Rapid method for determination of antimicrobial susceptibilities pattern of urinary bacteria

Method determines bacterial sensitivity to antimicrobial agents by measuring level of adenosine triphosphate remaining in the bacteria. Light emitted during reaction of sample with a mixture of luciferase and luciferin is measured

Towards population-based structural health monitoring, part IV : heterogeneous populations, transfer and mapping

Population-based structural health monitoring (PBSHM) involves utilising knowledge from one set of structures in a population and applying it to a different set, such that predictions about the health states of each member in the population can be performed and improved. Central ideas behind PBSHM are those of knowledge transfer and mapping. In the context of PBSHM, knowledge transfer involves using information from a structure, defined as a source domain, where labels are known for a given feature, and mapping these onto the unlabelled feature space of a different, target domain structure. If the mapping is successful, a machine learning classifier trained on the transformed source domain data will generalise to the unlabelled target domain data; i.e. a classifier built on one structure will generalise to another, making Structural Heath Monitoring (SHM) cost-effective and applicable to a wide range of challenging industrial scenarios. This process of mapping features and labels across source and target domains is defined as domain adaptation, a subcategory of transfer learning. However, a key assumption in conventional domain adaptation methods is that there is consistency between the feature and label spaces. This means that the features measured from one structure must be the same dimension as the other (i.e. the same number of spectral lines of a transmissibility), and that labels associated with damage locations, classification and assessment, exist on both structures. These consistency constraints can be restrictive, limiting to which types of population domain adaptation can be applied. This paper, therefore, provides a mathematical underpinning for when domain adaptation is possible in a structural dynamics context, with reference to topology of a graphical representation of structures. By defining when conventional domain adaptation is applicable in a structural dynamics setting, approaches are discussed that could overcome these consistency restrictions. This approach provides a general means for performing transfer learning within a PBSHM context for structural dynamics-based features

Can Oxygen Set Thermal Limits in an Insect and Drive Gigantism?

Contains fulltext : 111575.pdf (publisher's version ) (Open Access

Common origin of the gelsolin gene variant in 62 Finnish AGel amyloidosis families

Finnish gelsolin amyloidosis (AGel amyloidosis) is an autosomal dominantly inherited systemic disorder with ophthalmologic, neurologic and dermatologic symptoms. Only the gelsolin (GSN) c.640G>A variant has been found in the Finnish patients thus far. The purpose of this study was to examine whether the Finnish patients have a common ancestor or whether multiple mutation events have occurred at c.640G, which is a known mutational hot spot. A total of 79 Finnish AGel amyloidosis families including 707 patients were first discovered by means of patient interviews, genealogic studies and civil and parish registers. From each family 1-2 index patients were chosen. Blood samples were available from 71 index patients representing 64 families. After quality control, SNP array genotype data were available from 68 patients from 62 nuclear families. All the index patients had the same c.640G>A variant (rs121909715). Genotyping was performed using the Illumina CoreExome SNP array. The homozygosity haplotype method was used to analyse shared haplotypes. Haplotype analysis identified a shared haplotype, common to all studied patients. This shared haplotype included 17 markers and was 361 kb in length (GRCh37 coordinates 9:124003326–124364349) and this level of haplotype sharing was found to occur highly unlikely by chance. This GSN haplotype ranked as the largest shared haplotype in the 68 patients in a genome-wide analysis of haplotype block lengths. These results provide strong evidence that although there is a known mutational hot spot at GSN c.640G, all of the studied 62 Finnish AGel amyloidosis families are genetically linked to a common ancestor.Peer reviewe

Texture classification of proteins using support vector machines and bio-inspired metaheuristics

6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process