Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors

The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

Switching between molecular switch types by module rearrangement:Ca -enabled, H-driven 'off-on-off', H-driven YES and PASS 0 as well as H, Ca-driven AND logic operations

Several logic gates and switches can be accessed from two different combinations of a single set of fluorophore, receptor and spacer components.

A Preliminary Evaluation of a New Life Science Module for Year One Nursing and Midwifery Students

This report outlines the rationale for the design and implementation of a new life sciences module for year one nursing and midwifery students. It describes our experience to date in running the new module and presents some preliminary results which describe an improved student performance compared to our previous year one bioscience module.

A-level Mathematics Module Choice and Subsequent Performance in First Year of an Engineering Degree

The A-level Mathematics qualification is based on a compulsory set of pure maths modules and a selection of applied maths modules. The flexibility in choice of applied modules has led to concerns that many students would proceed to study engineering at university with little background in mechanics. A survey of aerospace and mechanical engineering students in our university revealed that a combination of mechanics and statistics (the basic module in both) was by far the most popular choice of optional modules in A-level Mathematics, meaning that only about one-quarter of the class had studied mechanics beyond the basic module within school mathematics. Investigation of student performance in two core, first-year engineering courses, which build on a mechanics foundation, indicated that any benefits for students who studied the extra mechanics at school were small. These results give concern about the depth of understanding in mechanics gained during A-level Mathematics.

Helping the most vulnerable out of the poverty trap and reducing inequality: Policies, strategies, and services for individuals with Autism Spectrum Disorder, including intellectual and neurodevelopmental disabilities: BASE Project Report (Volume 2) NILT Survey Autism Module

The primary purpose of the BASE Project was to establish how to help individuals with Autism Spectrum Disorder out of poverty by promoting social inclusion. In order to achieve this, a range of methodologies were utilised that aimed to provide a baseline against which the effect of the Autism Act (NI) 2011 and the associated Autism Strategy (2013-2020) and Action Plans can be measured. The BASE Project is reported in 5 volumes. Volume 2 reports on the analysis of the autism module of the Northern Ireland Life and Times (NILT) Survey that assessed public awareness, attitudes, knowledge, and projected behaviours with regard to individuals with ASD (all primary data and technical reports are available at www.ark.ac.uk/nilt/).

The NILT (2012) survey first ever autism module (n=1204) offered a baseline against which the impact of new autism legislation, policies, and strategies can be measured. Key findings:

• 82% awareness: Most people in Northern Ireland are aware of autism (n=989).
• 50% of all participants knew someone with autism personally (n=606).

Of those who were aware of autism:
• 19% had a close family member with autism (n=186), and/or a friends/acquaintance (n=296), and/or a work colleague (n=79) with autism.
• Autism awareness was particularly low for those from ethnic minorities and those with no internet access.
• Awareness of autism specific legislation was low (20%).
• Good levels of knowledge about autism strengths and challenges, slight tendency to overestimate the occurrence of special talents.
• Prevalence of autism was underestimated (62% thought autism was much less prevalent than official figures or did not know).
• Fairly accurate perception about causes of autism, i.e., not caused by poor parenting (84%).
• Strong support for evidence-based behavioural interventions (77%), but confusion about interventions that are not evidence-based (64%).
• Strong positive attitudes towards children and adults in social, educational and employment settings.
• Autism not viewed as necessarily ‘lifelong’ (58%); support for independent living (78%), e.g., driving a car (83%).
• More business for employers who employ people with autism (12%).
• Strong support for families caring rather than residential care (64%).
• Confusion about service responsibility: education (26%) health (33%) or both (28%).

Given increasing prevalence rates of ASD it is important that the general population is aware of autism and able to respond responsibly to the associated strengths and challenges. The results of the NILT (2012) first ever autism module show that the general public was well aware of autism, had positive attitudes, and was relatively knowledgeable about the issues faced by individuals and families affected directly. However, there was a lack of clarity about responsibility for effective service delivery. The NILT results show that a shift in focus is necessary from ‘awareness raising campaigns’ to an approach that delivers clarity with regard to intervention and accountability.

Predicting invasive species impacts: a community module functional response approach reveals context dependencies

Summary
-Predatory functional responses play integral roles in predator–prey dynamics, and their assessment promises greater understanding and prediction of the predatory impacts of invasive species.
-Other interspecific interactions, however, such as parasitism and higher-order predation, have the potential to modify predator–prey interactions and thus the predictive capability of the comparative functional response approach.
-We used a four-species community module (higher-order predator; focal native or invasive predators; parasites of focal predators; native prey) to compare the predatory functional responses of native Gammarus duebeni celticus and invasive Gammarus pulex amphipods towards three invertebrate prey species (Asellus aquaticus, Simulium spp., Baetis rhodani), thus, quantifying the context dependencies of parasitism and a higher-order fish predator on these functional responses.
-Our functional response experiments demonstrated that the invasive amphipod had a higher predatory impact (lower handling time) on two of three prey species, which reflects patterns of impact observed in the field. The community module also revealed that parasitism had context-dependent influences, for one prey species, with the potential to further reduce the predatory impact of the invasive amphipod or increase the predatory impact of the native amphipod in the presence of a higher-order fish predator.
-Partial consumption of prey was similar for both predators and occurred increasingly in the order A. aquaticus, Simulium spp. and B. rhodani. This was associated with increasing prey densities, but showed no context dependencies with parasitism or higher-order fish predator.
-This study supports the applicability of comparative functional responses as a tool to predict and assess invasive species impacts incorporating multiple context dependencies.

Identifying PV module mismatch faults by a thermography-based temperature distribution analysis

Photovoltaic (PV) solar power generation is proven to be effective and sustainable but is currently hampered by relatively high costs and low conversion efficiency. This paper addresses both issues by presenting a low-cost and efficient temperature distribution analysis for identifying PV module mismatch faults by thermography. Mismatch faults reduce the power output and cause potential damage to PV cells. This paper first defines three fault categories in terms of fault levels, which lead to different terminal characteristics of the PV modules. The investigation of three faults is also conducted analytically and experimentally, and maintenance suggestions are also provided for different fault types. The proposed methodology is developed to combine the electrical and thermal characteristics of PV cells subjected to different fault mechanisms through simulation and experimental tests. Furthermore, the fault diagnosis method can be incorporated into the maximum power point tracking schemes to shift the operating point of the PV string. The developed technology has improved over the existing ones in locating the faulty cell by a thermal camera, providing a remedial measure, and maximizing the power output under faulty conditions.

Evidence reasoning for event inference in smart transport video surveillance

In this paper we present a new event recognition framework, based on the Dempster-Shafer theory of evidence, which combines the evidence from multiple atomic events detected by low-level computer vision analytics. The proposed framework employs evidential network modelling of composite events. This approach can effectively handle the uncertainty of the detected events, whilst inferring high-level events that have semantic meaning with high degrees of belief. Our scheme has been comprehensively evaluated against various scenarios that simulate passenger behaviour on public transport platforms such as buses and trains. The average accuracy rate of our method is 81% in comparison to 76% by a standard rule-based method.

Global Sensitivity Analysis for MAP Inference in Graphical Models

We study the sensitivity of a MAP configuration of a discrete probabilistic graphical model with respect to perturbations of its parameters. These perturbations are global, in the sense that simultaneous perturbations of all the parameters (or any chosen subset of them) are allowed. Our main contribution is an exact algorithm that can check whether the MAP configuration is robust with respect to given perturbations. Its complexity is essentially the same as that of obtaining the MAP configuration itself, so it can be promptly used with minimal effort. We use our algorithm to identify the largest global perturbation that does not induce a change in the MAP configuration, and we successfully apply this robustness measure in two practical scenarios: the prediction of facial action units with posed images and the classification of multiple real public data sets. A strong correlation between the proposed robustness measure and accuracy is verified in both scenarios.