Pan-serotypic detection of foot-and-mouth disease virus using a minor groove binder probe reverse transcription polymerase chain reaction assay

A novel assay for the pan-serotypic detection of foot-and-mouth disease virus (FMDV) was designed using a 5' conjugated minor groove binder (MGB) probe real-time RT-PCR system. This assay targets the 3D region of the FMDV genome and is capable of detecting 20 copies of a transcribed RNA standard. The linear range of the test was eight logs from 2 x 10(1) to 2 x 10(8) copies and amplification time was approximately 2 h. Using a panel of 83 RNA samples from representative FMDV isolates, the diagnostic sensitivity of this test was shown to be equivalent to a TaqMan real-time RT-PCR that targets the 5' untranslated region of FMDV. Furthermore, the assay does not detect viruses causing similar clinical diseases in pigs such as swine vesicular disease virus and vesicular stomatitis virus, nor does it detect marine caliciviruses causing vesicular exanthema. The development of this assay provides a useful tool for the differential diagnosis of FMD, potentially for use in statutory or emergency testing programmes, or for detection of FMDV RNA in research applications. (C) 2011 Elsevier B.V. All rights reserved.

Perception of patterns of musical beat distribution in phonological developmental dyslexia:significant longitudinal relations with word reading and reading comprehension

In a recent study, we reported that the accurate perception of beat structure in music ('perception of musical meter') accounted for over 40% of the variance in single word reading in children with and without dyslexia (Huss et al., 2011). Performance in the musical task was most strongly associated with the auditory processing of rise time, even though beat structure was varied by manipulating the duration of the musical notes.

UTILISATION OF BIT LEVEL SYSTOLIC ARRAYS IN WORD LEVEL SYSTEMS.

Bit level systolic array structures for computing sums of products are studied in detail. It is shown that these can be sub-divided into two classes and that, within each class, architectures can be described in terms of a set of constraint equations. It is further demonstrated that high performance system level functions with attractive VLSI properties can be constructed by matching data flow geometries in bit level and word level architectures.

The last word:Family members' descriptions of end-of-life care in long-term care facilities

A postal survey was used to collect data from family members of deceased residents of six long-term care (LTC) facilities in order to explore end-of-life (EOL) care using the Family Perception of Care Scale. This article reports on the results of thematic analysis of family member comments provided while completing the survey. Family comments fell into two themes: 1) appreciation for care and 2) concerns with care. The appreciation for care theme included the following subthemes: psychosocial support, family care, and spiritual care. The concerns with care theme included the subthemes: physical care, staffing levels, staff knowledge, physician availability, communication, and physical environment. This study identified the need for improvement in EOL care skills among LTC staff and attending physicians. As such, there is a need to implement continuing education to address these issues. © 2006 Centre for Bioethics, IRCM.

Interpretable and Reconfigurable Clustering of Document Datasets by Deriving Word-based Rules

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop two clustering algorithms toward the outlined goal of building interpretable and reconfigurable cluster models. They generate clusters with associated rules that are composed of conditions on word occurrences or nonoccurrences. The proposed approaches vary in the complexity of the format of the rules; RGC employs disjunctions and conjunctions in rule generation whereas RGC-D rules are simple disjunctions of conditions signifying presence of various words. In both the cases, each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. Rules of the latter kind are easy to interpret, whereas the former leads to more accurate clustering. We show that our approaches outperform the unsupervised decision tree approach for rule-generating clustering and also an approach we provide for generating interpretable models for general clusterings, both by significant margins. We empirically show that the purity and f-measure losses to achieve interpretability can be as little as 3 and 5%, respectively using the algorithms presented herein.