Network Compression as a Quality Measure for Protein Interaction Networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients

Theoretical and Practical Aspects of Lance Skulling and Slag Foaming in BOF Vessels

In a BOF vessel, the supersonic jet is impinges on the slag and metal surfaces and the lance can become coated with the splashed materials. This leads to an increase in the outer diameter of the lance. This phenomenon of increase in the diameter of the lance due to deposition of slag is called ‘lance skulling’. The skulling can progress layer by layer, leading to a gradual increase in the diameter of the lance. Lance skulling in BOF vessels is undesirable both from the point of view of the control of the slag formation process and for the productivity of the shop. Whereas a lance with a heavy skull has to be replaced and cleaned, heavy slopping results in the loss of material form the vessel. Skulling (due to solid slag formation) precedes slopping. Solid slag formation often results in a high phosphorus and high carbon content of metal and low steel temperature at tap. Lance height and oxygen flow rate, during the progress of the blow, should be so adjusted that the content of FeO and the activity of FeO in the slag are optimally controlled. If the FeO in the slag is reduced to a level below 5-10%, then the slag starts to become solid and the lance skulling begins. This is also manifested by spitting and the consequent changes in exhaust gas composition, and a rise in the temperature and the flow rate of the waste gases. In the present work, the morphological aspects of the lance skulls obtained from five steel plants have been investigated. The phases of varying composition are formed and ejected as droplets from/around the jet impact zone. The study of skulled samples has provided an insight into an alternative mechanism of lime dissolution occurring in the neighborhood of the jet impact region in the BOF process. Skulled samples collected from a co-jet lance have also been investigated. In addition to the morphological studies carried out in the present work, a heat transfer model has been developed to correlate the changes in the lance exit cooling water temperature with the changes in the lance height and the thickness of the lance skull. For the first time, the effect of radiation from hot spot has been considered in the heat transfer model with an objective to predict the start of lance skulling as well as the progress of the formation of skull during the progress blow in a BOF. Post combustion calculations are presented for the actual heats in a 150 ton vessel. The post combustion model can be used to control the slag formation by dynamically changing the lance height and the oxygen flow rate

An Emi Sensor for non - destructive corrosion estimation in concrete

A portable Electromagnetic Induction (EMI) sensor for non-destructive concrete corrosion estimation is disclosed. The sensor, which uses a Multiple Loop Coil (MLC), is able to detect and differentiate the chemical contents present in a concrete structure. The sensor is integrated with various component such as batteries, measuring sensor head, micro controller, memory storage for saving measured data, LED for indication and graphical display of measurement on the device. The MLC technique enables detection of corrosion of concrete structures for different chemical contents at any stage of corrosion occurring in real-time

Precision phenotyping of dilated cardiomyopathy using multidimensional data.

BACKGROUND: Dilated cardiomyopathy (DCM) is a final common manifestation of heterogenous etiologies. Adverse outcomes highlight the need for disease stratification beyond ejection fraction. OBJECTIVES: The purpose of this study was to identify novel, reproducible subphenotypes of DCM using multiparametric data for improved patient stratification. METHODS: Longitudinal, observational UK-derivation (n = 426; median age 54 years; 67% men) and Dutch-validation (n = 239; median age 56 years; 64% men) cohorts of DCM patients (enrolled 2009-2016) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments. Machine learning with profile regression identified novel disease subtypes. Penalized multinomial logistic regression was used for validation. Nested Cox models compared novel groupings to conventional risk measures. Primary composite outcome was cardiovascular death, heart failure, or arrhythmia events (median follow-up 4 years). RESULTS: In total, 3 novel DCM subtypes were identified: profibrotic metabolic, mild nonfibrotic, and biventricular impairment. Prognosis differed between subtypes in both the derivation (P < 0.0001) and validation cohorts. The novel profibrotic metabolic subtype had more diabetes, universal myocardial fibrosis, preserved right ventricular function, and elevated creatinine. For clinical application, 5 variables were sufficient for classification (left and right ventricular end-systolic volumes, left atrial volume, myocardial fibrosis, and creatinine). Adding the novel DCM subtype improved the C-statistic from 0.60 to 0.76. Interleukin-4 receptor-alpha was identified as a novel prognostic biomarker in derivation (HR: 3.6; 95% CI: 1.9-6.5; P = 0.00002) and validation cohorts (HR: 1.94; 95% CI: 1.3-2.8; P = 0.00005). CONCLUSIONS: Three reproducible, mechanistically distinct DCM subtypes were identified using widely available clinical and biological data, adding prognostic value to traditional risk models. They may improve patient selection for novel interventions, thereby enabling precision medicine

A Prototype of an Electromagnetic Induction Sensor for Non-Destructive Estimation of the Presence of Corrosive Chemicals Ensuing Concrete Corrosion

The corrosion of steel reinforcement in concrete often leads to huge unbudgeted expenses for maintaining, monitoring and renovating an infrastructure. This is mainly due to the presence of salts or chemical chlorides that pose a danger to the concrete structures. The determination of the existence of these corrosive salts is vital for defining the service life of concrete. This research looked at developing an electromagnetic induction (EMI) sensor for the detection of corrosive salts. The first design adopted a single-loop coil (SLC) concept, and the second was based on a multiple-loop coil (MLC) one using copper wire. Tests were conducted on these two techniques, and with the results obtained, the latter seemed more promising; thus, a prototype sensor was developed using the MLC concept. As this new prototype sensor was able to detect the manifestation of chemical contents in a concrete structure, it could be used as a non-destructive evaluation (NDE) technique for the detection of corrosive chemicals in concrete and has the further possibility of detecting corrosion in concrete

Daily Flood Forecasts with Intelligent Data Analytic Models: Multivariate Empirical Mode Decomposition-Based Modeling Methods

Flood causes massive damages to infrastructure, agriculture, livelihood and leads to loss of life. This chapter designs M5 tree-based machine learning model integrated with advanced multivariate empirical mode decomposition (i.e., MEMD-M5 Tree) for daily flood index (FI) forecasting for Lockyer Valley in southeast Queensland, Australia, using data from January 01, 1950, to December 31, 2012. The MEMD-M5 tree is evaluated against MEMD-RF, standalone M5 tree, and RF models via statistical metrics, diagnostic plots with error distributions between simulated and observed daily flood index. The results indicate that MEMD-M5 tree outperforms the comparative models by attaining maximum values of r = 0.990, WI = 0.992, ENS = 0.979, and L = 0.920. The MEMD-M5 tree outperforms other models by registering the least value of RMSE and MAE and can precisely emulate 97.94% of daily FI value. Graphical diagnostic analysis and forecast error histograms further reveal that the MEMD-M5 tree has a greater resemblance to that of the observed data supporting the outcomes of statistical evaluation. Such advancements in flood prediction models, attained through data intelligent analytical methods, are very vital and effective in ensuring better mitigation and civil protection in emergency providing an early warning system, disaster risk reduction, disaster policy suggestions, and reduction of the property damage