Search Results (1,996)

Background: The purpose of this study was to detect proteomic alterations and corresponding signaling pathways involved in the formation of age-related cataract (ARC), diabetic cataract (DC), and post-vitrectomy cataract (PVC). Methods: Three sample types, the aqueous humor (AH), the anterior capsule (AC), and the content of the phaco cassette, were collected during phacoemulsification surgery. The samples were obtained from 12 participants without diabetes mellitus (DM), 11 participants with DM, and 7 participants without DM, with a history of vitrectomy surgery in the past 12 months. The Sp3 protocol (Single-Pot, Solid-Phase, Sample-Preparation) was used for the sample preparation. The recognition and quantification of proteins were carried out with liquid chromatography online with tandem mass spectrometry. The DIA-NN software was applied for the identification and quantification of peptides/proteins. Statistical analysis and data visualization were conducted on Perseus software. Data are available via ProteomeXchange. Results: A very rich atlas of the lens and AH proteome has been generated. Glycosaminoglycan biosynthesis and the non-canonical Wnt receptor signaling pathway were differentially expressed in ARC compared to both the DC and PVC groups. In the PVC group, complement activation was differentially expressed in AH samples, while glutathione metabolism and oxidoreductase activity were differentially expressed in AC samples. Microfilament motor activity, microtubule cytoskeleton organization, and microtubule binding were differentially expressed in the DC and PVC groups in both AH and AC samples. Conclusions: The results of this study expand the existing knowledge on pathways involved in the pathophysiology of cataract, and suggest possible important druggable targets for slower progression or even prevention of cataract. Full article

Production and consumption of vegetable crops has seen a sharp increase in the recent past owing to an increasing recognition of their nutraceutical benefits. In tandem, there has been unwarranted application of agrochemicals such as insecticides to enhance productivity and vegetable quality, at the cost of human health, and fundamental environmental and ecosystem functions and services. This study was conducted to evaluate the efficacy of neem and gliricidia botanical extracts in managing harmful insect pest populations in leaf mustard. Our results report that neem and gliricidia plant extracts enhance the yield and quality of leaf mustard by reducing the prevalence and feeding activity of harmful insect pests in a manner similar to synthetic insecticides. Some of the key insect pests reduced were Lipaphis erysimi, Pieris oleracea, Phyllotreta Cruciferae, Melanoplus sanguinipes, and Murgantia histrionica. However, compared to synthetic insecticides, neem and gliricidia plant extracts were able to preserve beneficial insects such as the Coccinellidae spp., Trichogramma minutum, Araneae spp., Lepidoptera spp., and Blattodea spp. Furthermore, plant extracts did not significantly alter sensory attributes, especially taste and odor, whereas the visual appearance of leaf mustard was greater in plants sprayed with neem and synthetic insecticides. Physiologically, plant extracts were also able to significantly lower leaf membrane damage as shown through the electrolyte leakage assay. Therefore, these plant extracts represent promising pesticidal plant materials and botanically active substances that can be leveraged to develop environmentally friendly commercial pest management products. Full article

In real-world scenarios, posture variation and low-quality image resolution are two well-known factors that compromise the accuracy and reliability of face recognition system. These challenges can be overcome using various methods, including Generative Adversarial Networks (GANs). Despite this, concerns over the accuracy and reliability of GAN methods are increasing as the facial recognition market expands rapidly. The existing framework such as Two-Pathway GAN (TP-GAN) method has demonstrated that it is superior to numerous GAN methods that provide better face-texture details due to its unique deep neural network structure that allows it to perceive local details and global structure in a supervised manner. TP-GAN overcomes some of the obstacle associated with face frontalization tasks through the use of landmark detection and synthesis functions, but it remains challenging to achieve the desired performance across a wide range of datasets. To address the inherent limitations of TP-GAN, we propose a novel face frontalization method (NFF) combining landmark detection, decision forests, and data augmentation. NFF provides 2D landmark detection to integrate global structure with local details of the generator model so that more accurate facial feature representations and robust feature extractions can be achieved. NFF enhances the stability of the discriminator model over time by integrating decision forest capabilities into the TP-GAN discriminator core architecture that allows us to perform a wide range of facial pose tasks. Moreover, NFF uses data augmentation techniques to maximize training data by generating completely new synthetic data from existing data. Our evaluations are based on the Multi-PIE, FEI, and CAS-PEAL datasets. NFF results indicate that TP-GAN performance can be significantly enhanced by resolving the challenges described above, leading to high quality visualizations and rank-1 face identification. Full article

Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. For example, to reduce the noise in a disturbed signal, smoothing kernels can be effectively used. This is achievedby convolving the distributed signal with smoothing kernels. In addition, orthogonal moments (OMs) are a crucial technique in signal preprocessing, serving as key descriptors for signal analysis and recognition. OMs are obtained by the projection of orthogonal polynomials (OPs) onto the signal domain. However, when dealing with 3D signals, the traditional approach of convolving kernels with the signal and computing OMs beforehand significantly increases the computational cost of computer vision algorithms. To address this issue, this paper develops a novel mathematical model to embed the kernel directly into the OPs functions, seamlessly integrating these two processes into a more efficient and accurate approach. The proposed model allows the computation of OMs for smoothed versions of 3D signals directly, thereby reducing computational overhead. Extensive experiments conducted on 3D objects demonstrate that the proposed method outperforms traditional approaches across various metrics. The average recognition accuracy improves to 83.85% when the polynomial order is increased to 10. Experimental results show that the proposed method exhibits higher accuracy and lower computational costs compared to the benchmark methods in various conditions for a wide range of parameter values. Full article

In recent years, machine learning methods have garnered significant attention in the field of crop recognition, playing a crucial role in obtaining spatial distribution information and understanding dynamic changes in planting areas. However, research in smaller plots within mountainous regions remains relatively limited. This study focuses on Shangzhou District in Shangluo City, Shaanxi Province, utilizing a dataset of high-resolution remote sensing images (GF-1, ZY1-02D, ZY-3) collected over seven months in 2021 to calculate the normalized difference vegetation index (NDVI) and construct a time series. By integrating field survey results with time series images and Google Earth for visual interpretation, the NDVI time series curve for maize was analyzed. The Random Forest (RF) classification algorithm was employed for maize recognition, and comparative analyses of classification accuracy were conducted using Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Artificial Neural Network (ANN). The results demonstrate that the random forest algorithm achieved the highest accuracy, with an overall accuracy of 94.88% and a Kappa coefficient of 0.94, both surpassing those of the other classification methods and yielding satisfactory overall results. This study confirms the feasibility of using time series high-resolution remote sensing images for precise crop extraction in the southern mountainous regions of China, providing valuable scientific support for optimizing land resource use and enhancing agricultural productivity. Full article

Visual clarity is crucial for shoulder arthroscopy, directly influencing surgical precision and outcomes. Despite advances in imaging technology, intraoperative bleeding remains a significant obstacle to optimal visibility, with subjective evaluation methods lacking consistency and standardization. This study proposes a novel image processing system to objectively quantify bleeding and assess surgical effectiveness. The system uses color recognition algorithms to calculate a bleeding score based on pixel ratios by incorporating multiple color spaces to enhance accuracy and minimize errors. Moreover, 200 three-second video clips from prior arthroscopic rotator cuff repairs were evaluated by three senior surgeons trained on the system’s color metrics and scoring process. Assessments were repeated two weeks later to test intraobserver reliability. The system’s scores were compared to the average score given by the surgeons. The average surgeon-assigned score was 5.10 (range: 1–9.66), while the system scored videos from 1 to 9.46, with an average of 5.08. The mean absolute error between system and surgeon scores was 0.56, with a standard deviation of 0.50, achieving agreement ranging from [0.96,0.98] with 96.7% confidence (ICC = 0.967). This system provides a standardized method to evaluate intraoperative bleeding, enabling the precise detection of blood variations and supporting advanced technologies like autonomous arthropumps to enhance arthroscopy and surgical outcomes. Full article

Childhood glaucoma encompasses a group of rare but severe ocular disorders characterized by increased intraocular pressure (IOP), posing significant risks to vision and quality of life. Primary congenital glaucoma has a prevalence of one in 10,000–68,000 people in Western countries. More worryingly, it is responsible for 5–18% of all childhood blindness cases. According to the Childhood Glaucoma Research Network (CGRN), this spectrum of disease is classified into primary glaucoma (primary congenital glaucoma and juvenile open-angle glaucoma) and secondary glaucomas (associated with non-acquired ocular anomalies, non-acquired systemic disease, acquired conditions, and glaucoma after cataract surgery). They present very specific ocular characteristics, such as buphthalmos or progressive myopic shift, corneal modifications such as Haab striae, corneal edema or increased corneal diameter, and also glaucoma findings including high intraocular pressure, specific visual fields abnormalities, and optic nerve damage such as increased cup-disc ratio, cup-disc ratio asymmetry of at least 0.2 and focal rim thinning. Surgical intervention remains the cornerstone of treatment, and initial surgical options include angle surgeries such as goniotomy and trabeculotomy, aimed at improving aqueous outflow. For refractory cases, trabeculectomy and glaucoma drainage devices (GDDs) serve as second-line therapies. Advanced cases may require cyclodestructive procedures, including transscleral cyclophotocoagulation, reserved for eyes with limited visual potential. All in all, with appropriate management, the prognosis of PCG may be quite favorable: stationary disease has been reported in 90.3% of cases after one year, with a median visual acuity in the better eye of 20/30. Immediate recognition of the specific signs and symptoms by caregivers, primary care providers, and ophthalmologists, followed by prompt diagnosis, comprehensive surgical planning, and involving the caregivers in the follow-up schedule remain critical for optimizing outcomes in childhood glaucoma management. Full article

Background: Neuroprosthetics for vision restoration have advanced significantly, incorporating technologies like retinal implants, cortical implants, and non-invasive stimulation methods. These advancements hold the potential to tackle major challenges in visual prosthetics, such as enhancing functionality, improving biocompatibility, and enabling real-time object recognition. Aim: The aim of this review overview is to provide a comprehensive analysis of the latest advancements in ocular neuroprostheses. Methods: A narrative review was conducted, focusing on the latest developments in visual neuroprosthetics. Comprehensive searches were carried out on Google Scholar, PubMed, and Scopus using specific keywords. A specific narrative checklist was applied, alongside a tailored quality assessment methodology, to evaluate the quality of the studies included. A total of sixteen relevant studies from the past three years were included in the review. Results and discussion: The integration of artificial retinas, cortical implants, high technology-enabled prosthetics, gene therapies, nanotechnology, and bioprinting has shown significant promise in enhancing the quality and functionality of vision restoration systems, offering potential to address complex visual impairments and improve independence and mobility for individuals with blindness. These innovations appear to have the potential to transform healthcare systems in the future by enabling more efficient and personalized therapies and prosthetic devices. However, challenges such as energy efficiency, scalability, and the neural dynamics of vision restoration persist, requiring continued interdisciplinary collaboration to refine these technologies, overcome ethical and regulatory hurdles, and ensure their effectiveness in real-world applications. Conclusions: While visual neuroprosthetics have made remarkable progress, addressing challenges related to energy consumption and regulatory and ethical concerns will be crucial for ensuring that neuroprosthetic devices can effectively meet the needs of individuals with visual impairments. Full article

The utilization of augmented reality (AR) is becoming increasingly prevalent in the integration of virtual reality (VR) elements into the tangible reality of the physical world. It facilitates a more straightforward comprehension of the interconnections, interdependencies, and spatial context of data. Furthermore, the presentation of analyses and the combination of spatial data with annotated data are facilitated. This is particularly evident in the context of mobile applications, where the combination of real-world and virtual imagery facilitates enhances visualization. This paper presents a proposal for the development of a multimodal system that is capable of identifying roof types in real time and visualizing them in AR on mobile devices. The current approach to roof identification is based on data made available by public administrations in an open-source format, including orthophotos and building contours. Existing computer processing technologies have been employed to generate objects representing the shapes of building masses, and in particular, the shape of roofs, in three-dimensional (3D) space. The system integrates real-time data obtained from multiple sources and is based on a mobile application that enables the precise positioning and detection of the recipient’s viewing direction (pose estimation) in real time. The data were integrated and processed in a Docker container system, which ensured the scalability and security of the solution. The multimodality of the system is designed to enhance the user’s perception of the space and facilitate a more nuanced interpretation of its intricacies. In its present iteration, the system facilitates the extraction and classification/generalization of two categories of roof types (gable and other) from aerial imagery through the utilization of deep learning methodologies. The outcomes achieved suggest considerable promise for the advancement and deployment of the system in domains pertaining to architecture, urban planning, and civil engineering. Full article

The composition of plastic and multilayer packaging waste was assessed in the mixed municipal solid waste (MSW) streams of the Kaunas (Lithuania), Daugavpils (Latvia) and Tallinn (Estonia) municipalities. For the analysis of samples in the mixed MSW streams, the authors used manual sorting and a visual recognition method. Composition analysis of plastic and multilayer packaging waste from separately collected waste of multi-family and single-family households was performed in the Kaunas and Tallinn municipalities. For the analysis of samples in the separately collected waste streams, the research group combined manual sorting and near-infrared (NIR) spectroscopy methods. The findings reveal that the percentage distribution of plastic and multilayer packaging waste within the municipal solid waste (MSW) stream is relatively consistent across the municipalities of Kaunas, Daugavpils and Tallinn, comprising 40.16%, 36.83% and 35.09%, respectively. However, a notable variation emerges when examining separately collected plastic and multilayer packaging waste streams. In this category, the proportion of plastic and multilayer packaging within the total separately collected packaging waste stream ranges from 62.05% to 74.7% for multi-family residential buildings and from 44.66% to 56.89% for single-family residential buildings. The authors provided further insights for the enhanced recycling potential of different plastic materials through improved sorting. Full article

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve the accuracy and efficiency of HAR. Three distinct, two-dimensional radar processing techniques, specifically range-fast Fourier transform (FFT)-based time-range maps, time-Doppler-based short-time Fourier transform (STFT) maps, and smoothed pseudo-Wigner–Ville distribution (SPWVD) maps, are evaluated in combination with four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2. This study positions radar-generated maps as a form of visual data, bridging radar signal processing and image representation domains while ensuring privacy in sensitive applications. In total, twelve CNN and preprocessing configurations are analyzed, focusing on the trade-offs between preprocessing complexity and recognition accuracy, all of which are essential for real-time applications. Among these results, MobileNetV2, combined with STFT preprocessing, showed an ideal balance, achieving high computational efficiency and an accuracy rate of 96.30%, with a spectrogram generation time of 220 ms and an inference time of 2.57 ms per sample. The comprehensive evaluation underscores the importance of interpretable visual features for resource-constrained environments, expanding the applicability of radar-based HAR systems to domains such as augmented reality, autonomous systems, and edge computing. Full article

Currently, various deep learning methods can assist in medical diagnosis. Coronary Digital Subtraction Angiography (DSA) is a medical imaging technology used in cardiac interventional procedures. By employing X-ray sensors to visualize the coronary arteries, it generates two-dimensional images from any angle. However, due to the complexity of the coronary structures, the 2D images may sometimes lack sufficient information, necessitating the construction of a 3D model. Camera-level 3D modeling can be realized based on deep learning. Nevertheless, the beating of the heart results in varying degrees of arterial vasoconstriction and vasodilation, leading to substantial discrepancies between DSA sequences, which introduce errors in 3D modeling of the coronary arteries, resulting in the inability of the 3D model to reflect the coronary arteries. We propose a coronary DSA video sequence keyframe recognition method, CDKD-w+, based on w+ space encoding. The method utilizes a pSp encoder to encode the coronary DSA images, converting them into latent codes in the w+ space. Differential analysis of inter-frame latent codes is employed for heartbeat keyframe localization, aiding in coronary 3D modeling. Experimental results on a self-constructed coronary DSA heartbeat keyframe recognition dataset demonstrate an accuracy of 97%, outperforming traditional metrics such as L1, SSIM, and PSNR. Full article

To explore the memory advantage and the underlying mechanisms of bridge experts, this study conducted two experiments. Experiment 1 investigated the effects of the suit categorization rule and the rank ordering rule on the memory performance of bridge experts when memorizing hands. The findings revealed that the suit categorization rule significantly influenced the memory advantage of bridge experts, regardless of whether the task involved recognition or free recall. Conversely, the rank ordering rule had no discernible effect on their memory performance, though the honor card information within this rule notably impacted their memory. Building on the first experiment, Experiment 2 further examined the roles of visual familiarity induced by the suit categorization and honor card rules, alongside the abstract knowledge embedded in these rules, on the memory performance of bridge experts. The results demonstrated that visual familiarity influenced recognition among bridge experts, while both visual familiarity and abstract knowledge jointly contributed to recall performance. These research findings concurrently support both chunking/template theory and SEEK theory. Full article

The accurate identification of natural disasters is crucial in ensuring effective post-disaster relief efforts. However, the existing models for disaster classification often incur high costs. To address this, we propose leveraging the most advanced pre-trained large language models, which offer superior generative and multimodal understanding capabilities. Using a question-answering approach, we extract textual descriptions and category prediction probabilities for disaster scenarios, which are then used as input to our proposed Mamba Multimodal Disaster Recognition Network (Mamba-MDRNet). This model integrates a large pre-trained model with the Mamba mechanism, enabling the selection of the most reliable modality information as a robust basis for scene classification. Extensive experiments demonstrate consistent performance improvements across various visual models with heterogeneous architectures. Notably, integrating EfficientNet within Mamba-MDRNet yielded 97.82% accuracy for natural scene classification, surpassing the performance of the CNN (91.75%), ViT (94.50%), and ResNet18 (97.25%). These results highlight the potential of multimodal models combining large models and the Mamba mechanism for disaster type prediction. Full article

As artificial intelligence increasingly automates the recognition and analysis of visual content, it poses significant risks to privacy, security, and autonomy. Computer vision systems can surveil and exploit data without consent. With these concerns in mind, we introduce a novel method to control whether images can be recognized by computer vision systems using reversible adversarial examples. These examples are generated to evade unauthorized recognition, allowing only systems with permission to restore the original image by removing the adversarial perturbation with zero-bit error. A key challenge with prior methods is their reliance on merely restoring the examples to a state in which they can be correctly recognized by the model; however, the restored images are not fully consistent with the original images, and they require excessive auxiliary information to achieve reversibility. To achieve zero-bit error restoration, we utilize the differential evolution algorithm to optimize adversarial perturbations while minimizing distortion. Additionally, we introduce a dual-color space detection mechanism to localize perturbations, eliminating the need for extra auxiliary information. Ultimately, when combined with reversible data hiding, adversarial attacks can achieve reversibility. Experimental results demonstrate that the PSNR and SSIM between the restored images by the method and the original images are ∞ and 1, respectively. The PSNR and SSIM between the reversible adversarial examples and the original images are 48.32 dB and 0.9986, respectively. Compared to state-of-the-art methods, the method maintains high visual fidelity at a comparable attack success rate. Full article