Specific Regional Characteristics Research Articles

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. In addition, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimer's disease, suggesting its potential in clinical research and applications.

Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing.

Biometric systems are vulnerable to presentation attacks (PAs) performed using various PA instruments (PAIs). Even though there are numerous PA detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with de-folding and de-mixing to derive the task-specific representation for PAD. During de-folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing the generative loss. While de-mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing the interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with the state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% equal error rate (EER) in OULU-NPU and MSU-MFSD, exceeding the baseline performance by 9.54%. The source code of the proposed technique is available at https://github.com/kongzhecn/dfdm.

Radiomics in oncology: A mini-review of principles, applications and challenges

Radiomics, a quantitative approach to medical imaging, employs computational methods to extract features from the images, revealing hidden characteristics of specific regions. This emerging field leverages advanced techniques to analyze a spectrum of features from modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans, aiming to decode tissue characteristics, disease progression, and treatment responses. The radiomics workflow integrates image acquisition, segmentation, feature selection, and data integration, utilizing advanced techniques such as deep learning, machine learning, and data mining. Radiomics demonstrates considerable potential in cancer detection and management, exhibiting high sensitivity and specificity in distinguishing between benign and malignant tumors and predicting outcomes. However, challenges such as imaging protocol variability, overfitting, and standardization requirements impede its broad clinical adoption. Innovations in multi-modal radiomics, deep learning, and genomics integration strive to mitigate these constraints. This review elucidates radiomics’ capabilities, current applications, benefits, challenges, and future directions in oncology.

Visualising quantum innovation: A regional case study.

At the beginning of this century, the advent of a second generation of 'quantum technologies' was announced together with its revolutionary potential to change existing information technologies. Despite the rapidly increasing paid to quantum technological development, there has been little attention paid to the specific characteristics or relationships within emerging quantum ecosystems. The aim of this study is to visualize the innovation structures and relationships that are emerging to shape these technological developments. As these structures typically depend on specific regional features, we have specifically focused on the Spanish case, as it is potentially indicative of the differences between European innovation models and other regional patterns. This objective was achieved by researching the funding network of the ecosystem, collected from a systematic review of various official sources and relevant previous literature. The resulting dataset was framed using the Innovation Ecosystem model and broken down through network analysis theory, as well as characterized through descriptive statistics. This framework identified the significant role that projects play in European scientific and technological innovation, which work as hubs to concentrate resources and incentive cooperation between actors. This is relevant because current work on quantum technologies neglects their importance, as their analysis focuses on the quantity of institutions rather than their relations. Moreover, this paper points out the prominence of public funding to drive quantum innovation, largely stemming from the European Commission. This is another key mechanism that is missed by the existing literature. Finally, it also sheds light on the recipients of this funding, who are mostly research centres. These results allow us to conceptualize the Spanish quantum ecosystem and offer the opportunity for comparative studies with other quantum technologies ecosystems.

Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks

ObjectiveIn this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.MethodsSpatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male–female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.ResultsThe proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.ConclusionThe proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

Predicting functional UTR variants by integrating region-specific features.

The untranslated region (UTR) of messenger ribonucleic acid (mRNA), including the 5'UTR and 3'UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5'UTR and A/T-related sequence composition in 3'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5'UTR and 0.85 for 3'UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.

Mendelian randomization analyses support causal relationships between tinnitus of different stages and severity and structural characteristics of specific brain regions

This study aims to delineate the causal relationships between idiopathic tinnitus in different stages and severity and the morphological properties in specific brain regions. We utilized a two-sample bidirectional Mendelian randomization (MR) analysis to ascertain the causal effects of brain structural attributes on varying severities and stages of tinnitus. Our approach involved harnessing genetic variables derived from extensive genome-wide association studies as instrumental variables, centered mainly on pertinent single-nucleotide polymorphisms associated with tinnitus. Subsequently, we integrated this data with brain structural imaging inputs to facilitate the MR analysis. We also applied reverse MR analysis to pinpoint the critical brain regions implicated in the onset of tinnitus. Our analysis revealed a demonstrable causal relationship between tinnitus and brain structural alterations, including changes primarily within the auditory cortex and hub regions of the limbic system, as well as portions of the frontal-temporal-occipital circuit. We found that individuals exhibiting cortical thickness alterations in the bilateral peri-calcarine and right superior occipital gyrus might have previously experienced tinnitus. Changes in the cortical areas of the right rectus, left inferior frontal gyrus, and right pars-orbitalis appeared unrelated to tinnitus. Furthermore, moderate tinnitus patients showed more pronounced structural alterations. This study substantiates that tinnitus could instigate substantial structural alterations mainly within the auditory-limbic-frontal-visual system, while the reciprocal causality was not supported. Moreover, the data underscores that moderate, rather than severe, tinnitus precipitates the most significant structural changes. Morphological alterations in several specific brain areas either indicate a history of tinnitus or bear no relation to it.

Whole-Russian Scientific and Practical Conference «Well-Being Measuring and Analyzing»

The article contains an overview of reports presented at the Whole-Russian scientific and practical conference «Well-being Measuring and Analyzing», held on 25–27 January 2024 at St. Petersburg State Economic University (UNECON). The program of the conference included 142 reports in face-to-face and online formats. Approximately a quarter of all the conference papers were presented by young researchers – students, undergraduates, and postgraduate students – who co-authored work with academic supervisors. Scientists from educational and research institutions from 19 constituent entities of the Russian Federation and seven foreign countries delivered their presentations.The conference participants discussed the accumulated domestic and international experience in measuring well-being, highlighting specific features and regional characteristics of this socio-economic phenomenon. Aspects of the application of statistical and econometric methods of analysis and forecasting in studying issues of welfare and social inequality were considered, including measuring the level of poverty, the impact of pension system reform, changes in consumer behavior of the population in the housing market, ways to solve social problems in certain regions, including, Arctic zone. The participants identified directions for improving the methodology for measuring well-being and measures to address social inequality at the federal and regional levels.

Divided by borders, united in healthcare? Regional heterogeneities and technocratic solidarity during the Covid-19 pandemic

ABSTRACT It is not only the states of the European Union (EU) that are heterogeneous in many respects, but also the regions existing within them. In this paper, we make use of a conjoint experiment on a particular type of technocratic solidarity – joint procurement of medical countermeasures (JPMC) – to explore how old and new regional-level heterogeneities affect public support for such type of solidarity. To this end, we exploit an experimental dataset, covering five EU countries at different phases of the Covid-19 pandemic in spring 2020. Our experimental results show that a broad policy scope, allocation based on need and EU governance all raise support for JPMC. Moreover, economically weaker regions and areas more severely hit by the pandemic exhibit stronger public backing for JPMC. Turning to the link between regional heterogeneities and specific design features of JPMC, evidence suggests that wealthier regions display greater public backing for a broader scope of the policy. Thus, our results suggest that public preferences for technocratic solidarity are generally strong, but also depend to some extent on regional collective rationalism.

Exploring the impact of social stress on the adaptive dynamics of COVID-19: Typing the behavior of naïve populations faced with epidemics

In the context of natural disasters, human responses inevitably intertwine with natural factors. The COVID-19 pandemic, as a significant stress factor, has brought to light profound variations among different countries in terms of their adaptive dynamics in addressing the spread of infection outbreaks across different regions. This emphasizes the crucial role of cultural characteristics in natural disaster analysis. The theoretical understanding of large-scale epidemics primarily relies on mean-field kinetic models. However, conventional SIR-like models failed to fully explain the observed phenomena at the onset of the COVID-19 outbreak. These phenomena included the unexpected cessation of exponential growth, the reaching of plateaus, and the occurrence of multi-wave dynamics. In situations where an outbreak of a highly virulent and unfamiliar infection arises, it becomes crucial to respond swiftly at a non-medical level to mitigate the negative socio-economic impact. Here we present a theoretical examination of the first wave of the epidemic based on a simple SIRSS model (SIR with Social Stress). We conduct an analysis of the socio-cultural features of naïve population behaviors across various countries worldwide. The unique characteristics of each country/territory are encapsulated in only a few constants within our model, derived from the fitted COVID-19 statistics. These constants also reflect the societal response dynamics to the external stress factor, underscoring the importance of studying the mutual behavior of humanity and natural factors during global social disasters. Based on these distinctive characteristics of specific regions, local authorities can optimize their strategies to effectively combat epidemics until vaccines are developed.

International terrorism and extremism in Central Asia: Regional specific features

Relevance. The relevance of the published study is due to the large-scale and consistent conceptualization of the problems of regional specific features of international terrorism and extremism in Central Asia over the past decades. Purpose. This publication is aimed at a thorough analysis of the current situation and the probable future of international terrorism and extremism in Central Asia, taking into account regional peculiarities. Methodology. The methodology for investigating regional features of international terrorism and extremism in Central Asia was to refer to the most represented scientific databases, first of all Cochrane Library, Campbell Collaboration, ERIC, JSTOR, NCJRS, ProQuest, PsycInfo, SCOPUS, ScienceDirect, IEEE Xplore, Web of Science. The selection of valid studies and surveys was carried out using the method of snowball. Results. The key findings highlight the historical context and current challenges posed by international terrorist organizations in Central Asia. The data underscores the threat of extremism and terrorism in the region, emphasizing the return of foreign fighters from conflict zones and the complex dynamics of radicalization and recruitment within Central Asian countries. Additionally, the repatriation efforts by governments showcase the ongoing struggle to address the risks associated with terrorism and extremism in the region. Conclusions. The conclusion of the research article emphasizes the threat of international terrorism and extremism in Central Asia, exacerbated by the support of radical movements by the local population, and highlights the challenges of identifying common drivers of radicalization while calling for further in-depth research into the regional characteristics of terrorism and extremism. Keywords: radicalization of society; security hazard; regional situation; endopathic causes; political power; foreign fighters Orumbayeva M, Kurmangali A. International terrorism and extremism in Central Asia: Regional specific features. Sci Herald Uzhhorod Univ Ser Phys. 2024;(56):527-534. DOI: 10.54919/physics/56.2024.52bye7

DMA-HPCNet: Dual Multi-level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.

Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disease (AD). In particular, convolutional neural network (CNN)-based methods are highly sensitive to subtle changes caused by brain atrophy in medical images (e.g., magnetic resonance imaging, MRI). Due to computational resource constraints, most CAD methods focus on quantitative features in specific regions, neglecting the holistic nature of the images, which poses a challenge for a comprehensive understanding of pathological changes in AD. To address this issue, we propose a lightweight dual multi-level hybrid pyramid convolutional neural network (DMA-HPCNet) to aid clinical diagnosis of AD. Specifically, we introduced ResNet as the backbone network and modularly extended the hybrid pyramid convolution (HPC) block and the dual multi-level attention (DMA) module. Among them, the HPC block is designed to enhance the acquisition of information at different scales, and the DMA module is proposed to sequentially extract different local and global representations from the channel and spatial domains. Our proposed DMA-HPCNet method was evaluated on baseline MRI slices of 443 subjects from the ADNI dataset. Experimental results show that our proposed DMA-HPCNet model performs efficiently in AD-related classification tasks with low computational cost.

TAILORING SUSTAINABLE TOURISM INDICATORS FOR THE HYDROPOWER RESERVOIR AREAS: THE CASE FROM HOA BINH, VIETNAM

Recently, lakes and hydropower reservoirs have emerged as attractive tourist destinations, serving as a viable alternative to coastal destinations, which have become overcrowded in many countries. Despite the proliferation of sustainable tourism assessment guidelines tailored for diverse landscapes, there is a lack of scholarly discussion about hydropower reservoir tourism. Various researchers and organizations at the global level have developed comprehensive criteria for evaluating the sustainability of tourism. However, there is an increasing demand to customize these frameworks to accommodate specific regional characteristics and variations. This study responds to this call for sensitive areas, such as the hydropower reservoirs in the mountainous region of Vietnam. We developed a set of indicators and criteria tailored to the peculiarities of the reservoir lake area, drawing on well-established sets of criteria and data from surveys and interviews. Our study makes a significant contribution to the advancement of sustainable tourism evaluation criteria both in general and specifically tailored to local regions. We argue that conducting these assessments at the early phases of tourist development can enable stakeholders, ranging from policymakers to local residents, to design well-informed and flexible policies.

Specific regional features of modern euroscepticism: A comparative analysis

This study investigated the topical issue of regional variation of Euroscepticism in the modern EU. The growing opposition to European integration in different parts of the continent threatens the unity and legitimacy of the European project, which necessitates a thorough study of the specifics of this phenomenon in a comparative perspective. The pur- pose of this study was to carry out a comprehensive analysis of the specific regional features of modern Euroscepticism based on theoretical understanding and empirical verification of its determinants and manifestations in different con- texts. The study employed methods of comparison, structural, and functional analysis, cluster analysis, discourse analysis, and case studies to identify common patterns and differences in the nature and dynamics of Euroscepticism in different countries and regions. The findings of the study suggest that Euroscepticism is a multidimensional and context-specific phenomenon, the intensity and nature of which is determined by a complex of historical, socio-economic, political, and cultural factors. The comparative analysis has found three main regional models of Euroscepticism – Central European, Southern European, and Northwestern – each with a distinctive profile and determinants. Despite the differences, a com- mon pattern is the link between the dynamics of Euroscepticism and the asymmetric effects of integration at the local level – the growth of Euroscepticism is most pronounced in peripheral regions and communities that have been margin- alised by economic development and feel threatened by their own identity. At the same time, the penetration of moderate Euroscepticism into the political mainstream poses risks of normalising populist criticism of the EU

Mutual influence of tourism and transport on the territory of "Sayan crossroads"

In our study, we focused our attention on identifying general patterns and specific regional features of the development of tourism, transport infrastructure and the interests of indigenous peoples on the territory of the Sayan Crossroads. Regional and federal authorities consider it necessary to develop tourism in the areas under consideration in order to improve the quality of life of the local population. A sharp increase in the number of tourists, as well as the transport development of the territory without taking into account the opinion of the local population, can lead to serious contradictions and conflicts, contribute to the degradation and destruction of traditional cultural landscapes. Thus, in the system "local population - transport - tourism" there are complex multi-aspect and multi-vector connections and mutual influences. These connections have both common fundamental patterns and their own regional and local features. All of them must be understood and taken into account when planning the sustainable development of the territory. Field work was carried out in the Okinsky district of Buryatia, Todzhinsky kozhuun of Tyva, in the Khankh somon of Khubsugul aimag of Mongolia in 2021-2022. Keywords: Okinsky district, Todzhunsky kozhuun, Todzha, Oka, Sayan crossroads, tourism, transport infrastructure

Features of interethnic relations in the perceptions of residents of a multiethnic region (a case study of the Kabardino-Balkar Republic)

The aim of this article is to present the results of an analysis of interethnic relations among the population of the Kabardino-Balkar Republic, with a focus on identifying local peculiarities and key trends in everyday life that impact ethnic interactions. The research methodology is based on a comprehensive approach to the study of the phenomenon of interethnic relations in a comparative context. For the purposes of this research, this approach combines quantitative and qualitative methods of sociology, statistical data, elements of expert analysis, and personal interviews in various situations. The integration of qualitative and quantitative strategies allows us to examine interethnic relations in the republic from different perspectives. Comparative sociological analysis reveals the spatial levels of interethnic relations and the extent to which they are influenced by specific local-territorial contexts. The use of data from mass republican surveys and materials from interviews and group discussions has enabled the presentation of assessments of interethnic relations, the analysis of the specifics of ethno-cultural composition and land use, the historical memory of the people, the features of ethnic self-awareness, and issues related to ethnic representation. The heterogeneity of the socio-economic space, varying levels of urbanisation in the territory, persistent unemployment levels, and active migration processes were taken into account as circumstances that influence the interethnic climate in the republic. The analysis conducted revealed specific regional features and issues that directly and indirectly determine the level of interethnic tension and stimulate mobilisation strategies. The obtained results can be a part of the social diagnosis of development resources for the republic within the multiethnic North Caucasus macro-region and the development of an effective strategy for regulating interethnic relations that meet the needs and interests of the residents of Kabardino-Balkaria.

Epigenetics of the far northern Yakutian population

BackgroundYakuts are one of the indigenous populations of the subarctic and arctic territories of Siberia characterized by a continental subarctic climate with severe winters, with the regular January average temperature in the regional capital city of Yakutsk dipping below − 40 °C. The epigenetic mechanisms of adaptation to such ecologies and environments and, in particular, epigenetic age acceleration in the local population have not been studied before.ResultsThis work reports the first epigenetic study of the Yakutian population using whole-blood DNA methylation data, supplemented with the comparison to the residents of Central Russia. Gene set enrichment analysis revealed, among others, geographic region-specific differentially methylated regions associated with adaptation to climatic conditions (water consumption, digestive system regulation), aging processes (actin filament activity, cell fate), and both of them (channel activity, regulation of steroid and corticosteroid hormone secretion). Further, it is demonstrated that the epigenetic age acceleration of the Yakutian representatives is significantly higher than that of Central Russia counterparts. For both geographic regions, we showed that epigenetically males age faster than females, whereas no significant sex differences were found between the regions.ConclusionsWe performed the first study of the epigenetic data of the Yakutia cohort, paying special attention to region-specific features, aging processes, age acceleration, and sex specificity.

Regional Specific Features of the Age Structure of the Siberian Population

Regional Specific Features of the Age Structure of the Siberian Population

E3ID: An efficient end to end person search model

Person Search has recently emerged as a challenging task that aims to jointly solve Pedestrian Detection and Person Re-identification (Re-ID). However, the existing approaches still stay at the image processing stage. In this paper, we proposed E3ID, an efficient end-to-end person search model for video, to better solve the person search problem in real densely populated areas. To speed up the model, a low-quality image filter is proposed to adaptively adjust the sampling frequency of Yolov5 according to the moving speed of pedestrians in the video with minimal computational effort. And the cropped pedestrian images are stored in the gallery library for the Re-ID step. Then, we exploit color feature enhancer to mine color features in specific regions that contribute significantly to the Re-ID process. Compared to other state-of-the-art methods, our proposed E3ID can reach 77.94 % on the evaluation index rank 1, which creates a feasibility of using this model in other complex real-world scenarios.

HPAC: a forest tree species recognition network based on multi- scale spatial enhancement in remote sensing images

ABSTRACT Forest tree species recognition is a pivotal subject in the field of remote sensing. To address this, deep learning has been extensively applied. Thus far, most classification methods have generally relied on learning certain global features, yet often overlook the characteristics of specific regions, thereby struggling to adequately handle the similarity between classes. Furthermore, due to the singular nature of features, effectively representing the attributes of tree species images becomes challenging, consequently impacting classification performance. To tackle these issues, a novel approach for forest tree species classification in remote sensing images is proposed, based on the Hollow Pyramid Attention Combination (HPAC) network. Initially, a Shallow Multi-scale Hollow Fusion (SMHF) module is introduced before the 7 × 7 convolution in the ResNet-50 network and the first residual block’s first layer. This module employs dilated convolutions to achieve varying receptive fields. Moreover, it incorporates positional feature information, significantly enhancing the shallow-level feature extraction capabilities, resulting in a richer feature representation. Subsequently, to minimize network parameters and computational workload while bolstering the capacity to recognize deep-level features, the last residual block of the ResNet-50 differentiation is substituted with a Maxpool Avgpool Fusion (MAF) module. This replacement serves to enhance classification accuracy. The classification process is ultimately concluded with a Softmax classifier. Experimental results underscore the effectiveness of the proposed method, achieving a classification accuracy of 95.89% on the PCANDVI dataset of forest tree species data (FTSD). In summary, the introduced HPAC network proves to be both feasible and effective.