bluuebunny/crossref_metadata_2025 huggingface dataset:
Applications of Supercomputers in Sequence Analysis and Genome Annotation
Authors: Dumancas Gerard G.
Published: May 2019

In the modern era of science, bioinformatics play a critical role in unraveling the potential genetic causes of various diseases. Two of the most important areas of bioinformatics today, sequence analysis and genome annotation, are essential for the success of identifying the genes responsible for different diseases. These two emerging areas utilize highly intensive mathematical calculations in order to carry out the processes. Supercomputers facilitate such calculations in an efficient and time-saving manner generating high-throughput images. Thus, this chapter thoroughly discusses the applications of supercomputers in the areas of sequence analysis and genome annotation. This chapter also showcases sophisticated software and algorithms utilized by the two mentioned areas of bioinformatics.
Big Data Analysis Techniques for Visualization of Genomics in Medicinal Plants
Authors: Kumar Hithesh , Chandramohan Vivek , Simon Smrithy M. , Yadav Rahul , Kumar Shashi
Published: May 2019

In this chapter, the complete overview and application of Big Data analysis in the field of health care industries, Clinical Informatics, Personalized Medicine and Bioinformatics is provided. The major tools and databases used for the Big Data analysis are discussed in this chapter. The development of sequencing machines has led to the fast and effective ways of generating DNA, RNA, Whole Genome data, Transcriptomics data, etc. available in our hands in just a matter of hours. The complete Next Generation Sequencing (NGS) huge data analysis work flow for the medicinal plants are discussed in the chapter. This chapter serves as an introduction to the big data analysis in Next Generation Sequencing and concludes with a summary of the topics of the remaining chapters of this book.
Cancer Biomarker Assessment Using Evolutionary Rough Multi-Objective Optimization Algorithm
Authors: Sarkar Anasua , Maulik Ujjwal
Published: May 2019

A hybrid unsupervised learning algorithm, which is termed as Evolutionary Rough Multi-Objective Optimization (ERMOO) algorithm, is proposed in this chapter. It comprises a judicious integration of the principles of the rough sets theory with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation deals with the incompleteness in the dynamic classification method with the quality of classification coefficient as the classificatory competence measurement, it enables faster convergence of the Pareto-archived evolution strategy. It incorporates both the rough set-based dynamic archive classification method in this algorithm. A measure of the amount of domination between two solutions is incorporated in this chapter to determine the acceptance probability of a new solution with an improvement in the spread of the non-dominated solutions in the Pareto-front by adopting rough sets theory. The performance is demonstrated on real-life breast cancer dataset for identification of Cancer Associated Fibroblasts (CAFs) within the tumor stroma, and the identified biomarkers are reported. Moreover, biological significance tests are carried out for the obtained markers.
Analysis of Microarray Data Using Artificial Intelligence Based Techniques
Authors: Raza Khalid
Published: May 2019

Microarray is one of the essential technologies used by the biologists to measure genome-wide expression levels of genes in a particular organism under some particular conditions or stimuli. As microarrays technologies have become more prevalent, the challenges of analyzing these data for getting better insight about biological processes have essentially increased. Due to availability of artificial intelligence based sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired algorithms, it is possible to analyse microarray gene expression data in a better way. In this chapter, we present artificial intelligence based techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been suggested.
Implanted Cardiac Pacemaker Mathematical Modeling and Research Based on the Volume Conduction
Authors: Feng Lixiao , Bai Junjie , Chen Chengyuan , Peng Jun , Chen Guorong
Published: May 2019

The research of the communication between implanted Cardiac Pacemaker and external devices is a focus. In this paper, a data communications model based Volume Conduction is creatively presented, in this way the human body conductive ability will been utilized to transmit current, which is more effective and decrease for harm of the human body than the other ways, such as: RF, Optical Transcutaneous, etc. As the frequency increases the KHz level, the effect of background biological noise is considered negligible, the channel is thus modeled as AWGN channel in these frequencies. From Shannon information theory, in two-dimensional modulation, the volume conduction channel capacity formula was derived, further derivation: with extremely low SNR using in the two-level modulation can be very effective use of channel capacity, with high SNR a multi-level modulation is used in order to make full use of the channel capacity. Matlab software is used to the channel simulation, the input and output signal waveforms and eye diagram comparison, the curves of the BER and SNR.
Bioremediation
Authors: Singh Vikram
Published: May 2019

Pollution is the biggest menace to the living being in this planet today. Enzyme bioremediation is a “breakthrough technology” that holds the potential of pollutant eradication through exploiting the enzyme potential by using the various techniques. Enzyme biocatalysis is referred as white biotechnology and work by green chemistry concept. Moreover, developments in the design and application of enzyme cocktails, mutienzyme complexes, promiscuous enzymes and protein families (cupin and VOC superfamily) has recently emerged a new opportunity in bioremediation. The implementation of various enzyme modification approaches intended for potential bioremediation has been done by adopting enzyme immobilization using magnetic nanoparticles, designer enzymes generation through enzyme engineering, nano-technological advancement for single enzyme nanoparticle generations, electro-bioremediation and carbon nanotube construction. Hence, enzyme bioremediation have greater positive effects and propose significant promise to pollutant bioremediation. In conclusion, the enzymatic bioremediation open the new era of pollutant eradication for clean, safe and green environment.
Mycoremediation of Lignocelluloses
Authors: Vara Saritha
Published: May 2019

The most abundant aromatic biopolymer on earth Lignin is extremely recalcitrant to degradation. It creates a barrier to solutions or enzymes by linking to both hemicellulose and cellulose preventing the penetration of lignocellulolytic enzymes into the interior lignocellulosic structure. Global attention has been gained by fungi owing to the potential use of their versatile enzymes for agriculture, medicines, industries and bioremediation. The combination of extracellular ligninolytic enzymes, mediators, organic acids and accessory enzymes make some of the basidiomycete white-rot fungi to be able to degrade lignin efficiently. This review describes remediation of lignocelluloses by fungi, properties of fungi, their spatial distribution and the mechanisms of action which render them attractive candidates in biotechnological applications like biopulping, animal feed, genetic engineering and space exploration.
Higher Order Sliding Mode Control for Blood Glucose Regulation of Type 1 Diabetic Patients
Authors: Djouima Mounir , Azar Ahmad Taher , Drid Saïd , Mehdi Driss
Published: May 2019

Type 1 diabetes mellitus (T1DM) treatment depends on the delivery of exogenous insulin to obtain near normal glucose levels. This article proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on comparing the first order sliding mode control (FOSMC) with a higher order SMC based on the super twisting control algorithm. The higher order sliding mode is used to overcome chattering, which can induce some undesirable and harmful phenomena for human health. In order to test the controller in silico experiments, Bergman's minimal model is used for studying the dynamic behavior of the glucose and insulin inside human body. Simulation results are presented to validate the effectiveness and the good performance of this control technique. The obtained results clearly reveal improved performance of the proposed higher order SMC in regulating the blood glucose level within the normal glycemic range in terms of accuracy and robustness.
Genome Sequence Analysis in Distributed Computing Using Spark
Authors: Ap. Sagar , Mehta Pooja , J. Anuradha , Tripathy B.K.
Published: May 2019

Integration of Computer Science with Bio Science has led to new field Computational Biology which created an opportunity in speeding up the process of analyzing the Bio-data. DNA sequence analysis especially finding the base pairs that helps in identifying the order of nucleotides present in all living beings, it also helps in forensics for DNA profiling and parenting testing. This sequence analysis has been a challenging task in Computational Biology due to large volumes of data and need of more computational resources. Using a distributed file system with distributed computation of tasks can be one of the solutions to above problem. In this paper, the authors use Spark a query engine for large-scale data processing in analyzing the DNA sequence and extracting the base pairs and also they try to improve base pair extraction with improvised algorithms.
A Romance of the Three Kingdoms
Authors: Hanel Petr , He Jie , Fu Jingyan , Reid Susan , Niosi Jorge E.
Published: May 2019

The cornerstone of China's strategy for developing high-technology activities is construction of scientific parks to create conditions emulating the successful High-technology clusters in the West. This is a comparative case study of biotechnology clusters in Guangdong province and the better documented ones in Beijing and Shanghai and Shenzhen. The experience of the government-controlled and supported ‘construction' and evolution of biotechnology clusters in Beijing, Shanghai and Guangdong province is compared with high-tech clustering in the West. The paper documents the existence and performance of the hitherto largely ignored Guangzhou biopharmaceutical cluster. Despite the rapid development of biopharmaceutical activities in Guangdong province, biotechnology is not yet a significant high-tech/high value-added alternative to Guangdong's specialization the labor-intensive export industries.
The Turkish Biotechnology System
Authors: Cetindamar Dilek
Published: May 2019

This chapter empirically examines biotechnology innovation system in order to present the concerns of developing countries. Even though it is not possible to create standard prescriptions across countries, this paper aims to develop a solid understanding of how biotechnology and institutions co-evolve that might shed light to innovation policy issues for biotechnology across developing countries. The immediate goal is the Turkish policy makers but it will surely have policy implications for developing countries in general. Through mapping innovation processes/functions over time, it is possible to develop insights of the dynamics of innovation systems. This mapping is carried out for the Turkish biotechnology system, and the findings are summarized.
Data Science and Computational Biology
Authors: Jyothi Singaraju , P Bhargavi
Published: May 2019

Data Science and Computational biology is an interdisciplinary program that brings together the domain specific knowledge of science and engineering with relevant areas of computing and bioinformatics. Data science has the potential to revolutionise healthcare, and respond to the increasing volume and complexity in biomedical and bioinformatics data. From genomics to clinical records, from imaging to mobile health and personalised medicine, the data volume in biomedical research presents urgent challenges for computer science. This chapter elevates the researchers in what way data science play important role in Computational Biology such as Bio-molecular Computation, Computational Photonics, Medical Imaging, Scientific Computing, Structural Biology, Bioinformatics and Bio-Computing etc. Big data analytics of biological data bases, high performance computing in large sequence of genome database and Scientific Visualization are also discussed in this chapter.
Start-Ups and Spin-Offs in Biotechnology Sector in Poland
Authors: Białek-Jaworska Anna , Gabryelczyk Renata
Published: May 2019

This chapter concerns the subject of research-developmental activity of biotech spin-offs in Poland with particular reference to their strategy, determinants of their development and determinants of their financial standing. In the chapter, the authors analyse the determinants of biotech spin-offs and start-ups development in Poland in the light of the research commercialisation cooperation on the universities-business line. The literature overview contains the definition of a process for the commercialisation of the results of research and development (R&D) activity and components of companies' business models. The chapter defines key activities in the development of business models in the context of the commercialisation process and the life cycle of the company, especially at the start up and early stage. Quality-quantitative analysis includes the business models of seven biotechnology spin-offs traded on the alternative market of the Warsaw Stock Exchange, especially the structure of their intellectual capital, R&D expenses in relation to received subsides and grants, third-party shares in start up equity, and the ability to realise the “Go Global” strategy.
Microbial Ligninolysis
Authors: Paliwal Rashmi , Giri Krishna , Rai J.P.N
Published: May 2019

Lignin is the second most abundant natural polymeric carbon source on earth after cellulose. It is a plant-originated polymer with three-dimensional network of dimethoxylated (syringyl), monomethoxylated (guaiacyl), and non-methoxylated (phydroxyphenyl) phenylpropanoid and acetylated units. The structural complexity and insolubility of lignin make it highly recalcitrant for degradation. Its biological degradation is critical to the global carbon cycle. Bioligninolysis involves application of microorganisms and their enzymes in degradation of lignin whichprovide environmental friendly technology for various industrial applications. As a major repository of aromatic chemical structures, lignin bears paramount significance for its removal from woody plants/lignocellulosic material, owing to potential application of bioligninolytic systems on commercial scale. This chapter provides an overview of microbial ligninolysis and its role in carbon cycling, various industrial process and pollution abatement for natural ecosystem management.
Smart Device Clickers
Authors: Mattos Érika Bertozzi de Aquino , Guimarães Isabelle Mazza , Gonçalves da Silva Alexander , Barreto Claudia Marcia Borges , Teixeira Gerlinde Agate Platais Brasil
Published: May 2019

In the traditional instructional paradigm, faculty members act like actors on a stage. They memorize their speech and deliver it to the audience, many times with very little to no interaction at all with the audience. On the other hand, in the student-centered learning paradigm, faculty members act like coaches interacting full time with their team. This chapter is based on a study conducted at a Brazilian Federal University. The study depicts the distance between science production and teaching, and reports on experiences using smart phone clickers to track and analyze students' content acquisition. The objective is to improve the interactive quality of teaching and learning, thus promoting steps to shift towards a student-centered instructional paradigm. Although smartphones were used in this study, with wearable technologies continuing to grow, other wearables such as smart glasses and smart watches could be used instead.
Artificial Intelligence Ethics in Biomedical-Engineering-Oriented Problems
Authors: Pavaloiu Alice
Published: May 2019

The field of artificial intelligence has recently encountered some ethical questions associated with the future of humankind. Although it is a common question that has been asked for years, the existence of humankind against badly configured intelligent systems is more important nowadays. As a result of rapid developments in intelligent systems and their increasing role in our life, there is a remarkable anxiety about dangerous artificial intelligence. Because of that, some research interests gathered under some topics like machine ethics, future of artificial intelligence, and even existential risks are drawing researchers' interest. As associated with this state, the objective of this chapter is to examine ethical factors in using intelligent systems for biomedical-engineering-oriented purposes. The chapter firstly gives essential information about the background and then considers possible scenarios that may require ethical adjustments during design and development of artificial-intelligence-oriented systems for biomedical engineering problems.
Is China Catching Up?
Authors: Hanel Petr
Published: May 2019

The chapter examines whether Chinese health-related biotechnology is catching up with leaders in the field. The approach is inspired by Malerba's Sectoral System of Innovation and Production, complemented by Mathew's insight into strategies for latecomer firms. The results show that Chinese scientists are quickly catching up in the output of scientific publications. However, the basic research remains insufficient for the development of a sustainable, innovative industry. The industrial production of biotechnology-based manufacturing of drugs and medical devices is growing slower than their knowledge base. Most firms still manufacture under license or contract low-value “me too” generic pharmaceutical and biosimilar ingredients medicines. The intensity of R&D and patenting in China increased dramatically, especially in the foreign-invested firms but China's share of biotechnology patenting in the US, EPO and Japan are very low. In summary, Chinese biotechnology ‘industry' is catching up with the West, but it has a long way to go.
Perspectives on Data Integration in Human Complex Disease Analysis
Authors: Van Steen Kristel , Malats Nuria
Published: May 2019

The identification of causal or predictive variants/genes/mechanisms for disease-associated traits is characterized by “complex” networks of molecular phenotypes. Present technology and computer power allow building and processing large collections of these data types. However, the super-rapid data generation is counterweighted by a slow-pace for data integration methods development. Most currently available integrative analytic tools pertain to pairing omics data and focus on between-data source relationships, making strong assumptions about within-data source architectures. A limited number of initiatives exist aiming to find the most optimal ways to analyze multiple, possibly related, omics databases, and fully acknowledge the specific characteristics of each data type. A thorough understanding of the underlying assumptions of integrative methods is needed to draw sound conclusions afterwards. In this chapter, the authors discuss how the field of “integromics” has evolved and give pointers towards essential research developments in this context.
Microbial Degradation of Azo Dyes
Authors: Tischler Dirk , Qi Jingxian , Ngo Anna Christina R. , Schlömann Michael
Published: May 2019

Azo dyes are considered as xenobiotic compounds, which are often recalcitrant to be biodegraded. Many dyes are relevant for staining in industries, especially textile and food related. Among other environmental problems, one obvious issue is the coloring effect on effluents from industrial sites and thus the release of xenobiotics into nature. Microorganisms (algae, fungi, yeast, and bacteria) have been found to decolorize a number of azo dyes. Decolorization of azo dyes by microbial cultures is summarized and this is mostly linked to initial activation or even cleavage of the azo bond (e.g., by azoreductases). However, it does not necessarily mean that the compound is degraded to non-toxic products. Various mechanisms of microbial decolorization processes were discovered, including adsorption, enzymatic degradation, or a combination of both. Oxidases and reductases were found to be involved, which contain azoreductase, lignin peroxidase, Mn peroxidase, laccase, tyrosinase, and so on. A focus is on the azoreductases including classification, activity, and applicability.
Biosorption of Heavy Metals
Authors: Babu Neelesh , Pathak Vinay Mohan , Akash  , Navneet
Published: May 2019

Large-scale production of commodities for mankind by industries did huge damage to the environment. Industrial waste contains lots of toxic materials including heavy metals were drained to water bodies like river, lakes, ponds, etc. These effluents drastically ruin water quality as well as the soil fertility. Type of industry and its raw material decides quantity and quality of the emerged wastes including both biodegradable as well as non-biodegradable. Among non-biodegradable wastes, copper, chromium, nickel, cadmium, etc. are widespread contaminants of soil, water, and these are most common heavy metals. Several heavy metals such as cadmium, mercury, and lead are highly poisonous and fatal to human as well as animals. Several plants as well as microbes respond to heavy metals by diverse biological processes like biosorption to their cell wall and entrapment in their capsule, oxidation and reduction, precipitation, complexation, etc. These responses may help significantly in the remediation of heavy metals from the contaminated sites.
Innovation and Intellectual Property Rights
Authors: Campi Mercedes
Published: May 2019

As a contribution to the open debate regarding the effect of Intellectual Property Rights (IPRs) on innovation, this chapter postulates that the adoption of strong IPRs is not a necessary condition to foster innovation in the plant breeding industry. The chapter studies the evolution of the soybean breeding industry in the US and Argentina and shows that regardless the level of intellectual property protection, if there is an attractive and profitable market, firms may search for different appropriability strategies rather than changing their innovative behavior. The chapter finds that the growth rates of new soybean varieties are similar in both countries and the adoption rate is faster in Argentina where the IPRs system is weaker.
Advances in the Reduction of the Costs Inherent to Fossil Fuels' Biodesulfurization Towards Its Potential Industrial Application
Authors: Paixão Susana M. , Silva Tiago P. , Arez Bruno F. , Alves Luís
Published: May 2019

Biodesulfurization (BDS) process consists on the use of microorganisms for the removal of sulfur from fossil fuels. Through BDS it is possible to treat most of the organosulfur compounds recalcitrant to the conventional hydrodesulfurization (HDS), the petroleum industry's solution, at mild operating conditions, without the need for molecular hydrogen or metal catalysts. This technique results in lower emissions, smaller residue production and less energy consumption, which makes BDS an eco-friendly process that can complement HDS making it more efficient. BDS has been extensively studied and much is already known about the process. Clearly, BDS presents advantages as a complementary technique to HDS; however its commercial use has been delayed by several limitations both upstream and downstream the process. This study will comprehensively review and discuss key issues, like reduction of the BDS costs, advances and/or challenges for a competitive BDS towards its potential industrial application aiming ultra low sulfur fuels.
Reconstruction of Electrical Impedance Tomography Using Fish School Search, Non-Blind Search, and Genetic Algorithm
Authors: Barbosa Valter A. F. , Ribeiro Reiga R. , Feitosa Allan R. S. , Silva Victor L. B. A. , Rocha Arthur D. D. , Freitas Rafaela C. , Souza Ricardo E. , Santos Wellington P.
Published: May 2019

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that does not use ionizing radiation, with application both in environmental sciences and in health. Image reconstruction is performed by solving an inverse problem and ill-posed. Evolutionary Computation and Swarm Intelligence have become a source of methods for solving inverse problems. Fish School Search (FSS) is a promising search and optimization method, based on the dynamics of schools of fish. In this article the authors present a method for reconstruction of EIT images based on FSS and Non-Blind Search (NBS). The method was evaluated using numerical phantoms consisting of electrical conductivity images with subjects in the center, between the center and the edge and on the edge of a circular section, with meshes of 415 finite elements. The authors performed 20 simulations for each configuration. Results showed that both FSS and FSS-NBS were able to converge faster than genetic algorithms.
Laccase Catalysis
Authors: Patel Helina , Gupte Akshaya
Published: May 2019

The search for cost-effective and environmental benign practices for the production of bioactive compounds has gained considerable attention since last decade, due to increasing demand of eco-friendly processes. Many industries have started adopting routes for the development of green chemistry by employing enzymatic approaches to overcome the limitations of physico-chemical methods and environmental concerns. Laccase is one such enzyme which has gained considerable attention in recent years as a biocatalyst in organic synthesis. Laccases possess versatile biochemical properties and the reactions catalyzed by laccase require only molecular oxygen with concomitant release of water as a byproduct. They have been widely used for reactions such as dimerization, polymerization, coupling, and grafting reactions and for antibiotic modifications. This chapter summarizes the advances that have been made in developing technologies based on laccase mediated reactions in the field of medicine, agriculture, food, and pharmaceuticals.
Biopharma Innovation Models for Gulf Region in the Era of Globalisation
Authors: Rashmi Rakhi
Published: May 2019

Biopharmaceutical is the most upcoming segment of the pharmaceutical industry as the use of biotechnology has the potential to provide cures for the most life threatening and difficult ailments. At the same time for biopharma innovation, factors such as increasing costs, high continuous funding, and risk funding are of increasing concern for the emerging economies. This chapter measures the strength of biopharma innovation indicators in Gulf Cooperation Council (GCC) and explores the potential biopharma innovation models for gulf countries.
Numerical solution of a nonlinear eigenvalue problem arising in optimal insulation
Authors: Bartels Sören , Buttazzo Giuseppe
Published: May 2019

The optimal insulation of a heat conducting body by a thin film of variable thickness can be formulated as a nondifferentiable, nonlocal eigenvalue problem. The discretization and iterative solution for the reliable computation of corresponding eigenfunctions that determine the optimal layer thickness are addressed. Corresponding numerical experiments confirm the theoretical observation that a symmetry breaking occurs for the case of small available insulation masses and provide insight in the geometry of optimal films. An experimental shape optimization indicates that convex bodies with one axis of symmetry have favorable insulation properties.
Relaxation to a planar interface in the Mullins–Sekerka problem
Authors: Chugreeva Olga , Otto Felix , Westdickenberg Maria G.
Published: May 2019

We analyze the convergence rates to a planar interface in the Mullins–Sekerka model by applying a relaxation method based on relationships among distance, energy, and dissipation. The relaxation method was developed by two of the authors in the context of the 1-d Cahn–Hilliard equation and the current work represents an extension to a higher dimensional problem in which the curvature of the interface plays an important role. The convergence rates obtained are optimal given the assumptions on the initial data.
Global stability for solutions to the exponential PDE describing epitaxial growth
Authors: Liu Jian-Guo , Strain Robert M.
Published: May 2019

In this paper we prove the global existence, uniqueness, optimal large time decay rates, and uniform gain of analyticity for the exponential PDE

h_t=\Delta e^{-\Delta h}

in the whole space

\mathbb{R}^d_x

. We assume the initial data is of medium size in the Wiener algebra

A(\mathbb{R}^d)

; we use the initial condition

\Delta h_0 \in A(\mathbb{R}^d)

which is scale-invariant with respect to the invariant scaling of the exponential PDE. This exponential PDE was derived in [18] and more recently in [22].
Development, Reliability, and Validity of the Multiple Errands Test Home Version (MET–Home) in Adults With Stroke
Authors: Burns Suzanne Perea , Dawson Deirdre R. , Perea Jaimee D. , Vas Asha , Pickens Noralyn Davel , Neville Marsha
Published: May 2019

OBJECTIVE. Our objective was to perform initial psychometric analysis of the Multiple Errands Test Home Version (MET–Home), which was designed to assess the influence of poststroke executive dysfunction on in-home task performance.

METHOD. We examined the reliability and validity of the MET–Home in adults with stroke (n = 23) and individually matched control participants (n = 23). All participants completed a series of assessments during a single in-home visit.

RESULTS. Notable differences in MET-Home subscores were discovered between participants with stroke and control participants. Participants with stroke omitted more tasks, broke more rules, passed by tasks more often, and were less efficient than matched control participants. The MET–Home demonstrated evidence of adequate internal consistency, excellent interrater reliability, and significant moderate associations with several tests.

CONCLUSION. This preliminary study suggests that the MET–Home differentiates between adults with stroke and matched control participants. The MET–Home provides evidence of initial reliability and validity among adults with stroke.
Modelado espacial histórico y actual del oyamel (Abies religiosa [Kunth] Schltdl. & Cham.) en la Faja Volcánica Transmexicana
Authors: Manzanilla-Quiñones Ulises , Martínez-Adriano Cristian A. , Aguirre-Calderón Oscar A.
Published: May 2019

Introducción: Las condiciones climáticas en la Faja Volcánica Transmexicana durante el Holoceno medio fueron diferentes de las actuales, lo que pudo tener un efecto en las áreas de distribución histórica del oyamel (Abies religiosa [Kunth] Schltdl. & Cham.). 
Objetivo: Determinar si los requerimientos ambientales que delimitan la distribución actual del oyamel en la Faja Volcánica Transmexicana han cambiado desde hace 6 000 años.
Materiales y métodos: Los registros de A. religiosa se obtuvieron de la plataforma Niche Toolbox. Se descargaron las variables WorldClim versión 2.0 para los periodos actual (1970-2000) y Holoceno medio (modelos CNRMCM5 y MIROC_ESM). Los modelos de distribución se generaron en MaxEnt utilizando 75 % de los datos para entrenamiento y 25 % para validación. Las variables más importantes de cada periodo se determinaron con la prueba de Jackknife.
Resultados y discusión: Las superficies estimadas de oyamel fueron similares en ambos periodos. Aproximadamente, 86.5 % de la distribución del oyamel se encuentra en áreas naturales protegidas de la zona. Las variables ambientales limitantes de la distribución son la altitud, precipitación anual, precipitación de verano, temperatura media anual y oscilación diurna en temperatura. El Nevado de Toluca y la Ciudad de México presentaron superficies mayores de oyamel (+2 658.4 ha y +20 129.7 ha, respectivamente) durante el Holoceno medio. La precipitación anual era 80 a 224 mm mayor que la actual y la temperatura era 1 °C más fría.
Conclusión: Las variables ambientales de mayor importancia para la distribución del oyamel son similares a las reportadas en la literatura, lo que indica similitud entre el nicho ecológico actual e histórico de A. religiosa.
Aporte de nutrientes vía precipitación incidente, directa y escurrimiento fustal en especies de matorral en el noreste de México
Authors: Luna-Robles Erik O. , Cantú-Silva Israel , González-Rodríguez Humberto , Marmolejo-Monsiváis José G. , Yáñez-Díaz María I. , Béjar-Pulido Silvia J.
Published: May 2019

Introducción: La precipitación es un factor importante en el aporte de nutrientes a los ecosistemas. En algunos casos, mediante la lluvia pueden agregarse elementos que por las condiciones del área no están disponibles.
Objetivo: Comparar el aporte nutrimental en la precipitación incidente (Pi) y en el agua de lluvia que llega al suelo por precipitación directa (Pd) y escurrimiento fustal (Ef) de especies de matorral.
Materiales y métodos: Se evaluaron 41 eventos de lluvia en el noreste de México en el periodo septiembre 2016-septiembre 2017. Se registraron los volúmenes de precipitación y se analizó su composición química: pH, conductividad eléctrica y aporte de macro (Ca, Mg, K) y micronutrientes (Fe, Mn, Zn y Cu). 
Resultados y discusión: La Pi acumuló 508.97 mm. La Pd es la que deposita mayor cantidad de nutrientes al suelo. El aporte neto (Pd+Ef) indicó que el Fe fue el micronutriente más depositado con 2 938.29 g·ha-1·año-1, de los cuales Casimiroa greggii (S. Watson) F. Chiang aportó 31.59 %. El macronutriente con mayor presencia fue el Ca con 319.31 kg·ha-1·año-1, siendo Acacia farnesiana (L.) Willd. la que logró mayor flujo del mineral (22.66 %). El pH y conductividad eléctrica fueron similares (Kruskal-Wallis, P > 0.05) en los tres tipos de precipitación. 
Conclusión: La química de la precipitación se modifica después de pasar el dosel de cada especie, ya sea para enriquecer o remover las concentraciones de nutrientes, jugando un papel importante en los ciclos biogeoquímicos del ecosistema.
Characterization of a commercial lower-cost medium-precision non-dispersive infrared sensor for atmospheric CO2 monitoring in urban areas
Authors: Arzoumanian Emmanuel , Vogel Felix R. , Bastos Ana , Gaynullin Bakhram , Laurent Olivier , Ramonet Michel , Ciais Philippe
Published: May 2019

Abstract. CO2 emission estimates from urban areas can
be obtained with a network of in situ instruments measuring atmospheric
CO2 combined with high-resolution (inverse) transport modelling. Because
the distribution of CO2 emissions is highly heterogeneous in space and
variable in time in urban areas, gradients of atmospheric CO2 (here, dry
air mole fractions) need to be measured by numerous instruments placed at
multiple locations around and possibly within these urban areas. This calls
for the development of lower-cost medium-precision sensors to allow a
deployment at required densities. Medium precision is here set to be a random
error (uncertainty) on hourly measurements of ±1 ppm or less, a
precision requirement based on previous studies of network design in urban
areas. Here we present tests of newly developed non-dispersive infrared (NDIR) sensors manufactured by
Senseair AB performed in the laboratory and at actual field stations, the
latter for CO2 dry air mole fractions in the Paris area. The lower-cost
medium-precision sensors are shown to be sensitive to atmospheric pressure
and temperature conditions. The sensors respond linearly to CO2 when
measuring calibration tanks, but the regression slope between measured and
assigned CO2 differs between individual sensors and changes with time.
In addition to pressure and temperature variations, humidity impacts the
measurement of CO2, with all of these factors resulting in systematic errors.
In the field, an empirical calibration strategy is proposed based on parallel
measurements with the lower-cost medium-precision sensors and a
high-precision instrument cavity ring-down instrument for 6 months. The
empirical calibration method consists of using a multivariable regression
approach, based on predictors of air temperature, pressure and humidity. This
error model shows good performances to explain the observed drifts of the
lower-cost medium-precision sensors on timescales of up to 1–2 months when
trained against 1–2 weeks of high-precision instrument time series. Residual
errors are contained within the ±1 ppm target, showing the feasibility
of using networks of HPP3 instruments for urban CO2 networks. Provided
that they could be regularly calibrated against one anchor reference
high-precision instrument these sensors could thus collect the CO2 (dry
air) mole fraction data required as for top-down CO2 flux estimates.
RTTOV-gb v1.0 – updates on sensors, absorption models, uncertainty, and availability
Authors: Cimini Domenico , Hocking James , De Angelis Francesco , Cersosimo Angela , Di Paola Francesco , Gallucci Donatello , Gentile Sabrina , Geraldi Edoardo , Larosa Salvatore , Nilo Saverio , Romano Filomena , Ricciardelli Elisabetta , Ripepi Ermann , Viggiano Mariassunta , Luini Lorenzo , Riva Carlo , Marzano Frank S. , Martinet Pauline , Song Yun Young , Ahn Myoung Hwan , Rosenkranz Philip W.
Published: May 2019

Abstract. This paper describes the first official release (v1.0) of
RTTOV-gb. RTTOV-gb is a FORTRAN 90 code developed by adapting the
atmospheric radiative transfer code RTTOV, focused on satellite-observing
geometry, to the ground-based observing geometry. RTTOV-gb is designed to
simulate ground-based upward-looking microwave radiometer (MWR) observations
of atmospheric downwelling natural radiation in the frequency range from 22
to 150 GHz. Given an atmospheric profile of temperature, water vapor, and,
optionally, cloud liquid water content, and together with a viewing
geometry, RTTOV-gb computes downwelling radiances and brightness
temperatures leaving the bottom of the atmosphere in each of the channels of the
sensor being simulated. In addition, it provides the sensitivity of
observations to the atmospheric thermodynamical state, i.e., the Jacobians.
Therefore, RTTOV-gb represents the forward model needed to assimilate
ground-based MWR data into numerical weather prediction models, which is
currently pursued internationally by several weather services. RTTOV-gb is
fully described in a previous paper (De Angelis et al., 2016), while several
updates are described here. In particular, two new MWR types and a new
parameterization for the atmospheric absorption model have been introduced since
the first paper. In addition, estimates of the uncertainty associated with
the absorption model and with the fast parameterization are given here.
Brightness temperatures (TB) computed with RTTOV-gb v1.0 from
radiosonde profiles have been compared with ground-based MWR observations in six channels (23.8, 31.4, 72.5, 82.5, 90.0, and 150.0 GHz). The comparison
shows statistics within the expected accuracy. RTTOV-gb is now available to
licensed users free of charge from the Numerical Weather Prediction
Satellite Application Facility (NWP SAF) website, after registration.
Coefficients for four MWR instrument types and two absorption model
parameterizations are also freely available from the RTTOV-gb support
website.
SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION
Authors: Akbari D. , Moradizadeh M. , Akbari M.
Published: May 2019

Abstract. This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.
FACE RECOGNITION WITH LOW FALSE POSITIVE ERROR RATE
Authors: Bagrov N. U. , Konushin A. S. , Konushin V. S.
Published: May 2019

Abstract. Nowadays face recognition systems are widely used in the world. In China these systems are used in safe cities projects in production, in Russia they are used mostly in closed-loop systems like factories, business centers with biometric access control or stadiums. Closed loop means that we need to identify people from a fixed dataset: in factory it’s a list of employees, in stadium it’s a list of ticket owners. The most challenging task is to identify people from some large city with an open dataset: we don’t have a fixed set of people in the city, it’s rapidly changing due to migration. Another limit is the accuracy of the system: we can’t make a lot of false positive errors (when a person is incorrectly recognized as another person) because number of human operators is limited and they are expensive. We propose an approach to maximize face recognition accuracy for a fixed false positive error rate using limited amount of hardware.
VISION BASED AUTOMATED ANTHROPOLOGICAL MEASUREMENTS AND ANALYSIS
Authors: Knyaz V. A. , Maksimov A. A. , Novikov M. M.
Published: May 2019

Abstract. Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the vision-based techniques for anthropological measurement and investigation needed in various applications, such as morphometric analysis, craniofacial identification, face approximation and other. Photogrammetric methods and their practical implementation in the automatic system for accurate digital 3D reconstruction of anthropological objects are described.
MODELS OF IMAGES WITH RADIAL-CIRCULAR STRUCTURE
Authors: Krasheninnikov V. R. , Malenova O. E. , Subbotin A. U.
Published: May 2019

Abstract. The overwhelming majority of known image models are varieties of random fields defined on rectangular two-dimensional grids or grids of higher dimension, for example. In some practical situations, the images have an annular, radial or radial-circular structure. For example, images of the facies (thin film) of dried biological fluid, eyes, cut of a tree trunk or a fruit, blood vessel, erythrocyte, blast pattern, end face detail, etc. In addition, radar and other images are physically obtained in polar or spherical coordinates. These features of images require their consideration in their mathematical models. In this paper, an autoregressive models of homogeneous and inhomogeneous random fields defined on a circle or oval are considered as representations of images with radial or radial-circular structure.
ALGORITHMS FOR ADJUSTMENT OF SYMMETRY AXIS FOUND FOR 2D SHAPES BY THE SKELETON COMPARISON METHOD
Authors: Kushnir O. A. , Seredin O. S. , Fedotova S. A.
Published: May 2019

Abstract. Reflection symmetry detection for 2D shapes is a well-known task in Computer Vision, but there is a limited number of efficient and effective methods for its solution. Our previously proposed approach based on pair-wise comparison of sub-sequences of skeleton primitives finds the axis of symmetry within few seconds. In order to evaluate the value of symmetry relative to the found axis we use the Jaccard similarity measure. It is applied to the pixels subsets of a shape which are split by the axis. Often an axis found by the skeleton comparison method diverges more or less from the ground-truth axis found by the method of exhaustive search among all the potential candidates. That is why the algorithms that allow adjusting the axis found by the fast skeleton method are proposed. They are based on the idea of searching the axis which is located near the seed skeleton axis and has greater Jaccard similarity measure. The experimental study on the ”Flavia” and ”Butterflies” datasets shows that proposed algorithms find the ground-truth axis (or the axis which has slightly less Jaccard similarity value than the ground-truth axis) in near real time. It is considerably faster than any of the optimized brute-force methods.
IMAGE SHARPENING WITH BLUR MAP ESTIMATION USING CONVOLUTIONAL NEURAL NETWORK
Authors: Nasonov A. , Krylov A. , Lyukov D.
Published: May 2019

Abstract. We propose a method for choosing optimal values of the parameters of image sharpening algorithm for out-of-focus blur based on grid warping approach. The idea of the considered sharpening algorithm is to move pixels from the edge neighborhood towards the edge centerlines. Compared to traditional deblurring algorithms, this approach requires only scalar blur level value rather than a blur kernel. We propose a convolutional neural network based algorithm for estimating the blur level value.
IRIS IMAGE KEY POINTS DESCRIPTORS BASED ON PHASE CONGRUENCY
Authors: Protsenko M. А. , Pavelyeva E. A.
Published: May 2019

Abstract. In this article the new method for iris image features extraction based on phase congruency is proposed. Iris image key points are calculated using the convolutions with Hermite transform functions. At each key point the feature vector characterizing this key point is obtained based on the phase congruency method. Iris key point descriptor contains phase congruency values at points located on concentric circles around the key point. To compare the key points, Euclidean metric between the key points descriptors is calculated. The distance between the iris images is equal to the number of matched iris key points. The proposed method was tested using the images from CASIA−IrisV4−Interval database and the value of EER = 0.226% was obtained.
THE PROCEDURES OF VISUAL ANALYSIS FOR MULTIDIMENSIONAL DATA VOLUMES
Authors: Bondarev A. E.
Published: May 2019

Abstract. The paper is devoted to problems of visual analysis of multidimensional data sets using an approach based on the construction of elastic maps. This approach is quite suitable for processing and visualizing of multidimensional datasets. The elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. Then the points of dataset in question are projected to the map. The extension of designed map to a flat plane allows one to get an insight about the structure of multidimensional dataset. The paper presents the results of applying elastic maps for visual analysis of multidimensional data sets of medical origin. Previously developed data processing procedures are applied to improve the results obtained - pre-filtering of data, removal of separated clusters (flotation), quasi-Zoom.