RNApolis: Computational Platform for RNA Structure Analysis

In the 1970s, computer scientists began to engage in research in the field of structural biology. The first structural databases, as well as models and methods supporting the analysis of biomolecule structures, started to be created. RNA was put at the centre of scientific interest quite late. However, more and more methods dedicated to this molecule are currently being developed. This paper presents RNApolis - a new computing platform, which offers access to seven bioinformatic tools developed to support the RNA structure study. The set of tools include a structural database and systems for predicting, modelling, annotating and evaluating the RNA structure. RNApolis supports research at different structural levels and allows the discovery, establishment, and validation of relationships between the primary, secondary and tertiary structure of RNAs. The platform is freely available at http://rnapolis.pl

Extraction and Research of Crop Feature Points Based on Computer Vision

Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

Mappism: formalizing classical and artificial life views on mind and consciousness

Throughout centuries philosophers have attempted to understand the disparity between the conscious experience and the material world – i.e., the problem of consciousness and the apparent mind–body dualism. Achievements in the fields of biology, neurology, and information science in the last century granted us more insight into processes that govern our minds. While there are still many mysteries to be solved when it comes to fully understanding the inner workings of our brains, new discoveries suggest stepping away from the metaphysical philosophy of mind, and closer to the computational viewpoint. In light of the advent of strong artificial intelligence and the development of increasingly complex artificial life models and simulations, we need a well-defined, formal theory of consciousness. In order to facilitate this, in this work we introduce mappism. Mappism is a framework in which alternative views on consciousness can be formally expressed in a uniform way, thus allowing one to analyze and compare existing theories, and enforcing the use of the language of mathematics, i.e, explicit functions and variables. Using this framework, we describe classical and artificial life approaches to consciousness.

Texture Analysis of Imaging: What Radiologists Need to Know

OBJECTIVE

Radiologic texture is the variation in image intensities within an image and is an important part of radiomics. The objective of this article is to discuss some parameters that affect the performance of texture metrics and propose recommendations that can guide both the design and evaluation of future radiomics studies.

CONCLUSION

A variety of texture-extraction techniques are used to assess clinical imaging data. Currently, no consensus exists regarding workflow, including acquisition, extraction, or reporting of variable settings leading to poor reproducibility.

Detecting life signatures with RNA sequence similarity measures

The RNA World is currently the most plausible hypothesis for explaining the origins of life on Earth. The supporting body of evidence is growing and it comes from multiple areas, including astrobiology, chemistry, biology, mathematics, and, in particular, from computer simulations. Such methods frequently assume the existence of a hypothetical species on Earth, around three billion years ago, with a base sequence probably dissimilar from any in known genomes. However, it is often hard to verify whether or not a hypothetical sequence has the characteristics of biological sequences, and is thus likely to be functional. The primary objective of the presented research was to verify the possibility of building a computational 'life probe' for determining whether a given genetic sequence is biological, and assessing the sensitivity of such probes to the signatures of life present in known biological sequences. We have proposed decision algorithms based on the normalized compression distance (NCD) and Levenshtein distance (LD). We have validated the proposed method in the context of the RNA World hypothesis using short genetic sequences shorter than the error threshold value (i.e., 100 nucleotides). We have demonstrated that both measures can be successfully used to construct life probes that are significantly better than a random decision procedure, while varying from each other when it comes to detailed characteristics. We also observed that fragments of sequences related to replication have better discriminatory power than sequences having other molecular functions. In a broader context, this shows that the signatures of life in short RNA samples can be effectively detected using relatively simple means.

Sustainability in Material Purchasing: A Multi-Objective Economic Order Quantity Model under Carbon Trading

Sustainability in material purchasing is a growing area of research. Goods purchasing decisions strongly affect transportation path flows, vehicle consolidation, inventory levels, and related obsolescence costs. These choices have an economic impact on the supply chain, in terms of different logistic costs, and an environmental impact, in terms of the carbon emissions produced during goods transportation, storage and final recovery. In this paper, we initially analyze and compare the environmental economic policies established by the International Governments in relation to the carbon trading systems adopted. Then, we overcome a traditional single objective formulation, by developing a bi-objective lot-sizing model in which costs and emissions are kept separated and analyzed by using a Pareto frontier subject to a Cap and Trade mitigation policy. The model is useful in practice to support managers in understanding the Pareto frontier shape linked to a specific purchasing problem, defining the cost-optimal and emission-optimal solutions and identifying a sustainable quantity to purchase when a Cap and Trade mitigation policy is present. We further analyze the model behavior according to variation in market carbon price and we finally analytically demonstrate that today carbon prices are still far too low to motivate managers towards sustainable purchasing choices: there is still a gap of about 79%.

Optimal Design of Nanoparticle Enhanced Phan-Thien-Tanner Flow of a Viscoelastic Fluid in a Microchannel

The excellent thermal characteristics of nanoparticles have increased their application in the field of heat transfer. In this paper, a thermophysical and geometrical parameter study is performed to minimize the total entropy generation of the viscoelastic flow of nanofluid. Entropy generation with respect to volume fraction (<0.04), the Reynolds number (20,000-100,000), and the diameter of the microchannel (20-20,000 μm) with the circular cross-section under constant flux are calculated. As is shown, most of the entropy generation owes to heat transfer and by increasing the diameter of the channel, the Bejan number increases. The contribution of heat entropy generation in the microchannel is very poor and the major influence of entropy generation is attributable to friction. The maximum quantity of in-channel entropy generation happens in nanofluids with TiO₂, CuO, Cu, and Ag nanoparticles, in turn, despite the fact in the microchannel this behavior is inverted, the minimum entropy generation occurs in nanofluids with CuO, Cu, Ag, and TiO₂ nanoparticles, in turn. In the channel and microchannel for all nanofluids except water-TiO₂, increasing the volume fraction of nanoparticles decreases entropy generation. In the channel and microchannel the total entropy generation increases by augmentation the Reynolds number.

Universes and simulations: Civilizational development in nested embedding

The rapid development of technology has allowed computer simulations to become routinely used in an increasing number of fields of science. These simulations become more and more realistic, and their energetic efficiency grows due to progress in computer hardware and software. As humans merge with machines via implants, brain-computer interfaces and increased activity involving information instead of material objects, philosophical concepts and theoretical considerations on the nature of reality are beginning to concern practical, working models and testable virtual environments. This article discusses how simulation is understood and employed in computer science today, how software, hardware and the physical universe unify, how simulated realities are embedded one in another, how complicated it can get in application, practical scenarios, and the possible consequences of these situations. A number of basic properties of universes and simulations in such multiply nested structures are reviewed, and the relationship of these properties with a level of civilizational development is explored.

Direct Depth SLAM: Sparse Geometric Feature Enhanced Direct Depth SLAM System for Low-Texture Environments

This paper presents a real-time, robust and low-drift depth-only SLAM (simultaneous localization and mapping) method for depth cameras by utilizing both dense range flow and sparse geometry features from sequential depth images. The proposed method is mainly composed of three optimization layers, namely Direct Depth layer, ICP (Iterative closest point) Refined layer and Graph Optimization layer. The Direct Depth layer uses a range flow constraint equation to solve the fast 6-DOF (six degrees of freedom) frame-to-frame pose estimation problem. Then, the ICP Refined layer is used to reduce the local drift by applying local map based motion estimation strategy. After that, we propose a loop closure detection algorithm by extracting and matching sparse geometric features and construct a pose graph for the purpose of global pose optimization. We evaluate the performance of our method using benchmark datasets and real scene data. Experiment results show that our front-end algorithm clearly over performs the classic methods and our back-end algorithm is robust to find loop closures and reduce the global drift.

Computational Fluid Dynamic Accuracy in Mimicking Changes in Blood Hemodynamics in Patients with Acute Type IIIb Aortic Dissection Treated with TEVAR

Background: We aimed to verify the accuracy of the Computational Fluid Dynamics (CFD) algorithm for blood flow reconstruction for type IIIb aortic dissection (TBAD) before and after thoracic endovascular aortic repair (TEVAR). Methods: We made 3D models of the aorta and its branches using pre- and post-operative CT data from five patients treated for TBAD. The CFD technique was used to quantify the displacement forces acting on the aortic wall in the areas of endograft, mass flow rate/velocity and wall shear stress (WSS). Calculated results were verified with ultrasonography (USG-Doppler) data. Results: CFD results indicated that the TEVAR procedure caused a 7-fold improvement in overall blood flow through the aorta (p = 0.0001), which is in line with USG-Doppler data. A comparison of CFD results and USG-Doppler data indicated no significant change in blood flow through the analysed arteries. CFD also showed a significant increase in flow rate for thoracic trunk and renal arteries, which was in accordance with USG-Doppler data (accuracy 90% and 99.9%). Moreover, we observed a significant decrease in WSS values within the whole aorta after TEVAR compared to pre-TEVAR (1.34 ± 0.20 Pa vs. 3.80 ± 0.59 Pa, respectively, p = 0.0001). This decrease was shown by a significant reduction in WSS and WSS contours in the thoracic aorta (from 3.10 ± 0.27 Pa to 1.34 ± 0.11Pa, p = 0.043) and renal arteries (from 4.40 ± 0.25 Pa to 1.50 ± 0.22 Pa p = 0.043). Conclusions: Post-operative remodelling of the aorta after TEVAR for TBAD improved hemodynamic patterns reflected by flow, velocity and WSS with an accuracy of 99%.

Structural analysis of a Petri net model of oxidative stress in atherosclerosis

Atherosclerosis is a complex process of gathering sub-endothelial plaques decreasing lumen of the blood vessels. This disorder affects people of all ages, but its progression is asymptomatic for many years. It is regulated by many typical and atypical factors including the immune system response, a chronic kidney disease, a diet rich in lipids, a local inflammatory process and a local oxidative stress that is here one of the key factors. In this study, a Petri net model of atherosclerosis regulation is presented. This model includes also some information about stoichiometric relationships between its components and covers all mentioned factors. For the model, a structural analysis based on invariants was made and biological conclusions are presented. Since the model contains inhibitor arcs, a heuristic method for analysis of such cases is presented. This method can be used to extend the concept of feasible t-invariants.

A Batching Machine Model for Lot Scheduling on a Single Machine

A recently introduced lot scheduling problem is considered. It is to find a partition of jobs of n orders into lots and to sequence these lots on a single machine so that the total average completion time of the orders is minimized. A simple O(n log n) time algorithm is presented for this problem in the literature, with a relatively sophisticated proof of its optimality. We show that modeling this problem as a classic batching machine problem makes its optimal solution obvious.

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Software defect prediction is a promising approach aiming to increase software quality and, as a result, development pace. Unfortunately, the cost effectiveness of software defect prediction in industrial settings is not eagerly shared by the pioneering companies. In particular, this is the first attempt to investigate the cost effectiveness of using the DePress open source software measurement framework (jointly developed by Wroclaw University of Science and Technology, and Capgemini software development company) for defect prediction in commercial software projects. We explore whether defect prediction can positively impact an industrial software development project by generating profits. To meet this goal, we conducted a defect prediction and simulated potential quality assurance costs based on the best possible prediction results when using a default, non-tweaked DePress configuration, as well as the proposed Quality Assurance (QA) strategy. Results of our investigation are optimistic: we estimated that quality assurance costs can be reduced by almost 30% when the proposed approach will be used, while estimated DePress usage Return on Investment (ROI) is fully 73 (7300%), and Benefits Cost Ratio (BCR) is 74. Such promising results, being the outcome of the presented research, have caused the acceptance of continued usage of the DePress-based software defect prediction for actual industrial projects run by Volvo Group.

A Novel Decision Support Framework for Computing Expected Utilities from Linguistic Evaluations

The increase in the amount and variety of evaluations provided by the users of different websites regarding the products displayed is becoming an increasingly familiar scenario. That is, decision makers (DMs) constantly receive linguistic evaluations (LEs) from unknown evaluators when considering different choice alternatives. The imprecision of the LEs and the fact that the evaluators may have biased interests when describing a product must be considered by the DMs when computing their expected utilities. We define a Bayesian-updated probability (BUP) function that accounts for the fuzziness inherent in the LEs and the reputation of the evaluator to represent the beliefs of DMs. The proposed BUP process allows the DMs to subjectively adjust the probability mass that is shifted across evaluation intervals when updating their beliefs and computing their corresponding expected utilities. We illustrate the behavior of the BUP function numerically and describe potential decision support applications.

Simulating the origins of life: The dual role of RNA replicases as an obstacle to evolution

Despite years of study, it is still not clear how life emerged from inanimate matter and evolved into the complex forms that we observe today. One of the most recognized hypotheses for the origins of life, the RNA World hypothesis, assumes that life was sparked by prebiotic replicating RNA chains. In this paper, we address the problems caused by the interplay between hypothetical prebiotic RNA replicases and RNA parasitic species. We consider the coexistence of parasite RNAs and RNA replicases as well as the impact of parasites on the further evolution of replicases. For these purposes, we used multi-agent modeling techniques that allow for realistic assumptions regarding the movement and spatial interactions of modeled species. The general model used in this study is based on work by Takeuchi and Hogeweg. Our results confirm that the coexistence of parasite RNAs and replicases is possible in a spatially extended system, even if we take into consideration more realistic assumptions than Takeuchi and Hogeweg. However, we also showed that the presence of trade-off that takes into the account an RNA folding process could still pose a serious obstacle to the evolution of replication. We conclude that this might be a cause for one of the greatest transitions in life that took place early in evolution-the separation of the function between DNA templates and protein enzymes, with a central role for RNA species.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Bioinformatics Study of Structural Patterns in Plant MicroRNA Precursors

According to the RNA world theory, RNAs which stored genetic information and catalyzed chemical reactions had their contribution in the formation of current living organisms. In recent years, researchers studied this molecule diversity, i.a. focusing on small non-coding regulatory RNAs. Among them, of particular interest is evolutionarily ancient, 19–24 nt molecule of microRNA (miRNA). It has been already recognized as a regulator of gene expression in eukaryotes. In plants, miRNA plays a key role in the response to stress conditions and it participates in the process of growth and development. MicroRNAs originate from primary transcripts (pri-miRNA) encoded in the nuclear genome. They are processed from single-stranded stem-loop RNA precursors containing hairpin structures. While the mechanism of mature miRNA production in animals is better understood, its biogenesis in plants remains less clear. Herein, we present the results of bioinformatics analysis aimed at discovering how plant microRNAs are recognized within their precursors (pre-miRNAs). The study has been focused on sequential and structural motif identification in the neighbourhood of microRNA.

Estimating contaminant source in chemical industry park using UAV-based monitoring platform, artificial neural network and atmospheric dispersion simulation

The ANN-based source estimation approach can effectively locate and quantify the emission source using data from a UAV.

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classification of vector data, intuitively introduced by Kohonen. The prototype adaptation scheme relies on its attraction and repulsion during the learning providing an easy geometric interpretability of the learning as well as of the classification decision scheme. Although deep learning architectures and support vector classifiers frequently achieve comparable or even better results, LVQ models are smart alternatives with low complexity and computational costs making them attractive for many industrial applications like intelligent sensor systems or advanced driver assistance systems.

Nowadays, the mathematical theory developed for LVQ delivers sufficient justification of the algorithm making it an appealing alternative to other approaches like support vector machines and deep learning techniques.

This review article reports current developments and extensions of LVQ starting from the generalized LVQ (GLVQ), which is known as the most powerful cost function based realization of the original LVQ. The cost function minimized in GLVQ is an soft-approximation of the standard classification error allowing gradient descent learning techniques. The GLVQ variants considered in this contribution, cover many aspects like bordersensitive learning, application of non-Euclidean metrics like kernel distances or divergences, relevance learning as well as optimization of advanced statistical classification quality measures beyond the accuracy including sensitivity and specificity or area under the ROC-curve.

According to these topics, the paper highlights the basic motivation for these variants and extensions together with the mathematical prerequisites and treatments for integration into the standard GLVQ scheme and compares them to other machine learning approaches. For detailed description and mathematical theory behind all, the reader is referred to the respective original articles.

Thus, the intention of the paper is to provide a comprehensive overview of the stateof- the-art serving as a starting point to search for an appropriate LVQ variant in case of a given specific classification problem as well as a reference to recently developed variants and improvements of the basic GLVQ scheme.

Handling the Multiplicity of Solutions in a Moea Based PDA-THESEUS Framework for Multi-Criteria Sorting

This paper proposes the combination of the THESEUS multi-criteria sorting method with an evolutionary optimization-based preference-disaggregation analysis. The main features of the combined method are studied by performing an extensive computer experiment that explores many models of preferences and sizes of problems as well as different degrees of decision-maker involvement. As a result of the experiment, the effectiveness of the combined framework and the importance of the decision-maker’s involvement are characterized.

CAC DPLB MCN: A Distributed Load Balancing Scheme in Multimedia Mobile Cellular Networks

The problem of non-uniform traffic demand in different cells of a cellular network may lead to a gross imbalance in the system performance. Thus, the users in hot cells may suffer from low throughput. In this paper, an effective and simple load balancing scheme CAC_DPLB_MCN is proposed that can effectively reduce the overall call blocking. This model considers dealing with multi-media traffic as well as time-varying geographical traffic distribution. The proposed scheme uses the concept of cell-tiering thereby creating fractional frequency reuse environment. A message exchange based distributed scheme instead of centralized one is used which help the proposed scheme be implemented in a multiple hot cell environment also. Furthermore, concept of dynamic pricing is used to serve the best interest of the users as well as for the service providers. The performance of the proposed scheme is compared with two other existing schemes in terms of call blocking probability and bandwidth utilization. Simulation results show that the proposed scheme can reduce the call blocking significantly in highly congested cell with highest bandwidth utilization. Use of dynamic pricing also makes the scheme useful to increase revenue of the service providers in contrast with compared schemes.

Genetic Algorithms for Feature Selection for Brain–Computer Interface

The crucial problem that has to be solved when designing an effective brain–computer interface (BCI) is: how to reduce the huge space of features extracted from raw electroencephalography (EEG) signals. One of the strategies for feature selection that is often applied by BCI researchers is based on genetic algorithms (GAs). The two types of GAs that are most commonly used in BCI research are the classic algorithm and the Culling algorithm. This paper presents both algorithms and their application for selecting features crucial for the correct classification of EEG signals recorded during imagery movements of the left and right hand. The results returned by both algorithms are compared to those returned by an algorithm with aggressive mutation and an algorithm with melting individuals, both of which have been proposed by the author of this paper. While the aggressive mutation algorithm has been published previously, the melting individuals algorithm is presented here for the first time.

Simultaneous channel and feature selection of fused EEG features based on Sparse Group Lasso

Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs). Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.