Memories of childhood in post-war Grimsby

This paper details the vivid memories of the author’s childhood in the fishing port of Grimsby, shortly after the Second World War. It was a time of shortages, overcrowding, improvisation and cannibalisation of anything that could be re-used. In time it became a period of reconstruction but not without its upheavals and difficulties. It begins in the ‘old town’ of workers’ small terrace houses, typically in a poor state of repair. Then it moves to the ‘new’ council estates. Similarly, the narrative also begins with a ‘Victorian’ technology of steam, coal and horses with very few petrol-engined vehicles and moves to the very beginnings of early consumer society. The principal analytic content of the paper concerns the status of what is clearly a ‘personal history’ – if that is not too great a contradiction – or as the author suggests: my story. The obvious ‘critical’ response – that it could have been otherwise – is contrasted against the suggestion that this story is a non-negotiable foundation of the author’s identity and that this ‘critical’ response is not appropriate. Some of the interdisciplinary options thrown up by this problem are considered

Boolean Dynamics with Random Couplings

This paper reviews a class of generic dissipative dynamical systems called N-K models. In these models, the dynamics of N elements, defined as Boolean variables, develop step by step, clocked by a discrete time variable. Each of the N Boolean elements at a given time is given a value which depends upon K elements in the previous time step. We review the work of many authors on the behavior of the models, looking particularly at the structure and lengths of their cycles, the sizes of their basins of attraction, and the flow of information through the systems. In the limit of infinite N, there is a phase transition between a chaotic and an ordered phase, with a critical phase in between. We argue that the behavior of this system depends significantly on the topology of the network connections. If the elements are placed upon a lattice with dimension d, the system shows correlations related to the standard percolation or directed percolation phase transition on such a lattice. On the other hand, a very different behavior is seen in the Kauffman net in which all spins are equally likely to be coupled to a given spin. In this situation, coupling loops are mostly suppressed, and the behavior of the system is much more like that of a mean field theory. We also describe possible applications of the models to, for example, genetic networks, cell differentiation, evolution, democracy in social systems and neural networks.Comment: 69 pages, 16 figures, Submitted to Springer Applied Mathematical Sciences Serie

Revision and Update of the Consensus Definitions of Invasive Fungal Disease From the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium.

BACKGROUND: Invasive fungal diseases (IFDs) remain important causes of morbidity and mortality. The consensus definitions of the Infectious Diseases Group of the European Organization for Research and Treatment of Cancer and the Mycoses Study Group have been of immense value to researchers who conduct clinical trials of antifungals, assess diagnostic tests, and undertake epidemiologic studies. However, their utility has not extended beyond patients with cancer or recipients of stem cell or solid organ transplants. With newer diagnostic techniques available, it was clear that an update of these definitions was essential. METHODS: To achieve this, 10 working groups looked closely at imaging, laboratory diagnosis, and special populations at risk of IFD. A final version of the manuscript was agreed upon after the groups' findings were presented at a scientific symposium and after a 3-month period for public comment. There were several rounds of discussion before a final version of the manuscript was approved. RESULTS: There is no change in the classifications of "proven," "probable," and "possible" IFD, although the definition of "probable" has been expanded and the scope of the category "possible" has been diminished. The category of proven IFD can apply to any patient, regardless of whether the patient is immunocompromised. The probable and possible categories are proposed for immunocompromised patients only, except for endemic mycoses. CONCLUSIONS: These updated definitions of IFDs should prove applicable in clinical, diagnostic, and epidemiologic research of a broader range of patients at high-risk

Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization

<p>Abstract</p> <p/> <p>This paper demonstrates how a Neural Grammar Network learns to classify and score molecules for a variety of tasks in chemistry and toxicology. In addition to a more detailed analysis on datasets previously studied, we introduce three new datasets (BBB, FXa, and toxicology) to show the generality of the approach. A new experimental methodology is developed and applied to both the new datasets as well as previously studied datasets. This methodology is rigorous and statistically grounded, and ultimately culminates in a Wilcoxon significance test that proves the effectiveness of the system. We further include a complete generalization of the specific technique to arbitrary grammars and datasets using a mathematical abstraction that allows researchers in different domains to apply the method to their own work.</p> <p>Background</p> <p>Our work can be viewed as an alternative to existing methods to solve the quantitative structure-activity relationship (QSAR) problem. To this end, we review a number approaches both from a methodological and also a performance perspective. In addition to these approaches, we also examined a number of chemical properties that can be used by generic classifier systems, such as feed-forward artificial neural networks. In studying these approaches, we identified a set of interesting benchmark problem sets to which many of the above approaches had been applied. These included: ACE, AChE, AR, BBB, BZR, Cox2, DHFR, ER, FXa, GPB, Therm, and Thr. Finally, we developed our own benchmark set by collecting data on toxicology.</p> <p>Results</p> <p>Our results show that our system performs better than, or comparatively to, the existing methods over a broad range of problem types. Our method does not require the expert knowledge that is necessary to apply the other methods to novel problems.</p> <p>Conclusions</p> <p>We conclude that our success is due to the ability of our system to: 1) encode molecules losslessly before presentation to the learning system, and 2) leverage the design of molecular description languages to facilitate the identification of relevant structural attributes of the molecules over different problem domains.</p

Detecting controlling nodes of boolean regulatory networks

Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that each function can only depend on a few nodes. Biologically motivated constraints further show that functions found in Boolean regulatory networks belong to certain classes of functions, for example, the unate functions. It turns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem of detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks with unbalanced functions and functions of an average sensitivity less than 23k, where k is the number of controlling variables for a function. Further, we consider the class of 1-low networks which include unate networks, linear threshold networks, and networks with nested canalyzing functions. We show that the application of spectral learning algorithms leads to both better time and sample complexity for the detection of controlling nodes compared with algorithms based on exhaustive search. For a particular algorithm, we state analytical upper bounds on the number of samples needed to find the controlling nodes of the Boolean functions. Further, improved algorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied

Mutation Size Optimizes Speciation in an Evolutionary Model

The role of mutation rate in optimizing key features of evolutionary dynamics has recently been investigated in various computational models. Here, we address the related question of how maximum mutation size affects the formation of species in a simple computational evolutionary model. We find that the number of species is maximized for intermediate values of a mutation size parameter μ; the result is observed for evolving organisms on a randomly changing landscape as well as in a version of the model where negative feedback exists between the local population size and the fitness provided by the landscape. The same result is observed for various distributions of mutation values within the limits set by μ. When organisms with various values of μ compete against each other, those with intermediate μ values are found to survive. The surviving values of μ from these competition simulations, however, do not necessarily coincide with the values that maximize the number of species. These results suggest that various complex factors are involved in determining optimal mutation parameters for any population, and may also suggest approaches for building a computational bridge between the (micro) dynamics of mutations at the level of individual organisms and (macro) evolutionary dynamics at the species level

Excessive Biologic Response to IFNβ Is Associated with Poor Treatment Response in Patients with Multiple Sclerosis

Interferon-beta (IFNβ) is used to inhibit disease activity in multiple sclerosis (MS), but its mechanisms of action are incompletely understood, individual treatment response varies, and biological markers predicting response to treatment have yet to be identified.he relationship between the molecular response to IFNβ and treatment response was determined in 85 patients using a longitudinal design in which treatment effect was categorized by brain magnetic resonance imaging as good (n = 70) or poor response (n = 15). Molecular response was quantified using a customized cDNA macroarray assay for 166 IFN-regulated genes (IRGs).The molecular response to IFNβ differed significantly between patients in the pattern and number of regulated genes. The molecular response was strikingly stable for individuals for as long as 24 months, however, suggesting an individual ‘IFN response fingerprint’. Unexpectedly, patients with poor response showed an exaggerated molecular response. IRG induction ratios demonstrated an exaggerated molecular response at both the first and 6-month IFNβ injections.MS patients exhibit individually unique but temporally stable biological responses to IFNβ. Poor treatment response is not explained by the duration of biological effects or the specific genes induced. Rather, individuals with poor treatment response have a generally exaggerated biological response to type 1 IFN injections. We hypothesize that the molecular response to type I IFN identifies a pathogenetically distinct subset of MS patients whose disease is driven in part by innate immunity. The findings suggest a strategy for biologically based, rational use of IFNβ for individual MS patients