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Images are mainly viewed and analyzed by humans. Because of this, in the characterization of image quality and effectiveness of image processing, it is necessary to take into account the peculiarities of the human vision system and cognition that are very complex. Saliency maps as well as priority and meaning maps introduced recently are the attempts to incorporate specific features of human vision into image analysis and processing fields. Many authors that consider the aforementioned maps consider them from different viewpoints. Thus, the basic subject of this paper is the factors that influence and determine these maps. Among such factors, there are low-level features as well as social and psychological ones such as emotions, age, and life values. The main goal of this paper is to give a brief survey of these factors and to consider how maps are already used in image quality assessment and processing as well as how they can be employed in the future. The tasks of the paper are to provide a definition of saliency, priority, and meaning maps, to analyze the factors that influence these maps, and to evaluate what improvement can be obtained due to taking maps into account in the assessment of image visual quality and such image processing operations as quality assessment, denoising, and lossy compression. The main result is that, by taking saliency maps into account, image quality assessment and processing efficiency can be sufficiently improved, especially for applications oriented on image viewing and analysis by observers or customers. This can be done by the simple weighting of local estimates of a given metric with further aggregation as well as by approaches based on neural networks. Using different quantitative criteria, we show what positive results can be got due to incorporating maps into quality assessment and image processing. As conclusion, we present possible directions of future research that are mainly related to an adaptation of denoising and lossy compression parameters to peculiarities of human attention.

Abstract The impact of biomineralization and redox processes on the formation and growth of ferromanganese deposits in the World Ocean remains understudied. This problem is particularly relevant for the Arctic marine environment where sharp seasonal variations of temperature, redox conditions, and organic matter inflow significantly impact the biogenic and abiotic pathways of ferromanganese deposits formation. The microbial communities of the fast-growing Arctic Fe–Mn deposits have not been reported so far. Here, we describe the microbial diversity, structure and chemical composition of nodules, crust and their underlying sediments collected from three different sites of the Kara Sea. Scanning electron microscopy revealed a high abundance of microfossils and biofilm-like structures within the nodules. Phylogenetic profiling together with redundancy and correlation analyses revealed a positive selection for putative metal-reducers (Thermodesulfobacteriota), iron oxidizers (Hyphomicrobiaceae and Scalinduaceae), and Fe-scavenging Nitrosopumilaceae or Magnetospiraceae in the microenvironments of the Fe–Mn deposits from their surrounding benthic microbial populations. We hypothesize that in the Kara Sea, the nodules provide unique redox-stable microniches for cosmopolitan benthic marine metal-cycling microorganisms in an unsteady environment, thus focusing the overall geochemical activity of nodule-associated microbial communities and accelerating processes of ferromanganese deposits formation to uniquely high rates.

Розглянуто властивості квадратичних і кручених суперсингулярних кривих Едвардса, які утворюють пари квадратичних кручень з порядком над простим полем. Розглянуто модифікацію алгоритму CSIDH на основі ізогеній непарного ступеня цих кривих. Побудовано просту модель для реалізації алгоритму CSIDH у 3 мінімальних непарних ступенях ізогенії 3, 5, 7, з простим модулем поля та порядком суперсингулярних кривих. На етапі випадання розраховуються та зводяться в таблицю параметри ізогенних ланцюгів усіх ступенів для цих двох класів суперсингулярних кривих Едвардса. Наведено приклад реалізації алгоритму CSIDH як неінтерактивної схеми обміну секретами на основі секретного та відкритого ключів Аліси та Боба. Запропоновано новий рандомізований алгоритм CSIDH з випадковим рівноімовірним вибором однієї з кривих цих двох класів на кожному кроці ланцюга ізогенії. Вибір ступеня кожної ізогенії є випадковим. Проілюстровано роботу рандомізованого алгоритму на прикладі. Цей алгоритм розглядається як можлива альтернатива "CSIDH з постійним часом". Комбінація двох підходів можлива для протидії атакам на бокових каналах. Наведено оцінки ймовірності успішної атаки побічного каналу в рандомізованому алгоритмі. Зазначається, що всі обчислення в алгоритмі CSIDH, необхідні для обчислення загального секрету, зводяться лише до обчислення параметра ізогенної кривої та виконуються за допомогою польових і групових операцій, зокрема, множення скалярних точок і подвоєння точок ядра ізогенії. У новому алгоритмі ми пропонуємо відмовитися від обчислення ізогенної функції випадкової точки , що значно прискорює роботу алгоритму.

Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Here the authors present a meta-analysis empowered by a new statistical method covering thousands of ChIP-Seq experiments resulting in the identification of more than 500 thousand allele-specific binding (ASB) events in the human genome.

The position weight matrix, also called the position-specific scoring matrix, is the commonly accepted model to quantify the specificity of transcription factor binding to DNA. Position weight matrices are used in thousands of projects and software tools in regulatory genomics, including computational prediction of the regulatory impact of single-nucleotide variants. Yet, recently Yan et al. reported that "the position weight matrices of most transcription factors lack sufficient predictive power" if applied to the analysis of regulatory variants studied with a newly developed experimental method, SNP-SELEX. Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can adequately quantify transcription factor binding to alternative alleles.

The position weight matrix, also called the position-specific scoring matrix, is the commonly accepted model to quantify the specificity of transcription factor binding to DNA. Position weight matrices are used in thousands of projects and software tools in regulatory genomics, including computational prediction of the regulatory impact of single-nucleotide variants. Yet, recently Yan et al. reported that "the position weight matrices of most transcription factors lack sufficient predictive power" if applied to the analysis of regulatory variants studied with a newly developed experimental method, SNP-SELEX. Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can adequately quantify transcription factor binding to alternative alleles.

Cestodes Ctenotaenia marmotae are a widespread parasite of Eurasian ground squirrels. Being the only valid species of its genus, it has been recorded in ten host species from the genera Marmota and Spermophilus. Its definitive hosts live in similar ecological conditions and their area covers a wide geographical territory. Therefore, it remained unclear whether C. marmotae is a single species and how high the level of its genetic variability is. The present study analyzed the variability of two mitochondrial cestode genes from four host species from geographically distant localities. Phylogenetic analysis revealed that the newly obtained sequences form a species-level clade with already known sequences from the GenBank. Small genetic distances between the studied sequences indicate low intraspecific genetic variability within the C. marmotae, which may be a consequence of rapid expansion of the cestodes in ground squirrel species inhabiting the flat and mountain steppe landscapes in the past.

Cestodes Paranoplocephala kalelai, which parasitizes in the small intestine of Myodes voles and is distributed in northern Fennoscandia, was found in six habitats in the Asian part of Russia and eastern Kazakhstan, which indicates a wider distribution of P. kalelai on the continent. Analysis of mtDNA showed that P. kalelai is characterized by significant molecular variability in Eurasia. This study complements the data on the distribution of P. kalelai and provides the first molecular data from the territory of Russia and Kazakhstan. The sequence variability of two mitochondrial genes cox1 and nad1 of P. kalelai was studied in two species of voles: gray red-backed Myodes rufocanus and northern red-backed vole Myodes rutilus. Five haplotype groups in the cox1 and nad1 gene networks were identified, and the existence of two mtDNA lines in P. kalelai outside northern Fennoscandia was confirmed. The geographical distribution of the identified haplotypes suggests that the foothills of the Altai-Sayan mountains and southern West Siberia may serve as a refugium for P. kalelai during repeated glaciations.

Lossy compression is widely used to decrease the size of multichannel remote sensing data. Alongside this positive effect, lossy compression may lead to a negative outcome as making worse image classification. Thus, if possible, lossy compression should be carried out carefully, controlling the quality of compressed images. In this paper, a dependence between classification accuracy of maximum likelihood and neural network classifiers applied to three-channel test and real-life images and quality of compressed images characterized by standard and visual quality metrics is studied. The following is demonstrated. First, a classification accuracy starts to decrease faster when image quality due to compression ratio increasing reaches a distortion visibility threshold. Second, the classes with a wider distribution of features start to “take pixels” from classes with narrower distributions of features. Third, a classification accuracy might depend essentially on the training methodology, i.e., whether features are determined from original data or compressed images. Finally, the drawbacks of pixel-wise classification are shown and some recommendations on how to improve classification accuracy are given.

Multispectral remote sensing data may contain component images that are heavily corrupted by noise and the pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images—component images having relatively high quality and that are similar to the image subject to pre-filtering. Here, we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. To provide high efficiency of denoising, reference images have to be transformed using linear or nonlinear transformations. This paper proposes a practical approach to doing this. Examples of denoising tests and real-life images demonstrate high efficiency of the proposed approach.

The position weight matrix, also called the position-specific scoring matrix, is the commonly accepted model to quantify the specificity of transcription factor binding to DNA. Position weight matrices are used in thousands of projects and software tools in regulatory genomics, including computational prediction of the regulatory impact of single-nucleotide variants. Yet, recently Yan et al. reported that 'the position weight matrices of most transcription factors lack sufficient predictive power' if applied to the analysis of regulatory variants studied with a newly developed experimental method, SNP-SELEX. Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can adequately quantify transcription factor binding to alternative alleles.

The position weight matrix, also called the position-specific scoring matrix, is the commonly accepted model to quantify the specificity of transcription factor binding to DNA. Position weight matrices are used in thousands of projects and software tools in regulatory genomics, including computational prediction of the regulatory impact of single-nucleotide variants. Yet, recently Yan et al. reported that 'the position weight matrices of most transcription factors lack sufficient predictive power' if applied to the analysis of regulatory variants studied with a newly developed experimental method, SNP-SELEX. Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can adequately quantify transcription factor binding to alternative alleles.

The commonly accepted model to quantify the specificity of transcription factor binding to DNA is the position weight matrix, also called the position-specific scoring matrix. Position weight matrices are used in thousands of projects and computational tools in regulatory genomics, including prediction of the regulatory potential of single-nucleotide variants. Yet, recently Yan et al. presented new experimental method for analysis of regulatory variants and, based on its results, reported that 'the position weight matrices of most transcription factors lack sufficient predictive power'. Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can successfully quantify transcription factor binding to alternative alleles.