data processing algorithms
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2021 ◽  
Vol 26 (jai2021.26(2)) ◽  
pp. 08-13
Author(s):  
Sprindzuk M ◽  
◽  
Vladyko A ◽  
Titov L ◽  
◽  
...  

Based on literature analysis and own bioinformatics and virology research experience, authors propose multistep data processing algorithms, designed for the objectives of assisting the SARS-CoV-2 epitope vaccine production. Epitope vaccines are expected to provoke a weaker but safer response of the vaccinated person. Methodologies of reverse bioengineering, vaccinology and synthetic peptide manufacturing have a promising future to combat COVID-19 brutal disease. The significant mutational variability and evolution of the SARS-CoV-2, which is more typical for natural animal-borne viruses, are the hurdle for the effective and robust vaccine application and therefore require multidisciplinary research and prevention measures on the international level of cooperation. However, we can expect that other viruses with different nature and content may be labelled as SARS-CoV-2. In this case metagenomics is an important discipline for COVID-19 discovery. High quality reliable virus detection is still an unresolved question for improvement and optimization. It is of upmost importance to develop the in silico and in vitro methods for the vaccine recipient reaction prediction and monitoring as techniques of the so-called modern personalized medicine. Many questions can`t be solved applying exclusively in silico techniques and only can be discovered in vitro and in vivo, demanding significant time and money investments. Future experiments also should be directed at the discovery of optimal vaccine adjuvants, vectors and epitope ensembles, as well as the personal characteristics of citizens of a certain region. This research would require several more years of meticulous large-scale laboratory and clinical work in various centers of biomedical institutions worldwide


Author(s):  
Л.Д. Егорова ◽  
Л.А. Казаковцев

В статье обсуждается применение методов фрактального анализа для решения задачи автоматической фильтрации сигнала ЭЭГ от артефактов различной природы. Изучается возможность использования показателя Херста в качестве информативного признака для алгоритмов интеллектуальной обработки данных. The article discusses the possibility of using fractal analysis to solve the problem of automatic filtering of the EEG signal from artifacts of various nature. The possibility of using the Hurst exponent as an informative feature for intelligent data processing algorithms is investigated


2021 ◽  
Author(s):  
Oleh Velychko ◽  
Tetyana Gordiyenko

National accreditation agencies in different countries have set quite strict requirements for accreditation of testing and calibration laboratories. Interlaboratory comparisons (ILCs) are a form of experimental verification of laboratory activities to determine technical competence in a particular activity. Successful results of conducting ILCs for the laboratory are a confirmation of competence in carrying out certain types of measurements by a specific specialist on specific equipment. To obtain reliable results of ILC accredited laboratories, it is necessary to improve the methods of processing these results. These methods are based on various data processing algorithms. Therefore, it is necessary to choose the most optimal method of processing the obtained data, which would allow to obtain reliable results. In addition, it is necessary to take into account the peculiarities of the calibration laboratories (CLs) when evaluating the results of ILС. Such features are related to the need to provide calibration of measuring instruments for testing laboratories. The evaluation results for ILCs for CLs are presented. The results for all participants of ILCs were evaluated using the En and z indexes. The obtained results showed that for the such ILCs it is also necessary to evaluate the data using the z index also.


Author(s):  
M. V. Sprindzuk ◽  
L. P. Titov ◽  
A. P. Konchits ◽  
L. V. Mozharovskaya

Analysis of bioinformatics data is an actual problem in modern computational biology and applied mathematics. With the development of biotechnology and tools for obtaining and processing such information, unresolved issues of the development and application of new algorithms and software have emerged.Authors propose practical algorithms and methods for processing transcriptomic data for efficient results of annotation, visualization and interpretation of bioinformatics data.


2021 ◽  
Vol 14 (7) ◽  
pp. 5071-5088 ◽  
Author(s):  
Olli Peltola ◽  
Toprak Aslan ◽  
Andreas Ibrom ◽  
Eiko Nemitz ◽  
Üllar Rannik ◽  
...  

Abstract. The eddy covariance (EC) technique has emerged as the prevailing method to observe the ecosystem–atmosphere exchange of gases, heat and momentum. EC measurements require rigorous data processing to derive the fluxes that can be used to analyse exchange processes at the ecosystem–atmosphere interface. Here we show that two common post-processing steps (time-lag estimation via cross-covariance maximisation and correction for limited frequency response of the EC measurement system) are interrelated, and this should be accounted for when processing EC gas flux data. These findings are applicable to EC systems employing closed- or enclosed-path gas analysers which can be approximated to be linear first-order sensors. These EC measurement systems act as low-pass filters on the time series of the scalar χ (e.g. CO2, H2O), and this induces a time lag (tlpf) between vertical wind speed (w) and scalar χ time series which is additional to the travel time of the gas signal in the sampling line (tube, filters). Time-lag estimation via cross-covariance maximisation inadvertently accounts also for tlpf and hence overestimates the travel time in the sampling line. This results in a phase shift between the time series of w and χ, which distorts the measured cospectra between w and χ and hence has an effect on the correction for the dampening of the EC flux signal at high frequencies. This distortion can be described with a transfer function related to the phase shift (Hp) which is typically neglected when processing EC flux data. Based on analyses using EC data from two contrasting measurement sites, we show that the low-pass-filtering-induced time lag increases approximately linearly with the time constant of the low-pass filter, and hence the importance of Hp in describing the high-frequency flux loss increases as well. Incomplete description of these processes in EC data processing algorithms results in flux biases of up to 10 %, with the largest biases observed for short towers due to the prevalence of small-scale turbulence. Based on these findings, it is suggested that spectral correction methods implemented in EC data processing algorithms are revised to account for the influence of low-pass-filtering-induced time lag.


Author(s):  
Henk Hogeveen ◽  
◽  
Mariska van der Voort ◽  

This chapter reviews advances in precision livestock farming techniques for monitoring dairy cattle welfare. It begins by describing the potential of PLF technology linked to the Five Domains framework, then goes into more detail by focusing on the use of precision livestock farming techniques for each of the five domains. Finally, the chapter reviews the need for data processing algorithms.


Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 750
Author(s):  
Iván Garrido ◽  
Jorge Erazo-Aux ◽  
Susana Lagüela ◽  
Stefano Sfarra ◽  
Clemente Ibarra-Castanedo ◽  
...  

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.


2021 ◽  
Author(s):  
Olli Peltola ◽  
Toprak Aslan ◽  
Andreas Ibrom ◽  
Eiko Nemitz ◽  
Üllar Rannik ◽  
...  

Abstract. The eddy covariance (EC) technique has emerged as the prevailing method to observe ecosystem - atmosphere exchange of gases, heat and momentum. EC measurements require rigorous data processing to derive the fluxes that can be used to analyse exchange processes at the ecosystem - atmosphere interface. Here we show that two common post-processing steps (time-lag estimation via cross-covariance maximisation, and correction for limited frequency response of the EC measurement system) are interrelated and this should be accounted for when processing EC gas flux data. These findings are applicable to EC systems employing closed- or enclosed-path gas analysers which can be approximated to be linear first-order sensors. These EC measurement systems act as a low-pass filters on the time-series of the scalar χ (e.g. CO2, H2O) and this induces a time-lag (tlpf) between vertical wind speed (w) and scalar χ time series which is additional to the travel time of the gas signal in the sampling line (tube, filters). Time-lag estimation via cross-covariance maximisation inadvertently accounts also for tlpf and hence overestimates the travel time in the sampling line. This results in a phase shift between the time-series of w and χ, which distorts the measured cospectra between w and χ and hence has an effect on the correction for dampening of EC flux signal at high frequencies. This distortion can be described with a transfer function related to the phase shift (Hp) which is typically neglected when processing EC flux data. Based on analyses using EC data from two contrasting measurement sites, we show that the low-pass filtering induced time-lag increases approximately linearly with the time constant of the low-pass filter, and hence the importance of Hp in describing the high frequency flux loss increases as well. Incomplete description of these processes in EC data processing algorithms results in flux biases of up to 10 %, with the largest biases observed for short towers due to prevalence of small scale turbulence. Based on these findings, it is suggested that spectral correction methods implemented in EC data processing algorithms are revised to account for the influence of low-pass filtering induced time-lag.


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