Neural network analysis of hyperspectral images of soil

The article approaches to the classification of high-resolution hyperspectral images in the problem of classification of soil species is proposed. A spectral-spatial convolutional neural network with compensation for lighting variations is used as a classifier. The effectiveness of the proposed approach in the problem of classification of hyperspectral images of soils obtained by a scanning hyperspectral camera is shown. The essence of the developed method is to use binary classification together with multiclass, thereby improving the result of the latter

Hyperspectral images neural network analysis of unstained micropreparations

The article presents the results of a study of hyperspectral imaging in microscopy to assess pathological changes in unstained medical micropreparations.Hyperspectral imaging was carried out using a system of synchronous shooting and movement of a movable table combined with a stepper motor. To improve the quality of theobtained images, software correction of the illumination of the spectral channels was used. The classification was carried out by a convolutional neural network. This method may be promising for assessing pathological changes in clinical practice. Experimental studies were carried out on histological preparations with different types of tissues without staining with contrasting medical dyes. To assess the reliability of the classification method, a comparison was made with thestandard method using staining of the studied samples

Three-Dimensional Endoscopy-Assisted Excision and Reconstruction for Metastatic Disease of the Dorsal and Lumbar Spine: Early Results

BACKGROUND: The aim of this study was to explore the role of three-dimensional (3D) endoscopy in surgical management of metastatic disease of the dorsal and lumbar spine. METHODS: This is a prospective study on 33 patients (15 men and 18 women, mean age of 61.6 ± 8.9 years) with biopsy-proven metastatic disease of the spine managed by sequential/staged posterior decompression-stabilization, followed by 3D endoscopy-assisted anterior corpectomy and stabilization with a mesh cage. All patients had significant extradural compression or spinal instability or both. Sixteen patients had neurological deficits. Visual analog scale (VAS), Frenkel grade (neurological deficits), Karnofsky performance status scale, and the 36-item short-form health survey (SF-36) were used for assessment preoperatively and at 3, 6, and 12 months from surgery. RESULTS: At a mean follow-up of 1.7 ± 0.7 years from surgery, 18 patients were alive. VAS showed significant improvement at the latest follow-up compared to preoperative levels (4.39 vs. 6.61, p = 0.001). Karnofsky status did not show any significant improvement. Frenkel grade improved in 5 patients, deteriorated in 4 patients, and remained unchanged in 24 patients. Regarding SF-36 parameters, general health showed deterioration, but role functioning—physical, role functioning—emotional, social functioning, and body pain showed statistically significant improvement. There was no change in physical health, viability, and mental health. Subjectively the surgeons felt better depth perception and smoother surgical experience with the 3D optics technology. The only complication was delayed wound healing in three patients who had a previous history of radiotherapy to the surgical site. CONCLUSIONS: 3D endoscopy is a valuable tool in the management of metastatic spinal disease requiring excision and reconstruction using the combined posterior and anterior approaches. These early results warrant confirmation with more data and longer follow-ups

Ophthalmic Bioengineering. Review

This article published the materials of the round table “Bioengineering in ophthalmology” (OphthalmicBioengineering), held on May 13, 2021 as part of the international conference Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). USBEREIT is held under the auspices of the IEEE Engineering in Medicine and Biology Society. The article presents reports on: metrological aspects of registration of tonometric and electrophysiological signals in ophthalmic diagnostics; approaches to modeling the processes of pulse blood filling of the eye with the determination of hemodynamic parameters; retinotoxicity based on electrophysiological signals; analysis of electrophysiological signals in the frequency-time domain and its application in clinical practice; extraction and analysis of specialized data obtained from the electrophysiological medical device; as well as diagnosing retinal diseases based on optical coherence tomography using machine learning. © 2023 Ophthalmology Publishing Group. All rights reserved

Deep learning-based video stream reconstruction in mass-production diffractive optical systems

Возможность существенно снизить массу и стоимость систем технического зрения привела к появлению большого числа работ, посвященных разработке новых оптических схем на основе дифракционной оптики и новых подходов к реконструкции получаемых изображений. Получаемые системы демонстрируют достаточное для прикладных систем технического зрения качество изображений. Однако при создании таких прикладных систем возможны источники дополнительных потерь качества получаемого видеопотока. В настоящей работе исследовано влияние на итоговое качество реконструируемого видеопотока таких факторов, как ограничения технологии массового производства дифракционной оптики, артефактов сжатия видеопотока с потерями, а также особенностей нейросетевого подхода к реконструкции. Предложена сквозная нейросетевая технология реконструкции изображений, позволяющая компенсировать дополнительные факторы потери качества и получить итоговый видеопоток с качеством, достаточным для решения прикладных задач технического зрения. Many recent studies have focused on developing image reconstruction algorithms in optical systems based on flat optics. These studies demonstrate the feasibility of applying a combination of flat optics and the reconstruction algorithms in real vision systems. However, additional causes of quality loss have been encountered in the development of such systems. This study investigates the influence on the reconstructed image quality of such factors as limitations of mass production technology for diffractive optics, lossy video stream compression artifacts, and specificities of a neural network approach to image reconstruction. The paper offers an end-to-end deep learning-based image reconstruction framework to compensate for the additional factors of quality losing. It provides the image reconstruction quality sufficient for applied vision systems.Теоретическая часть работы и разработка нейросетевых моделей выполнена при поддержке гранта РНФ 20-69-47110, экспериментальная часть выполнена при поддержке грантов РФФИ № 18-07-01390-А, а также в рамках государственного задания ИСОИ РАН – филиала Федерального научно-исследовательского центра «Кристаллография и фотоника» РАН (соглашение № 007-ГЗ/Ч3363/26)

Normal radial migration and lamination are maintained in dyslexia-susceptibility candidate gene homolog Kiaa0319 knockout mice

AbstractDevelopmental dyslexia is a common disorder with a strong genetic component, but the underlying molecular mechanisms are still unknown. Several candidate dyslexia-susceptibility genes, including KIAA0319, DYX1C1, and DCDC2, have been identified in humans. RNA interference experiments targeting these genes in rat embryos have shown impairments in neuronal migration, suggesting that defects in radial cortical migration could be involved in the disease mechanism of dyslexia. Here we present the first characterisation of a Kiaa0319 knockout mouse line. Animals lacking KIAA0319 protein do not show anatomical abnormalities in any of the layered structures of the brain. Neurogenesis and radial migration of cortical projection neurons are not altered, and the intrinsic electrophysiological properties of Kiaa0319-deficient neurons do not differ from those of wild-type neurons. Kiaa0319 overexpression in cortex delays radial migration, but does not affect final neuronal position. However, knockout animals show subtle differences suggesting possible alterations in anxiety-related behaviour and in sensorimotor gating. Our results do not reveal a migration disorder in the mouse model, adding to the body of evidence available for Dcdc2 and Dyx1c1 that, unlike in the rat in utero knockdown models, the dyslexia-susceptibility candidate mouse homolog genes do not play an evident role in neuronal migration. However, KIAA0319 protein expression seems to be restricted to the brain, not only in early developmental stages but also in adult mice, indicative of a role of this protein in brain function. The constitutive and conditional knockout lines reported here will be useful tools for further functional analyses of Kiaa0319

Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes

With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa) gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional annotation of those modules. Additionally, the expression patterns of genes across the treatments/conditions of an expression experiment comprise a second form of useful annotation

DYX1C1 is required for axonemal dynein assembly and ciliary motility

DYX1C1 has been associated with dyslexia and neuronal migration in the developing neocortex. Unexpectedly, we found that deleting exons 2–4 of Dyx1c1 in mice caused a phenotype resembling primary ciliary dyskinesia (PCD), a disorder characterized by chronic airway disease, laterality defects and male infertility. This phenotype was confirmed independently in mice with a Dyx1c1 c.T2A start-codon mutation recovered from an N-ethyl-N-nitrosourea (ENU) mutagenesis screen. Morpholinos targeting dyx1c1 in zebrafish also caused laterality and ciliary motility defects. In humans, we identified recessive loss-of-function DYX1C1 mutations in 12 individuals with PCD. Ultrastructural and immunofluorescence analyses of DYX1C1-mutant motile cilia in mice and humans showed disruptions of outer and inner dynein arms (ODAs and IDAs, respectively). DYX1C1 localizes to the cytoplasm of respiratory epithelial cells, its interactome is enriched for molecular chaperones, and it interacts with the cytoplasmic ODA and IDA assembly factor DNAAF2 (KTU). Thus, we propose that DYX1C1 is a newly identified dynein axonemal assembly factor (DNAAF4)

Information systems development of analysis company financial state based on the expert-statistical approach

The work is devoted to methods of analysis the company financial condition, including aggregated ratings. It is proposed to use the generalized solvency and liquidity indicator and the capital structure composite index. Mathematically, the generalized index is a sum of variables-characteristics and weighting factors characterizing the relative importance of individual characteristics composition. It is offered to select the significant features from a set of standard financial ratios, calculated according to enterprises balance sheets. To obtain the weighting factors values it is proposed to use one of the expert statistical approaches, the analytic hierarchy process. The method is as follows: we choose the most important characteristic and after the experts determine the degree of preference for the main feature based on the linguistic scale. Further, matrix of pairwise comparisons based on the assigned ranks is compiled, which characterizes the relative importance of attributes. The required coefficients are determined as elements of a vector of priorities, which is the first vector of the matrix of paired comparisons. The paper proposes a mechanism for finding the fields for rating numbers analysis. In addition, the paper proposes a method for the statistical evaluation of the balance sheets of various companies by calculating the mutual correlation matrices. Based on the considered mathematical methods to determine quantitative characteristics of technical objects financial and economic activities, was developed algorithms, information and software allowing to realize of different systems economic analysis