A universal fluid cell for the imaging of biological specimens in the atomic force microscope.

Recently, atomic force microscope (AFM) manufacturers have begun producing instruments specifically designed to image biological specimens. In most instances, they are integrated with an inverted optical microscope, which permits concurrent optical and AFM imaging. An important component of the set-up is the imaging chamber, whose design determines the nature of the experiments that can be conducted. Many different imaging chamber designs are available, usually designed to optimize a single parameter, such as the dimensions of the substrate or the volume of fluid that can be used throughout the experiment. In this report, we present a universal fluid cell, which simultaneously optimizes all of the parameters that are important for the imaging of biological specimens in the AFM. This novel imaging chamber has been successfully tested using mammalian, plant, and microbial cells.

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

Prospective universal application of mycobacterial interspersed repetitive-unit-variable-number tandem-repeat genotyping to characterize Mycobacterium tuberculosis isolates for fast identification of clustered and orphan cases.

The use of molecular tools for genotyping Mycobacterium tuberculosis isolates in epidemiological surveys in order to identify clustered and orphan strains requires faster response times than those offered by the reference method, IS6110 restriction fragment length polymorphism (RFLP) genotyping. A method based on PCR, the mycobacterial interspersed repetitive-unit-variable-number tandem-repeat (MIRU-VNTR) genotyping technique, is an option for fast fingerprinting of M. tuberculosis, although precise evaluations of correlation between MIRU-VNTR and RFLP findings in population-based studies in different contexts are required before the methods are switched. In this study, we evaluated MIRU-VNTR genotyping (with a set of 15 loci [MIRU-15]) in parallel to RFLP genotyping in a 39-month universal population-based study in a challenging setting with a high proportion of immigrants. For 81.9% (281/343) of the M. tuberculosis isolates, both RFLP and MIRU-VNTR types were obtained. The percentages of clustered cases were 39.9% (112/281) and 43.1% (121/281) for RFLP and MIRU-15 analyses, and the numbers of clusters identified were 42 and 45, respectively. For 85.4% of the cases, the RFLP and MIRU-15 results were concordant, identifying the same cases as clustered and orphan (kappa, 0.7). However, for the remaining 14.6% of the cases, discrepancies were observed: 16 of the cases clustered by RFLP analysis were identified as orphan by MIRU-15 analysis, and 25 cases identified as orphan by RFLP analysis were clustered by MIRU-15 analysis. When discrepant cases showing subtle genotypic differences were tolerated, the discrepancies fell from 14.6% to 8.6%. Epidemiological links were found for 83.8% of the cases clustered by both RFLP and MIRU-15 analyses, whereas for the cases clustered by RFLP or MIRU-VNTR analysis alone, links were identified for only 30.8% or 38.9% of the cases, respectively. The latter group of cases mainly comprised isolates that could also have been clustered, if subtle genotypic differences had been tolerated. MIRU-15 genotyping seems to be a good alternative to RFLP genotyping for real-time interventional schemes. The correlation between MIRU-15 and IS6110 RFLP findings was reasonable, although some uncertainties as to the assignation of clusters by MIRU-15 analysis were identified.

Breast cancer prognostic classification in the molecular era: the role of histological grade.

Breast cancer is a heterogeneous disease with varied morphological appearances, molecular features, behavior, and response to therapy. Current routine clinical management of breast cancer relies on the availability of robust clinical and pathological prognostic and predictive factors to support clinical and patient decision making in which potentially suitable treatment options are increasingly available. One of the best-established prognostic factors in breast cancer is histological grade, which represents the morphological assessment of tumor biological characteristics and has been shown to be able to generate important information related to the clinical behavior of breast cancers. Genome-wide microarray-based expression profiling studies have unraveled several characteristics of breast cancer biology and have provided further evidence that the biological features captured by histological grade are important in determining tumor behavior. Also, expression profiling studies have generated clinically useful data that have significantly improved our understanding of the biology of breast cancer, and these studies are undergoing evaluation as improved prognostic and predictive tools in clinical practice. Clinical acceptance of these molecular assays will require them to be more than expensive surrogates of established traditional factors such as histological grade. It is essential that they provide additional prognostic or predictive information above and beyond that offered by current parameters. Here, we present an analysis of the validity of histological grade as a prognostic factor and a consensus view on the significance of histological grade and its role in breast cancer classification and staging systems in this era of emerging clinical use of molecular classifiers.

Classification of immature mosquito species according to characteristics of the larval habitat in the subtropical province of Chaco, Argentina

To classify mosquito species based on common features of their habitats, samples were obtained fortnightly between June 2001-October 2003 in the subtropical province of Chaco, Argentina. Data on the type of larval habitat, nature of the habitat (artificial or natural), size, depth, location related to sunlight, distance to the neighbouring houses, type of substrate, organic material, vegetation and algae type and their presence were collected. Data on the permanence, temperature, pH, turbidity, colour, odour and movement of the larval habitat's water were also collected. From the cluster analysis, three groups of species associated by their degree of habitat similarity were obtained and are listed below. Group 1 consisted of Aedes aegypti. Group 2 consisted of Culex imitator, Culex davisi, Wyeomyia muehlensi and Toxorhynchites haemorrhoidalis separatus. Within group 3, two subgroups are distinguished: A (Psorophora ferox, Psorophora cyanescens, Psorophora varinervis, Psorophora confinnis, Psorophora cingulata, Ochlerotatus hastatus-oligopistus, Ochlerotatus serratus, Ochlerotatus scapularis, Culex intrincatus, Culex quinquefasciatus, Culex pilosus, Ochlerotatus albifasciatus, Culex bidens) and B (Culex maxi, Culex eduardoi, Culex chidesteri, Uranotaenia lowii, Uranotaenia pulcherrima, Anopheles neomaculipalpus, Anopheles triannulatus, Anopheles albitarsis, Uranotaenia apicalis, Mansonia humeralis and Aedeomyia squamipennis). Principal component analysis indicates that the size of the larval habitats and the presence of aquatic vegetation are the main characteristics that explain the variation among different species. In contrast, water permanence is second in importance. Water temperature, pH and the type of larval habitat are less important in explaining the clustering of species.

Universal features of personality traits from the observer's perspective: Data from 50 cultures

To test hypotheses about the universality of personality traits, college students in 50 cultures identified an adult or college-aged man or woman whom they knew well and rated the 11,985 targets using the 3rd-person version of the Revised NEO Personality Inventory. Factor analyses within cultures showed that the normative American self-report structure was clearly replicated in most cultures and was recognizable in all. Sex differences replicated earlier self-report results, with the most pronounced differences in Western cultures. Cross-sectional age differences for 3 factors followed the pattern identified in self-reports, with moderate rates of change during college age and slower changes after age 40. With a few exceptions, these data support the hypothesis that features of personality traits are common to all human groups.

20 de noviembre: Día universal de la infancia

Boletín semanal para profesionales sanitarios de la Secretaría General de Salud Pública, Inclusión y Calidad de Vida de la Consejería de Salud y Bienestar Social

Día universal de la infancia: una varita mágica para mejorar el mundo

Boletín semanal para profesionales sanitarios de la Secretaría General de Salud Pública y Participación Social de la Consejería de Salud

The Universal Protein Resource (UniProt) in 2010.

The primary mission of UniProt is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 3 weeks and can be accessed online for searches or download at http://www.uniprot.org.

Universal complex structures in written language

Quantitative linguistics has provided us with a number of empirical laws that characterise the evolution of languages and competition amongst them. In terms of language usage, one of the most influential results is Zipf’s law of word frequencies. Zipf’s law appears to be universal, and may not even be unique to human language. However, there is ongoing controversy over whether Zipf’s law is a good indicator of complexity. Here we present an alternative approach that puts Zipf’s law in the context of critical phenomena (the cornerstone of complexity in physics) and establishes the presence of a large-scale “attraction” between successive repetitions of words. Moreover, this phenomenon is scale-invariant and universal – the pattern is independent of word frequency and is observed in texts by different authors and written in different languages. There is evidence, however, that the shape of the scaling relation changes for words that play a key role in the text, implying the existence of different “universality classes” in the repetition of words. These behaviours exhibit striking parallels with complex catastrophic phenomena.