Human transforming growth factor a (TGF-alpha) is digested to a smaller (1-43), less biologically active, form in acidic gastric juice

Publisher version: http://gut.bmj.com/content/51/6/787.full.pd

Social Sensing of Floods in the UK

"Social sensing" is a form of crowd-sourcing that involves systematic analysis of digital communications to detect real-world events. Here we consider the use of social sensing for observing natural hazards. In particular, we present a case study that uses data from a popular social media platform (Twitter) to detect and locate flood events in the UK. In order to improve data quality we apply a number of filters (timezone, simple text filters and a naive Bayes `relevance' filter) to the data. We then use place names in the user profile and message text to infer the location of the tweets. These two steps remove most of the irrelevant tweets and yield orders of magnitude more located tweets than we have by relying on geo-tagged data. We demonstrate that high resolution social sensing of floods is feasible and we can produce high-quality historical and real-time maps of floods using Twitter.Comment: 24 pages, 6 figure

School-based curriculum development as reflective practice: a case study in Hong Kong

This paper examines a school-based curriculum development (SBCD) experience in Hong Kong. Traditionally, curriculum change in Hong Kong has normally been bureaucratic with teachers’ actions monitored. This qualitative case study investigates the lived experience of an SBCD practice. Semi-structured interviews were utilized to examine teachers’ perceptions of the reflective SBCD experience in their school and what adaptations they had made when delivering the school-based materials. The findings identified that all teachers held a positive attitude towards this reflective approach to SBCD and emphasized artistry in their teaching practice. Teachers also exercised discretion in response to their students’ level and interests when implementing the school-based curriculum at the classroom level. This research concludes that a reflective approach to curriculum planning with a bottom-up implementation can empower teachers reflecting their creativity, artistry, knowledge of the subject and related pedagogy, and knowledge of their students. The findings of this case study thus contrast sharply with previous research relating to Hong Kong government-led SBCD programs which focus more on meeting the requirements of the intended curriculum than on personalizing the curriculum to meet to learners’ needs

FALCON: a software package for analysis of nestedness in bipartite networks

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Nestedness is a statistical measure used to interpret bipartite interaction data in several ecological and evolutionary contexts, e.g. biogeography (species-site relationships) and species interactions (plant-pollinator and host-parasite networks). Multiple methods have been used to evaluate nestedness, which differ in how the metrics for nestedness are determined. Furthermore, several different null models have been used to calculate statistical significance of nestedness scores. The profusion of measures and null models, many of which give conflicting results, is problematic for comparison of nestedness across different studies. We developed the FALCON software package to allow easy and efficient comparison of nestedness scores and statistical significances for a given input network, using a selection of the more popular measures and null models from the current literature. FALCON currently includes six measures and five null models for nestedness in binary networks, and two measures and four null models for nestedness in weighted networks. The FALCON software is designed to be efficient and easy to use. FALCON code is offered in three languages (R, MATLAB, Octave) and is designed to be modular and extensible, enabling users to easily expand its functionality by adding further measures and null models. FALCON provides a robust methodology for comparing the strength and significance of nestedness in a given bipartite network using multiple measures and null models. It includes an “adaptive ensemble” method to reduce undersampling of the null distribution when calculating statistical significance. It can work with binary or weighted input networks. FALCON is a response to the proliferation of different nestedness measures and associated null models in the literature. It allows easy and efficient calculation of nestedness scores and statistical significances using different methods, enabling comparison of results from different studies and thereby supporting theoretical study of the causes and implications of nestedness in different biological contexts

On Approximation of the Eigenvalues of Perturbed Periodic Schrodinger Operators

This paper addresses the problem of computing the eigenvalues lying in the gaps of the essential spectrum of a periodic Schrodinger operator perturbed by a fast decreasing potential. We use a recently developed technique, the so called quadratic projection method, in order to achieve convergence free from spectral pollution. We describe the theoretical foundations of the method in detail, and illustrate its effectiveness by several examples.Comment: 17 pages, 2 tables and 2 figure

Multi-modalities in classroom learning environments

This paper will present initial findings from the second phase of a Horizon 2020 funded project, Managing Affective-learning Through Intelligent Atoms and Smart Interactions (MaTHiSiS). The project focusses on the use of different multi-modalities used as part of the project in classrooms across Europe. The MaTHiSiS learning vision is to develop an integrated learning platform, with re-usable learning components which will respond to the needs of future education in primary, secondary, special education schools, vocational environments and learning beyond the classroom. The system comprises learning graphs which attach individual learning goals to the system. Each learning graph is developed from a set of smart learning atoms designed to support learners to achieve progression. Cutting edge technologies are being used to identify the affect state of learners and ultimately improve engagement of learners. Much research identifies how learners engage with learning platforms (c.f. [1], [2], [3]). Not only do e-learning platforms have the capability to engage learners, they provide a vehicle for authentic classroom and informal learning [4] enabling ubiquitous and seamless learning [5] within a non-linear environment. When experiencing more enjoyable interaction learners become more confident and motivated to learn and become less anxious, especially those with learning disabilities or at risk of social exclusion [6], [13]. [7] identified the importance of understanding the affect state of learners who may experience emotions such as 'confusion, frustration, irritation, anger, rage, or even despair' resulting in disengaging with learning. The MaTHiSiS system will use a range of platform agents such as NAO robots and Kinects to measure multi-modalities that support the affect state: facial expression analysis and gaze estimation [8], mobile device-based emotion recognition [9], skeleton motion using depth sensors and speech recognition. Data has been collected using multimodal learning analytics developed for the project, including annotated multimodal recordings of learners interacting with the system, facial expression data and position of the learner. In addition, interviews with teachers and learners, from mainstream education as well as learners with profound multiple learning difficulties and autism, have been carried out to measure engagement and achievement of learners. Findings from schools based in the United Kingdom, mainstream and special schools will be presented and challenges shared

The response of perennial and temporary headwater stream invertebrate communities to hydrological extremes

The headwaters of karst rivers experience considerable hydrological variability, including spates and streambed drying. Extreme summer flooding on the River Lathkill (Derbyshire, UK) provided the opportunity to examine the invertebrate community response to unseasonal spate flows, flow recession and, at temporary sites, streambed drying. Invertebrates were sampled at sites with differing flow permanence regimes during and after the spates. Following streambed drying at temporary sites, dewatered surface sediments were investigated as a refugium for aquatic invertebrates. Experimental rehydration of these dewatered sediments was conducted to promote development of desiccation-tolerant life stages. At perennial sites, spate flows reduced invertebrate abundance and diversity, whilst at temporary sites, flow reactivation facilitated rapid colonisation of the surface channel by a limited number of invertebrate taxa. Following streambed drying, 38 taxa were recorded from the dewatered and rehydrated sediments, with Oligochaeta being the most abundant taxon and Chironomidae (Diptera) the most diverse. Experimental rehydration of dewatered sediments revealed the presence of additional taxa, including Stenophylax sp. (Trichoptera: Limnephilidae) and Nemoura sp. (Plecoptera: Nemouridae). The influence of flow permanence on invertebrate community composition was apparent despite the aseasonal high-magnitude flood events

Utilization of multifrequency permittivity measurements in addition to biomass monitoring

Heinrich C, Beckmann T, Büntemeyer H, Noll T. Utilization of multifrequency permittivity measurements in addition to biomass monitoring. BMC Proceedings. 2011;5(Suppl 8)

Utilization of multifrequency permittivity measurements in addition to biomass monitoring

Heinrich C, Beckmann T, Büntemeyer H, Noll T. Utilization of multifrequency permittivity measurements in addition to biomass monitoring. BMC Proceedings. 2011;5(Suppl 8)

On Non Commutative G2 structure

Using an algebraic orbifold method, we present non-commutative aspects of $G_2$ structure of seven dimensional real manifolds. We first develop and solve the non commutativity parameter constraint equations defining $G_2$ manifold algebras. We show that there are eight possible solutions for this extended structure, one of which corresponds to the commutative case. Then we obtain a matrix representation solving such algebras using combinatorial arguments. An application to matrix model of M-theory is discussed.Comment: 16 pages, Latex. Typos corrected, minor changes. Version to appear in J. Phys.A: Math.Gen.(2005