Search Results (411)

Ungulate browsing is a major driver of forest regeneration dynamics and habitat structure in managed temperate forests, influencing species composition, regeneration success, and long-term stand development. Traditional assessments of browsing impacts often rely on field-based indicators such as regeneration density or visual cover, but these metrics provide limited insight into three-dimensional habitat structure. Mobile handheld LiDAR offers highly detailed measurements of forest structure, enabling objective and reproducible quantification of structural complexity that complements and extends conventional field-based methods. In this study, we applied handheld LiDAR as an innovative indicator for habitat structure within the ungulate browsing zone (<2 m height) to evaluate structural development across sites differing in management context. Paired fenced and unfenced plots (12 × 12 m) were surveyed within the WiWaldI project framework in 2019 and 2023 and compared across three hunting regimes representing different degrees of ungulate population management. Structural complexity was quantified by deriving box-counting dimensions from LiDAR point clouds, providing a measure of spatial arrangement and density relevant to ungulate–vegetation interactions. To support interpretation and ecological context, we complemented LiDAR indicators with streamlined field assessments. Based on this framework, we assessed whether forest structural complexity and visual cover differ among regions and over time, and whether ungulate browsing induces detectable structural differences between fenced whether structural differences between fenced and unfenced plots are detectable. We further examined the relative importance of tree species composition, plant architecture, and hunting regime as drivers of three-dimensional habitat structure. A simplified octant method characterized the spatial distribution of woody regeneration, while a silhouette-based approach quantified visual cover from the perspective of a standard ungulate profile. These auxiliary measures contextualize visual and spatial aspects of structure that LiDAR metrics capture with minimal observer bias. LiDAR studies have previously demonstrated potential for linking high-resolution structural data to ungulate habitat use, and our approach extends this by focusing on structural complexity as a habitat indicator. Results show a consistent increase in LiDAR-derived structural complexity between 2019 and 2023 across all regions. This increase occurred across management contexts and was not consistently explained by fencing or hunting regime effects, suggesting that site conditions, forest composition, and successional processes were dominant drivers during the observation period. Hunting regime showed no statistically significant and no consistent effect on structural complexity across regions or years. Visual cover metrics varied strongly among regions and species and declined over time. These findings suggest that three-dimensional habitat structure information has the potential to enhance the evaluation of ungulate impacts and may support evidence-based forest and wildlife management, particularly when interpreted in the context of site conditions and successional dynamics. Beyond ungulate impact assessment, the presented handheld LiDAR approach provides a scalable remote sensing framework for precision forestry by capturing three-dimensional structural attributes that are directly linked to forest stability, resilience, growth dynamics, and stand-level species mixing, thereby supporting evidence-based forest management recommendations. Full article

Oxidative stress is considered one of the cancer hallmarks, influencing tumor initiation, progression, and metastasis. High levels of reactive oxygen species (ROS) impair the effectiveness of the immune response in cancer patients. We examined changes in oxidative stress during immunotherapy, exploring the relationship between the immune system and clinical parameters related to oxidative burden. Several T-cell and myeloid subsets from 79 metastatic non-oncogene-addicted non-small-cell lung cancer (NSCLC) patients were analyzed using flow cytometry. Additionally, 20 cytokines were measured in serum samples, and sNox2-dp levels, an indicator of NOX2 activity, were assessed by ELISA. Seventy-nine healthy donors served as controls. The data showed that cancer patients had higher levels of sNox2-dp compared to healthy donors (p < 0.0001). Elevated sNox2-dp levels were associated with inflammation-related comorbidities (p = 0.008) and platelet counts (p = 0.03) in NSCLC patients. Furthermore, sNox2-dp displayed a negative correlation with immune cells involved in activation, such as proliferating (Ki67⁺) CD8⁺, PD1⁺ and effector lymphocytes, and a positive correlation with immunosuppressive PMN-MDSCs and inflammatory soluble immune factors, including IL1α, IL1β, IL6, IL10, CCL3, and CCL4. Oxidation levels decreased after immunotherapy (p = 0.04) and increased only in non-responder patients (p = 0.02). Oxidative stress may be indirectly affected by immunotherapy and could serve as a novel tool to identify responding patients in the NSCLC setting. Full article

The longest-standing international treaty for wetland and waterbird protection, the Ramsar Convention has resulted in the establishment of more than 2500 protected areas covering over 2.5 million square kilometers around the world. However, its measures are not legally binding, and its effectiveness as a tool for wildlife conservation has rarely been quantitatively assessed. In Benin, West Africa, breeding waterbirds are subjected to intense hunting and egg harvesting for both commercial and subsistence purposes. We quantified count data of waterbirds and eggs taken by local hunters and trappers to assess the effectiveness of the Ramsar Convention as a wildlife conservation tool in southeastern Benin. During the six-month period between May and October 2022, 64 people reported harvesting a total of 12,053 breeding waterbirds and 63,987 eggs, comprising eight species in three families in Ramsar site 1018. Birds most heavily targeted included Allen’s Gallinule (Porphyrio alleni), with 4187 breeding birds taken (~35% of all birds captured), and the White-faced Whistling Duck (Dendrocygna viduata), with 24,491 eggs taken (~38% of all eggs taken) over the course of a single breeding season. The Eurasian Moorhen (Gallinula chloropus) and Lesser Moorhen (Paragallinula angulata) were the third and fourth most targeted bird species, respectively, followed by the African Swamphen (Porphyrio madagascariensis), Black Crake (Zapornia flavirostra), African Jacana (Actophilornis africanus), and African Crake (Cecropsis egregia). Captured waterbirds were sold live at local markets, while eggs were eaten by hunters, except eggs containing chicks, which were discarded. Our findings show heavy persecution of waterbirds during their breeding season, when nesting birds are especially vulnerable to human predation, on a scale that is likely unprecedented and threatens to drive declines of targeted species in Benin. As local residents do not currently appear to recognize any deterrents to the uncontrolled hunting of breeding waterbirds or the collection of eggs in Ramsar site 1018, there is an urgent need to better leverage the Ramsar Convention to enforce conservation practices in this region. Full article

Automated biological counting is essential for scaling wildlife monitoring and biodiversity assessments, as manual processing currently limits analytical effort and scalability. This review evaluates the integration of deep learning and computer vision across diverse acquisition platforms, including camera traps, unmanned aerial vehicles (UAVs), and remote sensing. Methodological paradigms ranging from Convolutional Neural Networks (CNNs) and one-stage detectors like You Only Look Once (YOLO) to recent transformer-based architectures and hybrid models are examined. The literature shows that these methods consistently achieve high accuracy—often exceeding 95%—across various taxa, including insect pests, aquatic organisms, terrestrial vegetation, and forest ecosystems. However, persistent challenges such as object occlusion, cryptic species differentiation, and the scarcity of high-quality, labeled datasets continue to hinder fully automated workflows. We conclude that while automated counting has fundamentally increased data throughput, future advancements must focus on enhancing model generalization through self-supervised learning and improved data augmentation techniques. These developments are critical for transitioning from experimental models to robust, operational tools for global ecological monitoring and conservation efforts. Full article

Protected national parks continue to face increased pressure from the expansion of human–wildlife interface zones, where habitat encroachment promotes human–wildlife contact and zoonotic disease transmission. Gastrointestinal parasites (GIPs) are a significant health issue in wild Asian elephants (Elephas maximus), affecting their fitness, survival, and potential for cross-species transmission. This study aimed to investigate the prevalence of GIPs among wild elephants at Khao Sip Ha Chan National Park in eastern Thailand. Direct smear, formalin-ethyl acetate sedimentation, flotation, and McMaster egg per gram (EPG) counting were used to examine 135 fecal samples from three populations. The findings showed that nematodes (Strongyle-type and Strongyloides spp.) and trematodes (Paramphistomum spp. and Fascioloides jacksoni) were identified. All samples were positive for at least one parasite species, and 84.4% were affected by mixed infections. The prevalence of Strongyle-type and Strongyloides spp. varied significantly among the studied populations, but Paramphistomum spp. had a moderate prevalence, and Fascioloides jacksoni had a low prevalence. The high parasite burden could be explained by environmental factors, host density, and movement patterns influencing parasite transmission, and these factors should be further investigated. These findings provide crucial baseline data and underscore the need for integrated parasite surveillance, alongside long-term conservation and future research. Full article

This study investigated the cyathostomin species composition, anthelmintic efficacy, and potential resistance-associated mutations in Chinese grazing horses. Fecal samples were collected from 90 adult horses. Fecal egg counts (FECs) were determined using a modified McMaster method. Third stage larvae (L3) cultured from the eggs were identified to species level by PCR amplification and sequencing of the internal transcribed spacer-2 (ITS-2). The main species included Cylicocyclus nassatus and Cylicostephanus spp. However, differences in the relative abundance of less common species, including Cyathostomum pateratum and Cylicocyclus ashworthi, suggests regional variation. Anthelmintic efficacy was assessed by a fecal egg count reduction test (FECRT) following albendazole administration. Mutations at six codons of the β-tubulin iso-type-1 (tbb-iso-1) gene were screened by gene sequencing. The results showed that, despite harboring an abundant and diverse cyathostomin community, this herd remained susceptible to albendazole. Overall, this study provides baseline data on cyathostomin species composition and anthelmintic susceptibility in horses from China, contributing to global surveillance of equine cyathostomin resistance. Full article

Herbage intake by grazers and browsers is of fundamental importance to agricultural ecosystems worldwide but is also notoriously difficult to quantify. The intake process is mediated by herbage comminution in the mouth. The attendant chew actions generate sound bursts that can be detected acoustically and analyzed to help elucidate the entire process. Goats consuming a single plant species were acoustically monitored in order to (i) determine the sensitivity of the chewing effort to the large variation in bite mass and satiety level and (ii) estimate how well the amount of herbage consumed can be predicted by counting chews. Experiments used hand-constructed patches containing bite-sized carob (Ceratonia siliqua L.) leaflets of a pre-determined mass that were presented to six goats, individually, with acoustic sensors attached to their horns. Experiment 1 determined the chewing effort and the sequence of bites and chews for three bite masses across five levels of total intake. Experiment 2 determined the chewing effort and the chew sequence at three levels of satiety, achieved by control of the feeding regime, using a single bite mass across three levels of total intake. In Experiment 1, the global chewing coefficient was ≈4 chews g⁻¹ fresh mass ingested (≈10 chews g⁻¹ dry matter). For an individual animal, the chewing coefficient was fairly stable, being influenced mildly by bite mass, but the variation between animals was large. In Experiment 2, the chewing coefficient was again fairly stable in an individual animal, although the chewing effort was slightly elevated at low satiety. At the population level, and for the most relevant range of intake levels, inverse regression of the pooled data from both experiments estimated the two-sided 95% confidence interval of the predicted intake of carob leaves to be <10% of the predicted value. If chewing coefficients can be estimated locally, usefully precise intake predictions should be attainable for the tested vegetation. These results are promising for the future potential of acoustic monitoring, although significant challenges remain. Full article

Viper envenomation in dogs represents a significant medical emergency in regions where vipers are endemic. Despite its clinical relevance, detailed data on the haematological and biochemical alterations in canine viper envenomation remain limited. This study aimed to evaluate the clinical presentation and haematological, biochemical and coagulative changes occurring in dogs following bites from the Vipera aspis species, and to assess their diagnostic and prognostic significance. Twelve dogs with suspected Vipera aspis envenomation were encompassed in the study. Clinical data were gathered and blood samples were collected at hospital admission (T1), 24 h (T2) and 48 h later (T3). Complete blood counts, biochemical profiles and coagulation parameters were analysed using standard automated systems. Common clinical signs included local pain and swelling, depression, fever, haematuria and melena. Haematological evaluation revealed progressive anaemia, leucocytosis and thrombocytopenia. Biochemical findings showed elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and creatine kinas (CK), indicating hepatic and muscular injury; however, no consistent evidence of renal failure was found. Coagulation analysis revealed a significant shortening of activated partial thromboplastin time (aPTT) and prothrombin time (PT) over time, alongside marked increases in fibrinogen and antithrombin III. This indicates an inflammatory rather than consumptive coagulopathy. Viper envenomation in dogs induces complex haematological and biochemical alterations, reflecting both direct venom toxicity and systemic inflammatory responses. Early recognition, supportive care and continuous laboratory monitoring are essential for improving prognosis. Full article

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article

Background: Perianal infections (PIs) are a serious threat in patients with acute myeloid leukemia (AML). While CPX-351 is designed to reduce gastrointestinal toxicity, its impact on the incidence of PIs is unknown. This study aims to evaluate the incidence and characteristics of PIs in a cohort of CPX-351-treated AML patients. Methods: We enrolled 22 adult patients diagnosed with secondary AML receiving CPX-351 between May 2020 and July 2025 at Policlinico Tor Vergata Hospital. Statistical analysis used descriptive statistics and multivariate analysis. Results: The incidence of PIs in the cohort was 31.8%. Microbiological cultures from the lesions commonly yielded Klebsiella pneumoniae and Enterococcus species. The development of a PI was associated with a significantly longer hospital stay (mean, 49.6 vs. 37.7 days; p = 0.034). An increased odds ratio of having PIs was noted for mucositis and positive rectal swabs (17.961, p = 0.062; 5.554, p = 0.391, respectively), with two patients (28.5%) having a positive pre-infection swab for Klebsiella pneumoniae. Surgical intervention was guided by patient pain levels and hematological criteria. Surgical patients had significantly higher pain levels (p = 0.001) and a platelet count greater than 20 × 10⁹/L (p = 0.028). All patients were alive at 30 days, with low rates of septic shock (14.2%, n = 1) and no infection-related mortality or recurrence. Conclusions: Despite CPX-351’s known reduced gastrointestinal toxicity, our study showed a significantly higher incidence of PIs compared to literature data. While the outcomes were favorable, PIs led to prolonged hospitalization. Routine rectal swab surveillance could be a valuable tool for risk stratification and preemptive strategies. Full article

Effective fishery management in coastal waters requires accurate assessments of species–environment relationships, particularly in data-rich but zero-inflated contexts (i.e., datasets with an excess of zero catches). Here, we used fishery-independent trawl survey data collected from 2018 to 2019 in the offshore waters of southern Zhejiang Province of China to investigate the spatio-temporal distribution of Setipinna taty (scaly hairfin anchovy) and its environmental determinants. Given the high frequency of zero catches, we fitted both zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models and selected the best-performing approach using the Akaike information criterion (AIC). Cross-validation indicated that the ZINB model (RMSE: 199.1, R²; 0.25) outperformed ZIP model (RMSE: 239.4, R²; 0.23). Temperature, depth, and salinity were key predictors of S. taty abundance, which generally occurred at depths of 20–40 m and salinities of 26–34 psu. We then applied the optimal ZINB model to predict S. taty distributions in spring, summer, and autumn of 2020. The predictions indicated a summer peak in abundance and a nearshore-to-offshore decreasing gradient, and were broadly consistent with the spatial distribution trends observed in the 2020 survey data. The highest predicted densities were located in nearshore areas off Wenzhou and Taizhou, west of 122° E. By clarifying the key environmental factors shaping S. taty distribution and applying zero-inflated count models to account for an excess of zero catches, which occur more frequently than expected under standard negative binomial models, this study provides an improved basis for effective conservation and sustainable utilization of S. taty resources in the southern offshore waters of Zhejiang; nevertheless, predictive performance could be further improved by incorporating additional environmental and biotic covariates together with extended spatio-temporal data. Full article

Free-living amoebae (FLA) are protozoa ubiquitous in nature, isolated from a variety of environments worldwide. In addition to their natural distribution, some species have been found to be pathogenic to humans. In the present study, FLA presence was evaluated and characterized at the molecular level from different water and soil samples in Fuerteventura Island, Canary Islands, Spain. A total of 31 samples were analyzed by culture and molecular assays (q-PCR and PCR). Moreover, the microbiological quality of the water samples was examined as required by current legislation and international standards. The obtained data revealed that the genus Acanthamoeba was the most prevalent genus of FLA in soil samples and the species Vermamoeba vermiformis was the most isolated in water samples collected from Fuerteventura by culture and molecular assays, q-PCR, and conventional PCR/Sanger sequencing. On the other hand, a microbiological analysis revealed heterogeneous contamination patterns. Escherichia coli was detected in several samples, with some exhibiting high counts while others showed no presence. Salmonella spp. appeared in multiple samples, particularly FTVW1, FTVW9, and FTVW13, whereas Shigella spp. was only found in one sample (FTVW1). Moreover, q-PCR detection offers advantages such as reduced detection time and cost. In addition, culture was proven to be more effective for confirming FLA viability and isolating a greater variety of FLA. Overall, the occurrence of potentially pathogenic free-living amoebae in habitats related to the human population, as reported in the present study, supports the relevance of FLA as a potential health threat to humans. Full article

Understanding life history parameters is key to assessing demography, biological productivity, and extinction risk of fishes. Age and growth analyses in chondrichthyan fishes (sharks, rays, and ghost sharks) is primarily undertaken through counting vertebral band pairs. For rare, threatened, and protected species such as river sharks (Carcharhinidae; Glyphis), obtaining sufficient vertebrae samples may not be possible. Here we use a very small sample size, selective size-class sampling, back-calculation techniques, and a Bayesian hierarchical model that accounts for repeated measures to provide age and growth information for the Speartooth Shark Glyphis glyphis from which comprehensive sampling is not possible. Ten individuals were selectively sampled from the Adelaide River, Northern Territory, Australia. Bayesian length-at-age models using a combination of informative and uninformative priors in a multi-model framework were applied to the observed and back-calculated data with the sexes combined. Band pair counts produced age estimates of 0–11 years and suggest that age at maturity is possibly >12 years. Most model parameter estimates for length-at-birth (L₀) and asymptotic length (L_∞) were biologically plausible. The Gompertz growth function, applied through a Bayesian hierarchical approach to back-calculated data, provided the best fitting and most biologically appropriate length-at-age parameters: L_∞ = 229.5 cm TL ± (14.6 SE), g_Gom = 0.16 yr⁻¹ ± (0.01 SE), and L₀ = 58.2 cm TL ± (1.4 SE). The results presented here are the first study to apply Bayesian methods to back-calculated length-at-age data while accounting for repeated measures. Full article

Rice is one of the world’s most important staple crops, and outbreaks of insect pests pose a persistent threat to yield stability and food security in major rice-growing regions. Reliable field-scale rice pest detection remains challenging due to limited datasets, heterogeneous imaging conditions, and inconsistent annotations. To address these limitations, we construct RicePest-30, a field-oriented dataset comprising 8848 images and 62,227 annotated instances covering 30 major rice pest species. Images were collected using standardized square-framing protocols to preserve spatial context and visual consistency under diverse illumination and background conditions. Based on RicePest-30, YOLOv11 was adopted as the primary detection framework and optimized through a systematic hyperparameter tuning process. The learning rate was selected via grid search within the range of 0.001–0.01, yielding an optimal value of 0.002. Training was conducted for up to 300 epochs with an early-stopping strategy to prevent overfitting. For fair comparison, YOLOv5s, YOLOv8s, Faster R-CNN, and RetinaNet were trained for the same number of epochs under unified settings, using the Adam optimizer with a learning rate of 0.001. Model performance was evaluated using Precision, Recall, AP@50, mAP@50:95, and counting error metrics. The experimental results indicate that YOLOv11 provides the most balanced performance across precision, localization accuracy, and counting stability. However, all models exhibit degraded performance in small-object scenarios, dense pest distributions, and visually similar categories. Error analyses further reveal that class imbalance and field-scene variability are the primary factors limiting detection robustness. Overall, this study contributes a high-quality, uniformly annotated rice pest dataset and a systematic benchmark of mainstream detection models under realistic field conditions. The findings highlight critical challenges in fine-grained pest recognition and provide a solid foundation for future research on small-object enhancement, adaptive data augmentation, and robust deployment of intelligent pest monitoring systems. Full article

This study presents the development and evaluation of a Quick Response (QR) code-integrated, web-based, and GIS-supported interactive learning model designed to enhance field-based plant learning in landscape architecture education. Conducted on the Görükle Campus of Bursa Uludağ University (BUU), the research systematically inventoried 6869 individual woody plants belonging to 172 taxa, georeferenced them using GPS, and visualized the data on an interactive campus map. Unique QR codes were generated for each taxon, providing instant access to plant profiles via a web platform and the Landscape Plants mobile application. The pedagogical effectiveness of the system was evaluated through a survey administered to 158 students, yielding a high internal reliability (Cronbach’s Alpha = 0.969). The findings indicated a high level of student satisfaction and a strong positive correlation between web-based and QR code applications (r = 0.941, p ≤ 0.001). This research represents the most comprehensive campus-scale digital plant learning system in Turkey, in terms of both species diversity and individual count. It provides a scalable and sustainable smart campus model which is applicable to nature-based disciplines worldwide. Full article