Auslander-Buchweitz approximation theory for triangulated categories

We introduce and develop an analogous of the Auslander-Buchweitz approximation theory (see \cite{AB}) in the context of triangulated categories, by using a version of relative homology in this setting. We also prove several results concerning relative homological algebra in a triangulated category \T, which are based on the behavior of certain subcategories under finiteness of resolutions and vanishing of Hom-spaces. For example: we establish the existence of preenvelopes (and precovers) in certain triangulated subcategories of \T. The results resemble various constructions and results of Auslander and Buchweitz, and are concentrated in exploring the structure of a triangulated category \T equipped with a pair (\X,\omega), where \X is closed under extensions and $\omega$ is a weak-cogenerator in \X, usually under additional conditions. This reduces, among other things, to the existence of distinguished triangles enjoying special properties, and the behavior of (suitably defined) (co)resolutions, projective or injective dimension of objects of \T and the formation of orthogonal subcategories. Finally, some relationships with the Rouquier's dimension in triangulated categories is discussed.Comment: To appear at: Appl. Categor. Struct. (2011); 22 page

Lifting and restricting recollement data

We study the problem of lifting and restricting TTF triples (equivalently, recollement data) for a certain wide type of triangulated categories. This, together with the parametrizations of TTF triples given in "Parametrizing recollement data", allows us to show that many well-known recollements of right bounded derived categories of algebras are restrictions of recollements in the unbounded level, and leads to criteria to detect recollements of general right bounded derived categories. In particular, we give in Theorem 1 necessary and sufficient conditions for a 'right bounded' derived category of a differential graded(=dg) category to be a recollement of 'right bounded' derived categories of dg categories. In Theorem 2 we consider the particular case in which those dg categories are just ordinary algebras.Comment: 29 page

Infection by the hepatitis C virus in chronic renal failure patients undergoing hemodialysis in Mato Grosso state, central Brazil: a cohort study

BACKGROUND: Hepatitis C virus (HCV) is a significant problem for patients undergoing hemodialysis therapy. This situation has never been studied in Mato Grosso state, central Brazil. This study was conducted aiming to estimate the prevalence of the anti-HCV and the incidence of seroconversion in the main metropolitan region of the state. METHODS: 433 patients from the six hemodialysis units were interviewed and anti-HCV was tested by a third-generation enzyme immunoassay. An open cohort of patients who tested negative for anti-HCV at the entry of the study was created and seroconversions was assessed monthly. The staff responsible for the units were interviewed to assess whether the infection control measures were being followed. Logistic and Cox regression analysis were performed in order to assess risk factor to HCV. RESULTS: The entry on the study took place between January 2002 and June 2005. 73 out of 433 (16.9%, CI95%: 13.3–20.8) was found to be anti-HCV reactive. The multivariate analysis indicated as risk factors associated to anti-HCV the duration of the hemodialysis treatment, the number of transfusions received, and the unit of treatment. An open cohort of 360 patients who tested negative for anti-HCV was created, with a following average of 24 (± 15) months. Forty seroconversions were recorded corresponding to an incidence density of 4.6/1000 patient-months, ranges 0 to 30 among the units. Cox regression indicated the time of hemodialysis (RR = 2.2; CI95%: 1.1–4.6; p < 0.05) and the unit where treatment was performed (RR = 42.4; CI95%: 9.9–180.5; p < 0.05) as risk factors for seroconversion. The three units with highest anti-HCV prevalence and incidence were identified as those that more frequently failed to apply control measures. CONCLUSION: The study demonstrated high prevalence and incidence of anti-HCV in some of the hemodialysis units. Time on hemodialysis therapy was an independent factor associated to HCV. Blood transfusion was associated with anti-HCV in initial survey but was not important in incident cases. Failure of applying control meaures was more evident in units with the highest HCV prevalence and incidence. The results suggest that nosocomial transmission was the main spread factor of HCV in the studied population

Multi-Class Clustering of Cancer Subtypes through SVM Based Ensemble of Pareto-Optimal Solutions for Gene Marker Identification

With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes

Understanding the programmatic and contextual forces that influence participation in a government-sponsored international student-mobility program

Although prior research establishes the forces that “push” and “pull” students to participate in foreign study, the transferability of findings from earlier studies is limited by the absence of theoretical grounding. In addition, relatively little is known about how a government-sponsored student mobility program promotes foreign study in a nation with a transitioning economy. Using case study methods, this study explores the characteristics of students who participate in such a program and identifies the programmatic characteristics and contextual forces that promote and limit participation. The findings shed light on the appropriate theoretical perspectives for understanding student participation in a government-sponsored mobility program and illustrate the need to consider how aspects of the national cultural, economic, and political context influence participation. The findings also raise several questions about how an international student mobility program should be structured to encourage participation and maximize benefits to individuals and society within a particular national context

Nanoscale structure of amyloid-β plaques in Alzheimer’s disease

Abstract Soluble amyloid-β (Aβ) is considered to be a critical component in the pathogenesis of Alzheimer’s disease (AD). Evidence suggests that these non-fibrillar Aβ assemblies are implicated in synaptic dysfunction, neurodegeneration and cell death. However, characterization of these species comes mainly from studies in cellular or animal models, and there is little data in intact human samples due to the lack of adequate optical microscopic resolution to study these small structures. Here, to achieve super-resolution in all three dimensions, we applied Array Tomography (AT) and Stimulated Emission Depletion microscopy (STED), to characterize in postmortem human brain tissue non-fibrillar Aβ structures in amyloid plaques of cases with autosomal dominant and sporadic AD. Ultrathin sections scanned with super-resolution STED microscopy allowed the detection of small Aβ structures of the order of 100 nm. We reconstructed a whole human amyloid plaque and established that plaques are formed by a dense core of higher order Aβ species (~0.022 µm3) and a peripheral halo of smaller Aβ structures (~0.003 µm3). This work highlights the potential of AT-STED for human neuropathological studies

Clustering gene expression data with a penalized graph-based metric

<p>Abstract</p> <p>Background</p> <p>The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets.</p> <p>Results</p> <p>In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with a highly penalized weight for connecting the subgraphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs as well as penalization functions. We show clustering results on several public gene expression datasets and simulated artificial problems to evaluate the behavior of the new metric.</p> <p>Conclusions</p> <p>In all cases the PKNNG metric shows promising clustering results. The use of the PKNNG metric can improve the performance of commonly used pairwise-distance based clustering methods, to the level of more advanced algorithms. A great advantage of the new procedure is that researchers do not need to learn a new method, they can simply compute distances with the PKNNG metric and then, for example, use hierarchical clustering to produce an accurate and highly interpretable dendrogram of their high-dimensional data.</p