Transient epileptic amnesia: an emerging late-onset epileptic syndrome.

Transient epileptic amnesia (TEA) is a distinct neurologic condition occurring in late-middle/old age and presenting with amnesic attacks of epileptic nature and interictal memory disturbances. For many years this condition has been associated with the nonepileptic condition of transient global amnesia (TGA) and still today is poorly recognized by clinicians. Despite the clinical and laboratory findings that distinguish TEA from TGA, differential diagnosis may be difficult in the individual patient. Every effort must be employed for an early diagnosis, since antiepileptic treatment may readily control both ictal episodes and memory disturbances

The North Wyke Farm Platform: GreenFeed System Methane and Carbon Dioxide Data

The North Wyke Farm Platform (NWFP) was established in 2010 to study and improve grassland livestock production at the farm-scale. The NWFP uses a combination of environmental sensors, routine field and lab-based measurements, and detailed management records to monitor livestock and crop production, emissions to water, emissions to air, soil health, and biodiversity. The rich NWFP datasets help researchers to evaluate the effectiveness of different grassland (and arable) farming systems, which in turn, contributes to the development of sustainable, resilient and net zero land management strategies. This document serves as a user guide to the methane (CH4) and carbon dioxide (CO2) emission data, sampled from housed cattle and sheep using GreenFeed systems. This document is associated with other dedicated user guides that detail the collection, and quality control processing of all the datasets produced on the NWFP

A Fast and Efficient Incremental Approach toward Dynamic Community Detection

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called $\Delta-screening$, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3x speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network

Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks

As most of the community discovery methods are researched by static thought, some community discovery algorithms cannot represent the whole dynamic network change process efficiently. This paper proposes a novel dynamic community discovery method (Phylogenetic Planted Partition Model, PPPM) for phylogenetic evolution. Firstly, the time dimension is introduced into the typical migration partition model, and all states are treated as variables, and the observation equation is constructed. Secondly, this paper takes the observation equation of the whole dynamic social network as the constraint between variables and the error function. Then, the quadratic form of the error function is minimized. Thirdly, the Levenberg–Marquardt (L–M) method is used to calculate the gradient of the error function, and the iteration is carried out. Finally, simulation experiments are carried out under the experimental environment of artificial networks and real net-works. The experimental results show that: compared with FaceNet, SBM + MLE, CLBM, and Pi-sCES, the proposed PPPM model improves accuracy by 5% and 3%, respectively. It is proven that the proposed PPPM method is robust, reasonable, and effective. This method can also be applied to the general social networking community discovery field

Agent-based interoperability for e-government

The provision of valuable e-government services depends upon the capacity to integrate the disperse provision of services by the public administration and thus upon the availability of interoperability platforms. These platforms are commonly built according to the principles of service oriented architectures, which raise the question of how to dynamically orchestrate services while preserving information security. Recently, it was presented an e-government interoperability model that preserves privacy during the dynamic orchestration of services. In this paper we present a prototype that implements that model using software agents. The model and the prototype are briefly described; an illustrative use case is presented; and the advantages of using software agents to implement the model are discussed. © Springer International Publishing Switzerland 2013