Search Results (6)

Forests are complex systems in which subtle variations in terrain can reveal much about plant community structure and interspecific interactions. Despite a wealth of studies focusing on broad-scale environmental gradients, the role of fine-scale topographic nuances often remains underappreciated, particularly in subtropical settings. In our study, we explore how minute differences in microtopography—encompassing local elevation, slope, aspect, terrain position index (TPI), terrain ruggedness index (TRI), and flow direction—affect neighborhood-scale interactions among plants. We established an 11.56-hectare dynamic plot in a subtropical forest at the northern margin of China’s subtropical zone, where both microtopographic factors and neighborhood indices (density, competition, diversity) were systematically measured using 5 m × 5 m quadrats. Parameter estimation and mixed-effects models were employed to examine how microtopography influences plant spatial patterns, growth, and competitive dynamics across various life stages. Our findings demonstrate that aspect and TPI act as key drivers, redistributing light and moisture to shape conspecific clustering, heterospecific competition, and tree growth. Remarkably, sun-facing slopes promoted sapling aggregation yet intensified competitive interactions, while shaded slopes maintained stable moisture conditions that benefited mature tree survival. Moreover, in contrast to broader-scale observations, fine-scale TRI was associated with reduced species richness, highlighting scale-dependent heterogeneity effects. The intensification of plant responses with life stage indicates shifting resource demands, where light is critical during early growth, and water becomes increasingly important for later survival. This study thus advances our multiscale understanding of forest dynamics and underscores the need to integrate fine-scale abiotic and biotic interactions into conservation strategies under global change conditions. Full article

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall. Full article

The environment has direct and indirect effects on mental health. Previous studies acknowledge that the poor design of communities and social environments leads to increased psychological distress, but methodological issues make it difficult to draw clear conclusions. Recent public health, leisure and recreation studies have tried to determine the relationship between recreation opportunities and mental health. However, previous studies have heavily focused on individual contexts rather than national or regional levels; this is a major limitation. It is difficult to reflect the characteristics of community environments effectively with such limited studies, because social environments and infrastructure should be analyzed using a spatial perspective that goes beyond an individual’s behavioral patterns. Other limitations include lack of socioeconomic context and appropriate data to represent the characteristics of a local community and its environment. To date, very few studies have tested the spatial relationships between mental health and recreation opportunities on a national level, while controlling for a variety of competing explanations (e.g., the social determinants of mental health). To address these gaps, this study used multi-level spatial data combined with various sources to: (1) identify variables that contribute to spatial disparities of mental health; (2) examine how selected variables influence spatial mental health disparities using a generalized linear model (GLM); (3) specify the spatial variation of the relationships between recreation opportunities and mental health in the continental U.S. using geographically weighted regression (GWR). The findings suggest that multiple factors associated with poor mental health days, particularly walkable access to local parks, showed the strongest explanatory power in both the GLM and GWR models. In addition, negative relationships were found with educational attainment, racial/ethnic dynamics, and lower levels of urbanization, while positive relationships were found with poverty rate and unemployment in the GLM. Finally, the GWR model detected differences in the strength and direction of associations for 3109 counties. These results may address the gaps in previous studies that focused on individual-level scales and did not include a spatial context. Full article

Background: Irritable bowel syndrome (IBS) is one of the most common functional gastrointestinal disorders, exhibiting complex and controversial pathological features. Both oxidative stress and inflammation-related reactive oxygen species production may be involved in IBS pathological development. Thus, we focused on several aspects regarding the causes of oxidative stress occurrence in IBS. Additionally, in the molecular context of oxidative changes, we tried to discuss these possible neurological implications in IBS. Methods: The literature search included the main available databases (e.g., ScienceDirect, Pubmed/Medline, Embase, and Google Scholar). Articles in the English language were taken into consideration. Our screening was conducted based on several words such as “irritable bowel syndrome”, “gut brain axis”, “oxidative stress”, “neuroendocrine”, and combinations. Results: While no consistent evidence suggests clear pathway mechanisms, it seems that the inflammatory response may also be relevant in IBS. The mild implication of oxidative stress in IBS has been described through clinical studies and some animal models, revealing changes in the main markers such as antioxidant status and peroxidation markers. Moreover, it seems that the neurological structures involved in the brain-gut axis may be affected in IBS rather than the local gut tissue and functionality. Due to a gut-brain axis bidirectional communication error, a correlation between neurological impairment, emotional over-responsiveness, mild inflammatory patterns, and oxidative stress can be suggested. Conclusions: Therefore, there is a possible correlation between neurological impairment, emotional over-responsiveness, mild inflammatory patterns, and oxidative stress that are not followed by tissue destruction in IBS patients. Moreover, it is not yet clear whether oxidative stress, inflammation, or neurological impairments are key determinants or in which way these three interact in IBS pathology. However, the conditions in which oxidative imbalances occur may be an interesting research lead in order to find possible explanations for IBS development. Full article

Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions. Full article

In this paper, we propose a new approach to recognize the motional patterns of human postures by introducing the data density functional method. Under the framework of the proposed method, sensed time signals will be mapped into specific physical spaces. The most probable cluster number within the specific physical space can be determined according to the principle of energy stability. Then, each corresponding cluster boundary can be measured by searching for the local lowest energy level. Finally, the configuration of the clusters in the space will characterize the most probable states of the motional patterns. The direction of state migration and the corresponding transition region between these states then constitute a significant motional feature in the specific space. Differing from conventional methods, only a single tri-axial gravitational sensor was employed for data acquirement in our hardware scheme. By combining the motional feature and the sensor architecture as prior information, experimental results verified that the most probable states of the motional patterns can be successfully classified into four common human postures of daily life. Furthermore, error motions and noise only offer insignificant influences. Eventually, the proposed approach was applied on a simulation of turning-over situations, and the results show its potential on the issue of elderly and infant turning-over monitoring. Full article