwall, UK

Concerns about the sustainability of an ageing farming population have brought interest in so called entry-exit issues in policy circles. Policy interventions to date have offered limited scope in stimulating farm transfer in UK, however, the increase in unconventional tenures which include partnerships, share farming and contract farming (collectively called joint ventures) would appear to offer new opportunities for those wishing to enter or leave farming. In recognition of this the Fresh Start initiative in Cornwall set up a matchmaking element with the aim of identifying and facilitating potential joint ventures agreements between new entrants and older farmers. The emphasis was on setting up long-term arrangements that would enable the new entrant to 'buy into' an existing farm business, gradually taking over managerial control. This paper examines the processes of matching partners for the possible formation of farm joint ventures, using qualitative data derived from interviews with the participants, deliverers and stakeholders involved in the matchmaking element of this initiative. The results reveal that there is a deep rooted reluctance amongst participants in the initiative to enter formal long term joint ventures due to differing motivations, expectations, and concerns about their respective responsibilities in the working relationship and about the validity of the legal framework. Only where a relationship had already been informally established was there a commitment to formalise a joint venture agreement. Future emphasis in policy should therefore be on helping to facilitate and formalise existing partnerships, rather than trying to artificially orchestrate matches where the parties do not know each other

UA30/1/1 New School of Music Building

A New School of Music Building includes elevations, cross sections, lintel schedule, 38 x 20

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Proteomic analysis of skin invasion by blood fluke larvae.

BackgroundDuring invasion of human skin by schistosome blood fluke larvae (cercariae), a multicellular organism breaches the epidermis, basement membrane, and dermal barriers of skin. To better understand the pathobiology of this initial event in schistosome infection, a proteome analysis of human skin was carried out following invasion by cercariae of Schistosoma mansoni.Methodology and resultsHuman skin samples were exposed to cercariae for one-half hour to two hours. Controls were exposed to water used to collect cercariae in an identical manner, and punctured to simulate cercarial tunnels. Fluid from both control and experimental samples was analyzed by LC/MS/MS using a linear ion trap in "triple play" mode. The coexistence of proteins released by cercariae and host skin proteins from epidermis and basement membrane confirmed that cercarial tunnels in skin were sampled. Among the abundant proteins secreted by cercariae was the cercarial protease that has been implicated in degradation of host proteins, secreted proteins proposed to mediate immune invasion by larvae, and proteins implicated in protection of parasites against oxidative stress. Components of the schistosome surface tegument, previously identified with immune serum, were also released. Both lysis and apoptosis of epidermal cells took place during cercarial invasion of the epidermis. Components of lysed epidermal cells, including desmosome proteins which link cells in the stratum granulosum and stratum spinosum, were identified. While macrophage-derived proteins were present, no mast cell or lymphocyte cytokines were identified. There were, however, abundant immunoglobulins, complement factors, and serine protease inhibitors in skin. Control skin samples incubated with water for the same period as experimental samples ensured that invasion-related proteins and host protein fragments were not due to nonspecific degeneration of the skin samples.ConclusionsThis analysis identified secreted proteins from invasive larvae that are released during invasion of human skin. Analysis of specific host proteins in skin invaded by cercariae served to highlight both the histolytic events facilitating cercarial invasion, and the host defenses that attempt to arrest or retard invasion. Proteins abundant in psoriatic skin or UV and heat-stressed skin were not abundant in skin invaded by cercariae, suggesting that results did not reflect general stress in the surgically removed skin specimen. Abundant immunoglobulins, complement factors, and serine protease inhibitors in skin form a biochemical barrier that complements the structural barrier of the epidermis, basement membrane, and dermis. The fragmentation of some of these host proteins suggests that breaching of host defenses by cercariae includes specific degradation of immunoglobulins and complement, and either degradation of, or overwhelming the host protease inhibitor repertoire

An error-tuned model for sensorimotor learning

Current models of sensorimotor control posit that motor commands are generated by combining multiple modules which may consist of internal models, motor primitives or motor synergies. The mechanisms which select modules based on task requirements and modify their output during learning are therefore critical to our understanding of sensorimotor control. Here we develop a novel modular architecture for multi-dimensional tasks in which a set of fixed primitives are each able to compensate for errors in a single direction in the task space. The contribution of the primitives to the motor output is determined by both top-down contextual information and bottom- up error information. We implement this model for a task in which subjects learn to manipulate a dynamic object whose orientation can vary. In the model, visual information regarding the context (the orientation of the object) allows the appropriate primitives to be engaged. This top-down module selection is implemented by a Gaussian function tuned for the visual orientation of the object. Second, each module's contribution adapts across trials in proportion to its ability to decrease the current kinematic error. Specifically, adaptation is implemented by cosine tuning of primitives to the current direction of the error, which we show to be theoretically optimal for reducing error. This error-tuned model makes two novel predictions. First, interference should occur between alternating dynamics only when the kinematic errors associated with each oppose one another. In contrast, dynamics which lead to orthogonal errors should not interfere. Second, kinematic errors alone should be sufficient to engage the appropriate modules, even in the absence of contextual information normally provided by vision. We confirm both these predictions experimentally and show that the model can also account for data from previous experiments. Our results suggest that two interacting processes account for module selection during sensorimotor control and learning.This work was financially supported by the Wellcome Trust (to DMW; WT097803MA, http://www.wellcome.ac.uk), the Royal Society Noreen Murray Professorship in Neurobiology (to DMW; https://royalsociety.org), Natural Sciences and Engineering Research Council of Canada (to JRF; RGPIN/04837, http://www.nserc.ca), the Canadian Institutes of Health Research (to JRF; 82837, http://www.cihr.ca). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Deleterious effect of suboptimal diet on rest-activity cycle in Anastrepha ludens manifests itself with age.

Activity patterns and sleep-wake cycles are among the physiological processes that change most prominently as animals age, and are often good indicators of healthspan. In this study, we used the video-based high-resolution behavioral monitoring system (BMS) to monitor the daily activity cycle of tephritid fruit flies Anastrepha ludens over their lifetime. Surprisingly, there was no dramatic change in activity profile with respect to age if flies were consistently fed with a nutritionally balanced diet. However, if flies were fed with sugar-only diet, their activity profile decreased in amplitude at old age, suggesting that suboptimal diet affected activity patterns, and its detrimental effect may not manifest itself until the animal ages. Moreover, by simulating different modes of behavior monitoring with a range of resolution and comparing the resulting conclusions, we confirmed the superior performance of video-based monitoring using high-resolution BMS in accurately representing activity patterns in an insect model

Predicting Graph Categories from Structural Properties

Complex networks are often categorized according to the underlying phenomena that they represent such as molecular interactions, re-tweets, and brain activity. In this work, we investigate the problem of predicting the category (domain) of arbitrary networks. This includes complex networks from different domains as well as synthetically generated graphs from five different network models. A classification accuracy of 96.6% is achieved using a random forest classifier with both real and synthetic networks. This work makes two important findings. First, our results indicate that complex networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of a new previously unseen network. Second, synthetic graphs are trivial to classify as the classification model can predict with near-certainty the network model used to generate it. Overall, the results demonstrate that networks drawn from different domains (and network models) are trivial to distinguish using only a handful of simple structural properties

Learning in the Permaculture Community of Practice in England: An Analysis of the Relationship between Core Practices and Boundary Processes

This article utilizes the Communities of Practice (CoP) framework to examine learning processes among a group of permaculture practitioners in England, specifically examining the balance between core practices and boundary processes. The empirical basis of the article derives from three participatory workshops and 14 interviews with permaculture practitioners distributed across England. The research found that permaculture practitioners are informally bound together by shared values, expertise and passion for the joint enterprise of permaculture, thus corresponding to a CoP. It found that core practices (situated learning, mutual engagement, joint enterprise and shared repertoire) are strong but also that boundary processes are active, enabling learning and interaction to take place with other learning systems, although this tends to be restricted to those with similar perspectives. This, and the strong cohesion and identity of the CoP, leads to some insularity. Scholars propose that innovative groups can strengthen the conventional Agricultural Knowledge System (AKS). This research, however, shows that the potential for the permaculture CoP to integrate with the conventional AKS is limited due to its insularity and self-reliance, in that the Permaculture Association fulfils the role of information provision and network facilitation. Most opportunities for integration lay in facilitating brokerage and dialogue between members at the periphery of the permaculture CoP and the AKS. The research provides a critique on the use and value of the CoP framework in a new context and offers insights into how learning takes place in the permaculture community

On the eigenvalues of Cayley graphs on the symmetric group generated by a complete multipartite set of transpositions

Given a finite simple graph \cG with $n$ vertices, we can construct the Cayley graph on the symmetric group $S_n$ generated by the edges of \cG, interpreted as transpositions. We show that, if \cG is complete multipartite, the eigenvalues of the Laplacian of \Cay(\cG) have a simple expression in terms of the irreducible characters of transpositions, and of the Littlewood-Richardson coefficients. As a consequence we can prove that the Laplacians of \cG and of \Cay(\cG) have the same first nontrivial eigenvalue. This is equivalent to saying that Aldous's conjecture, asserting that the random walk and the interchange process have the same spectral gap, holds for complete multipartite graphs.Comment: 29 pages. Includes modification which appear on the published version in J. Algebraic Combi