Sensitivity of asymmetric rate-dependent critical systems to initial conditions: insights into cellular decision making

The work reported here aims to address the effects of time-dependent parameters and stochasticity on decision-making in biological systems. We achieve this by extending previous studies that resorted to simple normal forms. Yet, we focus primarily on the issue of the system's sensitivity to initial conditions in the presence of different noise distributions. In addition, we assess the impact of two-way sweeping through the critical region of a canonical Pitchfork bifurcation with a constant external asymmetry. The parallel with decision-making in bio-circuits is performed on this simple system since it is equivalent in its available states and dynamics to more complex genetic circuits. Overall, we verify that rate-dependent effects are specific to particular initial conditions. Information processing for each starting state is affected by the balance between sweeping speed through critical regions, and the type of fluctuations added. For a heavy-tail noise, forward-reverse dynamic bifurcations are more efficient in processing the information contained in external signals, when compared to the system relying on escape dynamics, if it starts at an attractor not favoured by the asymmetry and, in conjunction, if the sweeping amplitude is large

Beacons of Liberty: International Free Soil and the Fight for Racial Justice in Antebellum America

One of the most original contributions to the history of the American antislavery movement, and antislavery thought more broadly,” Beacons of Liberty shows that different free-soil havens, such as Sierra Leone, Upper Canada, Haiti, and Mexico each offered distinct possibilities for freedom in Black Americans’ collective consciousness while providing “practical models of Black freedom” as well as destinations for emigrants

Letter to Philander Chase

A note from booksellers confirming that books were sent to Chase.https://digital.kenyon.edu/chase_letters/1627/thumbnail.jp

Establishing a Causal Link between Ankylosing Spondylitis and Inflammatory Bowel Disease: A Review of the Literature

The link between ankylosing spondylitis and inflammatory bowel disease is unclear, however it is hypothesized that there is a causal link between the inheritance of a human leukocyte antigen B27 allele and the development of inflammatory bowel disease symptoms in ankylosing spondylitis patients. Research articles assessing the relationship between ankylosing spondylitis, inflammatory bowel disease and the human leukocyte antigen B27 antigen were collected from the PubMed database. Patients expressing the human leukocyte antigen B27 allele have a demonstrated predisposition to developing symptoms of inflammatory bowel disease and sacroiliitis in ankylosing spondylitis. However, human leukocyte antigen B27 is considered to be just a contributing factor in the disease, as interleukin-23, natural killer cells, and alterations to the microbiome have also demonstrated an active role in the development of symptoms. More longitudinal studies using larger cohorts are needed to further substantiate a direct causal relationship between ankylosing spondylitis and inflammatory bowel disease

Climate change uncertainty evaluation, impacts modelling and resilience of farm scale dynamics in Scotland

This Thesis explored a range of approaches to study the uncertainty and impacts associated with climate change at the farm scale in Scotland. The research objective was to use a process of uncertainty evaluation and simulation modelling to provide evidence of how primary production components of agriculture in Scotland may change under a future climate. The work used a generic Integrated Modelling Framework to structure the following sequence of investigations: Evaluate a Regional Climate Model‟s hindcast estimates (1960-1990) against observed weather data; Develop bias correction „downscaling factors‟ to be applied to the Regional Climate Model‟s future estimates; Evaluate the impacts of weather data sources (observed and modelled) on estimates made by a cropping systems model (CropSyst); Estimate values for a range of agro-meteorological metrics using observed and estimated downscaled future weather data; Simulate spring barley and winter wheat growth using CropSyst with observed and modelled weather data; Develop CropSyst in order to represent grass growth, evaluate estimates against a set of a priori criteria and determine suitability for use in a whole farm model. Conduct counter-factual assessments of the impacts of climate change and potential adaptation options using a whole farm model (LADSS). The study aimed to use tools on a spectrum of land use modelling complexity: agro-meteorological metrics (simple), CropSyst (intermediate), and the whole-farm integrated model (complex). Such an approach had a path dependency, in that to use the livestock system model component within the whole farm model, CropSyst had to make estimates of an acceptable quality for grass production. CropSyst however failed to meet the a priori evaluation criteria. This, coupled with technical and time constraints in running LADSS, led to the decision not to run the whole farm model. The findings were organised within the concepts of resilience and adaptive capacity. Results gained showed that the HadRM3 Regional Climate Model was capable of making both good and poor estimates of weather variables in the UK, and that downscaling improved the match between hindcast and observed weather data significantly. A sensitivity analysis involving introducing uncertainty from weather data sources within CropSyst showed that care was needed in interpreting estimates of future crop production. The agro-meteorological metrics indicated that whilst growing season length increases, the date of end of field capacity does not. The projected changes in crop production will likely be more positive if crop responses to elevated CO2 are considered. However, there will be additional constraints on crop growth due to increases in duration and magnitude of periods of growth limiting soil water deficits. Without adaptation to crop varieties with slower phenological development, yield decreases are seen in spring barley and winter wheat. The thesis concludes, whilst recognising the caveats and limitations of the methods used and the multiple range of external influencing issues, that the biophysical impacts at the farm scale in Scotland are within the boundaries of resilience, given that achievable adaptation options exist and are undertaken. The dynamics of farm scale management will need to adjust to cope with higher levels of water stress, but opportunities will also arise for greater flexibility in land use mixes. Crop yield can increase due to more favourable growing conditions and cultivar adaptations. These conclusions, when placed within the context of climate change impacts and adaptive cycles at a global scale, indicate that agriculture in Scotland has the potential to cope with the impacts but that substantial changes are required in farming practices

Agrimod: The Agricultural Modelling Knowledge Hub Website

Agrimod is a new web-based Agricultural Modelling Knowledge Hub covering crop, livestock and trade models and the data they require, plus a wide range of supporting tools and resources. The purpose is to address the growing need, particularly in developing countries, of building national capabilities for researching agriculture and food security using models. To support research in this area, Agrimod provides a facility enabling users to access information and data needed to more successfully develop and employ agricultural modelling. Registered users can add new information about models, data, case studies, training, funding sources etc., whilst also being able to edit existing content  and contribute to discussion threads on key modelling issues. It will serve as a model, data and case study inventory. The vision is to unite the existing agricultural modelling community by providing a platform whereby models can be showcased, their applications discussed and new collaborations built, streamlining the process by which new model activities are developed. Moreover, Agrimod is intended to be a user–friendly information portal to people in other areas of research or new to agricultural modelling, looking to develop skills and acquire first-hand knowledge on agricultural modelling research. Thus Agrimod serves as a central knowledge hub for information on agricultural modelling activities worldwide and can be used by MACSUR as a complimentary information dissemination tool

Assessment of the ClimGen stochastic weather generator at Cameroon sites

Simulation of agricultural risk assessment and environmental management requires long series of daily weather data for the area being modelled. Acquiring and formatting this data can be very complex and time-consuming. This has led to the development of weather generation procedures and tools. Weather generators can produce time series of synthetic weather data of any length, interpolating observed data to produce synthetic weather data at new sites. Any generator must be tested to ensure that the data that it produces is satisfactory for the purposes for which it is to be used. The aim of this paper is to test a commonly used weather generator, ClimGen (version 4.1.05) at eight sites with contrasting climates in Cameroon. Statistical test were conducted, including t-test and F-test, to compare the differences between generated weather data versus 25 years observed weather data. The results showed that the generated weather series was similar to the observed data for its distribution of monthly precipitation and its variances, monthly means and variance of minimum and maximum air temperatures. Based on the results from this study, it can be concluded that ClimGen performs well in the simulation of weather statistics in Cameroon.Keywords: Weather generators, weather data, Cameroon, climate chang

Information to support input data quality and model improvement

Data quality is a key factor in determining the quality of model estimates and hence a models’ overall utility. Good models run with poor quality explanatory variables and parameters will produce meaningless estimates. Many models are now well developed and have been shown to perform well where and when good quality data is available. Hence a major limitation now to further use of models in new locations and applications is likely to be the availability of good quality data. Improvements in the quality of data may be seen as the starting point of further model improvement, in that better data itself will lead to more accurate model estimates (i.e. through better calibration), and it will facilitate reduction of model residual error by enabling refinements to model equations. This report sets out why data quality is important as well as the basis for additional investment in improving data quality