Epidemiology and the agreement rate of serological tests in human brucellosis in North East of Iran

Background: Brucellosis still remains a major health problem with different symptoms and various diagnostic methods. Diagnostic methods of brucellosis are usually based on detecting specific antibodies in the patient’s serum. Nowadays, many serological tests are applied for the diagnosis of human brucellosis. Most routine tests are serum agglutination tests based on Wright and 2-Mercaptoethanol (2-ME). Objectives: The aim of this study (cross sectional study) was to evaluate the prevalence of brucellosis and assess the degree of agreement among serum samples of suspected brucellosis serological tests routinely performed in Mashhad, Iran. Patients and Methods: This study was conducted in Mashhad from August 2011 to September 2012. Sera (2 - 3 mL) were collected from 83 cases suspected of brucellosis among 594 patients. Ten serum samples were collected from healthy subjects as control sera. Rose Bengal test for initial screening and Wright and 2 ME as standard tests were conducted to determine antibody titers. Thereafter, IgG and IgM levels were determined by the Enzyme Linked Immunosorbent Assay (ELISA) method. Results: Among 83 serum samples, Rose Bengal test was able to identify 20 (12%) positive specimens; the standard tube agglutination test was able to detect 30 (18%) positive samples, and the ELISA IgG and ELISA IgM were able to trace 42 (21%) and 13 (6.5%) positive samples, respectively. Ten control samples had negative results for the ELISA method. The results were calculated by the Kappa formula. The highest level of agreement was among 1 = KRB-SAT tests and the lowest level of agreement was among tests K ELISA IgM-IgG = 0.30. Conclusions: According to the results, brucellosis has remained endemic in this region. Most cases were detected by ELISA IgG. The highest kappa agreements were between tests KRB-SAT, KRB-IgG and KSAT-IgG, while the lowest levels of agreement were between tests SAT-IgM and ELISA IgM-IgG. Considering that ELISA IgM results are covered by SAT and ELISA IgG test results, applications of this test do not seem necessary. © 2015, Infectious Diseases and Tropical Medicine Research Center

Data granulation by the principles of uncertainty

Researches in granular modeling produced a variety of mathematical models, such as intervals, (higher-order) fuzzy sets, rough sets, and shadowed sets, which are all suitable to characterize the so-called information granules. Modeling of the input data uncertainty is recognized as a crucial aspect in information granulation. Moreover, the uncertainty is a well-studied concept in many mathematical settings, such as those of probability theory, fuzzy set theory, and possibility theory. This fact suggests that an appropriate quantification of the uncertainty expressed by the information granule model could be used to define an invariant property, to be exploited in practical situations of information granulation. In this perspective, a procedure of information granulation is effective if the uncertainty conveyed by the synthesized information granule is in a monotonically increasing relation with the uncertainty of the input data. In this paper, we present a data granulation framework that elaborates over the principles of uncertainty introduced by Klir. Being the uncertainty a mesoscopic descriptor of systems and data, it is possible to apply such principles regardless of the input data type and the specific mathematical setting adopted for the information granules. The proposed framework is conceived (i) to offer a guideline for the synthesis of information granules and (ii) to build a groundwork to compare and quantitatively judge over different data granulation procedures. To provide a suitable case study, we introduce a new data granulation technique based on the minimum sum of distances, which is designed to generate type-2 fuzzy sets. We analyze the procedure by performing different experiments on two distinct data types: feature vectors and labeled graphs. Results show that the uncertainty of the input data is suitably conveyed by the generated type-2 fuzzy set models.Comment: 16 pages, 9 figures, 52 reference

Decentralized Demand Side Management with Rooftop PV in Residential Distribution Network

In the past extensive researches have been conducted on demand side management (DSM) program which aims at reducing peak loads and saving electricity cost. In this paper, we propose a framework to study decentralized household demand side management in a residential distribution network which consists of multiple smart homes with schedulable electrical appliances and some rooftop photovoltaic generation units. Each smart home makes individual appliance scheduling to optimize the electric energy cost according to the day-ahead forecast of electricity prices and its willingness for convenience sacrifice. Using the developed simulation model, we examine the performance of decentralized household DSM and study their impacts on the distribution network operation and renewable integration, in terms of utilization efficiency of rooftop PV generation, overall voltage deviation, real power loss, and possible reverse power flows.Comment: 5 pages, 7 figures, ISGT 2018 conferenc

Role of inflammation in 6- hydroxydopamine model of Parkinson’s disease and its modulation by Peroxisome Proliferator Activated receptor gamma (PPAR-γ) agonist as a neuroprotective strategy

Parkinson’s disease is an age related progressive neurodegenerative disorder affecting ~2% of the population over the age of 65. The aetiology of Parkinson’s disease is not fully understood and treatment limited. Hence, the goal of ongoing research is to understand and target the mechanisms underlying the disease process to halt or slow down its progression. Parkinson’s disease is pathologically characterised by the loss of dopaminergic neurons in the substantial nigra that leads to the loss of innervation to the striatum, subsequently leading to the motor complications observed in Parkinson’s sufferers. Recent clinical and experimental evidence suggests that inflammation, characterised by over activation of the brain’s resident immune cells such as microglia/macrophages, play a detrimental role in Parkinson’s pathology. This project, for the first time aims to explore the time course of microglial activation in association with dopaminergic neuronal cell death in the substantia nigra utilising the 6-hydroxy dopamine rat model of Parkinson’s. The dynamics of morphological, immunophenotypic and phagocytic properties of activated microglia in the substantia nigra was assessed immunohistochemically. In addition we explored the cellular events between activated microglia and degenerating neurons in this model that had not been previously well defined. The role of matrix metalloproteinases as signalling molecules that activate microglia were also studied. Finally the significance of local microglial activation in the substantia nigra was elucidated by modulation of the microglial response via activation of the gamma subtype of peroxisome proliferator activated receptors. This project provided evidence that microglial activation preceded dopaminergic neuronal cell death in the substantia nigra and inhibition of microglial response serves as a neuroprotective strategy in Parkinson’s disease

Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome

We evaluate a version of the recently-proposed classification system named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a classification system for patterns represented as labeled graphs. However, since ODSE is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted to any input domain where it is possible to define a meaningful dissimilarity measure. Here we demonstrate the effectiveness of the ODSE classifier for sequences by considering an application dealing with the recognition of the solubility degree of the Escherichia coli proteome. Solubility, or analogously aggregation propensity, is an important property of protein molecules, which is intimately related to the mechanisms underlying the chemico-physical process of folding. Each protein of our dataset is initially associated with a solubility degree and it is represented as a sequence of symbols, denoting the 20 amino acid residues. The herein obtained computational results, which we stress that have been achieved with no context-dependent tuning of the ODSE system, confirm the validity and generality of the ODSE-based approach for structured data classification.Comment: 10 pages, 49 reference