Morphology-Driven Nanofiller Size Measurement Integrated with Micromechanical Finite Element Analysis for Quantifying Interphase in Polymer Nanocomposites.

This study focused on an innovative practical method using computer vision for particle size measurement, which serves as a key precursor for predicting the elastic modulus of polymer nanocomposites. This approach involved the morphological segmentation of the nanodispersed phase. It aimed, for the first time, to address the impractical conditions resulting from the assumption of idealized single-particle sizes in a monodispersed system during modeling. Subsequently, a micromechanical finite element framework was employed to determine the interphase thickness and modulus in ultrahigh molecular weight polyethylene/nanozeolite composites, following the quantification of nanoparticle sizes. The size measurement approach relied on morphological images extracted from scanning electron microscopy micrographs of impact-fractured surfaces. To compute the interphase thickness, experimental data was fitted to an interphase-inclusive upper-bound Hashin-Shtrikman model, with the measured average particle size per composition serving as a crucial input. Subsequently, the interphase elastic modulus was computed based on its thickness, employing a hybrid modified-Hashin-Hansen and Maxwell model. These estimated interfacial variables were then utilized as inputs for the finite element model to determine the tensile modulus. A comparison between the model results and measured data revealed a maximum discrepancy of 3.29%, indicating the effectiveness of the methodology employed in quantifying interfacial properties.

The relation between visual functions, functional vision, and bimanual function in children with unilateral cerebral palsy.

BACKGROUND: Accurate visual information is needed to guide and perform efficient movements in daily life. AIMS: To investigate the relation between visual functions, functional vision, and bimanual function in children with unilateral cerebral palsy (uCP). METHODS AND PROCEDURES: In 49 children with uCP (7-15 y), we investigated the relation between stereoacuity (Titmus Stereo Fly test), visual perception (Test of Visual Perceptual Skills), visuomotor integration (Beery Buktenica Test of Visual-Motor Integration) and functional vision (Flemish cerebral visual impairment questionnaire) with bimanual dexterity (Tyneside Pegboard Test), bimanual coordination (Kinarm exoskeleton robot, Box opening task), and functional hand use (Children's Hand-use Experience Questionnaire; Assisting Hand Assessment) using correlations (rs) and elastic-net regularized regressions (d). OUTCOMES AND RESULTS: Visual perception correlated with bimanual coordination (rs=0.407-0.436) and functional hand use (rs=0.380-0.533). Stereoacuity (rs=-0.404), visual perception (rs=-0.391 to -0.620), and visuomotor integration (rs=-0.377) correlated with bimanual dexterity. Functional vision correlated with functional hand use (rs=-0.441 to -0.458). Visual perception predicted bimanual dexterity (d=0.001-0.315), bimanual coordination (d=0.004-0.176), and functional hand use (d=0.001-0.345), whereas functional vision mainly predicted functional hand use (d=0.001-0.201). CONCLUSIONS AND IMPLICATIONS: Visual functions and functional vision are related to bimanual function in children with uCP highlighting the importance of performing extensive visual assessment to better understand children's difficulties in performing bimanual tasks. WHAT THIS PAPER ADDS: Previous findings showed that up to 62 % of children with unilateral cerebral palsy (uCP) present with visual impairment, which can further compromise their motor performance. However, the relation between visual and motor function has hardly been investigated in this population. This study makes a significant contribution to the literature by comprehensively investigating the multi-level relation between the heterogenous spectrum of visual abilities and bimanual function in children with uCP. We found that mainly decreased visual perception was related to decreased bimanual dexterity, bimanual coordination, and functional hand use while impairments in functional vision were only related to decreased functional hand use. Additionally, elastic-net regression models showed that visual assessments can predict bimanual function in children with uCP, however, effect sizes were only tiny to small. With our study, we demonstrated a relation between visual functions and bimanual function in children with uCP. These findings suggest the relevance of thoroughly examining visual functions in children with uCP to identify the presence of visual impairments that may further compromise their bimanual function.

Conscious vision in blindness: A new perceptual phenomenon implemented on the "wrong" side of the brain.

Patients with lesions in the visual cortex are blind in corresponding regions of the visual field, but they still may process visual information, a phenomenon referred to as residual vision or "blindsight". Here we report behavioral and fMRI observations with a patient who reports conscious vision across an extended area of blindness for moving, but not for stationary stimuli. This completion effect is shown to be of perceptual and not of conceptual origin, most likely mediated by spared representations of the visual field in the striate cortex. The neural output to extra-striate areas from regions of the deafferented striate cortex is apparently still intact; this is, for instance, indicated by preserved size constancy of visually completed stimuli. Neural responses as measured with fMRI reveal an activation only for moving stimuli, but importantly on the ipsilateral side of the brain. In a conceptual model this shift of activation to the "wrong" hemisphere is explained on the basis of an imbalance of excitatory and inhibitory interactions within and between the striate cortices due to the brain injury. The observed neuroplasticity indicated by this shift together with the behavioral observations provide important new insights into the functional architecture of the human visual system and provide new insight into the concept of consciousness.

Highly Stretchable and Oriented Wafer-Scale Semiconductor Films for Organic Phototransistor Arrays.

Stretchable organic phototransistor arrays have potential applications in artificial visual systems due to their capacity to perceive ultraweak light across a broad spectrum. Ensuring uniform mechanical and electrical performance of individual devices within these arrays requires semiconductor films with large-area scale, well-defined orientation, and stretchability. However, the progress of stretchable phototransistors is primarily impeded by their limited electrical properties and photodetection capabilities. Herein, wafer-scale and well-oriented semiconductor films were successfully prepared using a solution shearing process. The electrical properties and photodetection capabilities were optimized by improving the polymer chain alignment. Furthermore, a stretchable 10 × 10 transistor array with high device uniformity was fabricated, demonstrating excellent mechanical robustness and photosensitive imaging ability. These arrays based on highly stretchable and well-oriented wafer-scale semiconductor films have great application potential in the field of electronic eye and artificial visual systems.

Bifurcation detection in intravascular optical coherence tomography using vision transformer based deep learning.

Objective. Bifurcation detection in intravascular optical coherence tomography (IVOCT) images plays a significant role in guiding optimal revascularization strategies for percutaneous coronary intervention (PCI). We propose a bifurcation detection method using vision transformer (ViT) based deep learning in IVOCT.Approach. Instead of relying on lumen segmentation, the proposed method identifies the bifurcation image using a ViT-based classification model and then estimate bifurcation ostium points by a ViT-based landmark detection model.Main results. By processing 8640 clinical images, the Accuracy and F1-score of bifurcation identification by the proposed ViT-based model are 2.54% and 16.08% higher than that of traditional non-deep learning methods, are similar to the best performance of convolutional neural networks (CNNs) based methods, respectively. The ostium distance error of the ViT-based model is 0.305 mm, which is reduced 68.5% compared with the traditional non-deep learning method and reduced 24.81% compared with the best performance of CNNs based methods. The results also show that the proposed ViT-based method achieves the highest success detection rate are 11.3% and 29.2% higher than the non-deep learning method, and 4.6% and 2.5% higher than the best performance of CNNs based methods when the distance section is 0.1 and 0.2 mm, respectively.Significance. The proposed ViT-based method enhances the performance of bifurcation detection of IVOCT images, which maintains a high correlation and consistency between the automatic detection results and the expert manual results. It is of great significance in guiding the selection of PCI treatment strategies.

Home-monitoring for neovascular age-related macular degeneration in older adults within the UK: the MONARCH diagnostic accuracy study.

Background: Most neovascular age-related macular degeneration treatments involve long-term follow-up of disease activity. Home monitoring would reduce the burden on patients and those they depend on for transport, and release clinic appointments for other patients. The study aimed to evaluate three home-monitoring tests for patients to use to detect active neovascular age-related macular degeneration compared with diagnosing active neovascular age-related macular degeneration by hospital follow-up. Objectives: There were five objectives: Estimate the accuracy of three home-monitoring tests to detect active neovascular age-related macular degeneration. Determine the acceptability of home monitoring to patients and carers and adherence to home monitoring. Explore whether inequalities exist in recruitment, participants' ability to self-test and their adherence to weekly testing during follow-up. Provide pilot data about the accuracy of home monitoring to detect conversion to neovascular age-related macular degeneration in fellow eyes of patients with unilateral neovascular age-related macular degeneration. Describe challenges experienced when implementing home-monitoring tests. Design: Diagnostic test accuracy cohort study, stratified by time since starting treatment. Setting: Six United Kingdom Hospital Eye Service macular clinics (Belfast, Liverpool, Moorfields, James Paget, Southampton, Gloucester). Participants: Patients with at least one study eye being monitored by hospital follow-up. Reference standard: Detection of active neovascular age-related macular degeneration by an ophthalmologist at hospital follow-up. Index tests: KeepSight Journal: paper-based near-vision tests presented as word puzzles. MyVisionTrack®: electronic test, viewed on a tablet device. MultiBit: electronic test, viewed on a tablet device. Participants provided test scores weekly. Raw scores between hospital follow-ups were summarised as averages. Results: Two hundred and ninety-seven patients (mean age 74.9 years) took part. At least one hospital follow-up was available for 317 study eyes, including 9 second eyes that became eligible during follow-up, in 261 participants (1549 complete visits). Median testing frequency was three times/month. Estimated areas under receiver operating curves were < 0.6 for all index tests, and only KeepSight Journal summary score was significantly associated with the lesion activity (odds ratio = 3.48, 95% confidence interval 1.09 to 11.13, p = 0.036). Older age and worse deprivation for home address were associated with lower participation (χ2 = 50.5 and 24.3, respectively, p < 0.001) but not ability or adherence to self-testing. Areas under receiver operating curves appeared higher for conversion of fellow eyes to neovascular age-related macular degeneration (0.85 for KeepSight Journal) but were estimated with less precision. Almost half of participants called a study helpline, most often due to inability to test electronically. Limitations: Pre-specified sample size not met; participants' difficulties using the devices; electronic tests not always available. Conclusions: No index test provided adequate test accuracy to identify lesion diagnosed as active in follow-up clinics. If used to detect conversion, patients would still need to be monitored at hospital. Associations of older age and worse deprivation with study participation highlight the potential for inequities with such interventions. Provision of reliable electronic testing was challenging. Future work: Future studies evaluating similar technologies should consider: Independent monitoring with clear stopping rules based on test performance. Deployment of apps on patients' own devices since providing devices did not reduce inequalities in participation and complicated home testing. Alternative methods to summarise multiple scores over the period preceding a follow-up. Trial registration: This trial is registered as ISRCTN79058224. Funding: This award was funded by the National Institute of Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 15/97/02) and is published in full in Health Technology Assessment; Vol. 28, No. 32. See the NIHR Funding and Awards website for further award information.

Treatment for neovascular age-related macular degeneration, the most common cause of sight loss in those over 50 years, involves regular eye injections and frequent follow-up appointments. This is inconvenient for patients and causes capacity issues in the hospital eye service. Finding tests that could be undertaken at home that could detect if a further injection and hospital appointment was required or not would increase capacity to see those at highest risk of sight loss and also reduce the burden on patients and their carers. We investigated three different visual function tests, one paper-based and two applications on an iPod Touch_TM tablet (Apple, Cupertino, CA, USA). We wanted to see if they could detect an increase in disease activity that would require treatment, compared to the decision by a retinal specialist at a traditional hospital eye outpatient visit based on clinical examination and retinal imaging. To encourage those without a smartphone or home internet to participate, we provided both an iPod Touch and Mobile Wireless-Fidelity device with a mobile contract. None of the tests performed well enough to safely monitor patients at home. Those who were willing to participate tended to be younger, had previous experience of using smartphones, sending e-mail and internet access and were more well-off than those who chose not to participate. Some participants also experienced difficulties with the devices provided and successfully uploading the data which were not related to the extent of previous information technology experience. There were also significant technical challenges for the research team. The study helpline was used heavily, considerably more than we anticipated. These tests are not ready to be used in this context. Future studies involving mobile health technology need to carefully consider how to reach those unlikely to participate and provide sufficient technical support to support long-term follow-up.

Pre-processing visual scenes for retinal prosthesis systems: A comprehensive review.

BACKGROUND: Retinal prostheses offer hope for individuals with degenerative retinal diseases by stimulating the remaining retinal cells to partially restore their vision. This review delves into the current advancements in retinal prosthesis technology, with a special emphasis on the pivotal role that image processing and machine learning techniques play in this evolution. METHODS: We provide a comprehensive analysis of the existing implantable devices and optogenetic strategies, delineating their advantages, limitations, and challenges in addressing complex visual tasks. The review extends to various image processing algorithms and deep learning architectures that have been implemented to enhance the functionality of retinal prosthetic devices. We also illustrate the testing results by demonstrating the clinical trials or using Simulated Prosthetic Vision (SPV) through phosphene simulations, which is a critical aspect of simulating visual perception for retinal prosthesis users. RESULTS: Our review highlights the significant progress in retinal prosthesis technology, particularly its capacity to augment visual perception among the visually impaired. It discusses the integration between image processing and deep learning, illustrating their impact on individual interactions and navigations within the environment through applying clinical trials and also illustrating the limitations of some techniques to be used with current devices, as some approaches only use simulation even on sighted-normal individuals or rely on qualitative analysis, where some consider realistic perception models and others do not. CONCLUSION: This interdisciplinary field holds promise for the future of retinal prostheses, with the potential to significantly enhance the quality of life for individuals with retinal prostheses. Future research directions should pivot towards optimizing phosphene simulations for SPV approaches, considering the distorted and confusing nature of phosphene perception, thereby enriching the visual perception provided by these prosthetic devices. This endeavor will not only improve navigational independence but also facilitate a more immersive interaction with the environment.

A dataset of over one thousand computed tomography scans of battery cells.

Battery technology is increasingly important for global electrification efforts. However, batteries are highly sensitive to small manufacturing variations that can induce reliability or safety issues. An important technology for battery quality control is computed tomography (CT) scanning, which is widely used for non-destructive 3D inspection across a variety of clinical and industrial applications. Historically, however, the utility of CT scanning for high-volume manufacturing has been limited by its low throughput as well as the difficulty of handling its large file sizes. In this work, we present a dataset of over one thousand CT scans of as-produced commercially available batteries. The dataset spans various chemistries (lithium-ion and sodium-ion) as well as various battery form factors (cylindrical, pouch, and prismatic). We evaluate seven different battery types in total. The manufacturing variability and the presence of battery defects can be observed via this dataset. This dataset may be of interest to scientists and engineers working on battery technology, computer vision, or both.

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.

Abnormal ß-amyloid (Aß) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aß positivity (Aß+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aß + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aß + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

Behavioral profiling for adaptive video summarization: From generalization to personalization.

In today's world of managing multimedia content, dealing with the amount of CCTV footage poses challenges related to storage, accessibility and efficient navigation. To tackle these issues, we suggest an encompassing technique, for summarizing videos that merges machine-learning techniques with user engagement. Our methodology consists of two phases, each bringing improvements to video summarization. In Phase I we introduce a method for summarizing videos based on keyframe detection and behavioral analysis. By utilizing technologies like YOLOv5 for object recognition, Deep SORT for object tracking, and Single Shot Detector (SSD) for creating video summaries. In Phase II we present a User Interest Based Video summarization system driven by machine learning. By incorporating user preferences into the summarization process we enhance techniques with personalized content curation. Leveraging tools such as NLTK, OpenCV, TensorFlow, and the EfficientDET model enables our system to generate customized video summaries tailored to preferences. This innovative approach not only enhances user interactions but also efficiently handles the overwhelming amount of video data on digital platforms. By combining these two methodologies we make progress in applying machine learning techniques while offering a solution to the complex challenges presented by managing multimedia data.

Optic nerve compression associated with visual cortex functional alteration in dysthyroid optic neuropathy: A combined orbital and brain imaging study.

AIMS: To investigate the alterations of the optic nerve and visual cortex in dysthyroid optic neuropathy (DON), a subgroup of thyroid eye disease (TED). METHODS: Multiple orbital imaging biomarkers related to optic nerve compression and the amplitude of low-frequency fluctuations (ALFF) of the brain were obtained from 47 patients with DON, 56 TED patients without DON (nDON), and 37 healthy controls (HC). Correlation analyses and diagnostic tests were implemented. RESULTS: Compared with HC, the nDON group showed alterations in orbital imaging biomarkers related to optic nerve compression in posterior segments, as well as ALFF of the right inferior temporal gyrus and left fusiform gyrus. DON differed from nDON group mainly in the modified muscle index of the posterior segment of optic nerve, and ALFF of orbital part of right superior frontal gyrus, right hippocampus, and right superior temporal gyrus. Orbital and brain imaging biomarkers were significantly correlated with each other. Diagnostic models attained an area under a curve of 0.80 for the detection of DON. CONCLUSION: The combined orbital and brain imaging study revealed alterations of the visual pathway in patients with TED and DON as well as provided diagnostic value. The initiation of alterations in the visual cortex in TED may precede the onset of DON.

Contextual emotion detection in images using deep learning.

Introduction: Computerized sentiment detection, based on artificial intelligence and computer vision, has become essential in recent years. Thanks to developments in deep neural networks, this technology can now account for environmental, social, and cultural factors, as well as facial expressions. We aim to create more empathetic systems for various purposes, from medicine to interpreting emotional interactions on social media. Methods: To develop this technology, we combined authentic images from various databases, including EMOTIC (ADE20K, MSCOCO), EMODB_SMALL, and FRAMESDB, to train our models. We developed two sophisticated algorithms based on deep learning techniques, DCNN and VGG19. By optimizing the hyperparameters of our models, we analyze context and body language to improve our understanding of human emotions in images. We merge the 26 discrete emotional categories with the three continuous emotional dimensions to identify emotions in context. The proposed pipeline is completed by fusing our models. Results: We adjusted the parameters to outperform previous methods in capturing various emotions in different contexts. Our study showed that the Sentiment_recognition_model and VGG19_contexte increased mAP by 42.81% and 44.12%, respectively, surpassing the results of previous studies. Discussion: This groundbreaking research could significantly improve contextual emotion recognition in images. The implications of these promising results are far-reaching, extending to diverse fields such as social robotics, affective computing, human-machine interaction, and human-robot communication.

Classifying and quantifying changes in papilloedema using machine learning.

Background: Machine learning (ML) can differentiate papilloedema from normal optic discs using fundus photos. Currently, papilloedema severity is assessed using the descriptive, ordinal Frisén scale. We hypothesise that ML can quantify papilloedema and detect a treatment effect on papilloedema due to idiopathic intracranial hypertension. Methods: We trained a convolutional neural network to assign a Frisén grade to fundus photos taken from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT). We applied modified subject-based fivefold cross-validation to grade 2979 longitudinal images from 158 participants' study eyes (ie, the eye with the worst mean deviation) in the IIHTT. Compared with the human expert-determined grades, we hypothesise that ML-estimated grades can also demonstrate differential changes over time in the IIHTT study eyes between the treatment (acetazolamide (ACZ) plus diet) and placebo (diet only) groups. Findings: The average ML-determined grade correlated strongly with the reference standard (r=0.76, p<0.001; mean absolute error=0.54). At the presentation, treatment groups had similar expert-determined and ML-determined Frisén grades. The average ML-determined grade for the ACZ group (1.7, 95% CI 1.5 to 1.8) was significantly lower (p=0.0003) than for the placebo group (2.3, 95% CI 2.0 to 2.5) at the 6-month trial outcome. Interpretation: Supervised ML of fundus photos quantified the degree of papilloedema and changes over time reflecting the effects of ACZ. Given the increasing availability of fundus photography, neurologists will be able to use ML to quantify papilloedema on a continuous scale that incorporates the features of the Frisén grade to monitor interventions.

Experiences of Visually Impaired Mothers in Childcare and Their Expectations from Nurses.

Background and Purpose: Research on the childcare experiences of visually impaired mothers and their expectations from nurses in this context is relatively scarce. This study aims to explore the experiences of visually impaired mothers in caring for their children, as well as their expectations from nurses during this process. The goal is to provide strategic recommendations for nurses based on these expectations. Methods: The study utilized a phenomenological design and employed a qualitative methodology. Data for this study were collected in six different provinces of Turkey between March 2019 and May 2020. The study group consisted of 25 visually impaired mothers with nondisabled children aged 0-18 years. Results: The analysis yielded four themes: "Postpartum Emotions and Care at 0-1 Years," "Hygienic Care of the Child," "Experiences in the Child's Illness," and "Nursing Support: Expectations of Visually Impaired Mothers." While the first three themes are associated with childcare, the fourth theme encompasses both the expectations from nurses and recommendations for visually impaired mothers. The study found that visually impaired mothers had unique experiences and either received assistance or developed childcare strategies. However, it was observed that nurses struggled to empathize with visually impaired mothers, and efforts to address their specific needs were limited. Implications for Practice: Pediatric nurses should develop methods to facilitate childcare for visually impaired mothers. Successful outcomes in this field depend on nurses collaborating with visually impaired mothers and providing them with ongoing support.

Convergent technologies to tackle challenges of modern food authentication.

The authentication process involves all the supply chain stakeholders, and it is also adopted to verify food quality and safety. Food authentication tools are an essential part of traceability systems as they provide information on the credibility of origin, species/variety identity, geographical provenance, production entity. Moreover, these systems are useful to evaluate the effect of transformation processes, conservation strategies and the reliability of packaging and distribution flows on food quality and safety. In this manuscript, we identified the innovative characteristics of food authentication systems to respond to market challenges, such as the simplification, the high sensitivity, and the non-destructive ability during authentication procedures. We also discussed the potential of the current identification systems based on molecular markers (chemical, biochemical, genetic) and the effectiveness of new technologies with reference to the miniaturized systems offered by nanotechnologies, and computer vision systems linked to artificial intelligence processes. This overview emphasizes the importance of convergent technologies in food authentication, to support molecular markers with the technological innovation offered by emerging technologies derived from biotechnologies and informatics. The potential of these strategies was evaluated on real examples of high-value food products. Technological innovation can therefore strengthen the system of molecular markers to meet the current market needs; however, food production processes are in profound evolution. The food 3D-printing and the introduction of new raw materials open new challenges for food authentication and this will require both an update of the current regulatory framework, as well as the development and adoption of new analytical systems.

MetaV: A Pioneer in feature Augmented Meta-Learning Based Vision Transformer for Medical Image Classification.

Image classification, a fundamental task in computer vision, faces challenges concerning limited data handling, interpretability, improved feature representation, efficiency across diverse image types, and processing noisy data. Conventional architectural approaches have made insufficient progress in addressing these challenges, necessitating architectures capable of fine-grained classification, enhanced accuracy, and superior generalization. Among these, the vision transformer emerges as a noteworthy computer vision architecture. However, its reliance on substantial data for training poses a drawback due to its complexity and high data requirements. To surmount these challenges, this paper proposes an innovative approach, MetaV, integrating meta-learning into a vision transformer for medical image classification. N-way K-shot learning is employed to train the model, drawing inspiration from human learning mechanisms utilizing past knowledge. Additionally, deformational convolution and patch merging techniques are incorporated into the vision transformer model to mitigate complexity and overfitting while enhancing feature representation. Augmentation methods such as perturbation and Grid Mask are introduced to address the scarcity and noise in medical images, particularly for rare diseases. The proposed model is evaluated using diverse datasets including Break His, ISIC 2019, SIPaKMed, and STARE. The achieved performance accuracies of 89.89%, 87.33%, 94.55%, and 80.22% for Break His, ISIC 2019, SIPaKMed, and STARE, respectively, present evidence validating the superior performance of the proposed model in comparison to conventional models, setting a new benchmark for meta-vision image classification models.

The impact of using reverse polarity text for children with vision impairment assessed using light-adapted flicker electroretinogram.

CLINICAL RELEVANCE: Children with vision impairment can have difficulty accessing classroom reading material and knowledge of which students are likely to have improved performance reading performance with reverse polarity would be of value to educators. BACKGROUND: Printed material is typically presented as black text on a white background; however, reversing the polarity to white text on a black background may improve the reading speed for children with vision impairment. This study sought to identify the visual function or pathological features of children with vision impairment where reversing the polarity of text would improve their reading performance. METHODS: Forty-eight vision-impaired participants (27 male), aged 5-18 years with binocular visual acuities between 0.18-1.52 logMAR, were included. Reading performance was assessed by changes in Critical Print Size (ΔCPS), Maximum Reading Speed (ΔMRS) in normal and reverse polarity digital print, and numeric reading speed (ΔNRS) with normal and reverse polarity fonts. Correlations were made with 30 Hz flicker electroretinogram amplitude and high/low contrast acuity. Paired nonparametric tests evaluated significance in pathological condition groups. RESULTS: Significant negative correlations were only found between the 30 Hz flicker amplitude and ΔMRS (r = -.42, p = .028) and ΔNRS (r = -.46, p = .027). Follow-up pairwise comparisons based on pathology group only showed a significant effect of the retinal dystrophy group and CPS (n = 12, z = -2.24, p = .025). All other pairwise comparisons based on group were non-significant (p > .05). CONCLUSIONS: This study did not identify a specific pathological group or visual functional measure that could be used as a clinical marker to predict the impact of reversing polarity. However, significant improvements could be made in reading performance for some children and so a reading performance assessment is recommended for all children with vision impairment.

Machine Learning Inspired Nanowire Classification Method based on Nanowire Array Scanning Electron Microscope Images.

Background: This article introduces an innovative classification methodology to identify nanowires within scanning electron microscope images. Methods: Our approach employs advanced image manipulation techniques in conjunction with machine learning-based recognition algorithms. The effectiveness of our proposed method is demonstrated through its application to the categorization of scanning electron microscopy images depicting nanowires arrays. Results: The method's capability to isolate and distinguish individual nanowires within an array is the primary factor in the observed accuracy. The foundational data set for model training comprises scanning electron microscopy images featuring 240 III-V nanowire arrays grown with metal organic chemical vapor deposition on silicon substrates. Each of these arrays consists of 66 nanowires. The results underscore the model's proficiency in discerning distinct wire configurations and detecting parasitic crystals. Our approach yields an average F1 score of 0.91, indicating high precision and recall. Conclusions: Such a high level of performance and accuracy of ML methods demonstrate the viability of our technique not only for academic but also for practical commercial implementation and usage.

Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer.

Background: The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans. Methods: RViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification. Results: Evaluation on the Brain Tumor MRI Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.0), specificity (0.975), F1-score (0.984), Matthew's Correlation Coefficient (MCC) (0.972), and an overall accuracy of 0.986. Conclusion: RViT outperforms the standard Vision Transformer model and several existing techniques, highlighting its efficacy in medical imaging. The study confirms that integrating rotational patch embeddings improves the model's capability to handle diverse orientations, a common challenge in tumor imaging. The specialized architecture and rotational invariance approach of RViT have the potential to enhance current methodologies for brain tumor detection and extend to other complex imaging tasks.

RDH12 allows cone photoreceptors to regenerate opsin visual pigments from a chromophore precursor to escape competition with rods.

Capture of a photon by an opsin visual pigment isomerizes its 11-cis-retinaldehyde (11cRAL) chromophore to all-trans-retinaldehyde (atRAL), which subsequently dissociates. To restore light sensitivity, the unliganded apo-opsin combines with another 11cRAL to make a new visual pigment. Two enzyme pathways supply chromophore to photoreceptors. The canonical visual cycle in retinal pigment epithelial cells supplies 11cRAL at low rates. The photic visual cycle in Müller cells supplies cones with 11-cis-retinol (11cROL) chromophore precursor at high rates. Although rods can only use 11cRAL to regenerate rhodopsin, cones can use 11cRAL or 11cROL to regenerate cone visual pigments. We performed a screen in zebrafish retinas and identified ZCRDH as a candidate for the enzyme that converts 11cROL to 11cRAL in cone inner segments. Retinoid analysis of eyes from Zcrdh-mutant zebrafish showed reduced 11cRAL and increased 11cROL levels, suggesting impaired conversion of 11cROL to 11cRAL. By microspectrophotometry, isolated Zcrdh-mutant cones lost the capacity to regenerate visual pigments from 11cROL. ZCRDH therefore possesses all predicted properties of the cone 11cROL dehydrogenase. The human protein most similar to ZCRDH is RDH12. By immunocytochemistry, ZCRDH was abundantly present in cone inner segments, similar to the reported distribution of RDH12. Finally, RDH12 was the only mammalian candidate protein to exhibit 11cROL-oxidase catalytic activity. These observations suggest that RDH12 in mammals is the functional ortholog of ZCRDH, which allows cones, but not rods, to regenerate visual pigments from 11cROL provided by Müller cells. This capacity permits cones to escape competition from rods for visual chromophore in daylight-exposed retinas.