Synapsing variable-length crossover: Meaningful crossover for variable-length genomes

The synapsing variable-length crossover (SVLC algorithm provides a biologically inspired method for performing meaningful crossover between variable-length genomes. In addition to providing a rationale for variable-length crossover, it also provides a genotypic similarity metric for variable-length genomes, enabling standard niche formation techniques to be used with variable-length genomes. Unlike other variable-length crossover techniques which consider genomes to be rigid inflexible arrays and where some or all of the crossover points are randomly selected, the SVLC algorithm considers genomes to be flexible and chooses non-random crossover points based on the common parental sequence similarity. The SVLC algorithm recurrently "glues" or synapses homogenous genetic subsequences together. This is done in such a way that common parental sequences are automatically preserved in the offspring with only the genetic differences being exchanged or removed, independent of the length of such differences. In a variable-length test problem, the SVLC algorithm compares favorably with current variable-length crossover techniques. The variable-length approach is further advocated by demonstrating how a variable-length genetic algorithm (GA) can obtain a high fitness solution in fewer iterations than a traditional fixed-length GA in a two-dimensional vector approximation task.

A double-blind, placebo-controlled, crossover-designed probiotic feeding study in children diagnosed with autistic spectrum disorders

There is growing interest in the role of gastrointestinal (GI) pathology and clinical expression of autism. Recent studies have demonstrated differences in the faecal clostridial populations harboured by autistic and non-autistic children. The potential of Lactobacillus plantarum WCSF1 (a probiotic) to modulate the gut microbiota of autistic subjects was investigated during a double-blind, placebo-controlled, crossover-designed feeding study. The faecal microbiota, gut function and behaviour scores of subjects were examined throughout the 12-week study. Lactobacillus plantarum WCFS1 feeding significantly increased Lab158 counts (lactobacilli and enterococci group) and significantly reduced Erec482 counts (Clostridium cluster XIVa) compared to placebo. Probiotic feeding also resulted in significant differences in the stool consistency compared to placebo and behaviour scores (total score and scores for some subscales) compared to baseline. The major finding of this work was the importance of study protocol in relation to the specific considerations of this subject population, with an extremely high dropout rate seen (predominantly during the baseline period). Furthermore, the relatively high inter-individual variability observed suggests that subsequent studies should use defined subgroups of autistic spectrum disorders, such as regressive or late-onset autism. In summary, the current study has highlighted the potential benefit of L. plantarum WCFS1 probiotic feeding in autistic individuals.

Proof of concept of automated mapping of weeds in arable fields

The project investigated whether it would be possible to remove the main technical hindrance to precision application of herbicides to arable crops in the UK, namely creating geo-referenced weed maps for each field. The ultimate goal is an information system so that agronomists and farmers can plan precision weed control and create spraying maps. The project focussed on black-grass in wheat, but research was also carried out on barley and beans and on wild-oats, barren brome, rye-grass, cleavers and thistles which form stable patches in arable fields. Farmers may also make special efforts to control them. Using cameras mounted on farm machinery, the project explored the feasibility of automating the process of mapping black-grass in fields. Geo-referenced images were captured from June to December 2009, using sprayers, a tractor, combine harvesters and on foot. Cameras were mounted on the sprayer boom, on windows or on top of tractor and combine cabs and images were captured with a range of vibration levels and at speeds up to 20 km h-1. For acceptability to farmers, it was important that every image containing black-grass was classified as containing black-grass; false negatives are highly undesirable. The software algorithms recorded no false negatives in sample images analysed to date, although some black-grass heads were unclassified and there were also false positives. The density of black-grass heads per unit area estimated by machine vision increased as a linear function of the actual density with a mean detection rate of 47% of black-grass heads in sample images at T3 within a density range of 13 to 1230 heads m-2. A final part of the project was to create geo-referenced weed maps using software written in previous HGCA-funded projects and two examples show that geo-location by machine vision compares well with manually-mapped weed patches. The consortium therefore demonstrated for the first time the feasibility of using a GPS-linked computer-controlled camera system mounted on farm machinery (tractor, sprayer or combine) to geo-reference black-grass in winter wheat between black-grass head emergence and seed shedding.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

Prebiotic evaluation of cocoa-derived flavanols in healthy humans by using a randomized, controlled, double-blind, crossover intervention study

BACKGROUND: The absorption of cocoa flavanols in the small intestine is limited, and the majority of the flavanols reach the large intestine where they may be metabolized by resident microbiota. OBJECTIVE: We assessed the prebiotic potential of cocoa flavanols in a randomized, double-blind, crossover, controlled intervention study. DESIGN: Twenty-two healthy human volunteers were randomly assigned to either a high-cocoa flavanol (HCF) group (494 mg cocoa flavanols/d) or a low-cocoa flavanol (LCF) group (23 mg cocoa flavanols/d) for 4 wk. This was followed by a 4-wk washout period before volunteers crossed to the alternant arm. Fecal samples were recovered before and after each intervention, and bacterial numbers were measured by fluorescence in situ hybridization. A number of other biochemical and physiologic markers were measured. RESULTS: Compared with the consumption of the LCF drink, the daily consumption of the HCF drink for 4 wk significantly increased the bifidobacterial (P < 0.01) and lactobacilli (P < 0.001) populations but significantly decreased clostridia counts (P < 0.001). These microbial changes were paralleled by significant reductions in plasma triacylglycerol (P < 0.05) and C-reactive protein (P < 0.05) concentrations. Furthermore, changes in C-reactive protein concentrations were linked to changes in lactobacilli counts (P < 0.05, R(2) = -0.33 for the model). These in vivo changes were closely paralleled by cocoa flavanol-induced bacterial changes in mixed-batch culture experiments. CONCLUSION: This study shows, for the first time to our knowledge, that consumption of cocoa flavanols can significantly affect the growth of select gut microflora in humans, which suggests the potential prebiotic benefits associated with the dietary inclusion of flavanol-rich foods. This trial was registered at clinicaltrials.gov as NCT01091922.

The concept of a new video detector for ghosted IF signals

The effect of terrestrial television multipath signals on the intermediate frequency (IF) vestigial sideband filter and the video detector are discussed. A new detector is proposed which, by processing the detected phase quadrature information, derives the correct phase for synchronous detection in the presence of multipath effects. This minimizes dispersion and produces a detected video signal with the linear addition of any ghosts.

Cholesterol reduction using manufactured foods high in monounsaturated fatty acids: a randomized crossover study

In two separate studies, the cholesterol-lowering efficacy of a diet high in monounsaturated fatty acids (MUFA) was evaluated by means of a randomized crossover trial. In both studies subjects were randomized to receive either a high-MUFA diet or the control diet first, which they followed for a period of 8 weeks; following a washout period of 4–6 weeks they were transferred onto the opposing diet for a further period of 8 weeks. In one study subjects were healthy middle-aged men (n 30), and in the other they were young men (n 23) with a family history of CHD recruited from two centres (Guildford and Dublin). The two studies were conducted over the same time period using identical foods and study designs. Subjects consumed 38% energy as fat, with 18% energy as MUFA and 10% as saturated fatty acids (MUFA diet), or 13% energy as MUFA and 16% as saturated fatty acids (control diet). The polyunsaturated fatty acid content of each diet was 7%. The diets were achieved by providing subjects with manufactured foods such as spreads, ‘ready meals’, biscuits, puddings and breads, which, apart from their fatty acid compositions, were identical for both diets. Subjects were blind to which of the diets they were following on both arms of the study. Weight changes on the diets were less than 1 kg. In the groups combined (n 53) mean total and LDL-cholesterol levels were significantly lower at the end of the MUFA diet than the control diet by 0×29 (SD 0×61) mmol/l (P,0×001) and 0×38 (SD 0×64) mmol/l (P, 0×0001) respectively. In middle-aged men these differences were due to a mean reduction in LDL-cholesterol of ¹11 (SD 12) % on the MUFA diet with no change on the control diet (¹1×1 (SD 10) %). In young men the differences were due to an increase in LDL-cholesterol concentration on the control diet of þ6×2 (SD 13) % and a decrease on the MUFA diet of ¹7×8 (SD 20) %. Differences in the responses of middle-aged and young men to the two diets did not appear to be due to differences in their habitual baseline diets which were generally similar, but appeared to reflect the lower baseline cholesterol concentrations in the younger men. There was a moderately strong and statistically significant inverse correlation between the change in LDLcholesterol concentration on each diet and the baseline fasting LDL-cholesterol concentration (r¹0×49; P,0×0005). In conclusion, diets in which saturated fat is partially replaced by MUFA can achieve significant reductions in total and LDL-cholesterol concentrations, even when total fat and energy intakes are maintained. The dietary approach used to alter fatty acid intakes would be appropriate for achieving reductions in saturated fat intakes in whole populations.

A concept of approximated densities for efficient nonlinear estimation

This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques, the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy principle subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes theorem. Through simulation on a generic exponential model, the proposed nonlinear filter is implemented and the results prove to be superior to that of the extended Kalman filter and a class of nonlinear filters based on partitioning algorithms.