Using large-scale perturbations in gene network reconstruction

Background: Recent analysis of the yeast gene network shows that most genes have few inputs, indicating that enumerative gene reconstruction methods are both useful and computationally feasible. A simple enumerative reconstruction method based on a discrete dynamical system model is used to study how microarray experiments involving modulated global perturbations can be designed to obtain reasonably accurate reconstructions. The method is tested on artificial gene networks with biologically realistic in/out degree characteristics.Results: It was found that a relatively small number of perturbations significantly improve inference accuracy, particularly for low-order inputs of one or two genes. The perturbations themselves should alter the expression level of approximately 50-60% of the genes in the network.Conclusions: Time-series obtained from perturbations are a common form of expression data. This study illustrates how gene networks can be significantly reconstructed from such time-series while requiring only a relatively small number of calibrated perturbations, even for large networks, thus reducing experimental costs

Control of aphids and caterpillars on Brussels sprouts in British Columbia

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USE OF INFRARED THERMOGRAPHY TO DETECT SIGNS OF RABIES INFECTION IN RACCOONS (\u3ci\u3ePROCYON LOTOR\u3c/i\u3e)

Infrared thermography was evaluated as a technique to determine if raccoons (Procyon lotor) experimentally infected with rabies virus could be differentiated from non-infected raccoons. Following a 10-day adjustment period, raccoons (n = 6) were infected with a virulent rabies street strain raccoon variant by injection into the masseter muscle at a dose of 2 x 104 tissue-culture infectious dose (TCID50) in 0.2 ml (n = 4) or 105 TCID50 in 1 ml (n = 2). Five of the six raccoons developed prodromal signs of rabies 17 to 22 days post-inoculation (PI) and distinctive clinical signs of furious rabies between 19 and 24 days PI. At the time of euthanasia, which occurred 2 days after the onset of clinical signs of rabies, these five raccoons tested positive for rabies virus in brain tissue. Infrared thermal images of each raccoon were recorded twice daily during the preinoculation and PI periods. No apparent differences were identified among thermal temperatures compared among days for the eye, average body surface, and body temperature recorded from subcutaneous implants throughout the experiment for any of the six raccoons. However, increases in infrared surface temperature of the noses and differences in the visual thermal images of the noses were detected when animals began showing clinical signs of rabies. Differences were detected among the mean infrared nose temperatures for the disease progression intervals (F3,12 = 70.03, P \u3c 0.0001). The mean nose temperature in the clinical rabies stage (30.4 ± 3.5°C) was significantly elevated over the prodromal stage (F1,12 = 151.85, P\u3c 0.0001). This experiment provides data indicating that infrared thermography can be used in an experimental setting to detect raccoons in the infectious stage and capable of exhibiting clinical signs of rabies

Evaluation of soil properties of the Sudan Savannah ecological zone of Ghana for crop production

Low soil fertility and limited ability of farmers to purchase fertilizers in the Sudan savannah zone of Ghana has resulted in the decline in the yield of cereals over the years. There is, therefore, the need to identify soil parameters that are critical to crop production, to manage them effectively and improve fertilizer use efficiency to increase crop yield. To achieve this, an area of about 1.5 km2 was divided into grid cells (100m2) and characterised for their soil properties (organic carbon, pH, and soil texture). Data collected was used in a pedo-transfer function to estimate additional soil parameters that were not measured (i.e. wilting point, field capacity, available water and saturation). These were used as input to the crop simulation model (APSIM- Agriculture Productions Systems sIMulator) to simulate sorghum grain yield for each grid cell. Linear regression and factor analysis were also employed in explaining the data. Grain yield ranged from 402 to 1092 kg ha-1 with a mean of 673 kg ha-1 using 2005 weather data and 228 to 907 kg ha-1 with a mean of 427 kg ha-1 using 2000 weather data without fertilizer application. The model was sensitive to all input parameters. Soil texture and organic carbon were identified to have significant effect on crop yield. Soil organic carbon is, therefore, to be managed for the development of a good tilth and hence sustainable yields of sorghum at the study site

Using CERES-maize and ENSO as decision support tools to evaluate climate-sensitive farm management practices for maize production in the northern regions of Ghana

Open Access JournalMaize (Zea mays) has traditionally been a major cereal staple in southern Ghana. Through breeding and other crop improvement efforts, the zone of cultivation of maize has now extended to the northern regions of Ghana which, hitherto, were the home to sorghum and millet as the major cereals. Maize yield in the northern Ghana is hampered by three major biophysical constraints, namely, poor soil fertility, low soil water storage capacity and climate variability. In this study we used the DSSAT crop model to assess integrated water and soil management strategies that combined the pre-season El-Niño-Southern Oscillation (ENSO)-based weather forecasting in selecting optimal planting time, at four locations in the northern regions of Ghana. It could be shown that the optimum planting date for a given year was predictable based on February-to-April (FMA) Sea Surface Temperature (SST) anomaly for the locations with R2 ranging from 0.52 to 0.71. For three out of four locations, the ENSO-predicted optimum planting dates resulted in significantly higher maize yields than the conventional farmer selected planting dates. In Wa for instance, early optimum planting dates were associated with La Nina and El Niño (Julian Days 130-150; early May to late May) whereas late planting (mid June to early July) was associated with the Neutral ENSO phase. It was also observed that the addition of manure and fertilizer improved soil water and nitrogen use efficiency, respectively, and minimized yield variability, especially when combined with weather forecast. The use of ENSO-based targeted planting date choice together with modest fertilizer and manure application has the potential to improve maize yields and also ensure sustainable maize production in parts of northern Ghana

Mode of Biochar Application to Vertisols Influences Water Balance Components and Water Use Efficiency of Maize (Zea mays L.)

Vertisols belong to a group of soils with high fertility but poor physical properties of swelling when wet and shrinking and cracking when dry. The swelling inhibits infiltration, resulting in flooding, limiting the production of upland crops. Biochar (<BC) application has been shown to reduce the shrink-swell behaviour of Vertisols. However, the mode of biochar application to these soils may affect the effectiveness of the amendment. This study investigated the water relations and maize (Zea mays L.) growth under two BC application modes: (i) biochar applied into cracks that develop with drying, C, and (ii) biochar that was surface broadcast and incorporated into the topsoil, FM. A control treatment did not receive any BC amendment. Maize was grown on the BC-amended Vertisols using the two modes of application in a greenhouse under two seasonal water regimes of 610 and 450 mm. The results showed that the proportion of total water application lost to runoff was 37%, 49% and 53% for C, FM and control treatments, respectively. Both maize yield and Water Use Efficiency (WUE), for the C treatments were significantly (p < 0.05) higher than those for FM treatments. The maize yield under the C treatments was 19% over the control. Similarly, the WUE for the C treatments was 28% above the control treatment. It is concluded that the application of biochar into cracks is a more effective way of improving the water relations and upland crop productivity and WUE in Vertisols than the traditional surface incorporation

Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks

Digital Supply Chain Surveillance (DSCS) is the proactive monitoring and analysis of digital data that allows firms to extract information related to a supply network, without the explicit consent of firms involved in the supply chain. AI has made DSCS to become easier and larger-scale, posing significant opportunities for automated detection of actors and dependencies involved in a supply chain, which in turn, can help firms to detect risky, unethical and environmentally unsustainable practices. Here, we define DSCS, review priority areas using a survey conducted in the UK. Visibility, sustainability, resilience are significant areas that DSCS can support, through a number of machine-learning approaches and predictive algorithms. Despite anecdotal narrative on the importance of explainability of algorithmic results, practitioners often prefer accuracy over explainability; however, there are significant differences between industrial sectors and application areas. Using a case study, we highlight a number of concerns on the unchecked use of AI in DSCS, such as bias or misinterpretation resulting in erroneous conclusions, which may lead to suboptimal decisions or relationship damage. Building on this, we develop and discuss a number of illustrative cases to highlight risks that practitioners should be aware of, proposing key areas of further research

Potential effect of prior raccoonpox virus infection in raccoons on vaccinia-based rabies immunization

<p>Abstract</p> <p>Background</p> <p>The USDA, Wildlife Services cooperative oral rabies vaccination (ORV) program uses a live vaccinia virus-vectored (genus <it>Orthopoxvirus</it>) vaccine, Raboral V-RG^®(V-RG), to vaccinate specific wildlife species against rabies virus in several regions of the U.S. Several naturally occurring orthopoxviruses have been found in North America, including one isolated from asymptomatic raccoons (<it>Procyon lotor</it>). The effect of naturally occurring antibodies to orthopoxviruses on successful V-RG vaccination in raccoons is the focus of this study.</p> <p>Results</p> <p>Overall, raccoons pre-immunized (n = 10) with a recombinant raccoonpox virus vaccine (RCN-F1) responded to vaccination with V-RG with lower rabies virus neutralizing antibody (VNA) titers than those which were not pre-immunized (n = 10) and some failed to seroconvert for rabies VNA to detectable levels.</p> <p>Conclusion</p> <p>These results suggest that the success of some ORV campaigns may be hindered where raccoonpox virus or possibly other orthopoxvirus antibodies are common in wildlife species targeted for ORV. If these areas are identified, different vaccination strategies may be warranted.</p

Differential regulation drives plasticity in sex determination gene networks

Background: Sex determination networks evolve rapidly and have been studied intensely across many species, particularly in insects, thus presenting good models to study the evolutionary plasticity of gene networks.Results: We study the evolution of an unlinked gene capable of regulating an existing diploid sex determination system. Differential gene expression determines phenotypic sex and fitness, dramatically reducing the number of assumptions of previous models. It allows us to make a quantitative evaluation of the full range of evolutionary outcomes of the system and an assessment of the likely contribution of sexual conflict to change in sex determination systems. Our results show under what conditions network mutations causing differential regulation can lead to the reshaping of sex determination networks.Conclusion: The analysis demonstrates the complex relationship between mutation and outcome: the same mutation can produce many different evolved populations, while the same evolved population can be produced by many different mutations. Existing network structure alters the constraints and frequency of evolutionary changes, which include the recruitment of new regulators, changes in heterogamety, protected polymorphisms, and transitions to a new locus that controls sex determination

Blockchain-enabled supply chain traceability in the textile and apparel supply chain: A case study of the fiber producer, lenzing

Supply chain traceability is a dominant concern for many industries, regulators, and pol-icymakers. In the textile and apparel industries, social, environmental, and sustainability issues are frequent causes for concern, highlighting the need for effective traceability solutions. Blockchain technology has characteristics that make it attractive for supply chain traceability applications. However, the literature lacks discussion of empirical cases. We discuss current blockchain applications in the textile and apparel industries and analyze an exemplar of a prominent blockchain trace-ability solution adopted by a world-leading fiber producer, the Lenzing group. In this study, we identify two related objectives for traceability-product authentication and supply chain sustainabil-ity. The case study analysis has stimulated a rich discussion about the different levels of traceability achievable across the supply chain, the digital identification of products, and the extension of block-chain solutions across the whole supply network. Significant technical and business challenges exist in extending traceability to the upstream raw material supply chain and to the dispersed garment manufacturing networks downstream. More broadly, the study highlights the need to (1) clarify the objectives of a traceability initiative, and (2) scope a traceability solution appropriately, both hori-zontally across the supply chain and vertically with respect to the granularity of the items traced