Visual Research Articles

Maternal obesogenic diet operates at the tumor cell of origin to increase incidence and decrease latency of Neurofibromatosis Type 1 optic pathway glioma.

Pediatric low-grade glioma incidence has been rising in the U.S., mirroring the rising rates of pediatric and maternal obesity. Recently, children of obese mothers were demonstrated to develop brain tumors at higher rates. Importantly, obesity in the U.S. is largely driven by diet, given the prevalence of high fat and high sugar (HFHS) food choices. Since high-fat diet exposure can increase embryonic neuroglial progenitor cell (NPC) proliferation, the potential cells of origin for low-grade glioma, we hypothesized that in utero exposure to an obesogenic diet would modify pediatric brain penetrance and latency by affecting the tumor cell of origin. We employed several murine models of the Neurofibromatosis type 1 (NF1) pediatric brain tumor predisposition syndrome, in which optic pathway gliomas (Nf1-OPGs) arise from NPCs in the embryonic third ventricular zone (TVZ). We exposed dams and offspring to an obesogenic HFHS diet or control chow and analysed fetal neurodevelopment at E19.5 and tumor formation at 6w-3mo. Progeny from HFHS diet-exposed dams demonstrated increased TVZ NPC proliferation and glial differentiation. Dietary switch cohorts confirmed that these effects were dependent upon maternal diet, rather than maternal weight. Obesogenic diet (Ob) similarly accelerated glioma formation in a high-penetrance Nf1-OPG strain and increased glioma penetrance in two low-penetrance Nf1-OPG strains. In contrast, Ob exposure in the postnatal period alone did not recapitulate these effects. These findings establish maternal obesogenic diet as a risk factor for murine Nf1-OPG formation, acting in part through in utero effects on the tumor cell of origin.

Chemical defences indicate bold colour patterns with reduced variability in aposematic nudibranchs.

The selective factors that shape phenotypic diversity in prey communities with aposematic animals are diverse and coincide with similar diversity in the strength of underlying secondary defences. However, quantitative assessments of colour pattern variation and the strength of chemical defences in assemblages of aposematic species are lacking. We quantified colour pattern diversity using quantitative colour pattern analysis (QCPA) in 13 dorid nudibranch species (Infraorder: Doridoidei) that varied in the strength of their chemical defences. We accounted for the physiological properties of a potential predator's visual system (a triggerfish, Rhinecanthus aculeatus) and modelled the appearance of nudibranchs from multiple viewing distances (2 and 10 cm). We identified distinct colour pattern properties associated with the presence and strength of chemical defences. Specifically, increases in chemical defences indicated increases in colour pattern boldness (i.e. visual contrast elicited via either or potentially coinciding chromatic, achromatic and/or spatial contrast). Colour patterns were also less variable among species with chemical defences when compared to undefended species. Our results indicate correlations between secondary defences and diverse, bold colouration while showing that chemical defences coincide with decreased colour pattern variability among species. Our study suggests that complex spatiochromatic properties of colour patterns perceived by potential predators can be used to make inferences on the presence and strength of chemical defences.

Light wavelength modulates search behavior performance in zebrafish

Visual systems have evolved to discriminate between different wavelengths of light. The ability to perceive color, or specific light wavelengths, is important as color conveys crucial information about both biotic and abiotic features in the environment. Indeed, different wavelengths of light can drive distinct patterns of activity in the vertebrate brain, yet what remains incompletely understood is whether distinct wavelengths can invoke etiologically relevant behavioral changes. To address how specific wavelengths in the visible spectrum modulate behavioral performance, we use larval zebrafish and a stereotypic light-search behavior. Prior work has shown that the cessation of light triggers a transitional light-search behavior, which we use to interrogate wavelength-dependent behavioral modulation. Using 8 narrow spectrum light sources in the visible range, we demonstrate that all wavelengths induce motor parameters consistent with search behavior, yet the magnitude of search behavior is spectrum sensitive and the underlying motor parameters are modulated in distinct patterns across short, medium, and long wavelengths. However, our data also establishes that not all motor features of search are impacted by wavelength. To define how wavelength modulates search performance, we performed additional assays with alternative wavelengths, dual wavelengths, and variable intensity. Last, we also tested blind larvae to resolve which components of wavelength dependent behavioral changes potentially include signaling from non-retinal photoreception. These findings have important implications as organisms can be exposed to varying wavelengths in laboratory and natural settings and therefore impose unique behavioral outputs.

Predictive posture stabilization before contact with moving objects: equivalence of smooth pursuit tracking and peripheral vision.

Postural stabilization is essential to effectively interact with our environment. Humans preemptively adjust their posture to counteract impending disturbances, such as those encountered during interactions with moving objects, a phenomenon known as anticipatory postural adjustments (APAs). APAs are thought to be influenced by predictive models that incorporate object motion via retinal motion and extra-retinal signals. Building on our previous work that examined APAs in relation to the perceived momentum of moving objects, here we explored the impact of object motion within different visual field sectors on the human capacity to anticipate motion and prepare APAs for contact between virtual moving objects and the limb. Participants interacted with objects moving towards them under different gaze conditions. In one condition, participants fixated on either a central point (central fixation) or left-right of the moving object (peripheral fixation), while in another, they followed the moving object with smooth pursuit eye movements (SPEM). We found that APAs had the smallest magnitude in the central fixation condition and that no notable differences in APAs were apparent between the SPEM and peripheral fixation conditions. This suggests that the visual system can accurately perceive motion of objects in peripheral vision for posture stabilization. Using Bayesian Model Averaging, we also evaluated the contribution of different gaze variables, such as eye velocity and gain (ratio of eye and object velocity) and showed that both eye velocity and gain signals were significant predictors of APAs. Taken together, our study underscores the roles of oculomotor signals in modulation of APAs.

Brain-implantable needle-type CMOS imaging device enables multi-layer dissection of seizure calcium dynamics in the hippocampus

Objective: Current neuronal imaging methods mostly use bulky lenses that either impede animal behavior or prohibit multi-depth imaging. To overcome these limitations, we developed a lightweight lensless biophotonic system for neuronal imaging, enabling compact and simultaneous visualization of multiple brain layers. Approach: Our developed ‘CIS-NAIST’ device integrates a micro-CMOS image sensor, thin-film fluorescence filter, micro-LEDs, and a needle-shaped flexible printed circuit. With this device, we monitored neuronal calcium dynamics during seizures across the different layers of the hippocampus and employed machine learning techniques for seizure classification and prediction. Main results: The CIS-NAIST device revealed distinct calcium activity patterns across the CA1, molecular interlayer, and dentate gyrus. Our findings indicated an elevated calcium amplitude activity specifically in the dentate gyrus compared to other layers. Then, leveraging the multi-layer data obtained from the device, we successfully classified seizure calcium activity and predicted seizure behavior using Long Short-Term Memory and Hidden Markov models. Significance: Taken together, our ‘CIS-NAIST’ device offers an effective and minimally invasive method of seizure monitoring that can help elucidate the mechanisms of temporal lobe epilepsy.

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.

Not so early! Revisiting the question of visual pathway selectivity of saccadic suppression.

Not so early! Revisiting the question of visual pathway selectivity of saccadic suppression.

Causal effect of COVID-19 on optic nerve and visual pathway disorders: genetic evidence of lung-brain axis

PurposeTo investigate the potential causal association between COVID-19 exposure and optic nerve and visual pathway disorders through a two-sample bidirectional Mendelian randomization (MR) analysis, and to provide empirical support for the lung-brain axis.MethodsThis MR analysis utilized publicly accessible summary-level data from genome-wide association studies on COVID-19 (n=158,783) and optic nerve and visual pathway diseases (n=412,181), primarily involving individuals of European descent. The random-effect inverse-variance weighted estimation was applied as the main analytical approach, complemented by MR-Egger, weighted median, and weighted mode methods. The heterogeneity and pleiotropy of the instrumental variables were assessed using Cochran’s Q test, leave-one-out sensitivity analysis, MR-Egger intercept test, MR-PRESSO, and funnel plot evaluations.ResultsIn the forward analysis, the inverse-variance weighted method identified a significant causal effect of COVID-19 on optic nerve and visual pathway disorders (odds ratio = 1.697, 95% confidence interval: 1.086–2.652, p = 0.020). Directionally consistent results were also observed with MR-Egger regression, weighted median, and weighted mode approaches. Conversely, the reverse analysis revealed no causal effects of optic nerve and visual pathway disorders on COVID-19 susceptibility.ConclusionOur findings suggest that COVID-19 exposure may increase the risk of developing optic nerve and visual pathway disorders, supporting the lung-brain axis hypothesis. These results underscore the importance of vigilant monitoring of the visual system in patients recovering from COVID-19 and suggest potential avenues for future therapeutic strategies.

A Novel Attention-Based Generalized Efficient Layer Aggregation Network for Landslide Detection from Satellite Data in the Higher Himalayas, Nepal

Landslide event detection poses a significant challenge in the remote sensing community, especially with the advancements in computer vision technology. As computational capabilities continue to grow, the traditional manual and partially automated methods of landslide recognition from remote sensing data are transitioning towards automatic approaches using deep learning algorithms. Moreover, attention models, encouraged by the human visual system, have emerged as crucial modules in diverse applications including natural hazard assessment. Therefore, we suggest a novel and intelligent generalized efficient layer aggregation network (GELAN) based on two prevalent attention modules, efficient channel attention (ECA) and convolutional block attention module (CBAM), to enrich landslide detection techniques from satellite images. CBAM and ECA are separately integrated into GELAN at different locations. The experiments are conducted using satellite images of the Nepal Himalayan region. Standard metrics such as precision, recall, F-score, and mAP (mean average precision) are considered for quantitative evaluation. GELANc+CBAM (F-score = 81.5%) demonstrates the best performance. This study underscores the suitability of the proposed approach in up-to-date inventory creation and accurate landslide mapping for disaster recovery and response efforts. Moreover, it contributes to developing early prediction models for landslide hazards.

Image Segmentation Model for Vicinagearth Security Technology of Unmanned Aerial Vehicle Using Improved Pigeon-Inspired Optimization

The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle (UAV) security will become one of the evolving forms of its security technology, and how to improve the segmentation and recognition ability of UAV visual reconnaissance system for maritime targets through improvement will become the key to low-altitude security. Due to the fact that maritime target images are characterized by complex weather, strong interference, high speed requirement and large data volume, the traditional segmentation methods are not suitable for maritime small-target (MST) segmentation and recognition. Therefore, this paper proposes a threshold image segmentation (TIS) method based on an improved pigeon-inspired optimization (PIO) algorithm to provide a better method for segmentation and recognition of MST. First, this study proposes CCPIO based on the horizontal crossover search (HCS) and vertical crossover search (VCS) strategy, which effectively improves the search efficiency of PIO and the ability to jump out of local optimum. And the optimization performance of CCPIO is effectively verified by comparing it with 10 peer algorithms through benchmark function experiments. Further, in this paper, the proposed CCPIO-TIS segmentation model is proposed by combining CCPIO with non-local means, 2D histogram, and Kapur’s entropy. The proposed CCPIO-TIS model is also used for the segmentation and recognition of real MST images, and the results of the experimental comparison and evaluation analysis show that the proposed model has higher quality segmentation results than 12 models of the same type. In summary, this study can provide an efficient and accurate artificial intelligence model for segmentation and recognition of maritime small-target.

A neural modeling approach to study mechanisms underlying the heterogeneity of visual spatial frequency sensitivity in schizophrenia

Patients with schizophrenia exhibit abnormalities in spatial frequency sensitivity, and it is believed that these abnormalities indicate more widespread dysfunction and dysregulation of bottom-up processing. The early visual system, including the first-order Lateral Geniculate Nucleus of the thalamus (LGN) and the primary visual cortex (V1), are key contributors to spatial frequency sensitivity. Medicated and unmedicated patients with schizophrenia exhibit contrasting changes in spatial frequency sensitivity, thus making it a useful probe for examining potential effects of the disorder and antipsychotic medications in neural processing. We constructed a parameterized, rate-based neural model of on-center/off-surround neurons in the early visual system to investigate the impacts of changes to the excitatory and inhibitory receptive field subfields. By incorporating changes in both the excitatory and inhibitory subfields that are associated with pathophysiological findings in schizophrenia, the model successfully replicated perceptual data from behavioral/functional studies involving medicated and unmedicated patients. Among several plausible mechanisms, our results highlight the dampening of excitation and/or increase in the spread and strength of the inhibitory subfield in medicated patients and the contrasting decreased spread and strength of inhibition in unmedicated patients. Given that the model was successful at replicating results from perceptual data under a variety of conditions, these elements of the receptive field may be useful markers for the imbalances seen in patients with schizophrenia.

The eye of artificial intelligence - Convolutional Neural Networks

Inspired by the biological visual system, the convolutional neural network has been widely studied and invented in the field of artificial intelligence. As one of the important algorithms in artificial neural networks, convolutional neural networks have shown outstanding application potential in fields such as image recognition, computer vision, and natural language processing. This article will focus on exploring the powerful capabilities of convolutional neural networks in image processing. By delving into the implementation process of a convolutional neural network, readers will gain a deeper understanding of its working principles. In addition, this article will briefly introduce three classic models of convolutional neural networks, providing readers with more background knowledge. Next, this paper will analyze in detail two typical application cases of convolutional neural networks in the field of image processing: intelligent transportation systems and dental imaging technology. These cases demonstrate the successful application of convolutional neural networks in practical scenarios, pointing the way for their future development. In the future, convolutional neural networks will be more widely used in fields such as image and video processing as data scale increases and computing power improves. By using techniques such as model compression and hardware optimization, it is made more suitable for low-power and high-efficiency environments, and its interpretability and applicability are enhanced through data augmentation and model interpretation techniques.

Visible-light responsive CdS-QDs modified InGaZnO synapse for biologically plausible color-to-gray conversion

The energy-efficient perceptual capabilities of the human visual system have inspired the development of optoelectronic neuromorphic devices to replicate the essential properties. Color vision that is highly important for perception and cognitive behaviors, such as object recognition, esthetics, and communication, has recently been reported based on optoelectronic neuromorphic devices. The responses to different wavelengths of light are mainly dependent on power density, whereas the eye recognizes color images based on luminosity. This Letter presents a quantum dots (QDs) modified InGaZnO (IGZO) synaptic transistor based on an IGZO/CdS-QDs/PMMA heterojunction. These IGZO-based synapses show significant responsivity to 655-nm light through the spectrum broadening by QDs. More importantly, luminance-based color-to-gray conversion can be realized with this device using a spike encoding scheme, transforming color images into grayscale in parallel. Notably, the QDs modified IGZO-based synapses demonstrated a remarkable 34.3% improvement in recognition accuracy on the multicolor MNIST dataset in comparison with the pristine devices. Our results indicate that this visible-light responsive IGZO-based synapse has great potential for endowing the neuromorphic visual system with enhanced color vision and provides a biologically plausible approach for color-to-gray conversion.

Implementation of Integrated Development Environment for Machine Vision-Based IEC 61131-3

IEC 61131-3 is an international standard for developing standardized software for automation and control systems. Machine vision systems are a prominent technology in the field of computer vision and are widely used in various industries, such as manufacturing, robotics, healthcare, and automotive, and are often combined with AI technologies. In industrial automation systems, software developed for defect detection or product classification typically involves separate systems for automation and machine vision programs, leading to increased system complexity and unnecessary resource wastage. To address these limitations, this study proposes an IEC 61131-3-based integrated development environment for programmable machine vision. We selected 11 APIs commonly used in machine vision systems, evaluated their functions in an IEC 61131-3 compliant development environment, and measured the performance of representative machine vision applications. This approach demonstrates the feasibility of developing PLC and machine vision programs within a single-controller system. We investigated the impact of controller performance on function execution.

Interfacial Trap Effect Modulation of Organic Phototransistors for Artificial Vision System

Interfacial Trap Effect Modulation of Organic Phototransistors for Artificial Vision System

Scene‐content‐sensitive real‐time adaptive foveated rendering

AbstractIn recent years, techniques for accelerating rendering by exploiting the limitations of the human visual system have become increasingly prevalent. The foveated rendering method significantly reduces the computational requirements during rendering by reducing image quality in peripheral regions. In this paper, we propose a scene‐content‐sensitive real‐time adaptive foveated rendering method. First, we pre‐render the three‐dimensional (3D) scene at a low resolution. Then, we utilize the low‐resolution pre‐rendered image as input to extract edge, local contrast, and color features. Subsequently, we generate a screen‐space region division map based on the gaze point position. Next, we calculate the visual importance of each 16 × 16 pixel tile based on edge, local contrast, color, and screen‐space region. We then map the visual importance to the shading rate to generate a shading rate control map for the current frame. Finally, we complete the rendering of the current frame based on variable rate shading technology. Experimental results demonstrate that our method effectively enhances the visual quality of images near the foveal region while generating high quality foveal region images. Furthermore, our method can significantly improve performance compared to per‐pixel shading method and existing scene‐content‐based foveated rendering methods.

Filterless Bandpass Photodetectors Enabled by 2D/3D Perovskite Heterojunctions

AbstractBandpass photodetectors have tremendous applications in colorimetric analysis, light communication, imaging and machine vision systems. Compared to broadband photodetectors combined with external optical filters, solution‐processed filterless bandpass photodetectors enjoy high integration density and low cost, but suffer from broadened response edges and insufficient spectral rejection ratios (SRRs). Here, prototype filterless bandpass photodetectors with near square‐shape photoresponse are developed based on (CmH2m+1NH3)2PbI4/MAPbI3 (m = 4–8) and (CmH2m+1NH3)2PbBr4/MAPbBr3 (m = 2–8) perovskite heterojunctions. The strong and sharp excitonic absorption of 2D perovskites defines the ultranarrow response onset widths of 10 ± 1 nm. Besides, the strong photoluminescence (PL) self‐absorption of 2D perovskites suppresses the PL leakage from the front absorber to the back photoactive region, which is crucial to the achieved record high SRR of >2000. By integrating Br‐based 2D/3D perovskite photodetectors with onset edge wavelength discrimination of ≈20 nm, a multiple‐channel optical communication system with low spectral crosstalk is demonstrated.

Human Visual System applied to Facial Recognition

Research in the field of biometrics is constantly expanding. Numerous studies have been carried out to develop different techniques with the aim of ensuring reliable and efficient recognition systems. Of our five senses, vision takes up the most of the neurons in our brain. This makes the visual approach challenging. This paper proposes to develop a new facial recognition technique based on the concept of Human Visual System in order to simulate or imitate human perception. Our approach exploits the behavior of the Human Visual System in biometric systems to improve individual recognition. It focuses on Perceptual Channel Decomposition in order to generate images, confined around a certain frequency range, perfectly uncorrelated. For the extraction of characteristic vectors, our technique uses the Principal Component Analysis. The principle of the visual approach consists of exploiting one or more characteristics of the peripheral parts of the Human Visual System. These characteristics can integrate the sensitivity of the Human Visual System to spatial frequencies, its sensitivity to local contrast. The Principal Component Analysis uses a set of seventeen channels, each adjusted to a band of given radial frequencies and orientation. The seventeen output images contain the same spatial information but are perfectly uncorrelated from a spectral point of view. In the implementation phase, we limited ourselves to explore this technique by using only four frequency rings. The results obtained are conclusive and satisfactory.

Distributed representations of behavior-derived object dimensions in the human visual system.

Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioral goals, suggesting basic limitations of this category-centric framework. To address these limitations, we mapped a series of dimensions derived from a large-scale analysis of human similarity judgments directly onto the brain. Our results reveal broadly distributed representations of behaviorally-relevant information, demonstrating selectivity to a wide variety of novel dimensions while capturing known selectivities for visual features and categories. Behavior-derived dimensions were superior to categories at predicting brain responses, yielding mixed selectivity in much of visual cortex and sparse selectivity in category-selective clusters. This framework reconciles seemingly disparate findings regarding regional specialization, explaining category selectivity as a special case of sparse response profiles among representational dimensions, suggesting a more expansive view on visual processing in the human brain.

Effects of visual neuroplasticity training on visual perception, visual quality, and macular blood flow in patients with strabismus.

To observe the effects of visual neuroplasticity training on visual perception, visual quality, and macular blood flow in patients with concomitant strabismus postoperatively. In total, 108 patients underwent binocular strabismus correction operation, and some patients underwent neuroplasticity training. All patients underwent clinical ophthalmic examination, including measurement of best-corrected visual acuity, spherical equivalent, axis length, optical coherence tomography angiography, optical quality analysis system, and visual perception examinations. A total of 78 patients received neuroplasticity training for 1 month postoperatively, and 30 patients did not receive training. All patients underwent a visual perception examination preoperatively and at 1 day, 1 week, and 1 month postoperatively. Macular blood flow and visual quality were examined preoperatively and at 1 month postoperatively. Postoperative visual perception was better than preoperative visual perception (P < 0.05). After neuroplasticity training, the visual perception of the trained subjects was better than that of the untrained subjects (P < 0.05), and the blood flow in the macular area of the trained patients was lower than that of the untrained subjects (P < 0.05). The visual quality of the untrained subjects was lower than that of the trained patients (P < 0.05). Visual inspection system could accurately evaluate binocular visual perception in patients with concomitant strabismus. After surgical alignment of the strabismus patient, training can stimulate and integrate the formation of stereovision in a short period of time, maintain the visual quality of patients after surgery, and provide conditions for the formation of binocular visual signals and binocular stereovision, but in the short term, it will lead to the decrease of macular blood vessel density and perfusion density. However, the long-term effects of training have not been proven.