Search Results (195)

The spatial variation of forest ecosystem services at regional scales remains poorly understood, and few studies have explicitly analyzed how ecosystem services are distributed across different forest management types. This study assessed the spatial overlap between forest management types and ecosystem service hotspots in the Southeastern United States (SEUS) and the Pacific Northwest (PNW) forests. We used the InVEST suite of tools and GIS to quantify carbon storage and water yield. Carbon storage was estimated, stratified by forest group and age class, and literature-based biomass pool values were applied. Average annual water yield and its temporal changes (2001–2020) were modeled using the annual water yield model, incorporating precipitation, potential evapotranspiration, vegetation type, and soil characteristics. Ecosystem service outputs were classified to identify hotspot zones (top 20%) and to evaluate the synergies and tradeoffs between these services. Hotspots were then overlaid with forest management maps to examine their distribution across management types. We found that only 2% of the SEUS and 11% of the PNW region were simultaneous hotspots for both services. In the SEUS, ecological and preservation forest management types showed higher efficiency in hotspot allocation, while in PNW, production forestry contributed relatively more to hotspot areas. These findings offer valuable insights for decision-makers and forest managers seeking to preserve the multiple benefits that forests provide at regional scales. Full article

Background/Objectives: Anterior open bite (AOB) is a complex malocclusion characterized by the lack of vertical overlap between the upper and lower teeth during maximum intercuspation. It often results in functional impairments and aesthetic concerns. Traditional treatments for adult patients, including orthognathic surgery, are effective but invasive. Temporary anchorage devices (TADs) have emerged as a minimally invasive alternative. The aim of this systematic review and meta-analysis was to evaluate the effectiveness of TADs for molar intrusion in the correction of AOB. Methods: A systematic review was conducted according to the PRISMA 2020 guidelines. An electronic search was performed in PubMed and Scopus until March 2025. The inclusion criteria comprised clinical studies in humans published in English or Spanish in the last 10 years. The risk of bias was assessed using RoB 2, ROBINS-I, and the Joanna Briggs Institute tools. A random-effects meta-analysis was carried out to estimate pooled intrusion values, and heterogeneity was evaluated using Cochran’s Q test and the I² statistic. Results: Twelve studies were included. Molar intrusion using TADs achieved significant overbite improvements, with a pooled mean intrusion of 1.70 mm (95% CI: 0.53–2.87 mm). The heterogeneity among studies was high (I² = 88.5%). Despite variability in force magnitude and TAD type, lighter forces were generally associated with similar outcomes and fewer adverse effects. Conclusions: TADs offer a predictable and less invasive alternative to orthognathic surgery for AOB correction. When appropriately indicated and biomechanically managed, they provide effective vertical control and short- to medium-term stability in adult patients. Full article

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%. Full article

To address the widespread challenges of significant multi-category dental morphological variations and interference from overlapping anatomical structures in panoramic dental X-ray images, this paper proposes a dual-stream dental segmentation model based on Transformer heterogeneous feature complementarity. Firstly, we construct a parallel architecture comprising a Transformer semantic parsing branch and a Convolutional Neural Network (CNN) detail capturing pathway, achieving collaborative optimization of global context modeling and local feature extraction. Furthermore, a Pooling-Cooperative Convolutional Module was designed, which enhances the model’s capability in detail extraction and boundary localization through weighted centroid features of dental structures and a latent edge extraction module. Finally, a Semantic Transformation Module and Interactive Fusion Module are constructed. The Semantic Transformation Module converts geometric detail features extracted from the CNN branch into high-order semantic representations compatible with Transformer sequential processing paradigms, while the Interactive Fusion Module applies attention mechanisms to progressively fuse dual-stream features, thereby enhancing the model’s capability in holistic dental feature extraction. Experimental results demonstrate that the proposed method achieves an IoU of 91.49% and a Dice coefficient of 94.54%, outperforming current segmentation methods across multiple evaluation metrics. Full article

UAV-based small target detection is crucial in environmental monitoring, circuit detection, and related applications. However, UAV images often face challenges such as significant scale variation, dense small targets, high inter-class similarity, and intra-class diversity, which can lead to missed detections, thus reducing performance. To solve these problems, this study proposes a lightweight and high-precision model UAV-YOLO based on YOLOv8s. Firstly, a double separation convolution (DSC) module is designed to replace the Bottleneck structure in the C2f module with deep separable convolution and point-by-point convolution fusion, which can reduce the model parameters and calculation complexity while enhancing feature expression. Secondly, a new SPPL module is proposed, which combines spatial pyramid pooling rapid fusion (SPPF) with long-distance dependency modeling (LSKA) to improve the robustness of the model to multi-scale targets through cross-level feature association. Then, DyHead is used to replace the original detector head, and the discrimination ability of small targets in complex background is enhanced by adaptive weight allocation and cross-scale feature optimization fusion. Finally, the WIPIoU loss function is proposed, which integrates the advantages of Wise-IoU, MPDIoU and Inner-IoU, and incorporates the geometric center of bounding box, aspect ratio and overlap degree into a unified measure to improve the localization accuracy of small targets and accelerate the convergence. The experimental results on the VisDrone2019 dataset showed that compared to YOLOv8s, UAV-YOLO achieved an 8.9% improvement in the recall of mAP@0.5 and 6.8%, while the parameters and calculations were reduced by 23.4% and 40.7%, respectively. Additional evaluations of the DIOR, RSOD, and NWPU VHR-10 datasets demonstrate the generalization capability of the model. Full article

Accurate detection of Jinxiu Malus fruits in unstructured orchard environments is hampered by frequent overlap, occlusion, and variable illumination. To address these challenges, we propose YOLOv8-MSP-PD (YOLOv8 with Multi-Scale Pyramid Fusion and Proportional Distance IoU), a lightweight model built on an enhanced YOLOv8 architecture. We replace the backbone with MobileNetV4, incorporating unified inverted bottleneck (UIB) modules and depth-wise separable convolutions for efficient feature extraction. We introduce a spatial pyramid pooling fast cross-stage partial connections (SPPFCSPC) module for multi-scale feature fusion and a modified proportional distance IoU (MPD-IoU) loss to optimize bounding-box regression. Finally, layer-adaptive magnitude pruning (LAMP) combined with knowledge distillation compresses the model while retaining performance. On our custom Jinxiu Malus dataset, YOLOv8-MSP-PD achieves a mean average precision (mAP) of 92.2% (1.6% gain over baseline), reduces floating-point operations (FLOPs) by 59.9%, and shrinks to 2.2 MB. Five-fold cross-validation confirms stability, and comparisons with Faster R-CNN and SSD demonstrate superior accuracy and efficiency. This work offers a practical vision solution for agricultural robots and guidance for lightweight detection in precision agriculture. Full article

Background: Musculoskeletal injuries have a substantial impact on athletes, affecting sports performance and increasing the risk of future musculoskeletal disorders (e.g., osteoarthritis). Injury prevention programs are essential to reduce the risk of sport-related injuries and meta-analyses can provide a large amount of information in a single article. Objective: To summarize the pooled effects of injury prevention programs focused on any form of physical exercise in the incidence and risk of musculoskeletal injuries and reinjuries by sports and musculoskeletal body regions. Methods: The CINAHL (via EBSCOhost), Embase (via Elsevier), Epistemonikos, PubMed, Scopus, SPORTDiscus (via EBSCOhost), and the Cochrane Library e-databases were searched from inception to 7 October 2024. Systematic reviews with meta-analyses of randomized clinical trials were considered. The methodological quality of systematic reviews was assessed with AMSTAR 2. The degree of overlap between meta-analyses of interest was calculated. Results: Fourteen systematic reviews were included. Thirteen of these reviews were focused on soccer. Overall, meta-analyses including a specific injury prevention program (FIFA 11+ and FIFA 11+ kids) found that these programs may reduce the risk of musculoskeletal injuries among soccer players. Concretely, FIFA 11+ may reduce the risk of ankle, knee, hip/groin, and hamstring injuries, whereas FIFA 11+ kids may decrease the risk of ankle and knee injuries. Conclusions: FIFA 11+ and FIFA 11+ kids may reduce the risk of sports musculoskeletal injuries, mainly in the lower limbs. However, many clinical and methodological issues (e.g., the lack of meta-analyses in many types of sports) were discussed and highlighted the difficulty of making robust clinical recommendations with the current data. Full article

The Portuguese coast forms a key biogeographic transition zone where co-occurring kelp species show limited vertical overlap. This study aimed to understand whether temperature and light responses help explain the vertical niche differentiation of Laminaria ochroleuca, Saccorhiza polyschides, and Phyllariopsis brevipes. Results revealed that P. brevipes, despite occupying the southernmost range, showed a low thermal tolerance: 27 °C significantly increased respiration rates, indicating metabolic stress, and exposition at 30 °C caused physiological stress. In contrast, L. ochroleuca and S. polyschides exhibited a greater thermal resilience but displayed high light requirements, with evident stress at 30 °C. These results suggest that light availability may play a key role in shaping vertical zonation in a climate warming scenario, with species adapted to low light occupying deeper subtidal zones. S. polyschides, a high light-requiring species, dominates the shallow subtidal region, while L. ochroleuca, also high light-requiring and temperature-tolerant, is abundant in both intertidal pools and shallow subtidal habitats. These findings raise new hypotheses regarding future distribution patterns under climate change: while L. ochroleuca may continue expanding polewards and potentially replace other Laminaria spp. at shallow depths, low-light-adapted, cold-water species may retain a competitive advantage in deeper zones. Full article

Polyamines play a pivotal role in regulating the growth and metabolic adaptation of microalgae, yet their integrative regulatory roles remain underexplored. This review advances a comprehensive perspective of microalgae growth, integrating polyamine dynamics, amino acid metabolism, and redox balance. Polyamines (putrescine, spermidine, and spermine) biology in microalgae, particularly Chlamydomonas reinhardtii, is reviewed, exploring their critical function in modulating cell cycle progression, enzymatic activity, and stress responses through nucleic acid stabilization, protein synthesis regulation, and post-translational modifications. This review explores how the exogenous supplementation of polyamines modifies their intracellular dynamics, affecting growth phases and metabolic transitions, highlighting the complex regulation of internal pools of these molecules. Comparative analyses with Chlorella ohadii and Scenedesmus obliquus indicated species-specific responses to polyamine fluctuations, linking putrescine and spermine levels to important tunable metabolic shifts and fast growth phenotypes in phototrophic conditions. The integration of multi-omic approaches and computational modeling has already provided novel insights into polyamine-mediated growth regulation, highlighting their potential in optimizing microalgae biomass production for biotechnological applications. In addition, genomic-based modeling approaches have revealed target genes and cellular compartments as bottlenecks for the enhancement of microalgae growth, including mitochondria and transporters. System-based analyses have evidenced the overlap of the polyamines biosynthetic pathway with amino acids (especially arginine) metabolism and Nitric Oxide (NO) generation. Further association of the H₂O₂ production with polyamines metabolism reveals novel insights into microalgae growth, combining the role of the H₂O₂/NO rate regulation with the appropriate balance of the mitochondria and chloroplast functionality. System-level analysis of cell growth metabolism would, therefore, be beneficial to the understanding of the regulatory networks governing this phenotype, fostering metabolic engineering strategies to enhance growth, stress resilience, and lipid accumulation in microalgae. This review consolidates current knowledge and proposes future research directions to unravel the complex interplay of polyamines in microalgal physiology, opening new paths for the optimization of biomass production and biotechnological applications. Full article

Background: Nearly half of under-five deaths are linked to undernutrition. Most evidence on undernutrition relies on conventional anthropometric measures. Conventional anthropometric measures fail to capture its overlapping forms and are limited in providing the true burden and distinct disaggregated patterns of undernutrition. Using the Composite Index of Anthropometric Failure (CIAF), this study aims to provide updated regional and country-level pooled prevalence estimates of the overall burden and various single and coexisting patterns of undernutrition among children in Sub-Saharan Africa (SSA). Methods: We systematically searched Medline, CINAHL, Web of Science, Scopus, and Google Scholar for studies published between January 2006 and October 2023. Studies reporting the prevalence of aggregated CIAF or any of the disaggregated forms of CIAF (stunting only, wasting only, underweight only, stunting-underweight, wasting-underweight, and stunting-wasting-underweight) based on the 2006 World Health Organisation (WHO) growth standard were included. Data extraction was performed by two reviewers, and discrepancies were resolved by consensus. Pooled prevalences of various categories of undernutrition were estimated using a random effect model meta-analysis. Subgroup analysis and meta-regression were performed to identify possible sources of heterogeneity among the included studies. Publication bias was checked using the Asymmetry funnel plot and Egger’s test. The protocol was registered on PROSPERO (CRD42023458796). Result: This systematic review and meta-analysis identified 3898 published studies from the database search, of which 26 were included. In SSA, the overall pooled prevalence of undernutrition among children was 37.45% (95% Confidence Interval (CI): 31.97, 42.92). Of these, 10% (95% CI: 8.02, 11.98) of children experienced at least one coexisting form, and 25.5% (95% CI: 16.78, 33.72) experienced at least one single form of undernutrition. Stunting only [22.32% (95% CI: 18.26, 26.39)] was the most prevalent disaggregated pattern of undernutrition, followed by the coexistence of stunting with underweight [10.15% (95% CI: 8.17, 12.13)]. Conclusions: Over one in three children in SSA experienced at least one form of undernutrition. Nearly one-third of these undernourished children were affected by multiple forms of undernutrition. The high prevalence of coexisting undernutrition indicates the need to develop multi-indicator nutrition strategies that could simultaneously address the various dimensions of undernutrition in children. Full article

Background: Almost half of all deaths worldwide in children under five are related to malnutrition. Malnutrition encompasses a wide array of nutritional conditions and emerging evidence indicates a growing overlap of these different forms of malnutrition. Nutrimetry, which combines assessments of height-for-age (HAZ) with BMI-for-age (BMIZ) to offer a more integrated assessment of nutritional status, can be particularly useful in low-resource settings to correctly reflect the complex interplay of stunting and overweight. Objective: The objective of this study is to explore the impact of malnutrition on children in Madagascar and demonstrate how integrating HAZ assessments with BMIZ can reveal the double burden of malnutrition—encompassing both undernutrition and overnutrition—within the same population. Methods: This cross-sectional observational study employing Nutrimetry was carried out in rural communities in the Itasy region of Madagascar. A systematic random sampling method was used to choose the 500 women to invite to participate from the approximately 5000 who formed the pool of potential participants. A total of 437 were able to be invited and all invited women agreed to participate, resulting in 437 mother–child (0–24 months) pairs being included in the study. Results: Chronic undernutrition or thinness (31.6%), overweight and obesity (21.3%), and stunting (57.6%) were prevalent among the children included in the study. Among children with chronic undernutrition, 55.06% were identified as stunted. Among children with overweight or obese, 61.03% were identified as stunted. This highlights a significant overlap between inadequate weight and stunting. A socioeconomic analysis revealed significant barriers, including limited financial resources and poor dietary diversity, exacerbating malnutrition. Maternal nutritional status and breastfeeding practices also emerged as critical determinants of child nutritional outcomes. Conclusions: The study underscores the importance of prioritizing height assessments as a preliminary step in nutritional evaluations to prevent undetected acute malnutrition. Full article

Background/Objectives: The number of systematic reviews evaluating the incidence of anterior cruciate ligament (ACL) injuries in sports is increasing. To synthesize pooled incidence and prevalence rates of sport-related ACL injuries based on published systematic reviews with meta-analyses. Methods: An overview of systematic reviews with meta-analysis was conducted. The CINAHL, Embase, PubMed, and SPORTDiscus databases were searched from inception to 17 October 2023. AMSTAR 2 was used to assess the methodological quality of reviews. The degree of overlap between reviews was calculated when possible. Results: Seven systematic reviews including 51 meta-analyses of interest were included. The prevalence of ACL injuries was not meta-analyzed. Meta-analyses mainly showed that ACL injuries may have a high incidence in American football, basketball, European football/soccer, and volleyball, among other sports. In addition, ACL injuries may have a higher incidence in females than males in some sports. For example, the pooled incidence rates of ACL injuries in basketball ranged from 0.091 (95%CI, 0.074–0.111) to 0.110 (95%CI, 0.094–0.128) among female athletes, whereas this incidence ranged from 0.024 (95%CI, 0.016–0.034) to 0.027 (95%CI, 0.019–0.035) among male athletes. Conclusions: Sport-related ACL injuries may have a high incidence in sports such as American football, basketball, European football/soccer, or volleyball and show differences between sexes. Therefore, a sex-specific prevention of these injuries may be needed. Full article

Environmental control based on growth stage is critical for enhancing the yield and quality of industrially cultivated Pleurotus pulmonarius. Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. This study introduces a lightweight method called the real-time detection model for the growth stages of P. pulmonarius (GSP-RTMDet). A spatial pyramid pooling fast network with simple parameter-free attention (SPPF-SAM) was proposed, which enhances the backbone’s capability to extract key feature information. Additionally, it features an interactive attention mechanism between spatial and channel dimensions to build a cross-stage partial spatial group-wise enhance network (CSP-SGE), improving the feature fusion capability of the neck. The class-aware adaptive feature enhancement (CARAFE) upsampling module is utilized to enhance instance segmentation performance. This study innovatively fusions the improved methods, enhancing the feature representation and the accuracy of masks. By lightweight model design, it achieves real-time growth stage detection of P. pulmonarius and accurate instance segmentation, forming the foundation of an environmental control strategy. Model evaluations reveal that GSP-RTMDet-S achieves an optimal balance between accuracy and speed, with a bounding box mean average precision (bbox mAP) and a segmentation mAP (segm mAP) of 96.40% and 93.70% on the test set, marking improvements of 2.20% and 1.70% over the baseline. Moreover, it boosts inference speed to 39.58 images per second. This method enhances detection and segmentation outcomes in real-world environments of P. pulmonarius houses, offering a more accurate and efficient growth stage perception solution for environmental control. Full article

Object detection in low-light environments is often hampered by unfavorable factors such as low brightness, low contrast, and noise, which lead to issues like missed detections and false positives. To address these challenges, this paper proposes a low-light object detection algorithm named Dark-YOLO, which dynamically extracts features. First, an adaptive image enhancement module is introduced to restore image information and enrich feature details. Second, the spatial feature pyramid module is improved by incorporating cross-overlapping average pooling and max pooling to extract salient features while retaining global and local information. Then, a dynamic feature extraction module is designed, which combines partial convolution with a parameter-free attention mechanism, allowing the model to flexibly capture critical and effective information from the image. Finally, a dimension reciprocal attention module is introduced to ensure the model can comprehensively consider various features within the image. Experimental results show that the proposed model achieves an mAP@50 of 71.3% and an mAP@50-95 of 44.2% on the real-world low-light dataset ExDark, demonstrating that Dark-YOLO effectively detects objects under low-light conditions. Furthermore, facial recognition in dark environments is a particularly challenging task. Dark-YOLO demonstrates outstanding performance on the DarkFace dataset, achieving an mAP@50 of 49.1% and an mAP@50-95 of 21.9%, further validating its effectiveness for face detection under complex low-light conditions. Full article

Background/Objectives: Spinal cord injury (SCI) causes profound autonomic and endocrine dysfunctions, giving rise to adrenal insufficiency (AI), which is marked by a reduction in steroid hormone production. Left unaddressed, SCI-related AI (SCI-AI) can lead to life-threatening consequences such as severe hypotension and shock (i.e., adrenal crisis). However, symptoms are often non-specific, making AI challenging to distinguish from similar or overlapping cardiovascular conditions (e.g., orthostatic hypotension). Additionally, the etiology of SCI-AI remains unknown. This review aimed to synthesize the current literature reporting the prevalence, symptomology, and management of SCI-AI. Methods: A systematic search was performed to identify studies reporting AI following the cessation of glucocorticoid treatments in individuals with traumatic SCI. A random-effects meta-analysis was conducted to investigate the overall prevalence of SCI-AI. Results: Thirteen studies involving 545 individuals with traumatic SCI, most with cervical level injuries (n = 256), met the review criteria. A total of 4 studies were included in the meta-analysis. Primary analysis results indicated an SCI-AI pooled prevalence of 24.3% (event rate [ER] = 0.243, 95% confidence interval [CI] = 0.073–0.565, n = 4). Additional sensitivity analyses showed a pooled prevalence of 46.3% (ER = 0.463, 95%CI = 0.348–0.582, n = 2) and 10.8% (ER = 0.108, 95%CI = 0.025–0.368, n = 2) for case–control and retrospective cohort studies, respectively. High-dose glucocorticoid administration after SCI as well as the injury itself appear to contribute to the development of AI. Conclusions: The estimated prevalence of AI in people with traumatic SCI was high (24%). Prevalence was also greater among individuals with cervical SCI than those with lower-level lesions. Clinicians should be vigilant in recognizing the symptomatology and onset of SCI-AI. Further research elucidating its underlying pathophysiology is needed to optimize glucocorticoid administration for remediating AI in this vulnerable population. Full article