A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders

Publicado en Lecture Notes in Computer Science.The diagnosis and prognosis of cancer are among the more challenging tasks that oncology medicine deals with. With the main aim of fitting the more appropriate treatments, current personalized medicine focuses on using data from heterogeneous sources to estimate the evolu- tion of a given disease for the particular case of a certain patient. In recent years, next-generation sequencing data have boosted cancer prediction by supplying gene-expression information that has allowed diverse machine learning algorithms to supply valuable solutions to the problem of cancer subtype classification, which has surely contributed to better estimation of patient’s response to diverse treatments. However, the efficacy of these models is seriously affected by the existing imbalance between the high dimensionality of the gene expression feature sets and the number of sam- ples available for a particular cancer type. To counteract what is known as the curse of dimensionality, feature selection and extraction methods have been traditionally applied to reduce the number of input variables present in gene expression datasets. Although these techniques work by scaling down the input feature space, the prediction performance of tradi- tional machine learning pipelines using these feature reduction strategies remains moderate. In this work, we propose the use of the Pan-Cancer dataset to pre-train deep autoencoder architectures on a subset com- posed of thousands of gene expression samples of very diverse tumor types. The resulting architectures are subsequently fine-tuned on a col- lection of specific breast cancer samples. This transfer-learning approach aims at combining supervised and unsupervised deep learning models with traditional machine learning classification algorithms to tackle the problem of breast tumor intrinsic-subtype classification.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Downstream scour of combined flow over weirs and below gates

River morphodynamics and sediment transportSediment-structure interactio

Rare Earth Elements Recovery Using Selective Membranes via Extraction and Rejection.

Recently, demands for raw materials like rare earth elements (REEs) have increased considerably due to their high potential applications in modern industry. Additionally, REEs' similar chemical and physical properties caused their separation to be difficult. Numerous strategies for REEs separation such as precipitation, adsorption and solvent extraction have been applied. However, these strategies have various disadvantages such as low selectivity and purity of desired elements, high cost, vast consumption of chemicals and creation of many pollutions due to remaining large amounts of acidic and alkaline wastes. Membrane separation technology (MST), as an environmentally friendly approach, has recently attracted much attention for the extraction of REEs. The separation of REEs by membranes usually occurs through three mechanisms: (1) complexation of REE ions with extractant that is embedded in the membrane matrix, (2) adsorption of REE ions on the surface created-active sites on the membrane and (3) the rejection of REE ions or REEs complex with organic materials from the membrane. In this review, we investigated the effect of these mechanisms on the selectivity and efficiency of the membrane separation process. Finally, potential directions for future studies were recommended at the end of the review

Intelligent negotiation model for ubiquitous group decision scenarios

Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group decision-making process specially designed for ubiquitous contexts. Our approach can be used by researchers that intend to include arguments, complex algorithms and agents' modelling in a negotiation model. It uses a social networking logic due to the type of communication employed by the agents and it intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings. We propose a new look into this problematic by considering and defining strategies to deal with important points such as the type of attributes in the multicriteria problems, agents' reasoning and intelligent dialogues.This work has been supported by COMPETE Programme (operational programme for competitiveness) within project POCI-01-0145-FEDER-007043, by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the Projects UID/CEC/00319/2013, UID/EEA/00760/2013, and the João Carneiro PhD grant with the reference SFRH/BD/89697/2012 and by Project MANTIS - Cyber Physical System Based Proactive Collaborative Maintenance (ECSEL JU Grant nr. 662189).info:eu-repo/semantics/publishedVersio

DprE2 is a molecular target of the anti-tubercular nitroimidazole compounds pretomanid and delamanid

Abstract Mycobacterium tuberculosis is one of the global leading causes of death due to a single infectious agent. Pretomanid and delamanid are new antitubercular agents that have progressed through the drug discovery pipeline. These compounds are bicyclic nitroimidazoles that act as pro-drugs, requiring activation by a mycobacterial enzyme; however, the precise mechanisms of action of the active metabolite(s) are unclear. Here, we identify a molecular target of activated pretomanid and delamanid: the DprE2 subunit of decaprenylphosphoribose-2’-epimerase, an enzyme required for the synthesis of cell wall arabinogalactan. We also provide evidence for an NAD-adduct as the active metabolite of pretomanid. Our results highlight DprE2 as a potential antimycobacterial target and provide a foundation for future exploration into the active metabolites and clinical development of pretomanid and delamanid

Development of NMR and thermal shift assays for the evaluation of Mycobacterium tuberculosis isocitrate lyase inhibitors.

The enzymes isocitrate lyase (ICL) isoforms 1 and 2 are essential for Mycobacterium tuberculosis survival within macrophages during latent tuberculosis (TB). As such, ICLs are attractive therapeutic targets for the treatment of tuberculosis. However, there are few biophysical assays that are available for accurate kinetic and inhibition studies of ICL in vitro. Herein we report the development of a combined NMR spectroscopy and thermal shift assay to study ICL inhibitors for both screening and inhibition constant (IC50) measurement. Operating this new assay in tandem with virtual high-throughput screening has led to the discovery of several new ICL1 inhibitors

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Introduction: Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques. Methods: Sleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis. Results: Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30 and 89.93 accuracy, respectively. Discussion and Conclusion: Similar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders. © 2018 Alizadeh Savareh et al

Core-Shell catalyst particles for tandem catalysis: An experimental/numerical approach towards optimal design

Tandem catalysis is a promising approach to intensify chemical processes and increase their efficiency. On the other hand, the design of efficient, optimal and targeted tailored tandem catalysts is yet so challenging as the optimal catalyst loading is difficult to assess a priori. In this article, we present a concise route towards the design of optimal core-shell tandem catalyst particles on the example of coupled RWGS and FTS reactions for any specific spherical morphology. The route features five consecutive steps including: tandem system identification, catalyst synthesis, i.e. mono- and tandem-functional, catalyst characterization and initial performance test, kinetic modeling with parameter estimation if necessary, and optimal design of catalysts. The initial step features thermodynamic equilibrium calculations for RWGS and FTS showing a common operational window. Then, Pt and Co are selected as active metals and the formulation of the tandem catalyst is designed. For the second step, the synthesis route for the tandem catalyst Pt,CeO2@SiO2-Co, and the mono-functional catalysts, Pt@SiO2 and CeO2@SiO2-Co are presented. For the third step, all catalysts were tested for CO2 hydrogenation as an exemplary tandem process. A reduced transport model from literature was adjusted for RWGS and FTS reactions with kinetic expression from literature to enable numerical optimization. The kinetic parameters are estimated based on the performance tests of the mono-functional reference materials, i.e. Pt@SiO2 for RWGS and CeO2@SiO2-Co for FTS. The model is validated by cross comparison to the data from the tandem reaction setup. In the fifth step, the model was used for the numerical optimization of the catalyst loading on core and shell leading to the identification of the optimal design, resulting in a significant increase of C2+ Yield

Dynamic analysis of thick beams with functionally graded porous layers and viscoelastic support

This study presents dynamic responses of a composite thick beam with a functionally graded porous layer under dynamic sine pulse load. The boundary conditions of the composite beam are considered as viscoelastic supports. Three layers are considered, and face sheet layers have porous functionally graded materials in which the distribution of material gradation through the graded layer is described by the power law function, and the porosity is depicted by three different distributions (i.e., symmetric distribution, X distribution, and LOZENGE distribution). The layered composite thick beam is modeled as a two-dimensional plane stress problem. The equation of motion is obtained by Lagrange's equations. In formation of the problem, the finite element method is used with a 12-node 2D plane element. In the solution process of the dynamic problem, a numerical time integration method of the Newmark method is used. In numerical analyses, influences of stiffness and damping coefficients of viscoelastic supports, material gradation index, porosity parameter, and porosity models on the dynamic response of thick functionally graded porous beam are investigated under the pulse load

An evaluation model for the implementation of hospital information system in public hospitals using multi-criteria-decision-making (MCDM) approaches

Background: Hospital Information System (HIS) is implemented to provide high-quality patient care. The aim of this study is to identify significant dimensional factors that influence the hospital decision in adopting the HIS. Methods: This study designs the initial integrated model by taking the three main dimensions in adopting HIS technology. Accordingly, DEMATEL was utilized to test the strength of interdependencies among the dimensions and variables. Then ANP approach is adapted to determining how the factors are weighted and prioritized by professionals and main users working in the Iranian public hospitals, in-volved with the HIS system. Results: The results indicated that "Perceived Technical Competence" is a key factor in the Human dimension. The respondents also believed that "Relative Advantage," "Compatibility" and "Security Concern" of Technology dimension should be further assessed in relation to other factors. With respect to Organization dimension, "Top Management Support" and "Vendor Support" are considered more important than others. Conclusion: Applying the TOE and HOT-fit models as the pillar of our developed model with significant findings add to the growing literature on the factors associated with the adoption of HIS and also shed some light for managers of public hospitals in Iran to success-fully adopt the HIS