Search Results (116)

Our study was conducted in the Valle Nuevo National Park and included four habitat classes: tussock grass (Sabapa), pine forest (Pinoc), broadleaf forest (Boslat), and agricultural ecosystem (Ecoag). We had two main objectives: to comparatively describe millipede communities and to determine the relationships between population density/diversity and soil physicochemical variables. The research was cross-sectional and non-manipulative, with a descriptive and correlational scope; sampling followed a stratified systematic design, with eight transects and 32 quadrats of 1 m², covering 21.7 km. We found a sandy loam soil with an extremely acidic pH. The highest population density of millipedes was recorded in Sabapa, and the lowest in Ecoag. The highest alpha diversity was shared between Boslat (Margalef = 1.72) and Pinoc (Shannon = 2.53); Sabapa and Boslat showed the highest Jaccard similarity (0.56). The null hypothesis test using the weighted Shannon index revealed a statistically significant difference in diversity between the Boslat–Sabapa and Pinoc–Sabapa pairs. Two of the species recorded highly significant indicator values (IndVal) for two habitat classes. We found significant correlations (p < 0.05) between various soil physicochemical variables and millipede density and diversity. Full article

Alzheimer’s disease (AD) involves the accumulation of amyloid-β (Aβ) plaques, whose quantification plays a central role in understanding disease progression. Automated segmentation of Aβ deposits in histopathological micrographs enables large-scale analyses but is hindered by the high cost of detailed pixel-level annotations. Weakly supervised learning offers a promising alternative by leveraging coarse or indirect labels to reduce the annotation burden. We evaluated a weakly supervised approach to segment and analyze thioflavin-S-positive parenchymal amyloid pathology in AD and age-matched brains. Our pipeline integrates three key components, each designed to operate under weak supervision. First, robust preprocessing (including retrospective multi-image illumination correction and gradient-based background estimation) was applied to enhance image fidelity and support training, as models rely more on image features. Second, class activation maps (CAMs), generated by a compact deep classifier SqueezeNet, were used to identify, and coarsely localize amyloid-rich parenchymal regions from patch-wise image labels, serving as spatial priors for subsequent refinement without requiring dense pixel-level annotations. Third, a patch-based convolutional neural network, U-Net, was trained on synthetic data generated from micrographs based on CAM-derived pseudo-labels via an extensive object-level augmentation strategy, enabling refined whole-image semantic segmentation and generalization across diverse spatial configurations. To ensure robustness and unbiased evaluation, we assessed the segmentation performance of the entire framework using patient-wise group k-fold cross-validation, explicitly modeling generalization across unseen individuals, critical in clinical scenarios. Despite relying on weak labels, the integrated pipeline achieved strong segmentation performance with an average Dice similarity coefficient (≈0.763) and Jaccard index (≈0.639), widely accepted metrics for assessing segmentation quality in medical image analysis. The resulting segmentations were also visually coherent, demonstrating that weakly supervised segmentation is a viable alternative in histopathology, where acquiring dense annotations is prohibitively labor-intensive and time-consuming. Subsequent morphometric analyses on automatically segmented Aβ deposits revealed size-, structural complexity-, and global geometry-related differences across brain regions and cognitive status. These findings confirm that deposit architecture exhibits region-specific patterns and reflects underlying neurodegenerative processes, thereby highlighting the biological relevance and practical applicability of the proposed image-processing pipeline for morphometric analysis. Full article

This study presents a hybrid sensor placement methodology that combines criterion-based candidate selection with advanced optimization algorithms. Four established selection criteria—modal kinetic energy (MKE), modal strain energy (MSE), modal assurance criterion (MAC) sensitivity, and mutual information (MI)—are used to evaluate DOF sensitivity and generate candidate pools. These are followed by one of four optimization algorithms—greedy, genetic algorithm (GA), particle swarm optimization (PSO), or simulated annealing (SA)—to identify the optimal subset of sensor locations. A key feature of the proposed approach is the incorporation of constraint dynamics using the Udwadia–Kalaba (U–K) generalized inverse formulation, which enables the accurate expansion of structural responses from sparse sensor data. The framework assumes a noise-free environment during the initial sensor design phase, but robustness is verified through extensive Monte Carlo simulations under multiple noise levels in a numerical experiment. This combined methodology offers an effective and flexible solution for data-driven sensor deployment in structural health monitoring. To clarify the rationale for using the Udwadia–Kalaba (U–K) generalized inverse, we note that unlike conventional pseudo-inverses, the U–K method incorporates physical constraints derived from partial mode shapes. This allows a more accurate and physically consistent reconstruction of unmeasured responses, particularly under sparse sensing. To clarify the benefit of using the U–K generalized inverse over conventional pseudo-inverses, we emphasize that the U–K method allows the incorporation of physical constraints derived from partial mode shapes directly into the reconstruction process. This leads to a constrained dynamic solution that not only reflects the known structural behavior but also improves numerical conditioning, particularly in underdetermined or ill-posed cases. Unlike conventional Moore–Penrose pseudo-inverses, which yield purely algebraic solutions without physical insight, the U–K formulation ensures that reconstructed responses adhere to dynamic compatibility, thereby reducing artifacts caused by sparse measurements or noise. Compared to unconstrained least-squares solutions, the U–K approach improves stability and interpretability in practical SHM scenarios. Full article

The present study analyzed the vegetation diversity in three forests located in southern Romania and assessed their degree of similarity. Data were collected using frame quadrat sampling and species taxonomic identification. The methodology included the calculation of ecological indices (Shannon–Wiener, equitability, maximum entropy, Menhinick, Margalef, McIntosh, Gleason, and Simpson) and statistical analysis using ANOVA and Duncan tests (p < 0.05). Similarity between forests was evaluated using the Jaccard and Dice/Sørensen coefficients. The results showed that biodiversity increases with area size, and the forest ecosystem in Vlădila exhibited the highest number of woody and herbaceous species. Although the forest ecosystem in Studinița had the greatest floristic diversity, according to the Shannon–Wiener index, it also showed higher equitability (0.911 compared to 0.673 in Vlădila) due to a more uniform species distribution. The forest ecosystem in Studinița acted as an intermediate zone between those in Grădinile and Vlădila. Variations in diversity among the three areas reflect ecological differences influenced by location-specific factors such as soil type, climatic conditions, and human interventions. This suggests that ecological conditions and the physical characteristics of forests significantly impact the number and types of species that can coexist within an ecosystem. Full article

Background: Large language models (LLMs) like ChatGPT are increasingly being explored for medical applications. However, their reliability in providing medication advice for patients with complex clinical situations, particularly those with multiple comorbidities, remains uncertain and under-investigated. This study aimed to systematically evaluate the performance, consistency, and safety of ChatGPT in generating medication recommendations for complex cardiovascular disease (CVD) scenarios. Methods: In this simulation-based study (21 January–1 February 2024), ChatGPT 3.5 and 4.0 were prompted 10 times for each of 25 scenarios, representing five common CVDs paired with five major comorbidities. A panel of five cardiologists independently classified each unique drug recommendation as “high priority” or “low priority”. Key metrics included physician approval rates, the proportion of high-priority recommendations, response consistency (Jaccard similarity index), and error pattern analysis. Statistical comparisons were made using Z-tests, chi-square tests, and Wilcoxon Signed-Rank tests. Results: The overall physician approval rate for GPT-4 (86.90%) was modestly but significantly higher than that for GPT-3.5 (85.06%; p = 0.0476) based on aggregated data. However, a more rigorous paired-scenario analysis of high-priority recommendations revealed no statistically significant difference between the models (p = 0.407), indicating the advantage is not systematic. A chi-square test confirmed significant differences in error patterns (p < 0.001); notably, GPT-4 more frequently recommended contraindicated drugs in high-risk scenarios. Inter-model consistency was low (mean Jaccard index = 0.42), showing the models often provide different advice. Conclusions: While demonstrating high overall physician approval rates, current LLMs exhibit inconsistent performance and pose significant safety risks when providing medication advice for complex CVD cases. Their reliability does not yet meet the standards for autonomous clinical application. Future work must focus on leveraging real-world data for validation and developing domain-specific, fine-tuned models to enhance safety and accuracy. Until then, vigilant professional oversight is indispensable. Full article

Mediterranean countries represent a dynamic hub of cultural exchange, where wild plants play a significant role in culinary traditions. A substantial number of these plants belong to the Asteraceae family. The climate similarities across the region contribute to the common distribution ranges of the plants. While many species are widely distributed, others are confined to specific subregions, such as the western Mediterranean, eastern Mediterranean, or North Africa. Only six taxa of the traditionally consumed wild Asteraceae plants are endemic to just one country. This review focuses on wild plants from the Asteraceae family traditionally used as food across 13 study sites, comprising 11 countries in the Mediterranean and adjacent territories, including both mainland areas and three islands. The objective is to identify and analyze patterns of native distribution in relation to actual consumption. As a result, 167 edible wild plants from the Asteraceae family were identified. Their patterns of distribution and consumption are described and analyzed. The highest number of these edible wild plants from the Asteraceae family is consumed in Spain (n = 65), followed by southern Italy (n = 44) and Morocco (n = 32). A similar pattern of consumption is seen in Turkey (n = 24), Sicily (n = 23), Jordan and Palestine (n = 21), and Bulgaria (n = 21). It is notable that 106 plants are used as food in one particular country only, although most of them are distributed in several other countries. Many of the species consumed in certain countries are not used by neighboring populations, highlighting a limited cross-border transmission of ethnobotanical knowledge. The findings from a Jaccard index statistical analysis are discussed. Full article

Understanding the mindset of people who die by suicide remains a key research challenge. We map conceptual and emotional word–word co-occurrences in 139 genuine suicide notes and in reference word lists, an Emotional Recall Task, from 200 individuals grouped by high/low depression, anxiety, and stress levels on DASS-21. Positive words cover most of the suicide notes’ vocabulary; however, co-occurrences in suicide notes overlap mostly with those produced by individuals with low anxiety (Jaccard index of 0.42 for valence and 0.38 for arousal). We introduce a “words not said” method: It removes every word that corpus A shares with a comparison corpus B and then checks the emotions of “residual” words in $A-B$. With no leftover emotions, A and B are similar in expressing the same emotions. Simulations indicate this method can classify high/low levels of depression, anxiety and stress with 80% accuracy in a balanced task. After subtracting suicide note words, only the high-anxiety corpus displays no significant residual emotions. Our findings thus pin anxiety as a key latent feature of suicidal psychology and offer an interpretable language-based marker for suicide risk detection. Full article

Oak–lime–hornbeam forests are among the most biodiverse temperate forests. This study compared older managed stands with a strictly protected old-growth forest in terms of their features. Managed forests at various stages of silvicultural operations were selected: a mature stand where regeneration cuts had not yet begun, as well as stands where such treatments were in the initial or advanced stages. Stand features that may affect the diversity and density of avifauna were analyzed on the basis of 151 sample plots. In four successive breeding seasons, birds in these stands were surveyed. The stands differed significantly in volume, the density of large trees, regeneration, the vertical structure, and the amount of deadwood. The number of bird species was the highest in the initial and advanced gap-cut stands. Group-selection cutting in those stands led to a succession of non-forest bird species and, hence, a greater number of birds building nests on or close to ground as compared to the old-growth forest. The old-growth forest was the most similar to the mature managed stand in terms of bird species composition (Jaccard index = 0.76). The old-growth forest was characterized by the highest bird density (91 pairs per 10 ha), with more than half of the breeding pairs being cavity nesters. In the managed forest, the bird density was from 63 to 72 pairs per 10 ha. Based on the present study, it can be concluded that effective conservation of bird assemblages is possible in managed forests, provided that certain concessions are made. Drawing on the characteristics of old-growth forests, several guidelines can be proposed for forest management. First and foremost, it is essential to maintain a mosaic forest structure. Secondly, it is necessary to retain an adequate number of large, old trees within the stand and to ensure a sufficient volume and diversity of deadwood. Additionally, it is absolutely critical to shift timber harvesting activities outside of the bird breeding season. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) remains a challenging task due to heterogeneous tumor structures, varying intensities across modalities, and limited annotated data. Deep learning has significantly advanced segmentation accuracy; however, it often suffers from sensitivity to hyperparameter settings and limited generalization. To overcome these challenges, bio-inspired metaheuristic algorithms have been increasingly employed to optimize various stages of the deep learning pipeline—including hyperparameter tuning, preprocessing, architectural design, and attention modulation. This review systematically examines developments from 2015 to 2025, focusing on the integration of nature-inspired optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and novel hybrids including CJHBA and BioSwarmNet into deep learning-based brain tumor segmentation frameworks. A structured multi-query search strategy was executed using Publish or Perish across Google Scholar and Scopus databases. Following PRISMA guidelines, 3895 records were screened through automated filtering and manual eligibility checks, yielding a curated set of 106 primary studies. Through bibliometric mapping, methodological synthesis, and performance analysis, we highlight trends in algorithm usage, application domains (e.g., preprocessing, architecture search), and segmentation outcomes measured by metrics such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), Hausdorff Distance (HD), and ASSD. Our findings demonstrate that bio-inspired optimization significantly enhances segmentation accuracy and robustness, particularly in multimodal settings involving FLAIR and T1CE modalities. The review concludes by identifying emerging research directions in hybrid optimization, real-time clinical applicability, and explainable AI, providing a roadmap for future exploration in this interdisciplinary domain. Full article

Background and Objective: The use of computed tomography (CT)-based navigation systems has been shown to improve surgical accuracy in total hip arthroplasty. However, there is limited literature available about the application of CT-based navigation systems in severe hip dysplasia. This study aimed to evaluate the accuracy of a CT-based navigation system in patients with severe hip dysplasia using three-dimensional (3D)-printed bone models. Methods: 3D-printed bone models were generated from CT data of patients with severe hip dysplasia (Crowe type II, 10 hips; type III, 10 hips; and type IV, 10 hips). The accuracy of automatic segmentation, success rate, point-matching accuracy across different registration methods, and deviation values at reference points after registration were assessed. Results: For the combined cohort of Crowe II, III, and IV cases (n = 30), the Dice Similarity Coefficient and Jaccard Index were 0.99 ± 0.01 and 0.98 ± 0.02, respectively. These values indicate a high level of segmentation accuracy. The “Matching with true and false acetabulum + iliac crest” method achieved a 100% success rate across all groups, with mean deviations of 0.08 ± 0.28 mm in the Crowe II group, 0.12 ± 0.33 mm in the Crowe III group, and 0.14 ± 0.50 mm in the Crowe IV group (p = 0.572). In the Crowe IV group, the anterior superior iliac spine deviation was significantly lower using the “Matching with true and false acetabulum + iliac crest” method compared to the “Matching with true and false acetabulum” method (0.28 ± 0.49 mm vs. 3.29 ± 2.56 mm, p < 0.05). Conclusions: This study demonstrated the high accuracy of automatic AI-based segmentation, with a Dice Similarity Coefficient of 0.99 ± 0.01 and a Jaccard Index of 0.98 ± 0.02 in the combined cohort of Crowe type II, III, and IV cases (n = 30). The matching success rate was 100%, with additional points on the iliac crest, which improved matching accuracy and reduced deviations, depending on the case. Full article

The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity. Full article

As an important indicator species for ecological environments, birds can effectively reflect the ecological quality of urban parks through their diversity characteristics. This study takes Xuzhou Quanshan Forest Park as an example to systematically investigate avian diversity and habitat variations by using the line transect and direct counting methods. A total of 120 bird species from 16 orders and 40 families were recorded, accounting for 24.89% of the total bird species in Jiangsu Province, 45.28% in Xuzhou City, and 79% in Quanshan District. The results showed that the Shannon-Wiener diversity index (H’) was highest in wetland habitats (H’ = 2.40), while the lowest was found in coniferous forest habitats (H’ = 1.09). Jaccard similarity coefficient analysis revealed the highest similarity of bird communities between broadleaf forests and mixed coniferous-broadleaf forests (Cj = 0.363), and the lowest similarity between wetlands and coniferous forests (Cj = 0.071). From a zoogeographical perspective, widespread species dominated across different habitats. Resident birds were the most abundant, and passerines constituted the highest proportion of all birds recorded. Based on these results, recommendations such as optimizing vegetation structures, expanding wetland areas, and reducing human disturbance are proposed to enhance avian diversity and promote sustainable development of urban ecosystems. This study provides scientific evidence for ecological planning and avian conservation in urban parks. Full article

The automatic detection of objects in medical photographs is an essential component of the diagnostic procedure. The issue of early-stage brain tumor detection has progressed significantly with the use of deep learning algorithms (DLA), particularly convolutional neural networks (CNN). The issue lies in the fact that these algorithms necessitate a training phase involving a large database over several hundred images, which can be time-consuming and require complex computational infrastructure. This study aimed to comprehensively evaluate a proposed method, which relies on an active contour algorithm, for identifying and distinguishing brain tumors in magnetic resonance images. We tested the proposed algorithm using 50 brain images, specifically focusing on glioma tumors, while 2000 images were used for DLA from the BRATS Challenges 2021. The proposed segmentation method is made up of an active contour algorithm, an anisotropic diffusion filter for pre-processing, active contour segmentation (Chan-Vese), and morphologic operations for segmentation refinement. We evaluated its performance using various metrics, such as accuracy, precision, sensitivity, specificity, Jaccard index, Dice index, and Hausdorff distance. The proposed method provided an average of the first six performance metrics of 0.96, which is higher than most classical image segmentation methods and was comparable to the deep learning methods, which have an average performance score of 0.98. These results indicate its ability to detect brain tumors accurately and rapidly. The results section provided both numerical and visual insights into the similarity between segmented and ground truth tumor areas. The findings of this study highlighted the potential of computer-based methods in improving brain tumor identification using magnetic resonance imaging. Future work must validate the efficacy of these segmentation approaches across different brain tumor categories and improve computing efficiency to integrate the technology into clinical processes. Full article

In this study, we present the first-ever comparison of the effectiveness of SRAP and ISSR markers on three Allium species. In addition, to visualize the results of each dataset in a simpler way, the Fruchterman–Reingold algorithm was used to generate a link graph and neighbor-joining methods were used to obtain a phylogenetic tree. The genetic similarity matrices were compared using the Mantel test. Primers generated 59 ISSR and 72 SRAP fragments. There was no statistically significant difference between the polymorphism information content of the marker sets. In terms of the effective multiplex ratio, SRAP markers were higher than ISSR markers, with values of 6.700 for garlic, 6.400 for onion, and 5.800 for leek (3.490, 4.316, and 2.573, respectively). Similarly, the marker index was calculated as 2.820, 3.056, and 2.505 for SRAP and 1.903, 1.523, and 1.050 for ISSR in onion, garlic, and leek species, respectively. The highest value regarding cophenetic correlation coefficients was obtained from the Jaccard method. According to the neighbor-joining method, the tree drawn using SRAP and ISSR data together shows a more distinct hierarchical structure of genotypes. The results obtained proved that SRAPs have higher values in terms of sign efficiency criteria, but they are not sufficient for the homogeneous grouping of different Allium species. Full article