Open-angle glaucoma and Fuchs dystrophy.

A 62-year-old woman with a history of moderate myopia, long-standing open-angle glaucoma (OAG), and Fuchs dystrophy in both eyes was referred for consultative care. She had prior trabeculectomy in 1984 and 1992 in the left and right eyes, respectively. She is 3 months post-Descemet-stripping endothelial keratoplasty (DSEK) in the left eye, now referred with uncontrolled intraocular pressure (IOP) despite maximum tolerated medical therapy. Current medical therapy for IOP consists of acetazolamide 250 mg by mouth 2 times a day, brimonidine 2 times a day in the left eye, dorzolamide 2 times a day in the left eye, and timolol 2 times a day in the left eye. The patient has a history of presumed steroid response; however, her corneal surgeon has requested that the steroid be continued for the next several months because of the recent DSEK. The IOP in the left eye has ranged from the mid-20s to mid-30s since DSEK. The right eye has consistently had pressure in the low teens and below for many years without topical antihypertensive medications. Examination revealed stable visual acuity at 20/30 and 20/40 in the right and left eyes, respectively, IOP was 12 mm Hg in the right eye and 25 mm Hg in the left eye by Goldman applanation, irregular but reactive pupils without afferent defect, and full confrontational visual fields. Slitlamp examination showed superior low avascular bleb, moderate-to-severe guttae, and posterior chamber IOL in the right eye. The left eye showed superior low diffuse bleb, clear DSEK graft, quiet chamber, superonasal iridectomy, and posterior chamber IOL with an open posterior capsule. The conjunctiva was moderately scarred but a repeat trabeculectomy or Xen Gel stent (Abbvie) appeared possible. The angles were wide open in each eye. Fundus examination was normal aside from myopic, anomalous-appearing nerves with an approximate cup-to-disc ratio of 0.90 in both eyes. Humphrey visual field showed nonspecific changes on the right and moderate nasal defect on the left eye, stable to previous examinations dating back to 2018 (Figure 1JOURNAL/jcrs/04.03/02158034-202407000-00018/figure1/v/2024-07-10T174240Z/r/image-tiff and Figure 2JOURNAL/jcrs/04.03/02158034-202407000-00018/figure2/v/2024-07-10T174240Z/r/image-tiff). Optical coherence tomography (OCT) of the retinal nerve fiber layer (RNFL) revealed moderated thinning in both eyes that was also stable to prior examinations (Figure 3JOURNAL/jcrs/04.03/02158034-202407000-00018/figure3/v/2024-07-10T174240Z/r/image-tiff). Her axial length measured 25.23 and 26.34 mm in the right and left eyes, respectively. Central corneal thickness was 553 µm in the right eye and 563 µm in the left eye before her DSEK procedure. What would be your approach to management of this patient's left eye, addressing the following: Rationale for your procedure of choice? Would you over-rule the corneal surgeon and stop the steroid in an attempt to obviate the need for glaucoma surgery? Does the age of onset of glaucoma affect your surgical decision making? Note that patient age at the time of trabeculectomy was 22 years. Are some procedures better suited for patients after DSEK surgery?

Social signals predict contemporary art prices better than visual features, particularly in emerging markets.

What determines the price of an artwork? This article leverages a comprehensive and novel dataset on art auctions of contemporary artists to examine the impact of social and visual features on the valuation of artworks across global markets. Our findings indicate that social signals allow us to predict the price of artwork exceptionally well, even approaching the professionals' prediction accuracy, while the visual features play a marginal role. This pattern is especially pronounced in emerging markets, supporting the idea that social signals become more critical when it is more difficult to assess the quality. These results strongly support that the value of artwork is largely shaped by social factors, particularly in emerging markets where a stronger preference for "buying an artist" than "buying an artwork." Additionally, our study shows that it is possible to boost experts' performance, highlighting the potential benefits of human-machine models in uncertain or rapidly changing markets, where expert knowledge is limited.

Asymmetric Convolution: An Efficient and Generalized Method to Fuse Feature Maps in Multiple Vision Tasks.

Fusing features from different sources is a critical aspect of many computer vision tasks. Existing approaches can be roughly categorized as parameter-free or learnable operations. However, parameter-free modules are limited in their ability to benefit from offline learning, leading to poor performance in some challenging situations. Learnable fusing methods are often space-consuming and timeconsuming, particularly when fusing features with different shapes. To address these shortcomings, we conducted an in-depth analysis of the limitations associated with both fusion methods. Based on our findings, we propose a generalized module named Asymmetric Convolution Module (ACM). This module can learn to encode effective priors during offline training and efficiently fuse feature maps with different shapes in specific tasks. Specifically, we propose a mathematically equivalent method for replacing costly convolutions on concatenated features. This method can be widely applied to fuse feature maps across different shapes. Furthermore, distinguished from parameter-free operations that can only fuse two features of the same type, our ACM is general, flexible, and can fuse multiple features of different types. To demonstrate the generality and efficiency of ACM, we integrate it into several state-of-the-art models on three representative vision tasks: visual object tracking, referring video object segmentation, and monocular 3D object detection. Extensive experimental results on three tasks and several datasets demonstrate that our new module can bring significant improvements and noteworthy efficiency.

Severe Spontaneous Tilt of Scleral-Fixated Intraocular Lenses.

PURPOSE: To report and evaluate a multicenter series of 18 cases of severe, spontaneous IOL tilt involving the flanged intrascleral haptic fixation technique (FISHF). DESIGN: Clinical study with historical controls. METHODS: We report a cross-sectional study of 46 FISHF cases using the CT Lucia 602 IOL at a single academic center over a period of 24 weeks to determine the incidence of severe rotisserie-style rotational tilt. These rates were then compared with the same time-frame the prior year to help determine if this is a new phenomenon. Additional cases of severe tilt were solicited from another 4 academic centers. RESULTS: Among 46 FISHF cases at a single center, 5 developed severe tilt. No clear pattern in surgical technique, ocular history, or ocular anatomy was evident in these cases compared with controls, although the involved IOLs clustered within a narrow diopter range, indicative of a batch effect. In the same 24-week interval the year before, 33 FISHF cases were performed, none of which exhibited severe rotational tilt. In our multicenter dataset, 18 cases of tilt were identified. Surgeons included fellow and early-career physicians as well as surgeons with multiple years of experience with the Yamane technique. A variety of surgical approaches for FISHF were represented. In at least 8 of the cases, haptic rotation and/or dehiscence at the optic-haptic junction were documented. CONCLUSIONS: The identification of haptic rotation and dehiscence intraoperatively in several cases may reflect a new stability issue involving the optic-haptic junction.

Ulotaront, a Trace Amine-Associated Receptor 1/Serotonin 5-HT1A Agonist, in Patients With Parkinson Disease Psychosis: A Pilot Study.

Background and Objectives: Ulotaront (SEP-363856) is a trace amine-associated receptor 1 agonist with 5-HT1A receptor agonist activity currently in phase 3 clinical development for the treatment of schizophrenia. In this exploratory, flexibly dosed study, ulotaront was evaluated for the treatment of Parkinson disease psychosis (PDP). Methods: Patients with PDP requiring antipsychotic therapy were randomized, double-blind to ulotaront (25, 50, or 75 mg/d) or placebo. Mixed Model for Repeated Measures was used to assess change from baseline in the Scale for the Assessment of Positive Symptoms for Parkinson Disease (SAPS-PD) at 6 weeks (primary end point). Results: The efficacy analysis sample comprised 38 patients (ulotaront, n = 24; placebo, n = 14). SAPS-PD total scores were numerically reduced in ulotaront-treated vs placebo-treated patients from week 1 to week 6: Least squares mean (95% confidence interval) difference in change from baseline at week 6 was -1.1 (-6.5, 4.3, p = 0.681). PDP symptom complete remission (≥100% improvement [reduction] from baseline in SAPS-PD total score) was observed in 25% of ulotaront-treated vs 0% of placebo-treated patients. SAPS-PD and Neuropsychiatric Inventory hallucinations subscales were numerically reduced vs placebo, and SAPS-PD total scores were reduced in patients with greater cognitive impairment (baseline Mini-Mental State Examination [MMSE] scores ≤24). Ulotaront improved Scales for Outcomes in Parkinson Disease Sleep Scale - Daytime Sleepiness scores (p = 0.022). There was no worsening of Unified Parkinson Disease Rating Scale Part III motor score, MMSE, or vital signs. Adverse events (≥10%) with ulotaront vs placebo included hallucinations (24% vs 14%), confusional state (20% vs 14%), dizziness (16% vs 7%), nausea (12% vs 7%), and falls (12% vs 21%). Discussion: In this exploratory pilot study, ulotaront may decrease PDP symptoms without worsening motor function, particularly in patients with cognitive impairment. Trial Registration Information: ClinicalTrials.gov identifier: NCT02969369; submitted: November 17, 2016; study start date: December 31, 2016. Classification of Evidence: This Class II study was an exploratory pilot study that was underpowered to detect a statistically significant difference between ulotaront and placebo in the treatment of patients with Parkinson disease psychosis without worsening motor function.

Surgical decisions in the setting of zonulopathy.

An 85-year-old man with a history of type 2 diabetes, pseudoexfoliation (PXF) in both eyes, and tamsulosin use was referred for the evaluation of a dense cataract in the right eye and a subluxated intraocular lens (IOL) in the left eye. Unfortunately, his surgery in the left eye was complicated by diffuse zonulopathy. The referring surgeon placed a 3-piece IOL in the sulcus. However, the passively fixated 3-piece IOL moved inferiorly causing monocular diplopia for over a year. Because the patient was pleased with the IOL immediately postoperatively, a refixation procedure was performed in the form of sulcus placement with iris suture fixation in the left eye. Fortunately, the iris-fixated IOL in the left eye has remained well centered and stable without cystoid macular edema (CME) or chronic inflammation for over 8 months. The patient is on no ocular medications and has no family history of glaucoma. He now needs cataract surgery in the right eye and is extremely apprehensive because of his difficult course in the left eye. The corrected distance visual acuity is 20/70 in the right eye and 20/25 in the left eye. Intraocular pressures (IOPs) measure 20 mm Hg in the right eye and 14 mm Hg in the left eye by Goldmann tonometry. Pachymetry is 536 µm in the right eye and 543 µm in the left eye. Pupils are round with minimal reactivity and without a relative afferent pupillary defect. Extraocular motility is normal in both eyes, and confrontation visual fields is full in both eyes. Gonioscopy reveals an angle open to the pigmented trabecular meshwork (PTM) in the right eye and the ciliary body in the left eye with 1+ PTM and without peripheral anterior synechia in both eyes. The retinal nerve fiber layer and macular optical coherence tomography are normal in both eyes. On slitlamp examination, pertinent findings include pseudoexfoliative changes at the pupillary margin with poor dilation of 3.5 mm in both eyes; the anterior chamber (AC) is shallow but adequate in the right eye and deep and quiet with rare pigmented cells in the left eye. There is a 5+ nuclear sclerotic cataract with pseudoexfoliative changes on the anterior capsule and no obvious phacodonesis in the right eye and a 3-piece posterior chamber IOL in the sulcus fixated to the iris with 10-0 polypropylene sutures at 6 and 12 o'clock without pseudophacodonesis in the left eye. Dilated fundus examination reveals a cup-to-disc ratio of 0.4 with healthy neuroretinal rims in both eyes, posterior vitreous detachments in both eyes, and no evidence of diabetic retinopathy in both eyes. All other findings are unremarkable. How would you counsel this patient regarding his risk factors for surgery in the right eye? What surgical maneuvers would you use to remove the cataract safely? How would you stabilize the IOL if the capsule bag becomes compromised due to zonulopathy?

A Survey on Deep Learning Technique for Video Segmentation.

Video segmentation-partitioning video frames into multiple segments or objects-plays a critical role in a broad range of practical applications, from enhancing visual effects in movie, to understanding scenes in autonomous driving, to creating virtual background in video conferencing. Recently, with the renaissance of connectionism in computer vision, there has been an influx of deep learning based approaches for video segmentation that have delivered compelling performance. In this survey, we comprehensively review two basic lines of research - generic object segmentation (of unknown categories) in videos, and video semantic segmentation - by introducing their respective task settings, background concepts, perceived need, development history, and main challenges. We also offer a detailed overview of representative literature on both methods and datasets. We further benchmark the reviewed methods on several well-known datasets. Finally, we point out open issues in this field, and suggest opportunities for further research. We also provide a public website to continuously track developments in this fast advancing field: https://github.com/tfzhou/VS-Survey.

DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos.

Video anomaly detection (VAD) has been extensively studied for static cameras but is much more challenging in egocentric driving videos where the scenes are extremely dynamic. This paper proposes an unsupervised method for traffic VAD based on future object localization. The idea is to predict future locations of traffic participants over a short horizon, and then monitor the accuracy and consistency of these predictions as evidence of an anomaly. Inconsistent predictions tend to indicate an anomaly has occurred or is about to occur. To evaluate our method, we introduce a new large-scale benchmark dataset called Detection of Traffic Anomaly (DoTA)containing 4,677 videos with temporal, spatial, and categorical annotations. We also propose a new VAD evaluation metric, called spatial-temporal area under curve (STAUC), and show that it captures how well a model detects both temporal and spatial locations of anomalies unlike existing metrics that focus only on temporal localization. Experimental results show our method outperforms state-of-the-art methods on DoTA in terms of both metrics. We offer rich categorical annotations in DoTA to benchmark video action detection and online action detection methods. The DoTA dataset has been made available at: https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly.

Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection From Point Clouds.

Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i.e., point cloud videos. We empirically categorize the temporal information into short-term and long-term patterns. To encode the short-term data, we present a Grid Message Passing Network (GMPNet), which considers each grid (i.e., the grouped points) as a node and constructs a k-NN graph with the neighbor grids. To update features for a grid, GMPNet iteratively collects information from its neighbors, thus mining the motion cues in grids from nearby frames. To further aggregate long-term frames, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU), which contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module. STA and TTA enhance the vanilla GRU to focus on small objects and better align moving objects. Our overall framework supports both online and offline video object detection in point clouds. We implement our algorithm based on prevalent anchor-based and anchor-free detectors. Evaluation results on the challenging nuScenes benchmark show superior performance of our method, achieving first on the leaderboard (at the time of paper submission) without any "bells and whistles." Our source code is available at https://github.com/shenjianbing/GMP3D.

Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition.

One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes. To alleviate this problem, off-the-shelf image generation techniques based on Generative Adversarial Networks (GANs) can potentially create additional training images. However, these GAN-generated images are often not helpful for actually improving the accuracy of one-shot fine-grained recognition. In this paper, we proposes a meta-learning framework to combine generated images with original images, so that the resulting hybrid training images can improve one-shot learning. Specifically, the generic image generator is updated by a few training instances of novel classes, and a Meta Image Reinforcing Network (MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as image reinforcement. Our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks. Furthermore, our analysis shows that the reinforced images have more diversity compared to the original and GAN-generated images.

Segmenting Objects From Relational Visual Data.

In this article, we model a set of pixelwise object segmentation tasks - automatic video segmentation (AVS), image co-segmentation (ICS) and few-shot semantic segmentation (FSS) - in a unified view of segmenting objects from relational visual data. To this end, we propose an attentive graph neural network (AGNN) that addresses these tasks in a holistic fashion, by formulating them as a process of iterative information fusion over data graphs. It builds a fully-connected graph to efficiently represent visual data as nodes and relations between data instances as edges. The underlying relations are described by a differentiable attention mechanism, which thoroughly examines fine-grained semantic similarities between all the possible location pairs in two data instances. Through parametric message passing, AGNN is able to capture knowledge from the relational visual data, enabling more accurate object discovery and segmentation. Experiments show that AGNN can automatically highlight primary foreground objects from video sequences (i.e., automatic video segmentation), and extract common objects from noisy collections of semantically related images (i.e., image co-segmentation). AGNN can even generalize segment new categories with little annotated data (i.e., few-shot semantic segmentation). Taken together, our results demonstrate that AGNN provides a powerful tool that is applicable to a wide range of pixel-wise object pattern understanding tasks with relational visual data. Our algorithm implementations have been made publicly available at https://github.com/carrierlxk/AGNN.

Zero-Shot Video Object Segmentation With Co-Attention Siamese Networks.

We introduce a novel network, called CO-attention siamese network (COSNet), to address the zero-shot video object segmentation task in a holistic fashion. We exploit the inherent correlation among video frames and incorporate a global co-attention mechanism to further improve the state-of-the-art deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in short-term temporal segments. The co-attention layers in COSNet provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending co-attention responses into a joint feature space. COSNet is a unified and end-to-end trainable framework where different co-attention variants can be derived for capturing diverse properties of the learned joint feature space. We train COSNet with pairs (or groups) of video frames, and this naturally augments training data and allows increased learning capacity. During the segmentation stage, the co-attention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. Our extensive experiments over three large benchmarks demonstrate that COSNet outperforms the current alternatives by a large margin. Our implementations are available at https://github.com/carrierlxk/COSNet.

Plasmonic Anticounterfeit Tags with High Encoding Capacity Rapidly Authenticated with Deep Machine Learning.

Counterfeit goods create significant economic losses and product failures in many industries. Here, we report a covert anticounterfeit platform where plasmonic nanoparticles (NPs) create physically unclonable functions (PUFs) with high encoding capacity. By allowing anisotropic Au NPs of different sizes to deposit randomly, a diversity of surfaces can be facilely tagged with NP deposits that serve as PUFs and are analyzed using optical microscopy. High encoding capacity is engineered into the tags by the sizes of the Au NPs, which provide a range of color responses, while their anisotropy provides sensitivity to light polarization. An estimated encoding capacity of 270n is achieved, which is one of the highest reported to date. Authentication of the tags with deep machine learning allows for high accuracy and rapid matching of a tag to a specific product. Moreover, the tags contain descriptive metadata that is leveraged to match a tag to a specific lot number (i.e., a collection of tags created in the same manner from the same formulation of anisotropic Au NPs). Overall, integration of designer plasmonic NPs with deep machine learning methods can create a rapidly authenticated anticounterfeit platform with high encoding capacity.

Learning the generative principles of a symbol system from limited examples.

The processes and mechanisms of human learning are central to inquiries in a number of fields including psychology, cognitive science, development, education, and artificial intelligence. Arguments, debates, and controversies linger over the questions of human learning with one of the most contentious being whether simple associative processes could explain human children's prodigious learning, and in doing so, could lead to artificial intelligence that parallels human learning. One phenomenon at the center of these debates concerns a form of far generalization, sometimes referred to as "generative learning", because the learner's behavior seems to reflect more than co-occurrences among specifically experienced instances and to be based on principles through which new instances may be generated. In two experimental studies (N = 148) of preschool children's learning of how multi-digit number names map to their written forms and in a computational modeling experiment using a deep learning neural network, we show that data sets with a suite of inter-correlated imperfect predictive components yield far and systematic generalizations that accord with generative principles and do so despite limited examples and exceptions in the training data. Implications for human cognition, cognitive development, education, and machine learning are discussed.

A Case of Uveitis-Hyphema-Glaucoma Syndrome Due to EX-PRESS Glaucoma Filtration Device Implantation.

PURPOSE: To report a case of a 69-year-old patient who developed uveitis-glaucoma-hyphema syndrome (UGH) after an uneventful EX-PRESS mini shunt surgery for advanced primary open-angle glaucoma and to discuss management options and clinical implications. UGH syndrome is a rare, but serious complication usually described after cataract surgery. It is often described in anterior chamber intraocular lenses, sulcus lenses, and malpositioned or subluxed lenses resulting in chafing of the lens-iris interface. Clinical manifestations include increased intraocular pressure, anterior chamber inflammation, and recurrent hyphema. PATIENT AND METHODS: We report a case of a 69-year-old African American man who developed UGH syndrome 8 years after uneventful implantation of a P-50 EX-PRESS miniature shunt. Slit-lamp examination demonstrated persistent inflammation without evidence of iris atrophy nor intraocular lens dislocation; however, gonioscopy demonstrated localized iris atrophy under the shunt with surrounding iris billowing and a layered hyphema. RESULTS: A localized laser iridoplasty around the shunt was performed leading to resolution of uveitis and hyphema. No other complications occurred during follow-up. CONCLUSIONS: Given the increasing acceptance of glaucoma procedures involving implants, UGH syndrome may become more prevalent as new sources of intraocular devices may cause potential complications. Laser iridoplasty provides a minimally invasive approach to treating a localized source of chafing and reduce further surgical intervention.

Ciliochoroidal effusion syndrome with central serous-like chorioretinopathy and secondary angle closure following exogenous testosterone use.

PURPOSE: To report a unique presentation of ciliochoroidal effusion syndrome with central serous-like chorioretinopathy and secondary angle closure following exogenous testosterone use. OBSERVATIONS: A 37 year-old man presented with a two week history of blurred vision, elevated intraocular pressure, and myopic shift in his right eye. Gonioscopy showed angle closure. After YAG iridotomy, ultrasound biomicroscopy (UBM) showed ciliochoroidal effusion and anterior rotation of the ciliary processes. Subsequent color fundus photography, enhanced depth imaging optical coherence tomography (EDI-OCT) and near-infrared reduced-illuminance autofluorescence imaging (NIR-RAFI) showed macular striae, choroidal folds, and increased choroidal thickness without presence of subretinal fluid (SRF) or pigment epithelial detachment (PED). Further questioning revealed the patient was using dermal testosterone gel for six months for treatment of hypogonadism. The patient stopped using the testosterone gel, and his visual acuity and intraocular pressure significantly improved six weeks later. Follow-up UBM showed significant improvement of the ciliochoroidal effusion, and repeat multimodal images demonstrated resolution of the macular striae and choroidal folds, and slightly improved choroidal thickness. CONCLUSIONS AND IMPORTANCE: Our patient demonstrates a rare case of ciliochoroidal effusion, central serous-like chorioretinopathy, and secondary angle closure that dramatically improved with cessation of testosterone. We believe that this unique clinical constellation is the first to be reported associated with exogenous testosterone use.

How do infants start learning object names in a sea of clutter?

Infants are powerful learners. A large corpus of experimental paradigms demonstrate that infants readily learn distributional cues of name-object co-occurrences. But infants' natural learning environment is cluttered: every heard word has multiple competing referents in view. Here we ask how infants start learning name-object co-occurrences in naturalistic learning environments that are cluttered and where there is much visual ambiguity. The framework presented in this paper integrates a naturalistic behavioral study and an application of a machine learning model. Our behavioral findings suggest that in order to start learning object names, infants and their parents consistently select a set of a few objects to play with during a set amount of time. What emerges is a frequency distribution of a few toys that approximates a Zipfian frequency distribution of objects for learning. We find that a machine learning model trained with a Zipf-like distribution of these object images outperformed the model trained with a uniform distribution. Overall, these findings suggest that to overcome referential ambiguity in clutter, infants may be selecting just a few toys allowing them to learn many distributional cues about a few name-object pairs.

A View of Their Own: Capturing the Egocentric View of Infants and Toddlers with Head-Mounted Cameras.

Infants and toddlers view the world, at a basic sensory level, in a fundamentally different way from their parents. This is largely due to biological constraints: infants possess different body proportions than their parents and the ability to control their own head movements is less developed. Such constraints limit the visual input available. This protocol aims to provide guiding principles for researchers using head-mounted cameras to understand the changing visual input experienced by the developing infant. Successful use of this protocol will allow researchers to design and execute studies of the developing child's visual environment set in the home or laboratory. From this method, researchers can compile an aggregate view of all the possible items in a child's field of view. This method does not directly measure exactly what the child is looking at. By combining this approach with machine learning, computer vision algorithms, and hand-coding, researchers can produce a high-density dataset to illustrate the changing visual ecology of the developing infant.