{SCL(EQ)}: {SCL} for First-Order Logic with Equality

International audienceAbstract We propose a new calculus SCL(EQ) for first-order logic with equality that only learns non-redundant clauses. Following the idea of CDCL (Conflict Driven Clause Learning) and SCL (Clause Learning from Simple Models) a ground literal model assumption is used to guide inferences that are then guaranteed to be non-redundant. Redundancy is defined with respect to a dynamically changing ordering derived from the ground literal model assumption. We prove SCL(EQ) sound and complete and provide examples where our calculus improves on superposition

SCL(EQ): SCL for First-Order Logic with Equality

We propose a new calculus SCL(EQ) for first-order logic with equality thatonly learns non-redundant clauses. Following the idea of CDCL (Conflict DrivenClause Learning) and SCL (Clause Learning from Simple Models) a ground literalmodel assumption is used to guide inferences that are then guaranteed to benon-redundant. Redundancy is defined with respect to a dynamically changingordering derived from the ground literal model assumption. We prove SCL(EQ)sound and complete and provide examples where our calculus improves onsuperposition.<br

Finding the association of mRNA and miRNA using Next Generation Sequencing data of Kidney renal cell carcinoma

MicroRNAs (miRNAs) are a class of 22-nucleotide endogenous noncod- ing RNAs, plays important role in regulating target gene expression via repress- ing translation or promoting messenger RNAs (mRNA) degradation. Numerous re- searchers have found that miRNAs have serious effects on cancer. Therefore, study of mRNAs and miRNAs together through the integrated analysis of mRNA and miRNA expression profiling could help us in getting a deeper insight into the can- cer research. In this regards, High-Throughput Sequencing data of Kidney renal cell carcinoma is used here. The proposed method focuses on identifying mRNA- miRNA pair that has a signature in kidney tumor sample. For this analysis, Ran- dom Forests, Particle Swarm Optimization and Support Vector Machine classifier is used to have best sets of mRNAs-miRNA pairs. Additionally, the significance of selected mRNA-miRNA pairs is tested using gene ontology and pathway analysis tools. Moreover, the selected mRNA-miRNA pairs are searched based on changes in expression values of the used mRNA and miRNA dataset

Identification of lung cancer with high sensitivity and specificity by blood testing

<p>Abstract</p> <p>Background</p> <p>Lung cancer is a very frequent and lethal tumor with an identifiable risk population. Cytological analysis and chest X-ray failed to reduce mortality, and CT screenings are still controversially discussed. Recent studies provided first evidence for the potential usefulness of autoantigens as markers for lung cancer.</p> <p>Methods</p> <p>We used extended panels of arrayed antigens and determined autoantibody signatures of sera from patients with different kinds of lung cancer, different common non-tumor lung pathologies, and controls without any lung disease by a newly developed computer aided image analysis procedure. The resulting signatures were classified using linear kernel Support Vector Machines and 10-fold cross-validation.</p> <p>Results</p> <p>The novel approach allowed for discriminating lung cancer patients from controls without any lung disease with a specificity of 97.0%, a sensitivity of 97.9%, and an accuracy of 97.6%. The classification of stage IA/IB tumors and controls yielded a specificity of 97.6%, a sensitivity of 75.9%, and an accuracy of 92.9%. The discrimination of lung cancer patients from patients with non-tumor lung pathologies reached an accuracy of 88.5%.</p> <p>Conclusion</p> <p>We were able to separate lung cancer patients from subjects without any lung disease with high accuracy. Furthermore, lung cancer patients could be seprated from patients with other non-tumor lung diseases. These results provide clear evidence that blood-based tests open new avenues for the early diagnosis of lung cancer.</p

Multiple Sclerosis: MicroRNA Expression Profiles Accurately Differentiate Patients with Relapsing-Remitting Disease from Healthy Controls

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system, which is heterogenous with respect to clinical manifestations and response to therapy. Identification of biomarkers appears desirable for an improved diagnosis of MS as well as for monitoring of disease activity and treatment response. MicroRNAs (miRNAs) are short non-coding RNAs, which have been shown to have the potential to serve as biomarkers for different human diseases, most notably cancer. Here, we analyzed the expression profiles of 866 human miRNAs. In detail, we investigated the miRNA expression in blood cells of 20 patients with relapsing-remitting MS (RRMS) and 19 healthy controls using a human miRNA microarray and the Geniom Real Time Analyzer (GRTA) platform. We identified 165 miRNAs that were significantly up- or downregulated in patients with RRMS as compared to healthy controls. The best single miRNA marker, hsa-miR-145, allowed discriminating MS from controls with a specificity of 89.5%, a sensitivity of 90.0%, and an accuracy of 89.7%. A set of 48 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a specificity of 95%, a sensitivity of 97.6%, and an accuracy of 96.3%. While 43 of the 165 miRNAs deregulated in patients with MS have previously been related to other human diseases, the remaining 122 miRNAs are so far exclusively associated with MS. The implications of our study are twofold. The miRNA expression profiles in blood cells may serve as a biomarker for MS, and deregulation of miRNA expression may play a role in the pathogenesis of MS

High incidence of metastatic disease in primary high grade and large extremity soft tissue sarcomas treated without chemotherapy

BACKGROUND: The risk of metastasis and the survival in patients with primary extremity soft tissue sarcomas is worse when tumour size is large and the grade of malignancy is high. Such tumours may receive chemotherapy and/or radiation therapy (RTX) for optimising local control. Irradiation can either be applied preoperatively or after tumour resection. The question arises if the kind of RTX in the absence of chemotherapy influences the outcome concerning local control, metastatic disease, survival and complications. METHODS: We retrospectively reviewed the clinical outcome of 233 patients with a primary extremity soft tissue sarcoma treated between 1990 – 2000 with a mean follow-up of 35.8 (4–120) months in our institute. 41 patients had high grade, deep and large tumours (>8 cm), an AJCC stage III (no evidence of metastasis prior to treatment) and were treated with limb salvage surgery and irradiation but stayed without additional chemotherapy. Two groups of patients were compared: the first group received postoperative RTX after tumour resection (n = 33); the second group was treated with preoperative RTX (n = 8). Both groups did not differ concerning clinical parameters. We analysed primary and secondary outcomes. RESULTS: 56% (23/41) of the population developed metastatic disease, 24% (10/41) local recurrence. The risk of metastasis was higher in the group with preoperative irradiation (p = 0.046). The overall (p = 0.0248) and relapse free survival (p = 0.104) were worse in this group. The delay to tumour resection amounted 8 weeks on average in the preoperative group. Local control was not different (p = 0.38) in both study groups. Wound infections and other combined therapy related complications were equally distributed (p = 0.22). CONCLUSION: Without chemotherapy there remains a high risk of metastasis in AJCC grade 3 patients. In high risk patients treated without chemotherapy the elapsed time to tumour resection after preoperative radiation might contribute to the development of metastasis. This outcome may support the thesis that a combination of RTX and offensive multimodal treatment protocols is advantageous in such a subset of patient