Visual Research Articles

Visual Pathway Recovery Post Pituitary Adenoma Surgery: Insights from Retinal Structure, Vascular Density, and Neural Conduction Analysis.

This study investigates how surgery for pituitary adenoma (PA) affects the visual pathway, examining changes in the retina, blood vessel density, and nerve function. Since PAs often impair vision as a result of their location near visual structures, this research is key to understanding and improving vision recovery after surgery. Our study is based on a retrospective analysis of the historical data of 28 patients diagnosed with pituitary adenomas. We conducted assessments by reviewing preoperative and postoperative imaging records. These included optical coherence tomography (OCT) for retinal structure analysis, diffusion tensor imaging (DTI) for neural transmission evaluation, and optical coherence tomography angiography for assessing blood vessel density. These tools allowed for a detailed understanding of the structural and functional changes within the visual pathway following PA surgery. OCT findings show postoperative changes in the eye: thinning in average and nasal circumpapillary retinal nerve fiber layer, thickening in macular central 1mm inner plexus layer, ganglion cell complex, and nasal retinal nerve fiber layer. DTI reveals increased fractional anisotropy (FA) in the left optic chiasm and posterior optic nerve, decreased mid-segment optic nerve FA, and increased apparent diffusion coefficient (ADC) in the right optic chiasm and nerve segments. Early postoperative reduction in radial peripapillary capillaries plexus density is noted. Preoperative ganglion cell layer (GCL) thickness correlates with postoperative visual radiation FA and ADC values, especially in the inferior quadrant. A negative correlation exists between preoperative GCL thickness and postoperative visual field mean defect values, particularly on the temporal side and superior inner ring. All changes are statistically significant (P < 0.05). The study finds that surgery for PA has varied effects on vision. Early post surgery, there are changes in the retina and nerve signals. Macular GCL thickness before surgery might predict early visual recovery, influencing future research and treatment for vision issues related to PA.

Implicit Selective Attention: The Role of the Mesencephalic-basal Ganglia System.

The ability of the brain to recognize and orient attention to relevant stimuli appearing in the visual field is highlighted by a tuning process, which involves modulating the early visual system by both cortical and subcortical brain areas. Selective attention is coordinated not only by the output of stimulus-based saliency maps but is also influenced by top-down cognitive factors, such as internal states, goals, or previous experiences. The basal ganglia system plays a key role in implicitly modulating the underlying mechanisms of selective attention, favouring the formation and maintenance of implicit sensory-motor memories that are capable of automatically modifying the output of priority maps in sensory-motor structures of the midbrain, such as the superior colliculus. The article presents an overview of the recent literature outlining the crucial contribution of several subcortical structures to the processing of different sources of salient stimuli. In detail, we will focus on how the mesencephalic-basal ganglia closed loops contribute to implicitly addressing and modulating selective attention to prioritized stimuli. We conclude by discussing implicit behavioural responses observed in clinical populations in which awareness is compromised at some level. Implicit (emergent) awareness in clinical conditions that can be accompanied by manifest anosognosic symptomatology (i.e., hemiplegia) or involving abnormal conscious processing of visual information (i.e., unilateral spatial neglect and blind sight) represents interesting neurocognitive "test cases" for inferences about mesencephalic-basal ganglia closed-loops involvement in the formation of implicit sensory-motor memories.

The effect of expertise in music reading: cross-modal competence

We hypothesize that the fundamental difference between expert and learner musicians is the capacity to efficiently integrate cross-modal information. This capacity might be an index of an expert memory using both auditory and visual cues built during many years of learning and extensive practice. Investigating this issue through an eye-tracking experiment, two groups of musicians, experts and non-experts, were required to report whether a fragment of classical music, successively displayed both auditorily and visually on a computer screen (cross-modal presentation) was same or different. An accent mark,associated on a particular note, was located in a congruent or incongruent way according to musical harmony rules, during the auditory and reading phases. The cross-modal competence of experts was demonstrated by shorter fixation durations and less errors. Accent mark appeared for non-experts as interferences and lead to incorrect judgments. Results are discussed in terms of amodal memory for expert musicians that can be supported within the theoretical framework of Long-Term Working Memory (Ericsson and Kintsch, 1995).

Eye-specific detection and a multi-eye integration model of biological motion perception.

'Biological motion' refers to the distinctive kinematics observed in many living organisms, where visually perceivable points on the animal move at fixed distances from each other. Across the animal kingdom, many species have developed specialized visual circuitry to recognize such biological motion and to discriminate it from other patterns. Recently, this ability has been observed in the distributed visual system of jumping spiders. These eight-eyed animals use six eyes to perceive motion, while the remaining two (the principal anterior medial eyes) are shifted across the visual scene to further inspect detected objects. When presented with a biologically moving stimulus and a random one, jumping spiders turn to face the latter, clearly demonstrating the ability to discriminate between them. However, it remains unclear whether the principal eyes are necessary for this behavior, whether all secondary eyes can perform this discrimination, or whether a single eye-pair is specialized for this task. Here, we systematically tested the ability of jumping spiders to discriminate between biological and random visual stimuli by testing each eye-pair alone. Spiders were able to discriminate stimuli only when the anterior lateral eyes were unblocked, and performed at chance levels in other configurations. Interestingly, spiders showed a preference for biological motion over random stimuli - unlike in past work. We therefore propose a new model describing how specialization of the anterior lateral eyes for detecting biological motion contributes to multi-eye integration in this system. This integration generates more complex behavior through the combination of simple, single-eye responses. We posit that this in-built modularity may be a solution to the limited resources of these invertebrates' brains, constituting a novel approach to visual processing.

Blind Quality Assessment of Dense 3D Point Clouds with Structure Guided Resampling

Objective quality assessment of three-dimensional (3D) point clouds is essential for the development of immersive multimedia systems in real-world applications. Despite the success of perceptual quality evaluation for 2D images and videos, blind/no-reference metrics are still scarce for 3D point clouds with large-scale irregularly distributed 3D points. Therefore, in this article, we propose an objective point cloud quality index with Structure Guided Resampling (SGR) to automatically evaluate the perceptually visual quality of dense 3D point clouds. The proposed SGR is a general-purpose blind quality assessment method without the assistance of any reference information. Specifically, considering that the human visual system is highly sensitive to structure information, we first exploit the unique normal vectors of point clouds to execute regional pre-processing that consists of keypoint resampling and local region construction. Then, we extract three groups of quality-related features, including (1) geometry density features, (2) color naturalness features, and (3) angular consistency features. Both the cognitive peculiarities of the human brain and naturalness regularity are involved in the designed quality-aware features that can capture the most vital aspects of distorted 3D point clouds. Extensive experiments on several publicly available subjective point cloud quality databases validate that our proposed SGR can compete with state-of-the-art full-reference, reduced-reference, and no-reference quality assessment algorithms.

Diurnal predators in dim light: the ability of mantids to prey for supper.

Many insects rely heavily on visual cues in foraging and other life activities. Mantids are insect predators that usually ambush prey. The sophisticated visual system of mantids allows them to spot, track, and strike at prey with high accuracy. Mantids are categorized as diurnal animals in most cases, while our field observations suggested that they were active in foraging both day and night. Therefore, we hypothesize that predation in dim light is possible for mantids, while mantids are unable to capture prey in complete darkness. In this study, we experimentally examined whether different light conditions could affect the predation success and efficiency of mantid nymphs and adults, Hierodula chinensis Werner (Mantodea: Mantidae), through behavioral observations. Individual mantids were placed in individual chambers in complete darkness, simulated moonlight (0.1 lux), or simulated dusk (50 lux) conditions and were allowed to forage for prey items for 10 min. Our observations showed no evidence that H. chinensis could capture any prey in complete deprivation of light. The proportion of nymphs with successful predation in simulated moonlight was 50% higher than that in complete darkness and 45.83% lower than that in simulated dusk. The proportion of adults with successful predation in simulated moonlight was 42.11% higher than that in complete darkness and 57.89% lower than that in simulated dusk. Overall, the results provide new insights into the behavioral ecology of diurnal predators at night, with potential association with moonlight, starlight, and light pollution.

A voxel-based magnetic resonance study of cortical gray matter structures in intermittent exotropia

Objective: To explore the changes in gray matter volume of the cerebral cortex in patients with intermittent exotropia (IXT) using the voxel-based analysis and to analyze the correlation between these changes and clinical manifestations. Methods: This was a cross-sectional study. A collection of 15 consecutive patients diagnosed with IXT at Tianjin Eye Hospital from March 2021 to May 2022 formed the exotropia group, which comprised 8 males and 7 females, with an average age of (23.5±5.2) years. Ten healthy individuals, 3 males and 7 females, with an average age of (27.0±7.5) years, were selected as the control group. All participants underwent assessments of exotropia severity and Titmus stereoacuity. Three-dimensional high-resolution brain images were obtained through MRI scans. Voxel-based morphometry was employed to preprocess the MRI data, and the SPM toolbox in MATLAB was utilized to analyze differences of images between the two groups. Regions of interest (ROI) with structural abnormalities in the gray matter volume analysis were selected, and the ratio of gray matter voxel values in the ROI to the mean gray matter voxel values of the whole brain for each participant was calculated using the MarsBaR software. The correlation between this ratio and exotropia severity as well as the common logarithm of Titmus stereoacuity was analyzed. Results: The differences in age, gender distribution, and refractive error between the two groups were not statistically significant (all P>0.05). However, there were statistically significant differences in the degree of strabismus and Titmus stereoacuity (both P<0.001). Compared to the control group, patients in the strabismus group exhibited decreased gray matter volume in several brain regions, including the wedges of the medial surface of the cerebral hemisphere (decreased by 89 voxels), the left lingual gyrus (decreased by 176 voxels), the left calcarine sulcus V3 area (decreased by 30 voxels), the central anterior gyrus of the right frontal lobe (decreased by 192 voxels), the gray matter of the left hippocampal gyrus (decreased by 20 voxels), and the bilateral lateral geniculate nuclei (decreased by 100 and 40 voxels on the left and right sides, respectively). These differences were all statistically significant (all P<0.001). Additionally, there was an increase in gray matter volume in several brain regions, including the bilateral caudate nuclei (increased by 60 and 76 voxels on the left and right sides, respectively) and the left precentral gyrus (increased by 36 voxels). These differences were also statistically significant (all P<0.001). A group-level analysis identified 10 brain regions with structural differences between the two groups, which were used as ROI. The gray matter volume ratio was negatively correlated with the degree of exotropia (all P<0.05) in the ROI of the left wedges (r=-0.670), left calcarine sulcus V3 area (r=-0.610), and left lingual gyrus (r=-0.684). The gray matter volume ratio was negatively correlated with lgTS (all P<0.05) in the ROI of the left wedges (r=-0.568) and the central anterior gyrus of the right frontal lobe (r=-0.563). Conclusions: Patients with IXT exhibit decreased gray matter volume in the horizontal connection areas between the primary visual cortices V1 and V2. The reduction in gray matter volume of the lingual gyrus and the dorsal visual pathway V3 area becomes more pronounced with increasing exotropia severity, while the gray matter volume of the precentral gyrus (BA6 area) decreases with worsening stereoacuity.

Human perception of flicker-fused letters that are luminance balanced.

The Talbot-Plateau law specifies what combinations of flash frequency, duration, and intensity will yield a flicker-fused stimulus that matches the brightness of a steady stimulus. It has proven to be remarkably robust in its predictions, and here we provide additional support though the use of a contrast discrimination task. However, we also find that the visual system can register flicker-fused letters when the combination of frequency and duration is relatively low. The letters are recognized even though they have the same physical luminance as background. We hypothesize that the letters elicit synchronous oscillations that encode for stimulus attributes, which prevents the letter from blending into the background.

Imaging ultra-weak UV light below 100 pW cm-2 using a 4H-SiC photodetector with an Al2O3 interfacial layer.

Weak light detection is crucial in various practical applications such as night vision systems, flame monitoring, and underwater operations. Decreasing the dark current of a photodetector can effectively mitigate noises, consequently enhancing the signal-to-noise ratio and overall weak light detection performance. Herein, we demonstrate a 4H-SiC UV photodetector capable of detecting extremely weak UV light. This device comprises a photosensitive layer of 4H-SiC, two TiN electrodes and an atomically thin Al2O3 interfacial layer between TiN and the C surface of 4H-SiC. Under 360 nm UV light illumination, the proposed Al2O3 device demonstrates an ultra-low dark current of 18 fA, possibly benefiting from the effective passivation of interfacial carriers, and a boosted photo-to-dark current ratio of 6.7 × 107. Consequently, it achieves a weak-light detection limit as low as 31.8 pW cm-2, significantly outperforming the control device lacking Al2O3. When compared to previously reported SiC photodetectors, our Al2O3 device boasts an exceptional large linear dynamic range of 172 dB. Leveraging this, we construct a photodetector array capable of clearly imaging an object under ultra-weak light illumination below the 100 pW cm-2 level. The proposed photodetector represents a significant advancement in the development of highly sensitive image sensors for weak light detection.

An Overview of Optic Pathway Glioma With Neurofibromatosis Type 1: Pathogenesis, Risk Factors, and Therapeutic Strategies.

Optic pathway gliomas (OPGs) are most predominant pilocytic astrocytomas, which are typically diagnosed within the first decade of life. The majority of affected children with OPGs also present with neurofibromatosis type 1 (NF1), the most common tumor predisposition syndrome. OPGs in individuals with NF1 primarily affect the optic pathway and lead to visual disturbance. However, it is challenging to assess risk in asymptomatic patients without valid biomarkers. On the other hand, for symptomatic patients, there is still no effective treatment to prevent or recover vision loss. Therefore, this review summarizes current knowledge regarding the pathogenesis of NF1-associated OPGs (NF1-OPGs) from preclinical studies to seek potential prognostic markers and therapeutic targets. First, the loss of the NF1 gene activates 3 distinct Ras effector pathways, including the PI3K/AKT/mTOR pathway, the MEK/ERK pathway, and the cAMP pathway, which mediate glioma tumorigenesis. Meanwhile, non-neoplastic cells from the tumor microenvironment (microglia, T cells, neurons, etc.) also contribute to gliomagenesis via various soluble factors. Subsequently, we investigated potential genetic risk factors, molecularly targeted therapies, and neuroprotective strategies for tumor prevention and vision recovery. Last, potential directions and promising preclinical models of NF1-OPGs are presented for further research. On the whole, NF1-OPGs develop as a result of the interaction between glioma cells and the tumor microenvironment. Developing effective treatments require a better understanding of tumor molecular characteristics, as well as multistage interventions targeting both neoplastic cells and non-neoplastic cells.

Visual Eosin Y-Based Photosensitization Sensing Systems for Ultrasensitive Detection of Diclofenac with Single-Atom Co─N2O2 Site-Immobilized g-C3N4 Nanosheets.

It is highly desired to develop a visual sensing system for ultrasensitive detection of colorless diclofenac (DCF), yet with a significant challenge. Herein, a novel dye-based photosensitization sensing system has been successfully developed for detecting DCF for the first time, in which the used dye eosin Y (DeY) can strongly absorb visible light and then be decolorized obviously by transferring photogenerated electrons to g-C3N4 nanosheets (CN), while the built single-atomic Co─N2O2 sites on CN by boron-oxygen connection can competitively adsorb DCF to impede the photosensitization decoloration of DeY. This system exhibits a broad detection range from 8ngL-1 to 2 mgL-1 with 535nm light, an exceptionally low detection limit (3.5ngL-1), and remarkable selectivity. Through the time-resolved, in situ technologies, and theoretical calculations, the decolorization of DeY is attributed to the disruption of DeY's conjugated structure caused by the triplet excited state electron transfer from DeY to CN, meanwhile, the adsorbed oxygen facilitates the charge transfer process. The preferential adsorption of DCF mainly depends on the strong interactions between the as-constructed single-atom Co and Cl in DCF. This study opens an innovative light-driven sensing system by combining dye and single-atom metal/nanomaterial for visually intuitive detection of environmental pollutants.

Dual-Adaptive Heterojunction Synaptic Transistors for Efficient Machine Vision in Harsh Lighting Conditions.

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes.

The large-scale organization of shape processing in the ventral and dorsal pathways is dissociable from attention.

The two visual pathways model posits that visual information is processed through two distinct cortical systems: The ventral pathway promotes visual recognition, while the dorsal pathway supports visuomotor control. Recent evidence suggests the dorsal pathway is also involved in shape processing and may contribute to object perception, but it remains unclear whether this sensitivity is independent of attentional mechanisms that were localized to overlapping cortical regions. To address this question, we conducted two fMRI experiments that utilized different parametric scrambling manipulations in which human participants viewed novel objects in different levels of scrambling and were instructed to attend to either the object or to another aspect of the image (e.g. color of the background). Univariate and multivariate analyses revealed that the large-scale organization of shape selectivity along the dorsal and ventral pathways was preserved regardless of the focus of attention. Attention did modulate shape sensitivity, but these effects were similar across the two pathways. These findings support the idea that shape processing is at least partially dissociable from attentional processes and relies on a distributed set of cortical regions across the visual pathways.

Sound of the Police—Virtual Reality Training for Police Communication for High-Stress Operations

Police communication is a field with unique challenges and specific requirements. Police officers depend on effective communication, particularly in high-stress operations, but current training methods are not focused on communication and provide only limited evaluation methods. This work explores the potential of virtual reality (VR) for enhancing police communication training. The rise of VR training, especially in specific application areas like policing, provides benefits. We conducted a field study during police training to assess VR approaches for training communication. The results show that VR is suitable for communication training if factors such as realism, reflection and repetition are given in the VR system. Trainer feedback shows that assistive systems for evaluation and visualization of communication are highly needed. We present ideas and approaches for evaluation in communication training and concepts for visualization and exploration of the data. This research contributes to improving VR police training and has implications for communication training in VR in challenging contexts.

Utilizing Deep Learning for Defect Inspection in Hand Tool Assembly.

The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product's normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%.

Development of a Subjective Visual Vertical Test System Using a Smartphone With Virtual Reality Goggles for Screening of Otolithic Dysfunction: Observational Study.

The subjective visual vertical (SVV) test can evaluate otolith function and spatial awareness and is performed in dedicated vertigo centers using specialized equipment; however, it is not otherwise widely used because of the specific equipment and space requirements. An SVV test smartphone app was developed to easily perform assessments in outpatient facilities. This study aimed to verify whether the SVV test smartphone app with commercially available virtual reality goggles can be used in a clinical setting. The reference range was calculated for 15 healthy participants. We included 14 adult patients with unilateral vestibular neuritis, sudden sensorineural hearing loss with vertigo, and Meniere disease and investigated the correlation between the SVV test results and vestibular evoked myogenic potential (VEMP) results. The SVV reference range of healthy participants for the sitting front-facing position was small, ranging from -2.6º to 2.3º. Among the 14 patients, 6 (43%) exceeded the reference range for healthy participants. The SVV of patients with vestibular neuritis and sudden sensorineural hearing loss tended to deviate to the affected side. A total of 9 (64%) had abnormal cervical VEMP (cVEMP) values and 6 (43%) had abnormal ocular VEMP (oVEMP) values. No significant difference was found between the presence or absence of abnormal SVV values and the presence or absence of abnormal cVEMP and oVEMP values; however, the odds ratios (ORs) suggested a higher likelihood of abnormal SVV values among those with abnormal cVEMP and oVEMP responses (OR 2.40, 95% CI 0.18-32.88; P>.99; and OR 2, 95% CI 0.90-4.45; P=.46, respectively). The SVV app can be used anywhere and in a short period while reducing directional bias by using virtual reality goggles, thus making it highly versatile and useful as a practical otolith dysfunction screening tool.

Leveraging murine models of the neurofibromatosis type 1 cancer predisposition syndrome to elucidate the cellular circuits that drive pediatric low-grade glioma formation and progression.

Brain tumors are the leading cause of cancer-related death in children, where low-grade gliomas (LGGs) predominate. One common hereditary cause for LGGs involves neurofibromatosis-1 (NF1) gene mutation, as seen in individuals with the NF1 cancer predisposition syndrome. As such, children with NF1 are at increased risk of developing LGGs of the optic pathway, brainstem, cerebellum, and midline brain structures. Using genetically engineered mouse models, studies have revealed both cell-intrinsic (MEK signaling) and stromal dependencies that underlie their formation and growth. Importantly, these dependencies represent vulnerabilities against which targeted agents can be used for preclinical investigation prior to clinical translation.

Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms

Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs.

Peripuberty is a sensitive period for prefrontal parvalbumin interneuron activity to impact adult cognitive flexibility.

Developmental windows in which experiences can elicit long-lasting effects on brain circuitry and behavior are called 'sensitive periods' and reflect a state of heightened plasticity. The classic example of a sensitive period comes from studies of sensory systems, like the visual system, where early visual experience is required for normal wiring of primary visual cortex and proper visual functioning. At a mechanistic level, loss of incoming visual input results in a decrease in activity in thalamocortical neurons representing the affected eye, resulting in an activity-dependent reduction in the representation of those inputs in the visual cortex and loss of visual perception in that eye. While associative cortical regions like the medial prefrontal cortex (mPFC) do not receive direct sensory input, recent findings demonstrate that changes in activity levels experienced by this region during defined windows in early development may also result in long-lasting changes in prefrontal cortical circuitry, network function and behavior. For example, we recently demonstrated that decreasing the activity of mPFC parvalbumin-expressing (PV) interneurons during a period of time encompassing peripuberty (postnatal day P14) to adolescence (P50) led to a long-lasting decrease in their functional inhibition of pyramidal cells, as well as impairments in cognitive flexibility. While the effects of manipulating mPFC PV interneuron activity were selective to development, and not adulthood, the exact timing of the sensitive period for this manipulation remains unknown. To refine the sensitive period in which inhibiting mPFC PV cell activity can lead to persist effects on prefrontal functioning we used a chemogenetic approach to restrict our inhibition of mPFC PV activity to two distinct windows: 1) peripuberty (P14-P32) and 2) early adolescence (P33-P50). We then investigated adult behavior after P90. In parallel, we performed histological analysis of molecular markers associated with sensitive period onset and offset in visual cortex, to define the onset and offset of peak sensitive period plasticity in the mPFC. We found that inhibition of mPFC PV interneurons in peripuberty (P14-P32), but not adolescence (P33-P50), led to an impairment in set shifting behavior in adulthood manifest as an increase in trials to reach criterion performance and errors. Consistent with a pubertal onset of sensitive period plasticity in the PFC, we found histological markers of sensitive period onset and offset also demarcated P14 and P35, respectively. The time course of expression of these markers was similar in visual cortex. Both lines of research converge on the peripubertal period (P14-32) as one of heightened sensitive period plasticity in the mPFC. Further, our direct comparison of markers of sensitive period plasticity across the prefrontal and visual cortex suggests a similar time course of expression, challenging the notion that sensitive periods occur hierarchically. Together, these findings extend our knowledge about the nature and timing of sensitive period plasticity in the developing mPFC.

Fabric4show: real-time vision system for fabric defect detection and post-processing

The exploration of computer vision applications for fabric defect detection has immense potential value. However, current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency, while neglecting the practical industrial production requirements. Therefore, we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network (CNN) features (i.e., Faster R-CNN) for defect detection, defect localization and detection model evaluation. In addition, the proposed intelligent system incorporates novel approaches, such as a rearranged fabric dataset, anomaly detection, recommended clipping region division, and a replenishment device. This study illustrates an example of artificial intelligence (AI)-driven automated technology in fabric manufacturing. The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work. Experimental results demonstrate that the proposed approach achieves comparable performance to other models, while significantly reducing computational resource requirements. The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted, which is likely to have relevant implications for the textile industry.