Search Results (124)

Background/Objectives: Schizophrenia exhibits symptoms linked to the hippocampus and parahippocampal gyrus. This includes the entorhinal cortex (ERC) and perirhinal cortex (PRC) as anterior parts, along with the posterior segment known as the parahippocampal cortex (PHC). However, recent research has detailed atlases based on cytoarchitectural characteristics and the hippocampus divided into four subregions: cornu ammonis (CA), dentate gyrus (DG), subiculum (SUB), and hippocampal–amygdaloid transition (HATA). This study aimed to explore the functional connectivity (FC) changes between these hippocampal subregions and the parahippocampal gyrus structures (ERC, PRC, and PHC) as well as between hippocampal subregions and various functional brain networks in schizophrenia. Methods: In total, 50 individuals with schizophrenia and 50 matched healthy subjects were examined using resting state functional magnetic resonance imaging (rs-fMRI). Results: The results showed alterations characterized by increases and decreases in the strength of the positive connectivity between the parahippocampal gyrus structures and the four hippocampal subregions when comparing patients with schizophrenia with healthy subjects. Alterations were observed among the hippocampal subregions and functional brain networks, as well as the formation of new connections and absence of connections. Conclusions: There is strong evidence that the different subregions of the hippocampus have unique functions and their connectivity with the parahippocampal cortices and brain networks are affected by schizophrenia. Full article

Objectives: This study investigates whether scan time in the high-resolution magnetic resonance imaging (MRI) of human embryos can be reduced without compromising spatial resolution by applying zero-shot self-supervised learning (ZS-SSL), a deep-learning-based reconstruction method. Methods: Simulations using a numerical phantom were conducted to evaluate spatial resolution across various acceleration factors (AF = 2, 4, 6, and 8) and signal-to-noise ratio (SNR) levels. Resolution was quantified using a blur-based estimation method based on the Sparrow criterion. ZS-SSL was compared to conventional compressed sensing (CS). Experimental imaging of a human embryo at Carnegie stage 21 was performed at a spatial resolution of (30 μm)³ using both retrospective and prospective undersampling at AF = 4 and 8. Results: ZS-SSL preserved spatial resolution more effectively than CS at low SNRs. At AF = 4, image quality was comparable to that of fully sampled data, while noticeable degradation occurred at AF = 8. Experimental validation confirmed these findings, with clear visualization of anatomical structures—such as the accessory nerve—at AF = 4; there was reduced structural clarity at AF = 8. Conclusions: ZS-SSL enables significant scan time reduction in high-resolution MRI of human embryos while maintaining spatial resolution at AF = 4, assuming an SNR above approximately 15. This trade-off between acceleration and image quality is particularly beneficial in studies with limited imaging time or specimen availability. The method facilitates the efficient acquisition of ultra-high-resolution data and supports future efforts to construct detailed developmental atlases. Full article

This study assessed the accuracy of a long-term AI-based projection of signs of facial aging and their acceptance by consumers. Standardized photographs of 25 Chinese and 25 French women were first taken at T0 and graded using ethnic-specific skin aging atlases. An AI-based algorithm then aged the photographs by 10 (T10) and 15 (T15) years. A total of 246 women from China, France, and Thailand compared these images in pairs (T0 vs. T0 + 10 or T0 + 15) and provided feedback on their overall impressions, realism, and psychological acceptance via a questionnaire. Besides lower face ptosis (p < 0.01), the simulated images revealed that regardless of ethnicity, there were no significant differences in grading. Irrespective of ethnic background, 62–78% of overall panelists found the projections realistic and liked them, while 85–96% of panelists (Chinese and French) were willing to test them. A total of 47% of Thai panelists were reluctant to try, while 4–14% found it scary. This indicated some degree of cultural influence. This study confirms women’s acceptance of future facial appearance with some degree of cultural divergence. It also highlights a valid methodology to explore skin aging for a more realistic and personalized cosmetic improvement and innovation. Full article

Functional magnetic resonance imaging (fMRI) has become instrumental in researching the functioning of the brain. One application of fMRI is investigating the brains of people with autism spectrum disorder (ASD). The Autism Brain Imaging Data Exchange (ABIDE) facilitates this research through its extensive data-sharing initiative. While ABIDE offers raw data and data preprocessed with various atlases, independent component analysis (ICA) for dimensionality reduction remains underutilized. ICA is a data-driven way to reduce dimensionality without prior assumptions on delineations. Additionally, ICA separates the noise from the signal, and the signal components correspond well to functional brain networks called resting-state networks (RSNs). Currently, no large, readily available dataset preprocessed with ICA exists. Here, we address this gap by presenting ABIDE’s data preprocessed to extract ICA-based resting-state networks, which are publicly available. These RSNs unveil neural activation clusters without atlas constraints, offering a perspective on ASD analyses that complements the predominantly atlas-based literature. This contribution provides a resource for further research into ASD, benchmarking between methodologies, and the development of new analytical approaches. Full article

We examined three decades of changes in the mammal fauna of Estonia, Latvia, and Lithuania in the context of climate variability, land use transformation, and anthropogenic pressures. We compiled distributional, abundance, and status data from publications, atlases, official game statistics, and long-term monitoring programs, and we evaluated trends using compound annual growth rates or temporal indices. Our review identified losses such as regional extinctions of garden dormice and European mink, declines in small insectivores (e.g., pond bats and shrews) and herbivores (e.g., Microtus voles), and the contraction of boreal specialists (e.g., Siberian flying squirrels). However, we also identified gains, including increases in ungulate numbers (e.g., roe deer, red deer, fallow deer, moose, and wild boars before African swine fewer outbreak) and the recovery of large carnivores (e.g., wolves and lynxes). Invasions by non-native species (e.g., American mink, raccoon dog, and raccoon) and episodic disturbances, such as African swine fever and the “anthropause” caused by the SARS-CoV-2 pandemic, have further reshaped community composition. The drivers encompass climatic warming, post-socialist forest succession, intensified hunting management, and rewilding policies, with dispersal capacity mediating the responses of species. Our results underscore the dual legacy of historical land use and contemporary climate forcing in structuring the fauna dynamics of Baltic mammal communities in the face of declining specialists and invasive taxa. Full article

Suyukou Gully, located on the eastern slope of the Helan Mountains in northwest China, is a typical debris-flow-prone catchment characterized by a steep terrain, fractured bedrock, and abundant loose colluvial material. The area is subject to intense short-duration convective rainfall events, which often trigger destructive debris flows that threaten the Suyukou Scenic Area. To investigate the dynamics and risks associated with such events, this study employed the FLO-2D two-dimensional numerical model to simulate debris flow propagation, deposition, and hazard distribution under four rainfall return periods (10-, 20-, 50-, and 100-year scenarios). The modeling framework integrated high-resolution digital elevation data (original 5 m DEM resampled to 20 m grid), land-use classification, rainfall design intensities derived from regional storm atlases, and detailed field-based sediment characterization. Rheological and hydraulic parameters, including Manning’s roughness coefficient, yield stress, dynamic viscosity, and volume concentration, were calibrated using post-event geomorphic surveys and empirical formulations. The model was validated against field-observed deposition limits and flow depths, achieving a spatial accuracy within 350 m. Results show that the debris flow mobility and hazard intensity increased significantly with rainfall magnitude. Under the 100-year scenario, the peak discharge reached 1195.88 m³/s, with a maximum flow depth of 20.15 m and velocities exceeding 8.85 m·s⁻¹, while the runout distance surpassed 5.1 km. Hazard zoning based on the depth–velocity (H × V) product indicated that over 76% of the affected area falls within the high-hazard zone. A vulnerability assessment incorporated exposure factors such as tourism infrastructure and population density, and a matrix-based risk classification revealed that 2.4% of the area is classified as high-risk, while 74.3% lies within the moderate-risk category. This study also proposed mitigation strategies, including structural measures (e.g., check dams and channel straightening) and non-structural approaches (e.g., early warning systems and land-use regulation). Overall, the research demonstrates the effectiveness of physically based modeling combined with field observations and a GIS analysis in understanding debris flow hazards and supports informed risk management and disaster preparedness in mountainous tourist regions. Full article

The assessment of air pollution is an important and relevant issue that requires continuous monitoring and control, especially in urban spaces. However, using instrumental air quality measurement techniques and deploying meters throughout the city is extremely expensive, so a biological alternative can be used—a bioindicator, i.e., a species whose vital functions or morphological structure can reveal the qualitative state of the environment. In this work, the lichen Hypogymnia physodes L. was used to analyze air pollution in areas of the provincial city of Opole, southern Poland. Microscope and chemotaxonomy methods were used in the laboratory to confirm field identification of lichens (atlases and keys). The selected elements, Mn, Fe, Ni, Cu, Zn, Cd, and Pb, were determined using atomic absorption spectrometry, and direct mercury analyzer was used to analyzed Hg concentration. Factor analysis (FA) was performed to associate elements with possible sources of air pollution. The highest concentrations of analytes were found at measurement points close to railway roads (Fe = 5131 mg/kg) and streets with heavy traffic (Pb = 101 mg/kg). Statistically significant differences (p < 0.001) were found between the concentrations of individual elements, which have positive correlation coefficients higher than 0.65. Based on the research carried out, different anthropogenic and traffic-related activities can be considered as one of the main sources of air pollution in Opole City based on the results of FA. Using an additional lichen scale, it can be concluded that the areas surveyed in the town of Opole can be classified as zone IV—characterized by an increase in the number of leaf lichens (additionally co-occurring lichens of the Polycauliona candelaria species), i.e., an area with an average level of air pollution (based also on contamination factor [CF] and pollution load index [PLI]). Accumulation concentrations of heavy metals in lichen were metal-specific and varied spatially, thus reflecting local differences in heavy metal deposition. The research presented here proves that low-cost passive biomonitoring can effectively support classical methods of assessing air pollution in urban spaces. Full article

Background: The arcuate foramen (AF), an osseous foramen, is probably formatted from the ossification of the posterior atlanto-occipital membrane. When this morphologically ossified variant exists, it encloses the vertebral artery (VA) third segment (V3). This close relationship may cause compression to the VA with concomitant vertebrobasilar insufficiency, vertigo, headaches, or neck pain. In the published literature, no studies investigate the abovementioned potential compression pattern. The present study examines the AF ossification pattern (complete or partial type) and the variable VA diameter at the atlantal part (V3), concluding a potential risk for VA compression after correlating the relative diameters (AF and VA diameters). Materials and Methods: One hundred and fifty dried first cervical vertebrae (atlases) and one hundred fifty computed tomography (CT) scans were obtained for the present study. The presence of a complete or incomplete AF was evaluated, and when present, its diameter was measured. To correlate these findings with the vessel, 50 computed tomography angiographies (without AF presence) were obtained to measure the V3 segment diameter. Results: Out of the total 600 (N = 600) sides, 111 sides had incomplete AF (18.2%), and 67 sides had complete AF (11.1%). The AF mean diameter was 6.41 (1.12) mm. The diameter of the V3 segment ranged between 5.0 and 6.0 mm; therefore, three morphological stenosis patterns were identified. A low risk of compression (over 6.0 mm) was identified in 61.2% (N = 109 sides), a moderate risk (between 5.0–6.0 mm) was observed in 29.2% (N = 52 sides), and a high risk (under 5.0 mm) was recorded in 9.6% (N = 17 sides). There was no statistically significant correlation regarding sexes and age for the potential compression patterns. Conclusions: The present study revealed the morphological stenosis pattern of the AF to the V3 segment. The variation had a high risk of compression to the vessel in 9.6% of sides, indicating that it is not infrequent. Knowledge of these details is essential for clinicians when investigating vertebrobasilar insufficiency. Full article

Marbling is a crucial indicator that significantly impacts beef quality grading. Currently, Chinese beef processing enterprises rely on professional graders who visually assess marbling using national standard atlases. However, this manual evaluation method is highly subjective and time consuming. This study proposes a beef marbling grading algorithm based on an enhanced YOLOv8x model to address these challenges. The model integrates a convolutional neural network (CNN) augmented with an improved attention mechanism and loss function, along with a Region-of-Interest (ROI) preprocessing algorithm to automate the marbling grading process. A dataset comprising 1300 beef sample images was collected and split into training and test sets at an 8:2 ratio. Comparative experiments were conducted with other deep learning models as well as ablation tests to validate the proposed model’s effectiveness. The experimental results demonstrate that the improved YOLOv8x achieves a validation accuracy of 99.93%, a practical grading accuracy of 97.82%, and a detection time of less than 0.5 s per image. The proposed algorithm enhances grading efficiency and contributes to intelligent agricultural practices and livestock product quality assessment. Full article

Background/Objectives: Neuroimaging biomarkers could offer more objective measures of the pain experience. This study investigated rT1/T2 maps of the brain as a novel biomarker for chronic pain in patients with central post-stroke pain (PSP) and persistent spinal pain syndrome type 2 (PSPS-II). Methods: Patients with PSP and PSPS-II were retrospectively included alongside healthy controls. Bias correction and intensity normalization were applied to the T1-weighted and T2-weighted images to generate the rT1/T2 maps of the brain. Subsequently, rT1/T2 maps were spatially correlated with neurotransmitter atlases derived from molecular imaging. Results: In total, 15 PSPS-II patients, 11 PSP patients, and 18 healthy controls were included. No significant differences between patient and control demographics were found. Significant decreases in rT1/T2 signal intensity (p < 0.001) were observed in the dorsal and medial part of the thalamus, left caudate nucleus, cuneus, superior frontal gyrus, and dorsal cervicomedullary junction in PSP patients. No significant changes were found in rT1/T2 signal intensity in PSPS-II patients. Significant correlations were found with CB1-, 5HT2a-, and mGluR5-receptor maps (pFDR = 0.003, 0.030, and 0.030, respectively) for the PSP patients and with CB1-, 5HT1a-, 5HT2a-, KappaOp-, and mGluR5-receptor maps (pFDR = 0.003, 0.002, 0.002, 0.003, and 0.002, respectively) in PSPS-II patients. Conclusions: These findings suggest that microstructural alterations occur in the thalamus, cuneus, and dorsal cervicomedullary junction in patients with PSP. The lack of significant findings in rT1/T2 in PSPS-II patients combined with the significant correlations with multiple neurotransmitter maps suggests varying degrees of microstructural deterioration in both chronic pain syndromes, although further research is warranted. Full article

Background: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared five fully automated segmentation pipelines, including FSL, Freesurfer, volBrain, SPM12, and SimNIBS, with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls. Methods: We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared the results for seven brain structures, including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum. Results: We found volBrain’s vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer’s recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain’s vol2Brain and SimNIBS’ headreco to have the strongest correlations, depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL’s FIRST to give the strongest correlations, depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable. Conclusions: Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study, we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process that includes the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases. Full article

The application of artificial intelligence (AI) in anatomy teaching is gaining attention due to its potential to support personalized learning and its ability to provide customized, real-time feedback. While the potential impact of complete AI integration in medical education remains unclear, there is a suspicion that it could revolutionize pedagogical and assessment practices. Traditional anatomy teaching strategies that use donated human resources hinder continuous learning due to accessibility and ethical challenges. Existing resources, such as anatomy atlases, may not provide knowledge of spatial relationships. AI-powered applications enable students to access more flexible and accessible learning material beyond physical classrooms. This review critically evaluates current advances and the possible impacts of AI in learning anatomy based on the reported empirical original studies. Additionally, it recognizes the challenges and provides possible solutions for them. Most of the initiatives to integrate AI in anatomy teaching are directed towards the development of customized anatomy chatbots and their integration with virtual reality (VR). Although the crucial role of medical imaging in the anatomy curriculum is recognized, currently, no AI application has been developed to target this field. This review discusses the currently available AI tools for anatomy teaching. Additionally, the knowledge gaps and future directions of AI in medical education, especially anatomy education, are also discussed. With the present advances in AI technologies, their application in anatomical education is still deficient. This review paper provides an overview of recent tools used in anatomy teaching and learning. Full article

Climate change has global consequences, and Switzerland is no exception. The communication of climate change poses various challenges, and maps are often part of this process. This work presents three maps illustrating the impacts of climate change, developed for the Atlas of Switzerland (AoS), an interactive digital national atlas. The aim is to make climate change impacts understandable and visible. Three different indicators of climate change were visualized: the rise of the zero degree line, the evolution of glacial lakes, and changes in the flowering dates of plants. Various approaches were employed that leverage the strengths of the AoS, including temporal navigation, interactivity, 3D data visualizations, and map combinations. The feasibility of these visualizations are demonstrated through the presented maps and analysis of key considerations for their creation. We believe these map types should be included in national atlases and can contribute to the achievement of Sustainable Development Goal 13: “Climate Action”. Further research is needed to assess the effectiveness and user understandability of the proposed maps. Full article

Bone age (BA) reflects skeletal maturity and is crucial in clinical and forensic contexts, particularly for growth assessment, adult height prediction, and managing conditions like short stature and precocious puberty, often using X-ray, MRI, CT, or ultrasound imaging. Traditional BA assessment methods, including the Greulich-Pyle and Tanner–Whitehouse techniques, compare morphological changes to reference atlases. Despite their effectiveness, factors like genetics and environment complicate evaluations, emphasizing the need for new methods that account for comprehensive variations in skeletal maturity. The limitations of classical BA assessment methods increase the demand for automated solutions. The first automated tool, HANDX, was introduced in 1989. Researchers now focus on developing reliable artificial intelligence (AI)-driven tools, utilizing machine learning and deep learning techniques to improve accuracy and efficiency in BA evaluations, addressing traditional methods’ shortcomings. Recent reviews on BA assessment methods rarely compare AI-based approaches across imaging technologies. This article explores advancements in BA estimation, focusing on machine learning methods and their clinical implications while providing a historical context and highlighting each approach’s benefits and limitations. Full article

The light pollution of the night sky is already a widespread phenomenon, the spatial extent and magnitude of which are increasingly represented in the form of thematic maps and cartographic visualization. Its leading cause needs to be correctly designed or adequately installed outdoor lighting. The problem of excessive artificial light emission at night, together with its adverse effects, has already reached such a level that it has become necessary to develop usable and comprehensible methods for the cartographic representation of the distribution of the phenomenon. In practice, there are several ways to measure the intensity of this pollution. However, there are no uniform legal standards for the use of outdoor lighting and no guidance and guidelines for the visualization of measurement data. Such visualization should provide a consistent, reliable, and, above all, readable picture of the phenomenon adapted to the needs of different audiences. Examples of the representation of the results of measurements of light pollution of the night sky can be found in the literature or a few atlases. Still, they often differ in color scales, value divisions, and measurement units used. This paper reviews the scales and units available in the literature to describe this phenomenon. The differences between the approaches of specialists from different branches and their influence on the final interpretation of the data are also presented. In addition, an authorial solution is proposed to standardize methods of cartographic visualization of the spatial distribution of light smog measurement results. The article attempts to draw attention to the importance of the graphical description of light smog, which will shortly be the subject of increasing research and work on the unification of cartographic communication. Full article