The activity concentration of post-Chernobyl ¹³⁷Cs in the area of the Opole Anomaly (southern Poland)

During the years 2007 and 2010, the activity concentration of (137)Cs accumulated in soil, mosses Pleurozium schreberi and lichens Hypogymnia physodes was measured. The studies covered the areas of the so-called Opole Anomaly. In consequence of the Chernobyl nuclear power plant breakdown in 1986, relatively large amounts of this radionuclide were deposited in this area. In some areas of the Anomaly, over 100 times higher surface activity of (137)Cs was detected, compared to the lowest values registered in Poland. Currently, (137)Cs is still present in woodlands and wastelands. As at 2 April 2013, the surface activity concentration of (137)Cs in soil on the tested area was from 0.34 to 67.5 kBq m(-2). In comparison, the surface activity concentration of (137)Cs as at 1 June 1986, soon after deposition, was from 2.08 to over 125 kBq m(-2). The maximum specific activity concentrations of (137)Cs in mosses and lichens sampled for testing in 2010 were respectively 1234 and 959 Bq kg(-1). It was also proven that the changes in activity concentration of (137)Cs in the area of the Anomaly are mainly the consequence of the radioactive decay of this radionuclide.

Learning rule sets from survival data

Background

Survival analysis is an important element of reasoning from data. Applied in a number of fields, it has become particularly useful in medicine to estimate the survival rate of patients on the basis of their condition, examination results, and undergoing treatment. The recent developments in the next generation sequencing open new opportunities in survival study as they allow vast amount of genome-, transcriptome-, and proteome-related features to be investigated. These include single nucleotide and structural variants, expressions of genes and microRNAs, DNA methylation, and many others.

Results

We present LR-Rules, a new algorithm for rule induction from survival data. It works according to the separate-and-conquer heuristics with a use of log-rank test for establishing rule body. Extensive experiments show LR-Rules to generate models of superior accuracy and comprehensibility. The detailed analysis of rules rendered by the presented algorithm on four medical datasets concerning leukemia as well as breast, lung, and thyroid cancers, reveals the ability to discover true relations between attributes and patients’ survival rate. Two of the case studies incorporate features obtained with a use of high throughput technologies showing the usability of the algorithm in the analysis of bioinformatics data.

Conclusions

LR-Rules is a viable alternative to existing approaches to survival analysis, particularly when the interpretability of a resulting model is crucial. Presented algorithm may be especially useful when applied on the genomic and proteomic data as it may contribute to the better understanding of the background of diseases and support their treatments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1693-x) contains supplementary material, which is available to authorized users.

MAINE: a web tool for multi-omics feature selection and rule-based data exploration

SUMMARY

Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE-Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules.

AVAILABILITY AND IMPLEMENTATION

MAINE is freely available at maine.ibemag.pl as an online web application.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Censoring Weighted Separate-and-Conquer Rule Induction from Survival Data

Objectives: Rule induction is one of the major methods of machine learning. Rule-based models can be easily read and interpreted by humans, that makes them particularly useful in survival studies as they can help clinicians to better understand analysed data and make informed decisions about patient treatment. Although of such usefulness, there is still a little research on rule learning in survival analysis. In this paper we take a step towards rule-based analysis of survival data.

Methods: We investigate so-called covering or separate-and-conquer method of rule induction in combination with a weighting scheme for handling censored observations. We also focus on rule quality measures being one of the key elements differentiating particular implementations of separate-and-conquer rule induction algorithms. We examine 15 rule quality measures guiding rule induction process and reflecting a wide range of different rule learning heuristics.

Results: The algorithm is extensively tested on a collection of 20 real survival datasets and compared with the state-of-the-art survival trees and random survival forests algorithms. Most of the rule quality measures outperform Kaplan-Meier estimate and perform at least equally well as tree-based algorithms.

Conclusions: Separate-and-conquer rule induction in combination with weighting scheme is an effective technique for building rule-based models of survival data which, according to predictive accuracy, are competitive with tree-based representations.

The Influence of Body Mass Index on Growth Factor Composition in the Platelet-Rich Plasma in Patients with Knee Osteoarthritis

BACKGROUND

An abnormally high body mass index is strongly associated with knee osteoarthritis. Usually, obese patients are excluded from clinical trials involving PRP intra-articular injections. Growth factors have been demonstrated to have a disease-modifying effect on KOA treatment, even though data on their influence on treatment effectiveness in obese patients are lacking.

PURPOSE

To prospectively compare the level of selected growth factors including transforming growth factor-b (TGF-β), epidermal growth factor (EGF), fibroblast growth factor, insulin-like growth factor-1 (IGF-1), platelet-derived growth factor (PDGF), vascular endothelial growth factor (VEGF), and fibroblast growth factor-2 (FGF-2) in platelet-rich plasma (PRP) in obese patients and patients with normal BMI.

METHODS

A total of 49 patients were included in the study according to inclusion and exclusion criteria. The groups strongly differed in body mass index (median values 21.6 vs. 32.15). Concentrations of growth factors were measured with an enzyme-linked immunosorbent assay. Statistical significance was determined with the Mann-Whitney U test. The compliance of the distribution of the results with the normal distribution was checked using the Shapiro-Wilk test separately for both groups.

RESULTS

There were no statistically significant differences in median marker levels between groups. Statistically significant Pearson correlations were observed between IGF-1 serum level and age (weak negative, r = -0.294, p = 0.041) and gender (moderate positive, r = 0.392, 0.005).

CONCLUSIONS

BMI does not influence the level of selected growth factors in patients with knee osteoarthritis. Obese and non-obese patients had similar compositions of PDGF, TGF-β, EGF, FGF-2, IGF-1, and VEGF. PRP can be used in both groups with similar effects associated with growth factors' influence on articular cartilage.

Sensor-Based Predictive Maintenance with Reduction of False Alarms-A Case Study in Heavy Industry

In this paper, the problem of the identification of undesirable events is discussed. Such events can be poorly represented in the historical data, and it is predominantly impossible to learn from past examples. The discussed issue is considered in the work in the context of two use cases in which vibration and temperature measurements collected by wireless sensors are analysed. These use cases include crushers at a coal-fired power plant and gantries in a steelworks converter. The awareness, resulting from the cooperation with industry, of the need for a system that works in cold start conditions and does not flood the machine operator with alarms was the motivation for proposing a new predictive maintenance method. The proposed solution is based on the methods of outlier identification. These methods are applied to the collected data that was transformed into a multidimensional feature vector. The novelty of the proposed solution stems from the creation of a methodology for the reduction of false positive alarms, which was applied to a system identifying undesirable events. This methodology is based on the adaptation of the system to the analysed data, the interaction with the dispatcher, and the use of the XAI (eXplainable Artificial Intelligence) method. The experiments performed on several data sets showed that the proposed method reduced false alarms by 90.25% on average in relation to the performance of the stand-alone outlier detection method. The obtained results allowed for the implementation of the developed method to a system operating in a real industrial facility. The conducted research may be valuable for systems with a cold start problem where frequent alarms can lead to discouragement and disregard for the system by the user.

Data on methane concentration collected by underground coal mine sensors

Coal mining requires working in hazardous conditions. Miners in an underground coal mine can face several threats, such as, e.g. methane explosions. To provide protection for people working underground, systems for active monitoring of production processes are typically used. One of their fundamental applications is screening dangerous gas concentrations (methane in particular) to prevent spontaneous explosions. Such a system is the source of the data set containing raw data collected at an underground coal mine. The data is collected from 28 different sensors placed at various locations around the coal mine. All the attributes except one are numeric, and the examples collected form a time series. This data set can be used in a variety of analytical tasks, including classification, regression, time series and stream data analysis.

Cholecalciferol supplementation lowers leptin and TMAO but increases NO and VEGF-A levels in obese vitamin D deficient patients: Is it one of the potential cardioprotective mechanisms of vitamin D?

Background

Vitamin D deficiency is one of the most common health issues in developed countries. Obese patients are most at risk of having serum 25-hydroxyvitamin D₃ (25(OH)D₃) levels that are too low due to the accumulation of vitamin D in adipose tissue. While the effects of a deficiency on the skeletal or immune system are known, the effects on the cardiovascular system are not yet clear. Our study investigates the effect of cholecalciferol supplementation in obese patients on selected biomarkers associated with cardiovascular diseases (CVDs).

Methods

The study enrolled 33 obese patients with insufficient 25(OH)D₃ levels. For three months, the subjects supplemented with cholecalciferol at a dose of 2000 IU/day. Concentrations of nitric oxide (NO), vascular endothelial growth factor A (VEGF-A), leptin, trimethylamine N-oxide (TMAO) and soluble suppression of tumorigenicity 2 (sST2) were measured in baseline samples using ELISA (BioTek EPOCH). 25(OH)D₃ levels measured on Beckman Coulter DXI 800 by chemiluminescence method.

Results

After supplementation, 25(OH)D₃ levels increased significantly. Normal levels were achieved in most patients. A statistically significant reduction leptin and TMAO levels was observed. At the same time, NO and VEGF-A levels increased statistically significantly.

Conclusion

This study indicates that restoring normal 25(OH)D₃ levels in obese people reduces the concentration of pro-inflammatory factors associated with cardiovascular diseases. Reducing inflammation and the potential impact on vascular reactivity leads to the conclusion that cholecalciferol supplementation in obese patients may benefit the cardiovascular system.