Grid Service Discovery in the Financial Markets Sector

Investment Banking requires a diverse system set in supporting a range of markets from bonds to trading options on weather. The challenge to this community is the ability to adapt to new business requirements in an effective manner, utilizing their network of capabilities in a flexible, dynamic way. A semantic approach to discovery can be used in a pragmatic, practical manner. The use of richer explicit knowledge, that is system readable, provides the basis for discovering capabilities on this exemplar Business Grid—“the grid of services”. This design research project focuses on the utilization of disparate knowledge during discovery

Clonal Selection based Fuzzy C-Means Algorithm for Clustering

ABSTRACT In recent years, fuzzy based clustering approaches have shown to outperform state-of-the-art hard clustering algorithms in terms of accuracy. The difference between hard clustering and fuzzy clustering is that in hard clustering each data point of the data set belongs to exactly one cluster, and in fuzzy clustering each data point belongs to several clusters that are associated with a certain membership degree. Fuzzy c-means clustering is a well-known and effective algorithm, however, the random initialization of the centroids directs the iterative process to converge to local optimal solutions easily. In order to address this issue a clonal selection based fuzzy c-means algorithm (CSFCM) is introduced. CSFCM is compared with the basic Fuzzy C-Means (FCM) algorithm, a genetic algorithm based FCM (GAFCM) algorithm, and a particle swarm optimization based FCM (PSOFCM) algorithm

Weight Assignment of Semantic Match using User Values and a Fuzzy Approach

Abstract. Automatic discovery of services is a crucial task for the eScience and e-Business communities. Finding a suitable way to address this issue has become one of the key points to convert the Web into a distributed source of computation, as it enables the location of distributed services to perform a required functionality. To provide such an automatic location, the discovery process should be based on the semantic match between a declarative description of the service being sought and a description being offered. This problem requires not only an algorithm to match these descriptions, but also a language to declaratively express the capabilities of services. The proposed matchmaking approach is based on semantic descriptions for service attributes, descriptions and metadata. For the ranking of service matches a match score is calculated whereby the weight values are either given by the user or estimated using a fuzzy approach

Color Image Segmentation Using Fuzzy C-Regression Model

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms

Immune Network Algorithm applied to the Optimization of Composite SaaS in Cloud Computing

Abstract-In order to serve the different application needs of the different Cloud users efficiently and effectively, a possible solution is the decomposition of the software or so-called composite SaaS (Software as a Service). A composite SaaS constitutes a group of loosely-coupled applications that communicate with each other to form higher-level functionality. The benefits to the SaaS providers are reduced delivery cost and flexible SaaS functions, and the benefit for the users is the decreased cost of subscription. For this to be achieved effectively, the optimization of the process is required in order to manage the SaaS resources in the data center efficiently. In this paper, the optimization task of composite SaaS is investigated using an Immune network optimization approach. The approach makes use of activation and suppression that are mimicked by the natural immune system triggering an immune response not only when antibodies interact with antigens but also when they interact with other antibodies. Experiments are conducted with a series of SaaS configurations and the proposed immune network algorithm is compared with a formerly proposed grouping genetic algorithm. The results show that the immune network algorithm outperforms the grouping genetic algorithm

Segment-Based Genetic Programming

ABSTRACT Genetic Programming (GP) is one of the successful evolutionary computation techniques applied to solve classification problems, by searching for the best classification model applying the fitness evaluation. The fitness evaluation process greatly impacts the overall execution time of GP and is therefore the focus of this research study. This paper proposes a segment-based GP (SegGP) technique that reduces the execution time of GP by partitioning the dataset into segments, and using the segments in the fitness evaluation process. Experiments were done using four datasets and the results show that SegGP can obtain higher or similar accuracy results in shorter execution time compared to standard GP

MALDI-TOF High Mass Calibration up to 200 kDa Using Human Recombinant 16 kDa Protein Histidine Phosphatase Aggregates

Background: Protein histidine phosphatase (PHP) is an enzyme which removes phosphate groups from histidine residues. It was described for vertebrates in the year 2002. The recombinant human 16 kDa protein forms multimeric complexes in physiological buffer and in the gas phase. High-mass calibration in matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has remained a problem due to the lack of suitable standards. Large proteins can hardly be freed of their substructural microheterogeneity by classical purification procedures so that their use as calibrants is limited. A small adduct-forming protein of validated quality is a valuable alternative for that purpose. Methodology/Principal Findings: Three major PHP clusters of,113, 209 and.600 kDa were observed in gel filtration analysis. Re-chromatography of the monomer peak showed the same cluster distribution. The tendency to associate was detected also in MALDI-TOF MS measuring regular adducts up to 200 kDa. Conclusions/Significance: PHP forms multimers consisting of up to more than 35 protein molecules. In MALDI-TOF MS it generates adduct ions every 16 kDa. The protein can be produced with high quality so that its use as calibration compoun

The local soft tissue status and the prediction of local complications following fractures of the ankle region

INTRODUCTION Well-known risk factors (RF) for soft tissue complications following surgical treatment of fracture of the ankle region include diabetes, smoking, and the local soft tissue status. A weighted analysis might provide a risk profile that guides the surgical treatment strategy. The aim of this meta-analysis was to provide a risk profile for soft tissue complications following closed fractures of the ankle region. METHODS This review provides a meta-analysis of studies that investigate potential risk factors for complications in fractures of the ankle region. INCLUSION CRITERIA Original articles that were published between 2000 and 2020 in English or German language that calculated odds ratios (OR) of RF for soft tissue complications. Further, this study only includes articles that investigated fractures of the ankle region including pilon fracture, calcaneal fractures, and fractures of the malleoli. This study excluded articles that provide exploratory analyses, narrative reviews, and case reports. RF were stratified as patient specific systemic RF (PSS), patient specific local RF (PSL), and non-patient specific RF (NPS). PSS RF includes comorbidities, American society of anaesthesiology (ASA), requirement of medication, additional injuries, and smoking or substance abuse. PSL RF includes soft tissue status, wounds, and associated complications. NPS RF includes duration of surgery, staged procedure, or time to definitive surgery. Random effect (RE) models were utilized to summarize the effect measure (OR) for each group or specific RF. RESULTS Out of 1352 unique articles, 34 were included for quantitative analyses. Out of 370 complications, the most commonly assessed RF were comorbidities (34.6%). Local soft tissue status accounted for 7.5% of all complications. The overall rate for complication was 10.9% (standard deviation, SD 8.7%). PSS RF had an OR of 1.04 (95%CI 1.01 to 1.06, p = 0.006), PSL an OR of 1.79 (95% 1.28 to 2.49, p = 0.0006), and NPS RF an OR of 1.01 (95%CI 0.97 to 1.05, p = 0.595). Additional injuries did not predict complications (OR 1.23, 95%CI 0.44 to 3.45, p = 0.516). The most predictive RF were open fracture (OR 3.47, 95%CI 1.64 to 7.34, p < 0.001), followed by local tissue damage (OR 3.05, 95%CI 1.23 to 40.92, p = 0.04), and diabetes (OR 2.3, 95%CI 1.1 to 4.79, p = 0.26). CONCLUSION Among all RFs for regional soft tissue complications, the most predictive is the local soft tissue status, while additional injuries or NPS RF were less predictive. The soft tissue damage can be quantified and outweighs the cofactors described in previous publications. The soft tissue status appears to have a more important role in the decision making of the treatment strategy when compared with comorbidities such as diabetes

O6-Methylguanine-DNA Methyltransferase (MGMT) mRNA Expression Predicts Outcome in Malignant Glioma Independent of MGMT Promoter Methylation

Background: We analyzed prospectively whether MGMT (O(6)-methylguanine-DNA methyltransferase) mRNA expression gains prognostic/predictive impact independent of MGMT promoter methylation in malignant glioma patients undergoing radiotherapy with concomitant and adjuvant temozolomide or temozolomide alone. As DNA-methyltransferases (DNMTs) are the enzymes responsible for setting up and maintaining DNA methylation patterns in eukaryotic cells, we analyzed further, whether MGMT promoter methylation is associated with upregulation of DNMT expression. 12 Hide Figures Abstract Introduction Methods Results Discussion Acknowledgments Author Contributions References Reader Comments (0) Figures Abstract Background We analyzed prospectively whether MGMT (O6-methylguanine-DNA methyltransferase) mRNA expression gains prognostic/predictive impact independent of MGMT promoter methylation in malignant glioma patients undergoing radiotherapy with concomitant and adjuvant temozolomide or temozolomide alone. As DNA-methyltransferases (DNMTs) are the enzymes responsible for setting up and maintaining DNA methylation patterns in eukaryotic cells, we analyzed further, whether MGMT promoter methylation is associated with upregulation of DNMT expression. Methodology/Principal Findings: Adult patients with a histologically proven malignant astrocytoma (glioblastoma: N = 53, anaplastic astrocytoma: N = 10) were included. MGMT promoter methylation was determined by methylation-specific PCR (MSP) and sequencing analysis. Expression of MGMT and DNMTs mRNA were analysed by real-time qPCR. Prognostic factors were obtained from proportional hazards models. Correlation between MGMT mRNA expression and MGMT methylation status was validated using data from the Cancer Genome Atlas (TCGA) database (N = 229 glioblastomas). Low MGMT mRNA expression was strongly predictive for prolonged time to progression, treatment response, and length of survival in univariate and multivariate models (p<0.0001); the degree of MGMT mRNA expression was highly correlated with the MGMT promoter methylation status (p<0.0001); however, discordant findings were seen in 12 glioblastoma patients: Patients with methylated tumors with high MGMT mRNA expression (N = 6) did significantly worse than those with low transcriptional activity (p<0.01). Conversely, unmethylated tumors with low MGMT mRNA expression (N = 6) did better than their counterparts. A nearly identical frequency of concordant and discordant findings was obtained by analyzing the TCGA database (p<0.0001). Expression of DNMT1 and DNMT3b was strongly upregulated in tumor tissue, but not correlated with MGMT promoter methylation and MGMT mRNA expression. Conclusions/Significance: MGMT mRNA expression plays a direct role for mediating tumor sensitivity to alkylating agents. Discordant findings indicate methylation-independent pathways of MGMT expression regulation. DNMT1 and DNMT3b are likely to be involved in CGI methylation. However, their exact role yet has to be defined

miRIAD-integrating microRNA inter- and intragenic data

MicroRNAs (miRNAs) are a class of small (similar to 22 nucleotides) non-coding RNAs that post-transcriptionally regulate gene expression by interacting with target mRNAs. A majority of miRNAs is located within intronic or exonic regions of protein-coding genes (host genes), and increasing evidence suggests a functional relationship between these miRNAs and their host genes. Here, we introduce miRIAD, a web-service to facilitate the analysis of genomic and structural features of intragenic miRNAs and their host genes for five species (human, rhesus monkey, mouse, chicken and opossum). miRIAD contains the genomic classification of all miRNAs (inter-and intragenic), as well as classification of all protein-coding genes into host or non-host genes (depending on whether they contain an intragenic miRNA or not). We collected and processed public data from several sources to provide a clear visualization of relevant knowledge related to intragenic miRNAs, such as host gene function, genomic context, names of and references to intragenic miRNAs, miRNA binding sites, clusters of intragenic miRNAs, miRNA and host gene expression across different tissues and expression correlation for intragenic miRNAs and their host genes. Protein-protein interaction data are also presented for functional network analysis of host genes. In summary, miRIAD was designed to help the research community to explore, in a user-friendly environment, intragenic miRNAs, their host genes and functional annotations with minimal effort, facilitating hypothesis generation and in-silico validations