Search Results (911)

In the current work, the analysis covered 70 isolates of fungi belonging to Botryosphaeriaceae obtained in the years 2007–2017 during research on the mycobiota of F. excelsior trees with dieback symptoms in various regions of Poland. Five botryosphaeriaceous species were identified: Diplodia fraxini, D. seriata, D. sapinea, Dothiorella omnivora, and Do. sarmentorum, supported by morphological characteristics and nucleotide sequence data from three genes. The effect of temperature on the in vitro growth of colonies of five identified botryosphaeriaceous species was assessed. Dothiorella omnivora achieved optimal growth at 19.0 °C, while the other four species have shown optimal growth between 22.8 °C (Do. sarmentorum) and 25.7 °C (D. seriata). The pathogenicity test was performed in field conditions on nine-year-old F. excelsior seedlings. In total, wound inoculation was performed on 176 shoots, using 22 isolates of five identified fungal species. Each isolate was inoculated onto eight F. excelsior shoots. The symptoms on shoots were examined at 12 weeks after the inoculation. Among the tested fungal species, necrotic lesion was caused by D. fraxini, D. seriata, and Do. sarmentorum. The extent of damage they caused showed statistically significant differences. The highest pathogenic properties were demonstrated by D. fraxini, which caused necrotic lesion with a length of 34.25–50.50 mm (mean 40.13 mm) on inoculated trees. D. seriata showed the lowest degree of virulence. Half of its strains caused necrotic lesions, which did not differ significantly from the control. Diplodia sapinea and Do. omnivora did not cause any visible lesions. None of the control shoots developed necrosis. The role of Botryosphaeriaceae species in intensifying disease symptoms in ash trees in the context of Hymenoscyphus fraxineus invasion and climate changes was discussed. Full article

The survival and growth of mangroves along coastal China is threatened by invasive smooth cordgrass (Spartina alterniflora). Due to the high mortality and frequent replanting of mangrove trees and the impacts of invasive smooth cordgrass, the exact mangrove forest area in Zhejiang Province, China, is still unclear. Based on provincial-scale fine-resolution Unmanned Aerial Vehicle (UAV) imagery and a large number of field survey plots, this study mapped the distribution of mangroves and smooth cordgrass in 2023 using three machine learning classifiers, including Classification and Regression Tree (CART), Convolutional Neural Networks (CNNs), and Support Vector Machine (SVM). The accuracy assessment indicated that the CNN algorithm was superior to the other two algorithms and yielded an overall accuracy and Kappa coefficient of 97% and 0.96, respectively. The total areas of mangrove forest and smooth cordgrass were 140.83 ha and 52.95 ha, respectively, in 2023 in Zhejiang Province. The mangrove forest area was mostly concentrated in Yuhuan, Dongtou, Yueqing, and Longgang districts. The mean canopy coverage of mangrove trees was only 36.41%, with lower than 20% coverage in all northern and some central districts. At the spatial scale, the mangrove trees showed a scattered distribution pattern, and over 70.04% of the planting area had canopy coverage lower than 20%. Smooth cordgrass has widely invaded all 11 districts, accounting for about 13.7% of the total planting area of mangrove trees. Over 67.3% and 85.4% of the planting areas have been occupied by smooth cordgrass in Wenling and Jiaoxiang districts, respectively, which necessitates an intensive anthropogenic intervention to control its spread in these districts. Our study provides more accurate monitoring of the mangrove and smooth cordgrass distribution areas at a provincial scale. The findings will help guide the replanting and management activities of mangrove trees, control planning for smooth cordgrass, and provide a data basis for the accurate estimation of carbon stock for mangrove forests in Zhejiang Province. Full article

Recent intensive research on the cypress bark beetle, Phloeosinus aubei was prompted because of its invasion of Central Europe that caused serious damage to scale-leaved conifer ornamental trees. This dynamic also increased the risk of accidental introduction into North America. In contrast to other historically well-studied bark beetles infesting spruce, pine or broad-leaf trees, intense study of the pheromones and host plant kairomones of bark beetles associated with cupressaceous trees has only begun in the past decade. This highly specialized clade is represented by the genus Phloeosinus. The pressing need for semiochemical-baited traps demands the identification of behavior-modifying compounds. This challenge involves unraveling the various stimuli interacting in the complex communication system to reveal the composition of signal bouquets and the absolute configuration of their components capable of evoking behavior responses. In this short overview we describe the recent research results on host-finding and intraspecific chemical communication of P. aubei, with a short outlook on the species of this genus. Full article

Fast-growing invasive tree species management produces a significant amount of low-density and low-value biomass, which offers a chance for waste valorization in the environmentally friendly construction sector. This study examines the utilization potential of low-value natural waste materials of tree bark, obtained from invasive hardwood species, in the production of environmentally friendly building mortars. More specifically, this study focuses on mixing bark powder of black locust (Robinia pseudoacacia L.) and tree-of-heaven (Ailanthus altissima (Miller) Swingle), with two commercial commonly found lime-based mortar powders in five different ratios of bark content (0%, 5%, 10%, 20% and 30%) characterizing the produced composites, in terms of physical, hygroscopic and mechanical properties. Slightly lighter composites were created with the use of bark additives especially at the bark content of 20% and 30%. As regards the compressive strength, the bark shares of 10% and 20% exhibited the most beneficial performance among those studied, though only the weaker performance of mortar type (M1) benefited significantly from bark incorporation. For both mortars, the composites containing black locust bark presented higher resistance to compression strength and elasticity, demonstrating higher composite integration in general and milder, plastic fraction in relation to tree-of-heaven bark-based specimens, the properties of which are considered crucial for the durability of structural materials. However, black locust bark exhibited higher water absorption compared to tree-of-heaven-based specimens. Despite the drawback of higher hygroscopicity, the results show that black locust bark, especially at lower incorporation rates (10–20%), is a promising functional additive for generating lighter, more ductile mortars, supporting the creation of novel building materials and sustainable waste management. Full article

This study characterizes the chemical defense system of the invasive longhorn beetle Aromia bungii, a destructive pest of Prunus trees, addressing the limited understanding of chemical defensive mechanisms in Cerambycidae. High-speed cameras, environmental scanning electron microscopy (ESEM), dissection, and micro-CT imaging were used to investigate defensive behavior, and the structure of the defense system, in this beetle. Both sexes of A. bungii possess a pair of triangular, sac-like defensive glands symmetrically located in the metathorax, attached to the metasternum. Upon mechanical stimulation, white liquid defensive substances are rapidly ejected through a pair of slit-shaped openings (~200 µm) at the metasternum corners, without gland eversion, reaching over 50 cm. The average weight of substances ejected in first sprays was 7.95 ± 0.79 mg for females and 8.62 ± 2.13 mg for males (mean ± se), with no significant difference between sexes. However, the weight in second sprays after 10 days was significantly lower, at 2.93 ± 0.54 mg for females and 2.22 ± 0.40 mg for males (mean ± se), suggesting that the beetles cannot re-synthesize the substances soon after spray. The weight of ejected substances had no correlation with beetle body weight. Our findings represent the first detailed morphological and functional description of a chemical defense system in Cerambycidae, revealing a specialized metasternal gland and spray mechanism. The substantial but likely non-renewable defensive substances reflect an adaptive trade-off in energy allocation between reproduction and defense in this species that exhibits high fecundity but a short lifespan at the adult stage. Full article

Under the influence of multiple factors such as climate change and human activities, the frequency, intensity, and destructiveness of forest fires are increasing, which may trigger multiple ecological crises. Forest fires can be scientifically prevented, and their risks can be mitigated through specific approaches, particularly by managing forest combustible materials. Common methods include mechanical clearance, prescribed burning, and the establishment of biological firebreak belts, along with the application of grazing to regulate forest fuels. This paper presents a review of studies on grazing and fire risk, both domestically and internationally. Research indicates that livestock grazing has complex effects on forest fire risk: appropriate grazing can manage fuels and modify ecosystem structure to reduce fire hazards—for instance, by decreasing the accumulation of surface flammable materials and promoting the regeneration of fire-resistant tree species. Conversely, overgrazing may disrupt ecological balance and increase fire risk, such as by exacerbating soil erosion and encouraging the invasion of flammable weed species. Case studies from different ecological regions worldwide demonstrate varied effects of grazing on fire prevention, though research in this area exhibits geographical disparities. Adaptive management should integrate targeted grazing, prescribed burning, and mechanical treatments in a synergistic manner. Future efforts should prioritize cross-scale studies, investigate the mechanisms of woody fuel modulation, and refine fire ecology models to enhance the precision and global applicability of grazing-based fire management. Full article

Phloeosinus cupressi Hopkins is an invasive bark beetle that poses a serious threat to Cupressus trees, with potential ecological and economic impacts globally. Native to North America, it has spread to Australia and New Zealand, and climate change may further alter its range. Global trade increases the risk of spread, highlighting the need for predictive modeling in management. In this study, we employed CLIMEX and random forest (RF) models to project the potential global distribution of P. cupressi, incorporating host distribution data for Cupressus. Climatic suitability is concentrated in temperate, subtropical, and Mediterranean zones, including Europe, the U.S., South America, China, Australia, and New Zealand, totaling 10,165.22 × 10⁴ km². Coldest-quarter precipitation (bio19) and annual temperature range (bio7) were identified as the most influential variables. Under RCP6.0 scenarios, suitable areas are projected to expand northward, increasing by ~18%. Regional shifts include contraction in southern Europe and South China, expansion in southern Argentina, southeastern Australia, and coastal New Zealand. Temperature sensitivity is expected to exceed precipitation, enhancing colonization. Due to global Cupressus trade, quarantine and monitoring should focus on high-risk regions. Our findings support early detection, long-term monitoring, and control measures for managing P. cupressi under climate change. Full article

The success of invasive species relies heavily on the production, dispersal and genetic composition of propagules. For range expanding species, breeding strategy and level of reproductive investment will strongly influence their capacity to establish and invade new areas. A hermaphroditic lifestyle provides the advantage of increasing the number of seed bearing individuals within a population while a dioecious habit may enable more rapid adaptation to new environments, improve resource use efficiency, fecundity and dispersal. Pittosporum undulatum, a tree native to coastal areas of southeastern Australia, has many characteristics of an invasive species within and beyond its native range. A previous study detected a male bias within invasive populations, with a high proportion of fruit deriving from female-only trees, leading to recommendations for the removal of ‘matriarch’ trees as a simple management technique. We expanded that study and investigated breeding systems of different populations of P. undulatum by assessing tree density, gender, resource availability and fruit load of 871 individuals in seven native and seven invasive populations. All populations comprised either females (47%) or hermaphrodites. No male-only trees were observed within the study. More females produced more fruit than hermaphrodites, especially in the native site. This could not be attributed to environmental differences between sites. These data support the current management practices of targeting the removal of females as a simple method for containing invasions given the benefits of reducing the workload and spreading limited management resource. Our work highlights the value in understanding the breeding strategy employed by focal invasive species as a means of developing improved and more targeted control methods. Full article

Invasive insects pose significant ecological challenges due to their interactions with other species, which can have a considerable impact on pre-existent ecosystems. In the present study, we analysed the foraging behaviour of the invasive Polistes dominula, which was first detected in NW Patagonia in 2003, and the native wasp Hypodynerus labiatus. We evaluated their foraging behaviour in two types of environments: closed habitats with dense vegetation and open habitats without surrounding bushes and trees. Additionally, we recorded the wasps’ feeding choices at three different heights within each context. Our results showed that these sympatric wasps prefer to forage in different environments and in distinct microsite strata within each environment. Polistes dominula collected food from the ground level in both open and closed habitats, while H. labiatus was more frequently observed in closed areas, gathering resources from higher strata. The observed differences suggest that the collecting sites showed minimal overlap, which may facilitate their coexistence. These findings shed new light on the behavioural processes and interspecific interactions between a highly invasive wasp and a poorly studied native species that inhabit urban and semi urban environments in Patagonia. Full article

Kigelia africana (“sausage tree”) is an established medicinal plant in African traditional medicine, now recognized for its diverse bioactive constituents and emerging anticancer potential. This study systematically evaluates Kigelia africana fruit extract (KAE) in an in vitro model of HT-29 colorectal carcinoma cells, focusing on its cytotoxic effects, mechanistic impact on protein expression, and synergy with cisplatin chemotherapy. Across 42 oncology-related proteins, covering cell survival, apoptosis, adhesion, invasion, and signaling, KAE demonstrated extensive but typically moderate modulation, while cisplatin produced more pronounced responses in most markers. Protein changes linked to metastasis, therapy resistance, and survival were broadly suppressed, indicating significant antitumor activity. Notably, co-treatment with KAE and cisplatin in HT-29 cells resulted in marked synergistic cytotoxicity, permitting lower cisplatin doses while maintaining efficacy. LC-HRMS analyses revealed 14 metabolites in the extract, including phenolic acids naphthoquinones and iridoids, which may contribute to these effects. Full article

Facial palsy (FP) impairs voluntary control of facial muscles, resulting in facial asymmetry and difficulties in emotional expression. Traditional assessment methods to define the severity of FP (e.g., House–Brackmann score, HB) rely on visual examinations and, therefore, are highly examiner-dependent. This study proposes an alternative approach using facial surface electromyography (EMG) for automated HB prediction. Time-domain EMG features were extracted during different facial movements (i.e., smile, close eyes, and raise forehead) and analyzed through nine different machine learning (ML) models in 58 subjects (51.98 ± 1.67 years, 20 male) with variable facial nerve function (HB 1: n = 16, HB 2–3: n = 32; HB 4–6: n = 10). Model performances were evaluated based on accuracy, precision, recall, and F1-score. Among the evaluated models, ensemble-based approaches—particularly a random forest model with 100 trees and a decision tree ensemble—proved to be the most effective with classification accuracies ranging from 81.7 to 84.8% and from 81.7 to 84.7%, depending on the evaluated facial movement. The results indicate that ensemble-based ML models can reliably distinguish between different FP grades using non-invasive EMG data. The approach offers a robust alternative to subjective clinical scoring, potentially improving diagnostic consistency and supporting longitudinal monitoring in clinical and research applications. Full article

Background/Objectives: Cardiovascular diseases are the leading cause of mortality worldwide according to the World Health Organization (WHO), highlighting the need for accessible tools for early detection. Automated classification systems based on signal processing and machine learning offer a non-invasive alternative to support clinical diagnosis. Methods: This study implements and evaluates machine learning models for distinguishing normal and abnormal heart sounds using a hybrid feature extraction approach. Recordings labeled as normal, murmur, and extrasystolic were obtained from the PASCAL dataset and subsequently binarized into two classes. Multiple numerical datasets were generated through statistical features derived from Mel-Frequency Cepstral Coefficients (MFCCs) and Daubechies wavelet analysis. Each dataset was standardized and used to train four classifiers: support vector machines, logistic regression, random forests, and decision trees. Results: Model performance was assessed using accuracy, precision, recall, specificity, F1-score, and area under curve. All classifiers achieved notable results; however, the support vector machine model trained with 26 MFCCs and Daubechies-4 wavelet coefficients obtained the best performance. Conclusions: These findings demonstrate that the proposed hybrid MFCC–Wavelet framework provides competitive diagnostic accuracy and represents a lightweight, interpretable, and computationally efficient solution for computer-aided auscultation and early cardiovascular screening. Full article

Osteoarthritis (OA) of the hip (coxarthrosis) and knee (gonarthrosis) is a leading cause of disability worldwide. Differential diagnosis typically relies on imaging modalities such as X-rays and Magnetic Resonance Imaging (MRI). However, advanced imaging can be expensive and inaccessible, highlighting the need for non-invasive diagnostic tools. This study aimed to develop and validate an interpretable machine learning model to distinguish between hip and knee osteoarthritis using standard preoperative clinical and laboratory data. This model is designed to assist physicians in prioritizing whether to order a hip or a knee X-ray first, thereby saving time and medical resources. The study utilized retrospective data from 1792 patients treated at the City Clinical Hospital in Almaty, Kazakhstan. After applying inclusion and exclusion criteria, five machine learning algorithms were used for training and evaluation: Decision Tree, Random Forest, Logistic Regression, XGBoost, and CatBoost. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were employed to interpret predictions and determine the contribution of each feature. The XGBoost model demonstrated the best performance, achieving an accuracy of 93.85%, a precision of 95.15%, a recall of 90.51%, and an F1-score of 92.41%. SHAP analysis revealed that age, glucose and leukocyte levels, urea, and BMI made the greatest contributions to the model’s predictions, while local analysis using LIME indicated that age, leukocyte levels, glucose, erythrocytes, and platelets were the most influential features. These findings support the use of machine learning for cost-effective early osteoarthritis triage using routine preoperative data. Full article

Honey bees are essential both for many global ecosystems and apicultural production. The management of bee colonies remains labour-intensive, which drives a need for automated solutions. This work presents a proof-of-concept system to monitor honey bee activity by identifying the yearly lifecycle stages exhibited by the colony. A non-invasive vibration monitoring system was developed and placed on top of brood frames in Warsaw-type beehives to collect vibration data over a full apicultural season. The recorded vibration signals were analyzed using both Convolutional Neural Networks (CNNs) and classical machine learning approaches such as the extra trees method. Recursive Feature Elimination with Cross-Validation (RFECV) was performed to isolate the most important frequency bins for lifecycle period identification. The results demonstrate that the critical frequencies for recognizing yearly honey bee activity are concentrated below 1 kHz. The proposed machine learning models achieved a weighted accuracy score of over 95%. These findings have significant implications for future bee monitoring hardware design, indicating that sampling frequencies may be reduced to as low as 2 kHz without significantly compromising model accuracy. Full article

Background/Objectives: The assessment of endometriosis (EMS)-associated pain is important, but only very few studies address the potential use of questionnaires for non-invasive prediction of the disease. Methods: In a prospective observational study from 2016 to 2024 with patients (n = 228) using hormonal contraception, all women with suspected EMS answered two questionnaires and were examined physically and with transvaginal ultrasound (TVUS). If deep infiltrating EMS (DIE) was suspected, magnetic resonance imaging (MRI) was performed. EMS diagnosis was confirmed by histological examination. Statistical analysis was mainly performed using 2 × 2 contingency tables. The decision tree was created manually. Results: The mean numerical rating scales (NRSs) of EMS-positive compared to EMS-negative patients were ~4-fold higher (4.45 and 1.15, respectively). Patients with EMS have, significantly, ~3 times more significant parameters compared to patients without EMS (18.5 and 5.9, respectively). In combination with dysuria and lightning-like pain, this resulted in very good prediction. A decision tree yielded a sensitivity of 0.924, a specificity of 0.917, a positive predictive value (PPV) of 0.924, a negative predictive value (NPV) of 0.917, and a positive likelihood ratio of 11.2, indicating a very good diagnostic test. There is no typical endometriosis pain, but various pain patterns are predictive of EMS. Conclusions: Thus, a reliable non-invasive EMS diagnosis by questionnaires is possible and could reduce the delay in the detection of EMS. Full article