Evaluation of Color Difference Models for Wide Color Gamut and High Dynamic Range.

Color difference models (CDMs) are essential for accurate color reproduction in image processing. While CDMs aim to reflect perceived color differences (CDs) from psychophysical data, they remain largely untested in wide color gamut (WCG) and high dynamic range (HDR) contexts, which are underrepresented in current datasets. This gap highlights the need to validate CDMs across WCG and HDR.

LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection.

The detection of fetal ultrasound standard planes (FUSPs) is important for the diagnosis of fetal malformation and the prevention of perinatal death. As a promising deep-learning technique in FUSP detection, SonoNet's network parameters have a large size. In this paper, we introduced a light pyramid convolution (LPC) block into SonoNet and proposed LPC-SonoNet with reduced network parameters for FUSP detection.

Intelligent Human Operator Mental Fatigue Assessment Method Based on Gaze Movement Monitoring.

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task.

Evaluation of the age-specific relationship between PTH and vitamin D metabolites.

A commonly used method for determining vitamin D sufficiency is the suppression of excess PTH secretion. Conventionally, the main circulating vitamin D metabolite 25(OH)D is used for this assessment, however, the cut-off data for this parameter vary widely in the literature. The role of other metabolites as markers of vitamin D status is actively debated.

[Study of antitumor effects of human placenta hydrolysate on PC-3, OAW-42, BT-474 cell cultures].

Aim: To investigate the antitumor effects of human placenta hydrolysate (HPH) peptides on three hormone-dependent human cell lines: prostate adenocarcinoma, breast carcinoma, and ovarian cancer by metabolic analysis of cell cultures.

Materials And Methods: The effect of HPH on tumor and control tumor cell lines was evaluated. Study stages: (A) peptide sequencing by collision-induced dissociation mass spectrometry; (B) detection of peptides with anti-tumor properties; (C) expert analysis of the obtained lists of peptides.

[Prevention and treatment of COVID-19 based on post-genomic pharmacological analysis: Systematic computer analysis of 290,000 scientific articles on COVID-19].

The COVID-19 pandemic has highlighted pressing challenges in biomedical research methodology. It has become obvious that the rapid and effective development of treatments for "new" viral infections is impossible without the coordination of interdisciplinary research and in-depth analysis of data obtained within the framework of the post-genomic paradigm. Presents the results of a systematic computer analysis of 290,000 scientific articles on COVID-19, with an emphasis on the results of post-genomic studies of SARS-CoV-2.

High Reactivity of Supermolecular Nanoentities of a Vitamin B Derivative in Langmuir-Schaefer Films Toward Gaseous Toxins.

Recently, we have described the first supermolecular nanoentities (SMEs) of a vitamin B derivative, viz., a monocyano form of heptabutyl cobyrinate ((CN)Cby), unique nanoparticles with strong noncovalent intermolecular interactions, and emerging optical and redox properties. In this work, the fast response of thin films based on the SMEs of the B derivative to gaseous toxins (viz.

The Age of Information in Wireless Cellular Systems: Gaps, Open Problems, and Research Challenges.

One of the critical use cases for prospective fifth generation (5G) cellular systems is the delivery of the state of the remote systems to the control center. Such services are relevant for both massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) services that need to be supported by 5G systems. The recently introduced the age of information (AoI) metric representing the timeliness of the reception of the update at the receiver is nowadays commonly utilized to quantify the performance of such services.

Innovative aberration correction in ultrasound diagnostics with direct phase estimation for enhanced image quality.

The paper addresses a crucial challenge in medical radiology and introduces a novel general approach, which utilises applied mathematics and information technology techniques, for aberration correction in ultrasound diagnostics. Ultrasound imaging of inhomogeneous media inherently suffers from variations in ultrasonic speed between tissue. The characteristics of aberrations are unique to each patient due to tissue morphology.

Robust surface-correlation-function evaluation from experimental discrete digital images.

Correlation functions (CFs) are universal structural descriptors; surface-surface F_{ss} and surface-void F_{sv} CFs are a subset containing additional information about the interface between the phases. The description of the interface between pores and solids in porous media is of particular importance and recently Ma and Torquato [Phys. Rev.

OperatorEYEVP: Operator Dataset for Fatigue Detection Based on Eye Movements, Heart Rate Data, and Video Information.

Detection of fatigue is extremely important in the development of different kinds of preventive systems (such as driver monitoring or operator monitoring for accident prevention). The presence of fatigue for this task should be determined with physiological and objective behavioral indicators. To develop an effective model of fatigue detection, it is important to record a dataset with people in a state of fatigue as well as in a normal state.

Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods.

Objective: Brain Tumor diagnostic prediction is essential for assisting radiologists and other healthcare professionals in identifying and classifying brain tumors. For the diagnosis and treatment of cancer diseases, prediction and classification accuracy are crucial. The aim of this study was to improve ensemble deep learning models for classifing brain tumor and increase the performance of structure models by combining different model of deep learning to develop a model with more accurate predictions than the individual models.

Development of an anatomical breast phantom from polyvinyl chloride plastisol with lesions of various shape, elasticity and echogenicity for teaching ultrasound examination.

Purpose: The WHO reported an increasing trend in the number of new cases of breast cancer, making it the most prevalent cancer in the world. This fact necessitates the availability of highly qualified ultrasonographers, which can be achieved by the widespread implementation of training phantoms. The goal of the present work is to develop and test an inexpensive, accessible, and reproducible technology for creating an anatomical breast phantom for practicing ultrasound diagnostic skills in grayscale and elastography imaging, as well as ultrasound-guided biopsy sampling.

Supermolecular Nanoentities of Vitamin B Derivative as a Link in the Evolution of the Parent Molecules During Self-Assembly at the Air-Water Interface.

Nanoarchitectures with promising properties have now been formed from many important biomolecules. However, the preparation of nanoparticles of vitamin B and its derivatives remains an ongoing research challenge. This paper describes the formation of supermolecular nanoentities (SMEs) of vitamin B derivatives, unique nanoparticles with strong noncovalent intermolecular interactions, emerging properties, and activity.

Planar Gas Expansion under Intensive Nanosecond Laser Evaporation into Vacuum as Applied to Time-of-Flight Analysis.

A computational investigation of the dynamics of gas expansion due to intense nanosecond laser evaporation into vacuum has been carried out. The problem is solved in a one-dimensional approximation, which simplifies calculations and at the same time allows one to analyze the main features of the expansion dynamics. For analysis we use three different approaches.

Anti-phase boundary accelerated exsolution of nanoparticles in non-stoichiometric perovskite thin films.

Exsolution of excess transition metal cations from a non-stoichiometric perovskite oxide has sparked interest as a facile route for the formation of stable nanoparticles on the oxide surface. However, the atomic-scale mechanism of this nanoparticle formation remains largely unknown. The present in situ scanning transmission electron microscopy combined with density functional theory calculation revealed that the anti-phase boundaries (APBs) characterized by the a/2 < 011> type lattice displacement accommodate the excess B-site cation (Ni) through the edge-sharing of BO octahedra in a non-stoichiometric ABO perovskite oxide (LaSrNiTiO) and provide the fast diffusion pathways for nanoparticle formation by exsolution.

Pathfinding in stochastic environments: learning planning.

Among the main challenges associated with navigating a mobile robot in complex environments are partial observability and stochasticity. This work proposes a stochastic formulation of the pathfinding problem, assuming that obstacles of arbitrary shapes may appear and disappear at random moments of time. Moreover, we consider the case when the environment is only partially observable for an agent.

Deep Learning-Based Segmentation of Post-Mortem Human's Olfactory Bulb Structures in X-ray Phase-Contrast Tomography.

The human olfactory bulb (OB) has a laminar structure. The segregation of cell populations in the OB image poses a significant challenge because of indistinct boundaries of the layers. Standard 3D visualization tools usually have a low resolution and cannot provide the high accuracy required for morphometric analysis.

Discrete-velocity Boltzmann model: Regularization and linear stability.

A discrete-velocity Boltzmann model for a nine-velocity lattice is considered. Compared to the conventional lattice Boltzmann (LB) schemes the collisions for the model are defined explicitly. Space and time discretization of the model is based on the collide and stream method; in addition, the regularization of the collision term is proposed.

Hierarchical intrinsically motivated agent planning behavior with dreaming in grid environments.

Biologically plausible models of learning may provide a crucial insight for building autonomous intelligent agents capable of performing a wide range of tasks. In this work, we propose a hierarchical model of an agent operating in an unfamiliar environment driven by a reinforcement signal. We use temporal memory to learn sparse distributed representation of state-actions and the basal ganglia model to learn effective action policy on different levels of abstraction.

Quantal response equilibrium for the Prisoner's Dilemma game in Markov strategies.

Within the studies of human cooperation, there are gaps that require further investigation. One possible area for growth is developing theoretical concepts which describe high levels of cooperation. In this paper, we present a symmetrical quantal response equilibrium (QRE) in Prisoner's Dilemma game (PD) constructed in Markov strategies (tolerance to defection and mutual cooperation).

[New mathematical method for automating the analysis of fluorescein angiograms of the human fundus].

Unlabelled: Specific software for digital image analysis is essential for adequate analysis and objectification of ophthalmic images, assessment of the prevalence of the pathological process, as well as for planning the amount of necessary medical intervention in various diseases.

Purpose: To develop a method for automatic analysis of angiographic images of the fundus for early diagnosis of vascular disorders and complications in a number of ophthalmic diseases.

Material And Methods: Initial angiograms of patients (=117) with vascular eye diseases and in healthy condition were analyzed in automatic mode using a new mathematical approach to digital processing of fundus fluorescein angiograms based on a three-stage algorithm for extracting information from images.

[Cerebrolysin peptides as mood stabilizers].

Aim: To establish the molecular mechanisms of the mood stabilizing (normothymic) action of the neuroprotector Cerebrolysin.

Material And Methods: Mass-spectrometric analysis of the peptide composition of cerebrolysin followed by a complex bioinformatics analysis was utilized.

Results: Cerebrolysin contains considerable amounts of Leu- and Met-enkephalins, partial analogues of enkephalins, peptide fragments of beta-lipotropin.

Vitamin D deficiency in Kazakhstan: Cross-Sectional study.

Vitamin D deficiency (VDD) is one of the serious and highly debatable public health problems affecting at least one billion of world population. This study objected to evaluate Vitamin D status in adult population of both sexes residing in different geographical areas of Kazakhstan and to elucidate the possible contributing factors related to VDD. This cross-sectional study covered 6 regions of Kazakhstan and applied the systematic random sampling to recruit 1347 healthy adults (of whom 819 were females) with mean age 44 ± 14 years.