Search Results (352)

Accurate identification of predator–pest relationships is essential for implementing effective and sustainable biological control in agriculture. However, existing image-based methods struggle to recognize insect co-occurrence under complex field conditions, limiting their ecological applicability. To address this challenge, we propose a hybrid deep learning framework that integrates convolutional neural networks (CNNs) and Transformer architectures for multi-label recognition of predator–pest combinations. The model leverages a novel co-occurrence attention mechanism to capture semantic relationships between insect categories and employs a pairwise label matching loss to enhance ecological pairing accuracy. Evaluated on a field-constructed dataset of 5,037 images across eight categories, the model achieved an F1-score of 86.5%, mAP50 of 85.1%, and demonstrated strong generalization to unseen predator–pest pairs with an average F1-score of 79.6%. These results outperform several strong baselines, including ResNet-50, YOLOv8, and Vision Transformer. This work contributes a robust, interpretable approach for multi-object ecological detection and offers practical potential for deployment in smart farming systems, UAV-based monitoring, and precision pest management. Full article

Marine ecosystems are characterized by an immense biodiversity and represent a rich source of biological compounds with promising potential for the development of novel therapeutic drugs. This review describes the most promising marine-derived neuroprotective compounds with strong potential for the treatment of neurodegenerative disorders. We focus specifically on the retina and brain—two key components of the central nervous system—as primary targets for therapeutic interventions against neurodegeneration. Alzheimer’s disease and retinal degeneration diseases are used here as a representative model of neurodegenerative disorders, where complex molecular processes such as protein misfolding, oxidative stress, and neuroinflammation drive disease progression. We also examine gene therapy approaches inspired by marine biology, with particular attention to their application in retinal diseases, aimed at preserving or restoring photoreceptor function and vision. Full article

In recent years, the classification of underwater images has become one of the most remarkable areas of research in computer vision due to its useful applications in marine sciences, aquatic robotics, and sea exploration. Underwater imaging is pivotal for the evaluation of marine eco-systems, analysis of biological habitats, and monitoring underwater infrastructure. Extracting useful information from underwater images is highly challenging due to factors such as light distortion, scattering, poor contrast, and complex foreground patterns. These difficulties make traditional image processing and machine learning techniques struggle to analyze images accurately. As a result, these challenges and complexities make the classification difficult or poor to perform. Recently, deep learning techniques, especially convolutional neural network (CNN), have emerged as influential tools for underwater image classification, contributing noteworthy improvements in accuracy and performance in the presence of all these challenges. In this paper, we have proposed a modified ResNet-18 model for the binary classification of underwater images into raw and enhanced images. In the proposed modified ResNet-18 model, we have added new layers such as Linear, rectified linear unit (ReLU) and dropout layers, arranged in a block that was repeated three times to enhance feature extraction and improve learning. This enables our model to learn the complex patterns present in the image in more detail, which helps the model to perform the classification very well. Due to these newly added layers, our proposed model addresses various complexities such as noise, distortion, varying illumination conditions, and complex patterns by learning vigorous features from underwater image datasets. To handle the issue of class imbalance present in the dataset, we applied a data augmentation technique. The proposed model achieved outstanding performance, with 96% accuracy, 99% precision, 92% sensitivity, 99% specificity, 95% F1-score, and a 96% Area under the Receiver Operating Characteristic Curve (AUC-ROC) score. These results demonstrate the strength and reliability of our proposed model in handling the challenges posed by the underwater imagery and making it a favorable solution for advancing underwater image classification tasks. Full article

Background and Clinical Significance: Ocular cicatricial pemphigoid (OCP) is a rare autoimmune disease affecting the conjunctiva and oral mucosa. Chronic inflammation causes conjunctival scarring, leading to symblepharon, trichiasis, corneal damage, and possible blindness. Diagnosis is clinical, supported by biopsy and immunofluorescence. Treatment includes systemic corticosteroids, immunosuppressants, and biologics in refractory cases. Case Presentation: A 64-year-old male presented with ocular irritation, trichiasis, and counting fingers (CF) visual acuity in the left eye. Slit-lamp examination revealed conjunctival inflammation, corneal epithelial defect, and symblepharon in the left eye. Biopsy confirmed ocular cicatricial pemphigoid (OCP). He was treated with topical steroids, cyclosporine, subconjunctival injections, and systemic corticosteroids, followed by surgery, which improved BCVA to 0.10 logMAR. Two years later, disease progression resulted in severe inflammation and visual decline in both eyes. Systemic azathioprine and corticosteroids achieved partial control. Due to insufficient response, rituximab therapy was initiated, leading to significant reduction in inflammation and stabilization of disease. Right eye BCVA improved to 0.16 logMAR; the left remained at CF. The patient continues to receive rituximab during exacerbations and is under regular follow-up. Conclusions: Early diagnosis and timely systemic treatment are essential in preventing vision loss in OCP. In refractory cases, biologic agents like rituximab may offer effective disease control. Full article

Pastinaca L. is a small genus belonging to the Apiaceae family, traditionally used for both nutritional and medicinal purposes. Pastinaca sativa L. subsp. sativa is a biennial plant widely distributed in Europe and Asia, with recognized ethnopharmacological relevance. In this study, the essential oil (EO) obtained from the aerial parts of P. sativa subsp. sativa, collected in Alto Adige (Italy)—a previously unstudied accession—was analyzed by GC-MS, and the volatile profile has been compared with that of EOs previously studied in Bulgaria and Serbia. The EO was found to be rich in octyl acetate (38.7%) and octyl butanoate (26.7%), confirming that this species biosynthesizes these natural esters. The EO and its main constituents were tested to evaluate their antimicrobial properties. Furthermore, their biological potential was evaluated through antimicrobial, antibiofilm and antioxidant assays. This research work, in addition to evaluating possible chemotaxonomic differences at the geographical level of EOs of Pastinaca sativa subsp. sativa, has been extended to the determination of the biological properties of this accession never investigated before, with the aim of acquiring a broader vision of biofilm and antibacterial properties. Full article

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts. Full article

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios. Full article

Dragonfly classification is crucial for biodiversity conservation. Traditional taxonomic approaches require extensive training and experience, limiting their efficiency. Computer vision offers promising solutions for dragonfly taxonomy. In this study, we adapt the face recognition algorithms for the classification of dragonfly species, achieving efficient recognition of categories with extremely small differences between classes. Meanwhile, this method can also reclassify data that were incorrectly labeled. The model is mainly built based on the classic face recognition algorithm (ResNet50+ArcFace), and ResNet50 is used as the comparison algorithm for model performance. Three datasets with different inter-class data distributions were constructed based on two dragonfly image data sources: Data1, Data2 and Data3. Ultimately, our model achieved Top1 accuracy rates of 94.3%, 85.7%, and 90.2% on the three datasets, surpassing ResNet50 by 0.6, 1.5, and 1.6 percentage points, respectively. Under the confidence thresholds of 0.7, 0.8, 0.9, and 0.95, the Top1 accuracy rates on the three datasets were 96.0%, 97.4%, 98.7%, and 99.2%, respectively. In conclusion, our research provides a novel approach for species classification. Furthermore, it can calculate the similarity between classes while predicting categories, thereby offering the potential to provide technical support for biological research on the similarity between species. Full article

Usher syndrome, a rare genetic disorder causing both hearing and vision loss, presents significant diagnostic and therapeutic challenges due to its complex genetic basis. The identification of reliable biomarkers for early detection and intervention is crucial for improving patient outcomes. In this study, we present a machine learning-based hybrid sequential feature selection approach to identify key mRNA biomarkers associated with Usher syndrome. Beginning with a dataset of 42,334 mRNA features, our approach successfully reduced dimensionality and identified 58 top mRNA biomarkers that distinguish Usher syndrome from control samples. We employed a combination of feature selection techniques, including variance thresholding, recursive feature elimination, and Lasso regression, integrated within a nested cross-validation framework. The selected biomarkers were further validated using multiple machine learning models, including Logistic Regression, Random Forest, and Support Vector Machines, demonstrating robust classification performance. To assess the biological relevance of the computationally identified mRNA biomarkers, we experimentally validated candidates from the top 10 selected mRNAs using droplet digital PCR (ddPCR). The ddPCR results were consistent with expression patterns observed in the integrated transcriptomic metadata, reinforcing the credibility of our machine learning-driven biomarker discovery framework. Our findings highlight the potential of machine learning-driven biomarker discovery to enhance the detection of Usher syndrome. Full article

Microscopic cell classification is a fundamental challenge in both clinical diagnosis and biological research. However, existing methods still struggle with the complexity and morphological diversity of cellular images, leading to limited accuracy or high computational costs. To overcome these constraints, we propose an efficient classification method that balances strong feature representation with a lightweight design. Specifically, an Inception-Linear Attention-based Lightweight Vision Transformer (ILViT) model is developed for microscopic cell classification. The ILViT integrates two innovative modules: Dynamic Inception Convolution (DIC) and Contrastive Omni-Kolmogorov Attention (COKA). DIC combines dynamic and Inception-style convolutions to replace large kernels with fewer parameters. COKA integrates Omni-Dimensional Dynamic Convolution (ODC), linear attention, and a Kolmogorov-Arnold Network(KAN) structure to enhance feature learning and model interpretability. With only 1.91 GFLOPs and 8.98 million parameters, ILViT achieves high efficiency. Extensive experiments on four public datasets are conducted to validate the effectiveness of the proposed method. It achieves an accuracy of 97.185% on BioMediTech dataset for classifying retinal pigment epithelial cells, 97.436% on ICPR-HEp-2 dataset for diagnosing autoimmune disorders via HEp-2 cell classification, 90.528% on Hematological Malignancy Bone Marrow Cytology Expert Annotation dataset for categorizing bone marrow cells, and 99.758% on a white blood cell dataset for distinguishing leukocyte subtypes. These results show that ILViT outperforms the state-of-the-art models in both accuracy and efficiency, demonstrating strong generalizability and practical potential for cell image classification. Full article

The ocular surface is susceptible to a wide spectrum of inflammatory, degenerative, and neurotrophic diseases that can impair vision. The complex pathophysiology and limited therapeutic options associated with these conditions continue to pose significant clinical challenges. Nerve Growth Factor (NGF), a neurotrophin initially recognized for its role in neuronal survival and differentiation, has emerged as a key regulator of ocular surface homeostasis and repair. Beyond its neurotrophic functions, NGF is suggested to influence epithelial proliferation, immune responses, tear secretion, and angiogenesis. Experimental and clinical studies have implicated NGF in both the pathogenesis and potential treatment of various ocular surface diseases, including allergic conjunctivitis, neurotrophic keratopathy (NK), immune-mediated and herpetic keratitis, and dry eye disease (DED), as well as post-surgical corneal wound healing. Notably, recombinant human NGF (rhNGF, cenegermin) has been approved as the first topical biologic therapy for NK. Despite encouraging clinical outcomes, challenges such as high treatment costs, limited long-term data, and potential proangiogenic effects remain. This review consolidates current evidence on the role of NGF in ocular surface health and disease, highlighting its biological mechanisms, clinical applications, and future therapeutic potential. Full article

Background/Objectives: Diabetic retinopathy is an ocular disease caused by changes in the expression of inflammatory mediators and increased oxidative stress in the retina and is the leading cause of vision loss in diabetic patients. Currently, there is no treatment capable of reversing retinal damage, which represents a significant burden on the quality of life of patients. (1R)-1-Dodecylsulfonyl-5N,6O-oxomethylidenenojirimycin stands outs as a prototype of the sp²-iminoglycolipids family for its beneficial neuroprotective effect against this chronic eye disease. Critical issues related to the low solubility and bioavailability of this glycolipid in biological settings are overcome by its encapsulation in a Zeolitic-Imidazolate Framework (ZIF) structure, resulting in homogeneous and biocompatible GlycoZIF nanoparticles. Cell studies show an enhanced cellular uptake compared with the free glycolipid, and importantly, its bioactivity is preserved once released inside cells. Methods: Extensive in vitro and ex vivo assays with diabetic retinopathy models unveil the mechanistic pathways of the designed GlycoZIF. Results: A reduction in proinflammatory mediators, increased heme oxygenase-1 level, inhibition of NLRP3 inflammasome, and reduced reactive gliosis is shown. Conclusions: These findings demonstrate for the first time the potential of Glyco-modified ZIFs for the treatment of diabetes-related ocular problems by controlling the immune-mediated inflammatory response. Full article

Myopia patients wear rigid gas-permeable contact lenses during the day to achieve normal vision, but they might feel uncomfortable, since they are made of hard materials that can cause inappropriate friction and adhesion. These forces affect the biological tissues of the cornea and eyelid. In this study, an in vitro rigid gas-permeable contact lens friction testing method was established to mimic the friction between the eyelid and the rigid contact lens. The lens was rubbed against a gelatin membrane to investigate the tribological properties of artificial tear, saline, and two kinds of care solutions using a dedicated experimental setup. The viscosity, pH value, and surface tension of each lubricant was also analyzed. The friction coefficient of the artificial tear solution was the highest: 0.18 for its static friction and 0.09 for its dynamic friction. In contrast, polysaccharide-containing care solution demonstrated the lowest friction coefficient. The viscosity of artificial tear solutions ranged from 0.97 ± 00 to 1.15 ± 0.16 mPa·s, when the shear rate was increased from 19.2 to 192 1/s, while it ranged from 2.26 ± 1.12 to 2.91 ± 0.00 for polysaccharide-containing care solution. Although the physical–chemical properties of various lubricants could not explain the distinct tribological outcomes, the in vitro tribological testing method for rigid gas-permeable lenses was successfully established in this study. Full article

Spiders are important predatory natural enemies in agricultural and forestry ecosystems, yet the role of vision in their predatory behavior remains unclear. In this study, we screened three opsin genes—corresponding to ultraviolet-sensitive and medium-to-long wavelength-sensitive opsins—from the transcriptome sequencing database of Pardosa astrigera. All three genes possess seven transmembrane topological structures and a lysine residue on the second transmembrane domain, which are typical characteristics of opsins. Using quantitative real-time PCR (RT-qPCR), we analyzed the expression patterns of these opsin genes in different tissues, developmental stages, and under the induction of light at three wavelengths. The results showed that all three opsin genes were significantly expressed in the cephalothorax and expressed across developmental stages with no significant differences. Under light induction, their relative expression first increased and then decreased in both male and female adult spiders. Subsequently, RNA interference (RNAi) was used to individually knock down each opsin gene, confirming their involvement in color vision. These results suggest that the three opsin genes are involved in spider vision, laying the foundation for further elucidating the role of vision in spider predation, and offering a new perspective for reducing the unintended killing of natural enemies by insect traps. Full article

Biological studies on symmetry can be expanded to consider red (longer wavelengths) and blue (shorter wavelengths) shifts as antisymmetries (opposite-pattern symmetries), which may arise from similar underlying causes (invariant process symmetries). In this context, classic shift asymmetries of redder plumage in response to higher dietary carotenoids appear conceptually incomplete, as potential blue-shifted counterparts were not considered. A latent symmetric response is highlighted by recent evidence showing that the maximum absorbance bands of various colorful plumage pigments are red-shifted in birds with ultraviolet-sensitive (UVS) color vision but blue-shifted in those with violet-sensitive (VS) color vision. Blue-shifted responses to increased dietary carotenoid contents may also be underestimated, as relevant studies have focused on species-rich but uniformly UVS Passerida passerines. This study explored the relationship between pattern–process symmetries and diets of VS Piciformes–Coraciiformes by gauging the responses of their plumage reflectance to a modified diet index (Diet_c), where the overall rank carotenoid contents of food items were weight-averaged by three levels of importance in a species’ diet. In the case of both sexes, the main long-wavelength reflectance band for the three carotenoid-based pigment classes defined the same graded series of blue shifts in response to higher Diet_c. Yellow showed a strong absolute (negative slope) blue shift, orange showed a weaker absolute blue shift, and red exhibited only a blue shift (flat, non-significant slope) relative to absolute red shifts (positive slope). The secondary shorter-wavelength reflectance band was also unresponsive to Diet_c in the VS Piciformes–Coraciiformes (relative blue shift) compared with earlier evidence for it decreasing (absolute red shift) at higher Diet_c in UVS species. Results for the intervening minimum reflectance (maximum absorbance) band were intermediate between those for the other reflectance bands. No pigment class monopolized lower or higher Diet_c, but red was less variable overall. Phylogenetic independence, sexually similar responses, and specimen preservation reinforced characterizations. A review of avian perceptual studies suggested that VS models discriminate yellows and oranges extremely well, consistent with the importance of the corresponding carotenoids as Diet_c indicators. Both UVS and VS species appear to produce putatively more costly and possibly beneficial carotenoid metabolites and/or concentrations in response to higher Diet_c, supporting underlying invariant processes in relation to carotenoid limitations and honest signaling despite opposite plumage shifts and their different chemical bases. In symmetry parlance, pigment classes (red) or wavebands (short) that lack responses to Diet_c suggest broken pattern and process symmetry. The biology of VS Piciformes–Coraciiformes may favor such exceptions owing to selection for visual resemblance and tuning specializations, although universal constraints on physical and chemical properties of (particularly red) carotenoids may favor certain functional tendencies. Thus, symmetry principles organize carotenoid diversity into a simplified and predictive framework linked to color vision. Full article