Object Discrimination Research Articles

Social Pain Minimization Mediates Discrimination’s Effect on Sleep Health

ABSTRACT Objective Discrimination disrupts sleep and contributes to race-based health inequities for Black Americans, but less is known about the psychological mechanisms underlying this relation. The current work tests whether emotion invalidation, termed Social Pain Minimization (SPM), mediates discrimination’s negative effects on sleep quality. We focus on the experiences of Black Americans because of racism’s disproportionate effect on the health outcomes of Black individuals in the U.S. Methods Four studies with cross-sectional (Studies 1a-1b) and cross-lagged panel designs (Studies 2–3; N total = 1,176) were used. Discrimination and SPM were assessed with established measures and self-reported sleep quality was assessed with three different operationalizations including the short form Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep-Related Impairment (SRI) subscale and composites of the Insomnia Severity Index (ISI). Results Cross-sectional (Studies 1a-1b) and longitudinal mediation analyses (Studies 2–3) indicated discrimination’s negative effect on sleep quality partially operated through SPM (Study 1a, 95% CI = [−.24, −.016]); Study 1b, 95% CI = [−.22, −.03]; Study 2, 95% CI = [−.268, −.007]]; Study 3, 95% CI = [−0.043, −.002]). Conclusion Beyond its direct negative effect on sleep, discrimination also operates through SPM to reduce sleep quality. Emotion invalidation stemming from discrimination damages sleep.

Towards a Taxonomy Machine: A Training Set of 5.6 Million Arthropod Images

The taxonomic identification of organisms from images is an active research area within the machine learning community. Current algorithms are very effective for object recognition and discrimination, but they require extensive training datasets to generate reliable assignments. This study releases 5.6 million images with representatives from 10 arthropod classes and 26 insect orders. All images were taken using a Keyence VHX-7000 Digital Microscope system with an automatic stage to permit high-resolution (4K) microphotography. Providing phenotypic data for 324,000 species derived from 48 countries, this release represents, by far, the largest dataset of standardized arthropod images. As such, this dataset is well suited for testing the efficacy of machine learning algorithms for identifying specimens into higher taxonomic categories.

Application of Visual Transformer in Low-resolution Thermal Infrared Image Recognition

Abstract Addressing the challenges of inadequate accuracy and limited robustness exhibited by current lightweight object detection networks specifically tailored for low-resolution thermal infrared face detection scenarios, this paper delves into developing an ultra-lightweight thermal infrared face detection algorithm that leverages visual attention mechanisms. To ascertain the optimal neural network complexity, a series of comparative experiments are meticulously conducted. With Yolo-FastestDet serving as the benchmark, this study endeavors to compress the backbone network, striking a delicate balance between network depth and detection speed. Additionally, to bolster the network’s capacity for profound feature extraction and precise discrimination of target edges and small objects, a Transformer-Encoder-based visual attention module is seamlessly integrated. Consequently, a lightweight face detection algorithm, enriched with attention mechanisms, is formulated. Furthermore, to mitigate the scarcity of low-resolution infrared face image data, a self-constructed visible-infrared face dataset is employed for training and evaluation purposes. The experimental outcomes reveal that the proposed algorithm attains an impressive mAP@0.5 score of 0.953 on the test dataset while satisfying the stringent real-time detection criterion of 30 frames per second (FPS) when deployed on an embedded Raspberry Pi CPU.

Self-other voice discrimination task: A potential neuropsychological tool for clinical assessment of self-related deficits

BackgroundDeficits in self are commonly described through different neuro-pathologies, based on clinical evaluations and experimental paradigms. However, currently available approaches lack appropriate clinical validation, making objective evaluation and discrimination of self-related deficits challenging. MethodsWe applied a statistical standardized method to assess the clinical discriminatory capacity of a Self-Other Voice Discrimination (SOVD) task. This task, validated experimentally as a marker for self-related deficits, was administered to 17 patients eligible for neurosurgery due to focal hemispheric brain tumors or epileptic lesions. ResultsThe clinical discriminatory capacity of the SOVD task was evident in three patients who exhibited impairments for self-voice perception that could not be predicted by other neuropsychological deficits. Impairments in other-voice perception were linked to inhibitory neuropsychological deficits, suggesting a potential association with executive deficits in voice recognition. ConclusionsThis exploratory study highlights the clinical discriminatory potential of the SOVD task and suggests that it could complement the standard neuropsychological assessment, paving the way for enhanced diagnoses and tailored treatments for self-related deficits.

Multi-aperture telescopes at the quantum limit of superresolution imaging : Detecting subRayleigh object near a star

Angular resolution is a critical aspect of astronomical observation, as it determines the minimum resolvable angle between two objects. This is governed by the Rayleigh criterion, which states that the minimum resolvable angle is proportional to the wavelength (λ) over the diameter (D) of the aperture of a monolithic telescope. While larger aperture telescopes have advantages such as smaller angular resolution and higher sensitivity to dim and small exoplanets, they also have disadvantages such as higher launch cost, design intricacies, and high mission cost. Using multi-aperture telescopes can be a cost-effective alternative as they work on the principles of baseline interferometry, making the minimum resolvable angle proportional to λ/B, where B is the baseline. In this work, we compare the performance of a single monolithic telescope to a multi-aperture alternative with the same effective glass area in the context of hypothesis testing between two scenarios - H1 (a star) and H2 (a star with an exoplanet). We formulate the theory based on likelihood ratio tests and find that the multi-aperture telescope performs better than the monolithic telescope when direct detection is used on the focal plane. While this is expected, the performance can be further improved by using quantum-inspired detecting strategies. We utilize Quantum Binary Spatial Mode Demultiplexing (BSPADE) to process the point spread function (PSF) of the telescopes and find better performance compared to the respective direct detection measurement. Therefore, our efforts can be viewed as one of the initial steps toward employing quantum-inspired detection techniques in sparse aperture configurations for high-contrast imaging applications. In conclusion, our work shows that multi-aperture telescopes are an effective alternative to monolithic telescopes for object discrimination and potentially for super-resolution imaging and their performance can be further improved by using quantum-inspired detection strategies. With their cost-effectiveness (see Appendix C) and potential for high performance, multi-aperture telescopes can significantly advance our ability to observe and study exoplanets and other celestial objects. Future research can explore ways to optimize the performance of multi-aperture telescopes and further improve their capabilities for astronomical observation, potentially facilitating the detection and imaging of Earth-like exoplanets.

Object and spatial discrimination makes weakly supervised local feature better

Local feature extraction plays a crucial role in numerous critical visual tasks. However, there remains room for improvement in both descriptors and keypoints, particularly regarding the discriminative power of descriptors and the localization precision of keypoints. To address these challenges, this study introduces a novel local feature extraction pipeline named OSDFeat (Object and Spatial Discrimination Feature). OSDFeat employs a decoupling strategy, training descriptor and detection networks independently. Inspired by semantic correspondence, we propose an Object and Spatial Discrimination ResUNet (OSD-ResUNet). OSD-ResUNet captures features from the feature map that differentiate object appearance and spatial context, thus enhancing descriptor performance. To further improve the discriminative capability of descriptors, we propose a Discrimination Information Retained Normalization module (DIRN). DIRN complementarily integrates spatial-wise normalization and channel-wise normalization, yielding descriptors that are more distinguishable and informative. In the detection network, we propose a Cross Saliency Pooling module (CSP). CSP employs a cross-shaped kernel to aggregate long-range context in both vertical and horizontal dimensions. By enhancing the saliency of keypoints, CSP enables the detection network to effectively utilize descriptor information and achieve more precise localization of keypoints. Compared to the previous best local feature extraction methods, OSDFeat achieves Mean Matching Accuracy of 79.4% in local feature matching task, improving by 1.9% and achieving state-of-the-art results. Additionally, OSDFeat achieves competitive results in Visual Localization and 3D Reconstruction. The results of this study indicate that object and spatial discrimination can improve the accuracy and robustness of local feature, even in challenging environments. The code is available at https://github.com/pandaandyy/OSDFeat.

Gradient-Based Instance-Specific Visual Explanations for Object Specification and Object Discrimination.

We propose the gradient-weighted Object Detector Activation Maps (ODAM), a visual explanation technique for interpreting the predictions of object detectors. Utilizing the gradients of detector targets flowing into the intermediate feature maps, ODAM produces heat maps that show the influence of regions on the detector's decision for each predicted attribute. Compared to previous works on classification activation maps (CAM), ODAM generates instance-specific explanations rather than class-specific ones. We show that ODAM is applicable to one-stage, two-stage, and transformer-based detectors with different types of detector backbones and heads, and produces higher-quality visual explanations than the state-of-the-art in terms of both effectiveness and efficiency. We discuss two explanation tasks for object detection: 1) object specification: what is the important region for the prediction? 2) object discrimination: which object is detected? Aiming at these two aspects, we present a detailed analysis of the visual explanations of detectors and carry out extensive experiments to validate the effectiveness of the proposed ODAM. Furthermore, we investigate user trust on the explanation maps, how well the visual explanations of object detectors agrees with human explanations, as measured through human eye gaze, and whether this agreement is related with user trust. Finally, we also propose two applications, ODAM-KD and ODAM-NMS, based on these two abilities of ODAM. ODAM-KD utilizes the object specification of ODAM to generate top-down attention for key predictions and instruct the knowledge distillation of object detection. ODAM-NMS considers the location of the model's explanation for each prediction to distinguish the duplicate detected objects. A training scheme, ODAM-Train, is proposed to improve the quality on object discrimination, and help with ODAM-NMS.

Amyloid-β deposition in basal frontotemporal cortex is associated with selective disruption of temporal mnemonic discrimination.

Cerebral amyloid-beta (Aβ) accumulation, a hallmark pathology of Alzheimer's disease (AD), precedes clinical impairment by two to three decades. However, it is unclear whether Aβ contributes to subtle memory deficits observed during the preclinical stage. The heterogenous emergence of Aβ deposition may selectively impact certain memory domains, which rely on distinct underlying neural circuits. In this context, we tested whether specific domains of mnemonic discrimination, a neural computation essential for episodic memory, exhibit specific deficits related to early Aβ deposition. We tested 108 cognitively unimpaired human older adults (66% female) who underwent 18F-florbetapir positron emission tomography (Aβ-PET), and a control group of 35 young adults, on a suite of mnemonic discrimination tasks taxing object, spatial, and temporal domains. We hypothesized that Aβ pathology would be selectively associated with temporal discrimination performance due to Aβ's propensity to accumulate in the basal frontotemporal cortex, which supports temporal processing. Consistent with this hypothesis, we found a dissociation in which generalized age-related deficits were found for object and spatial mnemonic discrimination, while Aβ-PET levels were selectively associated with deficits in temporal mnemonic discrimination. Further, we found that higher Aβ-PET levels in medial orbitofrontal and inferior temporal cortex, regions supporting temporal processing, were associated with greater temporal mnemonic discrimination deficits, pointing to the selective vulnerability of circuits related to temporal processing early in AD progression. These results suggest that Aβ accumulation within basal frontotemporal regions may disrupt temporal mnemonic discrimination in preclinical AD, and may serve as a sensitive behavioral biomarker of emerging AD progression.

Inter-object discriminative graph modeling for indoor scene recognition

Variable scene layouts and coexisting objects across scenes make indoor scene recognition still a challenging task. Leveraging object information within scenes to enhance the distinguishability of feature representations has emerged as a key approach in this domain. Currently, most object-assisted methods use a separate branch to process object information, combining object and scene features heuristically. However, few of them pay attention to interpretably handling the hidden discriminative knowledge within object information. In this paper, we propose to leverage discriminative object knowledge to enhance scene feature representations. Initially, we capture the object-scene discriminative relationships from a probabilistic perspective, which are transformed into an Inter-Object Discriminative Prototype (IODP). Given the abundant prior knowledge from IODP, we subsequently construct a Discriminative Graph Network (DGN), in which pixel-level scene features are defined as nodes and the discriminative relationships between node features are encoded as edges. DGN aims to incorporate inter-object discriminative knowledge into the image representation through graph convolution and mapping operations (GCN). With the proposed IODP and DGN, we obtain state-of-the-art results on several widely used scene datasets, demonstrating the effectiveness of the proposed approach.

Electroacupuncture improves cognitive impairment after ischemic stroke based on regulation of mitochondrial dynamics through SIRT1/PGC-1α pathway

In recent years, the mechanism of acupuncture in the treatment of post-stroke cognitive impairment (PSCI) has not been fully elucidated. The balance between mitochondrial fission and fusion is important for PSCI. Our previous research demonstrated that electroacupuncture can improve learning and memory in middle cerebral artery ischemia reperfusion (MCAO/R) rats. However, the specific mechanism by which electroacupuncture improves learning and memory in MCAO/R rats by regulating mitochondrial fission and fusion needs to be further investigated. The MCAO/R rats was developed using the line-bolt method. The rats were randomly divided into sham-operated (Sham), model (MCAO/R), electroacupuncture (MCAO/R + EA) and sham-electroacupuncture (MCAO/R + sham EA) groups. Investigating the effects of EA on the expression of Sirtuin1 (SIRT1), peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α), Optic atrophy 1R + (OPA1) and Dynamin-related protein 1 (DRP1) in hippocampal neurons and on the morphology and function of hippocampal neurons and mitochondria. EA was able to reduce neurologic deficit scores and cerebral infarct volume and improve new object discrimination in MCAO/R rats, but there were no significant changes in these indices in the sham-electroacupuncture group. Moreover, EA increased the expression of SIRT1, PGC-1α, and OPA1 in hippocampal tissues, inhibited the expression of DRP1, attenuated neuronal and mitochondrial damage, and reduced mitochondrial fragmentation. The mechanism by which EA improves learning memory deficits in MCAO/R rats may be related to the inhibition of SIRT1/PGC-1α expression, the enhancement of mitochondrial fusion and the obstruction of its fission, and the reduction of hippocampal neuronal damage.

다문화주의의 관점에서 본 SF 게임 속 포스트휴먼

Kara, Connor, and Marcus, the central characters of <Detroit: Become Human>, gain ontological status by breaking away from the typical. What is important to note here is how they acquire ontological status. They do not try to 'humanize' themselves in order to attain the same status as 'humans'. For example, while Bicentennial Man(1999) ends with the “humanization of the inhuman,” Detroit establishes its identity through the “inhumanity of the inhuman,” i.e., the “dehumanization of the defective.” For androids, the “defective” is the “inhumanity” of the defective. For the androids, the 'defective' is rather 'human' and therefore acts as a threat to the 'human', which presupposes an ontological difference. Therefore, the moment a human 'defect' takes on 'human-being', the android becomes a substitute for a human being, and the confrontation becomes even worse. However, it does not become 'non-human' as a result of human alienation, but when it becomes 'non-human', it acquires an ontological status as good as 'human' and can only become a 'free being'. The origins of the recognition of the posthuman problem in <Detroit> are rooted in the problem of racial or ethnic discrimination. The 'androids', which are placed as objects of discrimination and hatred, show a pattern of intensifying confrontation as they get closer to 'humans', which is similar to the position of marginalized minorities in mainstream culture. In other words, when a marginalized minority perfectly fulfills the tasks assigned to them by the mainstream culture, they become an intruder that threatens the mainstream culture. <Detroit> offers a response to this situation. While showing the aspects of 'submission' in Kara, 'subordination' in Connor, and 'antagonism' in Marcus, it can be seen that their narratives acquire an ontological status equal to that of 'humans' when they establish their identity as 'defective goods', that is, when they realize 'becoming non-human'. <Detroit> provides a glimpse into how the marginalized are viewed in mainstream culture.

Development of emergent processes and threshold of consciousness with levels of processing.

The transition of experience from unconscious to conscious, the emergent process, is a crucial topic in consciousness studies. Three frameworks exist to explain the process: (1) consciousness arises in an all-or-none manner; (2) consciousness arises gradually; (3) consciousness arises either all at once or gradually, depending on the level of stimulus processing (low- vs. high-level). However, the development of emergent processes of consciousness remains unclear. This study examines the development of emergent processes of consciousness based on the level of stimulus processing framework. Ninety-nine children (5-12 year-olds) and adults participated in two online discrimination tasks. These tasks involved color discrimination as lower-level processing and number magnitude discrimination as higher-level processing, as well as backward masking with stimulus onset asynchronies (SOAs) varying from 16.7 to 266.7 ms. We measured objective discrimination accuracy and used a 4-scale Perceptual Awareness Scale (PAS) to assess subjective awareness. We fit the data to a four-parameter nonlinear function to estimate the center of the slope (threshold) and the range of the slope (gradualness, the measure of emergent process of consciousness) of the model. The results showed the threshold of objective discrimination was significantly higher in 5-6 year-olds than in 7-12 year-olds, but not of subjective awareness. The emergent process of objective discrimination in the number task was more gradual than in the color task. The findings suggest that the thresholds of subjective awareness in 5-6 year-olds and objective discrimination in 7-9 year-olds are similar to those in adults. Moreover, the emergent processes of subjective awareness and objective discrimination in 5-6 year-olds are also similar to those in adults. Our results support the level of processing hypothesis but suggest that its effects may differ across developmental stages.

Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging.

There is no clinical tool available to primary care physicians or dermatologists that could provide objective identification of suspicious skin cancer lesions. Multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy enables label-free biochemical and metabolic imaging of skin lesions. This study investigated the use of pixel-level maFLIM dermoscopy features for objective discrimination of malignant from visually similar benign pigmented skin lesions. Clinical maFLIM dermoscopy images were acquired from 60 pigmented skin lesions before undergoing a biopsy examination. Random forest and deep neural networks classification models were explored, as they do not require explicit feature selection. Feature pools with either spectral intensity or bi-exponential maFLIM features, and a combined feature pool, were independently evaluated with each classification model. A rigorous cross-validation strategy tailored for small-size datasets was adopted to estimate classification performance. Time-resolved bi-exponential autofluorescence features were found to be critical for accurate detection of malignant pigmented skin lesions. The deep neural network model produced the best lesion-level classification, with sensitivity and specificity of 76.84%±12.49% and 78.29%±5.50%, respectively, while the random forest classifier produced sensitivity and specificity of 74.73%±14.66% and 76.83%±9.58%, respectively. Results from this study indicate that machine-learning driven maFLIM dermoscopy has the potential to assist doctors with identifying patients in real need of biopsy examination, thus facilitating early detection while reducing the rate of unnecessary biopsies.

Auditory hemispheric asymmetry for actions and objects.

What is the function of auditory hemispheric asymmetry? We propose that the identification of sound sources relies on the asymmetric processing of two complementary and perceptually relevant acoustic invariants: actions and objects. In a large dataset of environmental sounds, we observed that temporal and spectral modulations display only weak covariation. We then synthesized auditory stimuli by simulating various actions (frictions) occurring on different objects (solid surfaces). Behaviorally, discrimination of actions relies on temporal modulations, while discrimination of objects relies on spectral modulations. Functional magnetic resonance imaging data showed that actions and objects are decoded in the left and right hemispheres, respectively, in bilateral superior temporal and left inferior frontal regions. This asymmetry reflects a generic differential processing-through differential neural sensitivity to temporal and spectral modulations present in environmental sounds-that supports the efficient categorization of actions and objects. These results support an ecologically valid framework of the functional role of auditory brain asymmetry.

"The eyes are the window to the representation": Linking gaze to memory precision and decision weights in object discrimination tasks.

Humans selectively attend to task-relevant information in order to make accurate decisions. However, selective attention incurs consequences if the learning environment changes unexpectedly. This trade-off has been underscored by studies that compare learning behaviors between adults and young children: broad sampling during learning comes with a breadth of information in memory, often allowing children to notice details of the environment that are missed by their more selective adult counterparts. The current work extends the exemplar-similarity account of object discrimination to consider both the intentional and consequential aspects of selective attention when predicting choice. In a novel direct input approach, we used trial-level eye-tracking data from training and test to replace the otherwise freely estimated attention dynamics of the model. We demonstrate that only a model imbued with gaze correlates of memory precision in addition to decision weights can accurately predict key behaviors associated with (a) selective attention to a relevant dimension, (b) distributed attention across dimensions, and (c) flexibly shifting strategies between tasks. Although humans engage in selective attention with the intention of being accurate in the moment, our findings suggest that its consequences on memory constrain the information that is available for making decisions in the future. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.

Weakly supervised object localization (WSOL), which trains object localization models using solely image category annotations, remains a challenging problem. Existing approaches based on convolutional neural networks (CNNs) tend to miss full object extent while activating discriminative object parts. Based on our analysis, this is caused by CNN's intrinsic characteristics, which experiences difficulty to capture object semantics at long distances. In this article, we introduce the vision transformer to WSOL, with the aim to capture long-range semantic dependency of features by leveraging transformer's cascaded self-attention mechanism. We propose the token semantic coupled attention map (TS-CAM) method, which first decomposes class-aware semantics and then couples the semantics with attention maps for semantic-aware activation. To capture object semantics at long distances and avoid partial activation, TS-CAM performs spatial embedding by partitioning an image to a set of patch tokens. To incorporate object category information to patch tokens, TS-CAM reallocates category-related semantics to each patch token. The patch tokens are finally coupled with attention maps which are semantic-agnostic to perform semantic-aware object localization. By introducing semantic tokens to produce semantic-aware attention maps, we further explore the capability of TS-CAM for multicategory object localization. Experiments show that TS-CAM outperforms its CNN-CAM counterpart by 11.6% and 28.9% on ILSVRC and CUB-200-2011 datasets, respectively, improving the state-of-the-art with large margins. TS-CAM also demonstrates superiority for multicategory object localization on the Pascal VOC dataset. The code is available at github.com/yuanyao366/ts-cam-extension.

Helmholtz–Galerkin Technique in Dipole Field Scattering from Buried Zero-Thickness Perfectly Electrically Conducting Disk

Non-invasive concealed object detection, identification, and discrimination have been of interest to the research community for decades due to the needs to preserve infrastructures and artifacts, guarantee safe conditions for the detection and location of landmines, etc. A modern approach is based on the use of an unmanned aerial vehicle equipped with ground-penetrating radar, which has the advantage of not requiring direct contact with the ground. Moreover, high-resolution underground images are obtained by coherently combining measurements by using a synthetic aperture radar algorithm. Due to the complexity of the real scenario, numerical analyses have always been welcomed to provide almost real-time information to make the best use of the potential of such kinds of techniques. This paper proposes an analysis of the scattering from a zero-thickness perfectly electrically conducting disk buried in a lossy half-space surrounded by air and illuminated by a field generated by a Hertzian dipole located in the air. It is carried out by means of a generalized form of the analytically regularizing Helmholtz–Galerkin technique, introduced and successfully applied by the authors to analyze the plane-wave scattering from a disk and a holed plane in a homogeneous medium. As clearly shown in the numerical results, the proposed method is very effective and drastically outperforms the commercial software CST Microwave Studio 2023.

Neuroprotective Effect of Brivaracetam and Perampanel Combination on Electrographic Seizures and Behavior Anomalies in Pentylenetetrazole-Kindled Mice.

Pentylenetetrazole (PTZ)-induced kindling is a broadly used experimental model to study the anticonvulsive potential of new and existing chemical moieties with the aim of discovering drugs hindering seizure progression and associated neurological comorbidities. In the present study, the impact of brivaracetam (BRV) (10 and 20 mg/kg) as monotherapy as well as in combination with 0.25 mg/kg of perampanel (PRP) was investigated on seizure progression with simultaneous electroencephalographic changes in PTZ kindling mouse model. Subsequently, mice were experimentally analyzed for anxiety, cognition, and depression after which their brains were biochemically evaluated for oxidative stress. The outcomes demonstrated that BRV alone delayed the kindling process, but BRV + PRP combination significantly (p < 0.0001) protected the mice from seizures of higher severity and demonstrated an antikindling effect. The PTZ-kindled mice exhibited anxiety, memory impairment, and depression in behavioral tests, which were remarkably less (p < 0.001) in animals treated with drug combination (in a dose-dependent manner) as these mice explored central, illuminated, and exposed zones of open-field test, light/dark box, and elevated plus maze. Moreover, memory impairment was demonstrated by kindled mice, which was significantly (p < 0.001) protected by BRV + PRP as animal's spontaneous alteration, object discrimination, and step-through latencies were increased in various tests employed for the assessment of cognitive abilities. The brains of PTZ-kindled mice had increased malondialdehyde and reduced antioxidant enzymes while treatment with BRV + PRP combination prevented kindling-induced elevation in oxidative markers. The outcomes of this study demonstrate that combining the PRP at low dose augmented the antiseizure properties of BRV as both drugs when administered simultaneously hindered the process of kindling by reducing PTZ-induced excessive electrical activity and oxidative stress in the brain.

A Second-Generation (44-Channel) Suprachoroidal Retinal Prosthesis: A Single-Arm Clinical Trial of Feasibility

To assess the feasibility of a second-generation (44-channel) suprachoroidal retinal prosthesis for provision of functional vision in recipients with end-stage retinitis pigmentosa (RP) over 2.7 years. Prospective, single-arm, unmasked interventional clinical trial. Four participants, with advanced RP and bare-light perception vision. The 44-channel suprachoroidal retinal prosthesis was implanted in the worse-seeing eye. Device stability, functionality, and adverse events were investigated at approximately 12-week intervals up to 140 weeks (2.7 years) postdevice activation. Serious adverse event (SAE) reporting, visual response outcomes, functional vision outcomes, and quality-of-life outcomes. All 4 participants (aged 39-66 years, 3 males) were successfully implanted in 2018, and there were no device-related SAEs over the duration of the study. A mild postoperative subretinal hemorrhage was detected in 2 recipients, which cleared spontaneously within 2weeks. OCT confirmed device stability and position under the macula. Improvements in localization abilities were demonstrated for all 4 participants in screen-based, tabletop, and orientation and mobility tasks. In addition, 3 of 4 participants recorded improvements in motion discrimination and 2 of 4 participants recorded substantial improvements in spatial discrimination and identification of tabletop objects. Participants reported their unsupervised use of the device included exploring new environments, detecting people, and safely navigating around obstacles. A positive effect of the implant on participants' daily lives in their local environments was confirmed by an orientation and mobility assessor and participant self-report. Emotional well-being was not impacted by device implantation or usage. The completed clinical study demonstrates that the suprachoroidal prosthesis raises no safety concerns and provides improvements in functional vision, activities of daily living, and observer-rated quality of life. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Detector size selection for long-range targeting performance in the extended shortwave infrared band

Interest in the eSWIR band is growing due to focal plane array technology advancements with mercury cadmium telluride and type-II superlattice materials. As design and fabrication processes improve, eSWIR detector size, weight, and power can now be optimized. For some applications, it is desirable to have a smaller detector size. Reduced solar illumination in the 2 to 2.5 μm spectral range creates a fundamental limit to passive imaging performance in the eSWIR band where the resolution benefit of small detectors cannot out-compete the reduced SNR in photon-starved environments. This research explores the underlying theory using signal-to-noise ratio radiometry and modeled target discrimination performance to assess the optimal detector size for eSWIR dependent upon illumination conditions. Finally, we model continuous-wave laser illumination in the eSWIR band to compare the effect of detector size on active and passive imaging for long-range object discrimination.