Detection of Malfunctions and Abnormal Working Conditions of a Coal Mill

Coal mill malfunctions are some of the most common causes of failing to keep the power plant crucial operating parameters or even unplanned power plant shutdowns. Therefore, an algorithm has been developed that enable online detection of abnormal conditions and malfunctions of an operating mill. Based on calculated diagnostic signals and defined thresholds, this algorithm informs about abnormal operating conditions. Diagnostic signals represent the difference between the measured and the modeled values of two selected mill operating parameters. Models of mill motor current and outlet temperature of pulverized fuel were developed based on the linear regression theory. Various data analysis and feature selection procedures have been performed to obtain the best possible model. The model based on linear regression has been compared with two alternative models. The algorithm validation was carried out based on historical data containing values of operating parameters from 10 months of mill operation. Historical data were downloaded from distributed control system (DCS) of a 200-MW coal-fired power plant. Tests carried out on historical data show that this algorithm can be successfully used to detect certain abnormal conditions and malfunctions of the operating mill, such as feeder blockage, lack of coal and mill overload

Predicting early brain metastases based on clinicopathological factors and gene expression analysis in advanced HER2-positive breast cancer patients

The overexpression or amplification of the human epidermal growth factor receptor 2 gene (HER2/neu) is associated with high risk of brain metastasis (BM). The identification of patients at highest immediate risk of BM could optimize screening and facilitate interventional trials. We performed gene expression analysis using complementary deoxyribonucleic acid-mediated annealing, selection, extension and ligation and real-time quantitative reverse transcription PCR (qRT-PCR) in primary tumor samples from two independent cohorts of advanced HER2 positive breast cancer patients. Additionally, we analyzed predictive relevance of clinicopathological factors in this series. Study group included discovery Cohort A (84 patients) and validation Cohort B (75 patients). The only independent variables associated with the development of early BM in both cohorts were the visceral location of first distant relapse [Cohort A: hazard ratio (HR) 7.4, 95 % CI 2.4–22.3; p < 0.001; Cohort B: HR 6.1, 95 % CI 1.5–25.6; p = 0.01] and the lack of trastuzumab administration in the metastatic setting (Cohort A: HR 5.0, 95 % CI 1.4–10.0; p = 0.009; Cohort B: HR 10.0, 95 % CI 2.0–100.0; p = 0.008). A profile including 13 genes was associated with early (≤36 months) symptomatic BM in the discovery cohort. This was refined by qRT-PCR to a 3-gene classifier (RAD51, HDGF, TPR) highly predictive of early BM (HR 5.3, 95 % CI 1.6–16.7; p = 0.005; multivariate analysis). However, predictive value of the classifier was not confirmed in the independent validation Cohort B. The presence of visceral metastases and the lack of trastuzumab administration in the metastatic setting apparently increase the likelihood of early BM in advanced HER2-positive breast cancer

Molecular EPISTOP, a comprehensive multi-omic analysis of blood from Tuberous Sclerosis Complex infants age birth to two years

We present a comprehensive multi-omic analysis of the EPISTOP prospective clinical trial of early intervention with vigabatrin for pre-symptomatic epilepsy treatment in Tuberous Sclerosis Complex (TSC), in which 93 infants with TSC were followed from birth to age 2 years, seeking biomarkers of epilepsy development. Vigabatrin had profound effects on many metabolites, increasing serum deoxycytidine monophosphate (dCMP) levels 52-fold. Most serum proteins and metabolites, and blood RNA species showed significant change with age. Thirty-nine proteins, metabolites, and genes showed significant differences between age-matched control and TSC infants. Six also showed a progressive difference in expression between control, TSC without epilepsy, and TSC with epilepsy groups. A multivariate approach using enrollment samples identified multiple 3-variable predictors of epilepsy, with the best having a positive predictive value of 0.987. This rich dataset will enable further discovery and analysis of developmental effects, and associations with seizure development in TSC.</p

Molecular EPISTOP, a comprehensive multi-omic analysis of blood from Tuberous Sclerosis Complex infants age birth to two years

We present a comprehensive multi-omic analysis of the EPISTOP prospective clinical trial of early intervention with vigabatrin for pre-symptomatic epilepsy treatment in Tuberous Sclerosis Complex (TSC), in which 93 infants with TSC were followed from birth to age 2 years, seeking biomarkers of epilepsy development. Vigabatrin had profound effects on many metabolites, increasing serum deoxycytidine monophosphate (dCMP) levels 52-fold. Most serum proteins and metabolites, and blood RNA species showed significant change with age. Thirty-nine proteins, metabolites, and genes showed significant differences between age-matched control and TSC infants. Six also showed a progressive difference in expression between control, TSC without epilepsy, and TSC with epilepsy groups. A multivariate approach using enrollment samples identified multiple 3-variable predictors of epilepsy, with the best having a positive predictive value of 0.987. This rich dataset will enable further discovery and analysis of developmental effects, and associations with seizure development in TSC

Smooth muscle contamination analysis in clinical oncology gene expression research

Gene expression profiling is one of the most explored methods for studying cancers and microarray data repositories have become a rich and important resource. The most common human cancers develop in organs that are walled by smooth muscles. The only method of sample extraction free of unintentional contamination with surrounding tissue is microdissection. Nevertheless, such an approach is implemented infrequently. In the light of the above, there is a possibility of smooth muscle contamination in a large portion of publicly available data. In this study, 2292 publicly available microarrays were analysed to develop a simple screening method for detecting smooth muscle contamination. Microarray Inspector software was used to perform the tests since it has the unique ability to use many selected genes and probesets in a single group as a tissue definition. Furthermore, the test was dataset-independent. Two strategies of tissue definition were explored and compared. The first one depended on Tissue Specific Genes Database (TiSGeD) and BioGPS web resources, which themselves were based on meta-analysis of thousands of microarrays. The second method was based on a differential gene expression analysis of a few hundred preselected arrays. The comparison of the two methods proved the latter to be superior. Among the tested samples of undefined contamination, nearly half were identified to possibly contain significant smooth muscle traces. The obtained results equip researches with a simple method of examining microarray data for smooth muscle contamination. The presented work serves as an example of how to create definitions when searching for other possible contaminations

Microarray Inspector: tissue cross contamination detection tool for microarray data

Microarray technology changed the landscape of contemporary life sciences by providing vast amounts of expression data. Researchers are building up repositories of experiment results with various conditions and samples which serve the scientific community as a precious resource. Ensuring that the sample is of high quality is of utmost importance to this effort. The task is complicated by the fact that in many cases datasets lack information concerning pre-experimental quality assessment. Transcription profiling of tissue samples may be invalidated by an error caused by heterogeneity of the material. The risk of tissue cross contamination is especially high in oncological studies, where it is often difficult to extract the sample. Therefore, there is a need of developing a method detecting tissue contamination in a post-experimental phase. We propose Microarray Inspector: customizable, user-friendly software that enables easy detection of samples containing mixed tissue types. The advantage of the tool is that it uses raw expression data files and analyses each array independently. In addition, the system allows the user to adjust the criteria of the analysis to conform to individual needs and research requirements. The final output of the program contains comfortable to read reports about tissue contamination assessment with detailed information about the test parameters and results. Microarray Inspector provides a list of contaminant biomarkers needed in the analysis of adipose tissue contamination. Using real data (datasets from public repositories) and our tool, we confirmed high specificity of the software in detecting contamination. The results indicated the presence of adipose tissue admixture in a range from approximately 4% to 13% in several tested surgical samples