Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Bioaccumulation of total mercury in the earthworm Eisenia andrei

Earthworms are a major part of the total biomass of soil fauna and play a vital role in soil maintenance. They process large amounts of plant and soil material and can accumulate many pollutants that may be present in the soil. Earthworms have been explored as bioaccumulators for many heavy metal species such as Pb, Cu and Zn but limited information is available for mercury uptake and bioaccumulation in earth- worms and very few report on the factors that influence the kinetics of Hg uptake by earthworms. It is known however that the uptake of Hg is strongly influenced by the presence of organic matter, hence the influence of ligands are a major factor contribut - ing to the kinetics of mercury uptake in biosystems. In this work we have focused on the uptake of mercury by earthworms ( Eisenia andrei ) in the presence of humic acid (HA) under varying physical conditions of pH and temperature, done to assess the role of humic acid in the bioaccumulation of mercury by earthworms from soils. The study was conducted over a 5-day uptake period and all earthworm samples were analysed by direct mercury analysis. Mercury distribution profiles as a function of time, bioac- cumulation factors (BAFs), first order rate constants and body burden constants for mercury uptake under selected conditions of temperature, pH as well as via the dermal and gut route were evaluated in one comprehensive approach. The results showed that the uptake of Hg was influenced by pH, temperature and the presence of HA. Uptake of Hg 2 + was improved at low pH and temperature when the earthworms in soil were in contact with a saturating aqueous phase. The total amount of Hg 2 + uptake decreased from 75 to 48 % as a function of pH. For earthworms in dry soil, the uptake was strongly influenced by the presence of the ligand. Calculated BAF values ranged from 0.1 to 0.8. Mercury uptake typically followed first order kinetics with rate constants determined as 0.2 to 1 h ? 1 .Scopus 201

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori

ADHERE: randomized controlled trial comparing renal function in de novo kidney transplant recipients receiving prolonged-release tacrolimus plus mycophenolate mofetil or sirolimus

ADHERE was a randomized, open-label, Phase IV study comparing renal function at Week 52 postkidney transplant, in patients who received prolongedrelease tacrolimus-based immunosuppressive regimens. On Days 0?27, patients received prolonged-release tacrolimus (initially 0.2 mg/kg/day), corticosteroids, and mycophenolate mofetil (MMF). Patients were randomized on Day 28 to receive either prolonged-release tacrolimus plus MMF (Arm 1) or prolongedrelease tacrolimus (?25% dose reduction on Day 42) plus sirolimus (Arm 2). The primary endpoint was glomerular filtration rate by iohexol clearance (mGFR) at Week 52. Secondary endpoints included eGFR, creatinine clearance (CrCl), efficacy failure (patient withdrawal or graft loss), and patient/graft survival. Tolerability was analyzed. The full-analysis set comprised 569 patients (Arm 1: 287; Arm 2: 282). Week 52 mean mGFR was similar in Arm 1 versus Arm 2 (40.73 vs. 41.75 ml/min/1.73 m2; P = 0.405), as were the secondary endpoints, except composite efficacy failure, which was higher in Arm 2 versus 1 (18.2% vs. 11.5%; P = 0.002) owing to a higher postrandomization withdrawal rate due to adverse events (AEs) (14.4% vs. 5.2%). Results from this study show comparable renal function between arms at Week 52, with fewer AEs leading to study discontinuation with prolonged-release tacrolimus plus MMF (Arm 1) versus lower dose prolonged-release tacrolimus plus sirolimus (Arm 2)

Design, development, and scientific performance of the Raman Laser Spectrometer EQM on the 2020 ExoMars (ESA) Mission

The Raman Laser Spectrometer (RLS) is one of the three Pasteur Payload instruments located within the rover analytical laboratory drawer (ALD), for ESA’s Aurora exploration programme, ExoMars 2020 mission. The instrument will analyse the crushed surface and subsurface samples that are positioned below the Raman optical head by the ALD carousel. The RLS engineering and qualification model (EQM) was delivered to ESA at the end of 2017, after a wide technical and scientific test characterization campaign. The scientific campaign comprised instrument calibration and detailed evaluation of the scientific requirements and overall performance. For spectral calibration, continuous emission standard lamps (such as Hg-Ar, Ne, and Xe) were utilized, as well as Raman spectra of pure liquids typically used as standards (cyclohexane and carbon tetrachloride (CCl4)). In addition, Raman spectra of the RLS calibration target (CT), a small disc of polyethylene terephthalate (PET) were obtained at various temperatures. This target, placed inside the rover, will be used for both Instrument health checks and calibration activities throughout Mars operations. For the scientific requirements and performance evaluations, several liquid and solid samples were analysed under a wide range of ambient conditions. The obtained spectral band parameters (peak position, relative peak intensity, peak width, and peak profile) were evaluated. Also, the instrument response (in terms of SNR) was characterized at different integration times and detector operating temperatures. In this paper, we provide a description of the development, verification, functional test, and overall scientific performance of the RLS instrument developed for ExoMars. Particular attention is placed on the performance of the EQM, which is the most representative instrument, in terms of engineering and functionality, of the flight model (FM) and in addition is used for performing all the mechanical, thermal, and radiation tests necessary for space qualification (for planetary applications). The data presented and analysed here, comprise part of the overall dataset obtained during the full instrument characterization campaign conducted at INTA before and during delivery and integration of the EQM in the rover ALD at TAS-I facilities (Torino, Italy). The results obtained confirm that the full functionality and scientific performance of the RLS instrument was maintained after integration.Proyecto MINECO Retos de la Sociedad. Ref. ESP2017-87690-C3-1-

Development of the intelligent knee osteoarthritis lifestyle app: A person-based approach

Background: Knee osteoarthritis is one of the most prevalent long term health conditions globally. Exercise and physical activity are now widely recognised to significantly reduce joint pain, improve physical function and quality of life in patients with knee osteoarthritis. However, prescribed exercise without regular contact with a healthcare professional often results in lower adherence and poorer health outcomes. Digital mobile health (mHealth) technologies offer great potential to support people with long-term conditions such as knee osteoarthritis more efficiently and effectively and with relatively lower cost than existing interventions. However, there are currently very few mHealth interventions for the self-management of knee osteoarthritis. The aim of the present study was to describe the development process of a mHealth app to extend the support for physical activity and musculoskeletal health beyond short-term, structured rehabilitation through self-management, personalised physical activity, education, and social support. Methods: The development of the intelligent knee osteoarthritis lifestyle application intervention involved an iterative and interconnected process comprising intervention ‘planning’ and ‘optimisation’ informed by the person-based approach framework for the development of digital health interventions. The planning phase involved a literature review and collection of qualitative data obtained from focus groups with individuals with knee osteoarthritis (n = 26) and interviews with relevant physiotherapists (n = 5) to generate ‘guiding principles’ for the intervention. The optimisation phase involved usability testing (n = 7) and qualitative ‘think aloud’ sessions (n = 6) with potential beneficiaries to refine the development of the intervention. Results: Key themes that emerged from the qualitative data included the need for educational material, modifying activities to suit individual abilities and preferences as well as the inclusion of key features such as rehabilitation exercises. Following a user-trial further changes were made to improve the usability of the application. Conclusions: Using a systematic person-based, development approach, we have developed the intelligent knee osteoarthritis lifestyle application to help people maintain physical activity behaviour. The app extends the support for physical activity and musculoskeletal health beyond short-term, structured rehabilitation through personalised physical activity guidance, education, and social support

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain

Small-scale field evaluation of PermaNet ® Dual (a long-lasting net coated with a mixture of chlorfenapyr and deltamethrin) against pyrethroid-resistant Anopheles gambiae mosquitoes from Tiassalé, Côte d’Ivoire

Background: Due to the rapid expansion of pyrethroid-resistance in malaria vectors in Africa, Global Plan for Insecticide Resistance Management (GPIRM) has recommended the development of long-lasting insecticidal nets (LLINs), containing insecticide mixtures of active ingredients with different modes of action to mitigate resistance and improve LLIN efficacy. This good laboratory practice (GLP) study evaluated the efficacy of the chlorfenapyr and deltamethrin-coated PermaNet® Dual, in comparison with the deltamethrin and synergist piperonyl butoxide (PBO)-treated PermaNet® 3.0 and the deltamethrin-coated PermaNet® 2.0, against wild free-flying pyrethroid-resistant Anopheles gambiae sensu lato (s.l.), in experimental huts in Tiassalé, Côte d’Ivoire (West Africa). Methods: PermaNet® Dual, PermaNet® 3.0 and PermaNet® 2.0, unwashed and washed (20 washes), were tested against free-flying pyrethroid-resistant An. gambiae s.l. in the experimental huts in Tiassalé, Côte d’Ivoire from March to August 2020. Complementary laboratory cone bioassays (daytime and 3-min exposure) and tunnel tests (nightly and 15-h exposure) were performed against pyrethroid-susceptible An. gambiae sensu stricto (s.s.) (Kisumu strain) and pyrethroid-resistant An. gambiae s.l. (Tiassalé strain). Results: PermaNet® Dual demonstrated significantly improved efficacy, compared to PermaNet® 3.0 and PermaNet® 2.0, against the pyrethroid-resistant An. gambiae s.l. Indeed, the experimental hut trial data showed that the mortality and blood-feeding inhibition in the wild pyrethroid-resistant An. gambiae s.l. were overall significantly higher with PermaNet® Dual compared with PermaNet® 3.0 and PermaNet® 2.0, for both unwashed and washed samples. The mortality with unwashed and washed samples were 93.6 ± 0.2% and 83.2 ± 0.9% for PermaNet® Dual, 37.5 ± 2.9% and 14.4 ± 3.9% for PermaNet® 3.0, and 7.4 ± 5.1% and 11.7 ± 3.4% for PermaNet® 2.0, respectively. Moreover, unwashed and washed samples produced the respective percentage blood-feeding inhibition of 41.4 ± 6.9% and 43.7 ± 4.8% with PermaNet® Dual, 51.0 ± 5.7% and 9.8 ± 3.6% with PermaNet® 3.0, and 12.8 ± 4.3% and − 13.0 ± 3.6% with PermaNet® 2.0. Overall, PermaNet® Dual also induced higher or similar deterrence, exophily and personal protection when compared with the standard PermaNet® 3.0 and PermaNet® 2.0 reference nets, with both unwashed and washed net samples. In contrast to cone bioassays, tunnel tests predicted the efficacy of PermaNet® Dual seen in the current experimental hut trial. Conclusion: The deltamethrin-chlorfenapyr-coated PermaNet® Dual induced a high efficacy and performed better than the deltamethrin-PBO PermaNet® 3.0 and the deltamethrin-only PermaNet® 2.0, testing both unwashed and 20 times washed samples against the pyrethroid-susceptible and resistant strains of An. gambiae s.l. The inclusion of chlorfenapyr with deltamethrin in PermaNet® Dual net greatly improved protection and control of pyrethroid-resistant An. gambiae populations. PermaNet® Dual thus represents a promising tool, with a high potential to reduce malaria transmission and provide community protection in areas compromised by mosquito vector resistance to pyrethroids

A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data

Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses.Results: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system.Conclusion: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences. <br /

Computational classifiers for predicting the short-term course of Multiple sclerosis

The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS). METHODS: We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center. RESULTS: We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later. CONCLUSIONS: The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability