Search Results (1,056)

Venomous invertebrates have provided a large diversity of toxins that selectively and potently modulate ion channels that are indispensable tools for elucidating the structure and underlying mechanisms of these channels. Voltage-gated sodium channels (VGSC) are responsible for the initiation and propagation of action potentials in excitable cells and represent an important target for a variety of diseases. The Na_v1.7 isoform, located in the peripheral nervous system, is central to pain signaling and is under intense investigation as a target for the treatment of pain. Closed-state inactivation (CSI) has been implicated in various disease states, such as arrhythmias and neuropathic pain. The investigation of venom toxins and VGSC CSI is poorly understood. However, many scorpion and spider toxins bind to site 3, characterized by a delay in steady-state inactivation, and interact with domain IV of the channel alpha subunit. In this study, two novel toxins were isolated from the venoms of Heteroctenus junceus and Poecilotheria regalis that demonstrated similar activity to site 3 modulators. Both toxins were shown to inhibit CSI while enhancing the rate at which CSI can occur. Taken together, this study demonstrates the need for additional investigation in CSI as well as the ability for toxins to modulate this phenomenon. Full article

Lygus pratensis is a major pest of cotton, causing serious damage to cotton production. This study designed species-specific PCR detection primers for L. pratensis, established a detection system to identify L. pratensis DNA in the intestinal contents of predatory natural enemies, and investigated the control potential of four species’ predatory natural enemies against L. pratensis. The results indicated that 826 predatory natural enemies were collected from cotton fields belonging to two classes, five orders, and twelve families. Among these, 9 species of insecta natural enemies accounted for 54.12% of the total number of predatory natural enemies collected, while 14 species of arachnida predatory natural enemies comprised 45.88%. Of the 806 natural enemies tested, 5.58% were found to be positive for L. pratensis, all of which were arachnid predators, specifically Ebrechtella tricuspidata, Xysticus ephippiatus, Hylyphantes graminicola, and Oxyopes sertatus. The predation response of these four spider species to the fourth to fifth instar nymphs and adults of L. pratensis adhered to the Holling II model. The theoretical predation (a′/Th), daily maximum predation rate (T/Th), and searching effect for the fourth to fifth instar nymphs and adults of L. pratensis of the four spider species were assessed. According to the results, the species can be ranked in terms of their predatory and searching efficiency as follows: O. sertatus > E. tricuspidata > X. ephippiatus > H. graminicola. Four species of spiders had the highest theoretical predation against L. pratensis nymphs, ranging from 23.71 to 60.86, and adults, ranging from 22.14 to 50.25. Therefore, these four spider species could be utilized for L. pratensis management. This study identified the main predatory natural enemies of L. pratensis and their pest control capabilities, providing a scientific basis for selecting and utilizing natural enemies in integrated pest management (IPM) strategies. This will help promote ecological and green pest control of L. pratensis in cotton-growing areas. Full article

With the rapid advancement of industrial intelligence, ensuring occupational safety has become an increasingly critical concern. Among the essential personal protective equipment (PPE), safety helmets play a vital role in preventing head injuries. There is a growing demand for real-time detection of helmet chinstrap wearing status during industrial operations. However, existing detection methods often encounter limitations such as user discomfort or potential privacy invasion. To overcome these challenges, this study proposes a non-intrusive approach for detecting the wearing state of helmet chinstraps, inspired by the mechanosensory hair arrays found on spider legs. The proposed method utilizes multiple MEMS inertial sensors to emulate the sensory functionality of spider leg hairs, thereby enabling efficient acquisition and analysis of helmet wearing states. Unlike conventional vibration-based detection techniques, posture signals reflect spatial structural characteristics; however, their integration from multiple sensors introduces increased signal complexity and background noise. To address this issue, an improved adaptive convolutional neural network (ICNN) integrated with a long short-term memory (LSTM) network is employed to classify the tightness levels of the helmet chinstrap using both single-sensor and multi-sensor data. Experimental validation was conducted based on data collected from 20 participants performing wall-climbing robot operation tasks. The results demonstrate that the proposed method achieves a high recognition accuracy of 96%. This research offers a practical, privacy-preserving, and highly effective solution for helmet-wearing status monitoring in industrial environments. Full article

Aster spp., a key grass species for the ecological restoration of alpine degraded grasslands on the Qinghai–Tibet Plateau, often suffers from pest damage during its flowering and seed maturation stages, severely limiting the effectiveness of ecological restoration and the sustainable utilization of germplasm resources. This study focused on nine widely distributed species of Aster in the Three River Source Region of Qinghai Province, systematically investigated the structure of arthropod communities and the spatiotemporal dynamics of pests, and developed an integrated pest management (IPM) strategy. Through systematic surveys at multiple sites, a total of 109 arthropod species were identified (57 families of insects, 96 species; 7 families of spiders, 13 species). The Diptera (Tephritidae) and Hemiptera (Miridae) were identified as dominant groups. Tephritis angustipennis was determined to be the key pest, with its population density reaching a peak in mid-to-late August (p < 0.05). Based on the occurrence patterns of the pest, an IPM strategy integrating physical, chemical, and biological control methods was proposed: flower head bagging as a physical barrier significantly reduced plant damage but required balancing the risk of seed sterility. A combination lure (broad-spectrum fruit fly lure + a mixture of sugar and vinegar) showed a significant effect in attracting and killing adult flies. In chemical control, spraying a combination of insecticides (DB: 10% β-Cypermethrin aqueous emulsion (9 mL/acre) + 5% avermectin (20 mL/acre)) during the leaf expansion stage to early flowering stage achieved approximately 80% pest mortality within 24 h; additionally, supplementary spraying of 5% broflanilide (30 mL/acre) during the full flowering stage prolonged the efficacy and delayed the development of insecticide resistance. In terms of natural enemy utilization, Lycosidae and Thomisidae demonstrated significant potential for naturally regulating pest populations. Physiological mechanism studies showed that the difference in responses between plant catalase (CAT) activity and insect glutathione S-transferase (GST) activity was a key factor driving control efficacy (the cumulative explanation rate reached 94%). This IPM strategy, by integrating physical barriers, dynamic trapping, targeted spraying, and natural enemy control, significantly enhances control efficiency and ecological compatibility, providing a theoretical basis and technical paradigm for the ecological restoration of degraded alpine grasslands and the sustainable management of medicinal plants in cold regions. Full article

Liver cirrhosis, a progressive and often irreversible condition, exerts widespread systemic effects, with the skin frequently serving as a visible window into the extent of hepatic dysfunction. Cutaneous manifestations, such as spider angiomas, palmar erythema, jaundice, and pruritus, not only reflect underlying pathophysiologic changes but also serve as important, non-invasive diagnostic and prognostic markers of disease severity. Early detection of such cutaneous findings may allow for early treatment, optimize patient management, and improve outcomes. This review addresses the various cutaneous manifestations of liver cirrhosis, their pathogenesis, and their prognostic and diagnostic importance, emphasizing the need for heightened clinical awareness of the improvement in patient care. Full article

Soil moisture serves as a critical factor in the hydrological cycle, affecting plant growth, ecosystem health, and groundwater reserves. Current methods for monitoring and predicting it fail to account for the complexities introduced by climatic variations and other influencing factors, such as the effects of atmospheric interference and data gaps, leading to reduced prediction accuracy. To address these challenges, this study introduces a novel soil moisture prediction model based on remote sensing and deep learning, utilizing the Adaptive Group Radial Lasso Regularized Basis Function Networks (AGRL-RBFN) optimized by the Hierarchical Correlated Spider Wasp Optimizer (HCSWO) and incremental learning (IL) techniques. The proposed method for monitoring soil moisture utilizes hyperspectral and soil moisture data from a 2017 campaign in Karlsruhe, encompassing variables such as datetime, soil moisture percentage, soil temperature, and remote sensing spectral bands. The proposed methodology begins with comprehensive preprocessing of historical remote sensing data to fill gaps, reduce noise, and correct atmospheric disturbances. It then employs a unique seasonal mapping and grouping technique, enhanced by the AdaK-MCC method, to analyze the impact of climatic changes on soil moisture patterns. The model’s innovative feature selection approach, using HCSWO, identifies the most significant predictors, ensuring optimal data input for the AGRL-RBFN model. The model achieves an impressive accuracy of 98.09%, a precision of 98.17%, a recall of 97.24%, and an F1-score of 98.95%, outperforming existing methods. Furthermore, it attains a mean absolute error (MAE) of 0.047 in gap filling and a Dunn Index of 4.897 for clustering. Although successful in many aspects, the study did not investigate the relationship between soil moisture levels and specific crops, which presents an opportunity for future research aimed at enhancing smart agricultural practices. Furthermore, the model can be refined by integrating a wider range of datasets and improving its resilience to extreme weather conditions, thereby providing a reliable tool for climate-responsive agricultural management and water conservation strategies. Full article

The two-spotted spider mite, Tetranychus urticae, is one of the polyphagous pests of several crops and forestry, resistant to numerous conventional chemicals. Due to the negative side effects of harmful chemical pesticides, such as environmental pollution, and risks to human health, the introduction of effective and low-risk alternatives is essential. The promising pesticidal effects of essential oils (EOs) isolated from Artemisia annua have been documented in recent studies. In the present study, the acaricidal effects of an A. annua EO, along with its two dominant monoterpenoids, 1,8-cineole and camphor, were investigated against adults of T. urticae. Artemisia annua EO, 1,8-cineole, and camphor, with 24 h-LC₅₀ values of 0.289, 0.533, and 0.64 µL/L air, respectively, had significant toxicity by fumigation against T. urticae adults. Along with lethality, A. annua EO and monoterpenoids had significant inhibitory effects on the activity of detoxifying enzymes, including α- and β-esterases, glutathione S-transferases, and cytochrome P-450 monooxygenase. According to the findings of the present study, A. annua EO and its two dominant monoterpenoids, 1,8-cineole and camphor, with significant toxicity and inhibitory effects on detoxifying enzymes, can be introduced as available, effective, and eco-friendly acaricides in the management of T. urticae. Full article

The bamboo carpenter bee, Xylocopa (Biluna) tranquebarorum tranquebarorum, is native to continental China and Taiwan, and the species invaded Japan around 2006. The bee utilizes bamboo culm for its nesting and hibernation, thereby causing structural damage to bamboo fencing and sting injuries to humans. Serious economic and ecological impacts were not expected in the early stage of its invasion. However, its distribution is rapidly expanding in Japan, and thus, its potential impacts need to be evaluated. Since the basic biology of the bee has not been examined in detail, even in its natural range, we examined the basic biology of X. t. tranquebarorum in its invasive range by evaluating its nesting preference and hibernation in several bamboo species collections in Kyoto, Japan. The field survey revealed that the bee prefers dead bamboo internodes with approximately16–28 mm of external diameter, which is well-congruent with previous studies, and does not have strict preference concerning the bamboo species, though the bee prefers Bambusa multiplex and Phyllostachys spp. in its native range. The hibernating bees in the culm sometimes share their nests with other invertebrates, including Anterhynchium gibbifrons, Dinoderus japonicus, Crematogaster matsumurai, unidentified spiders, shield bugs, and lepidopteran larvae. Within these co-habitants, the former two possibly negatively affect nesting and hibernation of the bees. Full article

In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global–local collaborative attention module and a spider web convolution module. The former enhances feature representation through bidirectional feature interaction and dynamic weight allocation, reducing false positives and missed detections. The latter introduces an asymmetric sampling topology and six-directional receptive field paths to effectively improve the recognition of irregular objects. Experiments on the Cityscapes, CamVid, and BDD100K datasets, collected using vehicle-mounted cameras, demonstrate that the proposed method performs excellently across multiple evaluation metrics, including mIoU, mRecall, mPrecision, and mAccuracy. Comparative experiments with classical segmentation networks, attention mechanisms, and convolution modules validate the effectiveness of the proposed approach. The proposed method demonstrates outstanding performance in sensor-based semantic segmentation tasks and is well-suited for environmental perception systems in autonomous driving. Full article

Recent advancements have been made in the precise prediction of protein structures within the Protein Folding Problem (PFP), particularly in relation to minimizing the energy function to achieve stable and biologically relevant protein structures. This problem is classified as NP-hard within computational theory, necessitating the development of various techniques and algorithms. Bio-inspired algorithms have proven effective in addressing NP-hard challenges in practical applications. This study introduces a novel hybrid algorithm, termed GRSABio, which integrates the strategies of Jumping Spider Algorithm (JSOA) with the Golden Ratio Simulated Annealing (GRSA) for peptide prediction. Furthermore, the GRSABio algorithm incorporates a Convolutional Neural Network for fragment prediction (FCNN), forms an enhanced methodology called GRSABio-FCNN. This integrated framework achieves improved structure refinement based on energy for protein prediction. The proposed enhanced GRSABio-FCNN approach was applied to a dataset of 60 peptides. The Wilcoxon and Friedman statistics test were employed to compare the GRSABio-FCNN results against recent state-of-the-art-approaches. The results of these tests indicate that the GRSABio-FCNN approach is competitive with state-of-the-art methods for peptides up to 50 amino acids in length and surpasses leading PFP algorithms for peptides with up to 30 amino acids. Full article

Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning models requiring extensive labeled data, limiting scalability for smallholder farmers. This article proposes AIM-Net (AI-based Infestation Mapping Network) by evaluating SwAV (Swapping Assignments between Views), a self-supervised learning framework, for classifying RSM infestation severity (Mild, Moderate, Severe) using a geo-referenced, field-acquired dataset of RSM infested tea-leaves, Cam-RSM. The methodology combines SwAV pre-training on unlabeled data with fine-tuning on labeled subsets, employing multi-crop augmentation and online clustering to learn discriminative features without full supervision. Comparative analysis against a fully supervised ResNet-50 baseline utilized 5-fold cross-validation, assessing accuracy, F1-scores, and computational efficiency. Results demonstrate SwAV’s superiority, achieving 98.7% overall accuracy (vs. 92.1% for ResNet-50) and macro-average F1-scores of 98.3% across classes, with a 62% reduction in labeled data requirements. The model showed particular strength in Mild_RSM-class detection (F1-score: 98.5%) and computational efficiency, enabling deployment on edge devices. Statistical validation confirmed significant improvements (p < 0.001) over baseline approaches. These findings establish self-supervised learning as a transformative tool for precision pest management, offering resource-efficient solutions for early infestation detection while maintaining high accuracy. Full article

Pardosa pseudoannulata plays an important role in the biological control of insect pests. The inclusion of yeast in the culture medium is very important for the growth, development, and reproduction of Drosophila melanogaster, but there have been few studies on the influence of nutrients in the culture medium on spider development. In order to explore the effects of different yeast treatments on the growth and development of D. melanogaster and as a predator, P.  pseudoannulata, three treatments (no yeast, active yeast added, and inactivated yeast added) were adopted to modify the conventional D. melanogaster culture medium. The addition of yeast to the medium shortened the development time from larva to pupation in D. melanogaster. The emergence and larval developmental times of D. melanogaster reared with activated yeast were shorter than those of the group without yeast addition, which promoted D. melanogaster emergence and increased body weight. The addition of yeast to the medium increased the fat, protein, and glucose content in D. melanogaster. The addition of activated yeast shortened the developmental time of P.  pseudoannulata at the second instar stage but had no effect on other instars. Different yeast treat-ments in the medium had no effect on the body length or body weight of P.  pseudoannulata. Adding yeast to D. melanogaster culture medium can increase the total fat content in P.  pseudoannulata, but it has no effect on glucose and total protein in P.  pseudoannulata. Our study shows the importance of yeast to the growth and development of fruit flies. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

Tetranychus urticae, commonly known as the two-spotted spider mite, is a highly adaptable and polyphagous arthropod in the family Tetranychidae, capable of feeding on over 1200 plant species, including strawberries (Fragaria × ananassa Duch.). The fitness and microbiota of herbivorous arthropods can vary significantly across different plant species and cultivars. In this study, we investigated the fecundity, longevity, growth rate, and microbiota composition of T. urticae reared on seven Chinese strawberry cultivars: Hongyan (HY), Yuexiu (YX), Tianshi (TS), Ningyu (NY), Xuetu (XT), Zhangjj (ZJ), and Xuelixiang (XLX). Our findings revealed significant differences among cultivars: mites reared on the XT cultivar exhibited the highest fecundity (166.56 ± 7.82 eggs), while those on XLX had the shortest pre-adult period (7.71 ± 0.13 days). Longevity was significantly extended in mites reared on XLX, XT, and NY cultivars (25.95–26.83 days). Microbiota analysis via 16S rRNA sequencing showed that Proteobacteria dominated (>89.96% abundance) across all mite groups, with Wolbachia as the predominant symbiont (89.58–99.19%). Male mites exhibited higher bacterial diversity (Shannon and Chao1 indices) than females, though Wolbachia abundance did not differ significantly between sexes or cultivars. Functional predictions highlighted roles of microbiota in biosynthesis, detoxification, and energy metabolism. These findings underscore the influence of host plant variety on T. urticae fitness and microbiota composition, suggesting potential strategies for breeding resistant strawberry cultivars and leveraging microbial interactions for pest management. Full article

Background/Objectives: Liver metastases are common among patients with breast cancer and have a poor prognosis if left untreated. The aim of this systematic review is to evaluate and compare chemoembolization (TACE) versus radioembolization (TARE) treatments in patients with breast cancer liver-dominant metastases in terms of overall survival (OS), local tumor control (LC), and toxicity. Methods: The S.P.I.D.E.R framework was used to address the clinical question. A systematic literature search using PubMed and Scopus was performed to identify full articles evaluating the efficacy of TACE and TARE in patients with liver metastases from breast cancer. Results: The literature search resulted in 10 articles for TACE, 13 articles for TARE and 1 for combined TACE/TARE, totaling 462 patients for the TACE group and 627 for the TARE group. The median LC was 68.7% for TACE and 78.9% for TARE. The median OS was 15.3 months for TACE and 11.9 for TARE. Progression at three months was 32.5% for TACE and 20.6% for TARE. Conclusions: The included studies were heterogeneous, varying widely in design, patient selection, and therapeutic protocols. Nonetheless, this systematic review suggests that locoregional therapies are effective in the treatment of liver metastases in patients with breast cancer and may improve tumor burden, alleviate symptoms and extend overall survival. The median LC of the liver metastases at three months was higher in the TARE group compared to TACE. However, the TARE group showed lower OS rates after treatment. Full article