Search

One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin. [ABSTRACT FROM AUTHOR]

Can people react to objects in their visual field that they do not consciously perceive? We investigated how visual perception and motor action respond to moving objects whose visibility is reduced, and we found a dissociation between motion processing for perception and for action. We compared motion perception and eye movements evoked by two orthogonally drifting gratings, each presented separately to a different eye. The strength of each monocular grating was manipulated by inducing adaptation to one grating prior to the presentation of both gratings. Reflexive eye movements tracked the vector average of both gratings (pattern motion) even though perceptual responses followed one motion direction exclusively (component motion). Observers almost never perceived pattern motion. This dissociation implies the existence of visual-motion signals that guide eye movements in the absence of a corresponding conscious percept. [ABSTRACT FROM PUBLISHER]

Abstract: Inspection time (IT) is the most popular simple psychometric measure that is used to account for a large part of the variance in human mental ability, with the estimated corrected correlation between IT and IQ being −0.50. In this study, we investigate the relationship between IT and the performance and oculomotor variables measured during three simple visual tasks. Participants’ ITs were first measured using a slight variation of the standard IT task, which was followed by the three simple visual tasks that were designed to test participants’ visual–attentional control and visual working memory under varying degrees of difficulty; they included a visual search task, a comparative visual search task, and a visual memorization task. Significant correlations were found between IT and performance variables for each of the visual tasks. The implications of the correlation between IT and performance-related variables are discussed. Oculomotor variables on the other hand only correlated significantly with IT during the retrieval phase of the visual memorization task, which is likely a product of differences in participants’ ability to memorize objects during the loading phase of the experiment. This leads us to the conclusion that the oculomotor variables we measured do not correlate with IT in general, but may in the case where a systematic benefit would be realized. [Copyright &y& Elsevier]

Abstract: Visual search is a fundamental and routine task of everyday life. Studying visual search promises to shed light on the basic attentional mechanisms that facilitate visual processing. To investigate visual attention during search processes, numerous studies measured the selectivity of observers’ saccadic eye movements for local display features. These experiments almost entirely relied on simple, artificial displays with discrete search items and features. The present study employed complex search displays and targets to examine task-driven (top-down) visual guidance by low-level features under more natural conditions. Significant guidance by local intensity, contrast, spatial frequency, and orientation was found, and its properties such as magnitude and resolution were analyzed across dimensions. Moreover, feature-ratio effects were detected, which correspond to distractor-ratio effects in simple search displays. These results point out the limitations of current purely stimulus-driven (bottom-up) models of attention during scene perception. [Copyright &y& Elsevier]

The Area Activation Model (Pomplun, Reingold, Shen, & Williams, 2000) is a computational model predicting the statistical distribution of saccadic endpoints in visual search tasks. Its basic assumption is that saccades in visual search tend to foveate display areas that provide a maximum amount of task-relevant information for processing during the subsequent fixation. In the present study, a counterintuitive prediction by the model is empirically tested, namely that saccadic selectivity towards stimulus features depends on the spatial arrangement of search items. We find good correspondence between simulated and empirically observed selectivity patterns, providing strong support for the Area Activation Model. [Copyright &y& Elsevier]

Discusses the comparative visual search as an experimental paradigm for investigating the underlying complex processes of perception and cognition. Modes of visual processing which correspond to consecutive stages; Role of perceptual grouping in visual search; Components of visual perception involved in a standard visual search task.

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain. We analyzed interictal iEEG data from 1928 contacts recorded from 20 children with drug-resistant epilepsy who became seizure-free after neurosurgery. We localized each iEEG contact in the MRI, created TF images (1–70 Hz) for each contact, and used a pre-trained VGG16 network to measure their visual complexity by extracting unsupervised activation energy (UAE) from 13 convolutional layers. We identified points of interest in the brain using the UAE values via patient- and layer-specific thresholds (based on extreme value distribution) and using a support vector machine classifier. Results show that contacts inside the seizure onset zone exhibit lower UAE than outside, with larger differences in deep layers (L10, L12, and L13: p < 0.001). Furthermore, the points of interest identified using the support vector machine, localized the EZ with 7 mm accuracy. In conclusion, we presented a pre-surgical computerized tool that facilitates the EZ localization in the patient's MRI without requiring long-term iEEG inspection. [ABSTRACT FROM AUTHOR]

The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images. The HCNN classification technique consists of the Ridgelet transform, feature computations, classifier module, and segmentation algorithm. Pixel stability during the decomposition process was improved by the Ridgelet transform, and the features were computed from the coefficient of the Ridgelet. These features were classified using the HCNN classification approach, and tumor pixels were detected using the segmentation algorithm. The experimental results were analyzed for meningioma tumor images by applying the proposed method to the BRATS 2019 and Nanfang dataset. The proposed HCNN-based meningioma detection system achieved 99.31% sensitivity, 99.37% specificity, and 99.24% segmentation accuracy for the BRATS 2019 dataset. The proposed HCNN technique achieved99.35% sensitivity, 99.22% specificity, and 99.04% segmentation accuracy on brain Magnetic Resonance Imaging (MRI) in the Nanfang dataset. The proposed system obtains 99.81% classification accuracy, 99.2% sensitivity, 99.7% specificity and 99.8% segmentation accuracy on BRATS 2022 dataset. The experimental results of the proposed HCNN algorithm were compared with those of the state-of-the-art meningioma detection algorithms in this study. [ABSTRACT FROM AUTHOR]

The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images. The HCNN classification technique consists of the Ridgelet transform, feature computations, classifier module, and segmentation algorithm. Pixel stability during the decomposition process was improved by the Ridgelet transform, and the features were computed from the coefficient of the Ridgelet. These features were classified using the HCNN classification approach, and tumor pixels were detected using the segmentation algorithm. The experimental results were analyzed for meningioma tumor images by applying the proposed method to the BRATS 2019 and Nanfang dataset. The proposed HCNN-based meningioma detection system achieved 99.31% sensitivity, 99.37% specificity, and 99.24% segmentation accuracy for the BRATS 2019 dataset. The proposed HCNN technique achieved99.35% sensitivity, 99.22% specificity, and 99.04% segmentation accuracy on brain Magnetic Resonance Imaging (MRI) in the Nanfang dataset. The proposed system obtains 99.81% classification accuracy, 99.2% sensitivity, 99.7% specificity and 99.8% segmentation accuracy on BRATS 2022 dataset. The experimental results of the proposed HCNN algorithm were compared with those of the state-of-the-art meningioma detection algorithms in this study. [ABSTRACT FROM AUTHOR]

Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%. [ABSTRACT FROM AUTHOR]

A previous study (Vision Research 51 (2011) 1192–1205) found evidence for semantic guidance of visual attention during the inspection of real-world scenes, i.e., an influence of semantic relationships among scene objects on overt shifts of attention. In particular, the results revealed an observer bias toward gaze transitions between semantically similar objects. However, this effect is not necessarily indicative of semantic processing of individual objects but may be mediated by knowledge of the scene gist, which does not require object recognition, or by known spatial dependency among objects. To examine the mechanisms underlying semantic guidance, in the present study, participants were asked to view a series of displays with the scene gist excluded and spatial dependency varied. Our results show that spatial dependency among objects seems to be sufficient to induce semantic guidance. Scene gist, on the other hand, does not seem to affect how observers use semantic information to guide attention while viewing natural scenes. Extracting semantic information mainly based on spatial dependency may be an efficient strategy of the visual system that only adds little cognitive load to the viewing task. [ABSTRACT FROM AUTHOR]

Highlights: [•] Focus of peri-saccadic mislocalization follows salient stimuli, not necessarily the saccade goal. [•] Attracting effect of salient stimuli is not diminished by visible background. [•] Absence of salient stimulus including saccade target results in less compression. [•] A strong visual reference can influence perceived location of peri-saccadic flashes. [ABSTRACT FROM AUTHOR]

Abstract: The perception of objects in our visual world is influenced by not only their low-level visual features such as shape and color, but also their high-level features such as meaning and semantic relations among them. While it has been shown that low-level features in real-world scenes guide eye movements during scene inspection and search, the influence of semantic similarity among scene objects on eye movements in such situations has not been investigated. Here we study guidance of eye movements by semantic similarity among objects during real-world scene inspection and search. By selecting scenes from the LabelMe object-annotated image database and applying latent semantic analysis (LSA) to the object labels, we generated semantic saliency maps of real-world scenes based on the semantic similarity of scene objects to the currently fixated object or the search target. An ROC analysis of these maps as predictors of subjects’ gaze transitions between objects during scene inspection revealed a preference for transitions to objects that were semantically similar to the currently inspected one. Furthermore, during the course of a scene search, subjects’ eye movements were progressively guided toward objects that were semantically similar to the search target. These findings demonstrate substantial semantic guidance of eye movements in real-world scenes and show its importance for understanding real-world attentional control. [Copyright &y& Elsevier]

E-readers are fast rivaling print as a dominant method for reading. Because they offer accessibility options that are impossible in print, they are potentially beneficial for those with impairments, such as dyslexia. Yet, little is known about how the use of these devices influences reading in those who struggle. Here, we observe reading comprehension and speed in 103 high school students with dyslexia. Reading on paper was compared with reading on a small handheld e-reader device, formatted to display few words per line. We found that use of the device significantly improved speed and comprehension, when compared with traditional presentations on paper for specific subsets of these individuals: Those who struggled most with phoneme decoding or efficient sight word reading read more rapidly using the device, and those with limited VA Spans gained in comprehension. Prior eye tracking studies demonstrated that short lines facilitate reading in dyslexia, suggesting that it is the use of short lines (and not the device per se) that leads to the observed benefits. We propose that these findings may be understood as a consequence of visual attention deficits, in some with dyslexia, that make it difficult to allocate attention to uncrowded text near fixation, as the gaze advances during reading. Short lines ameliorate this by guiding attention to the uncrowded span. [ABSTRACT FROM AUTHOR]

People with dyslexia, who ordinarily struggle to read, sometimes remark that reading is easier when e-readers are used. Here, we used eye tracking to observe high school students with dyslexia as they read using these devices. Among the factors investigated, we found that reading using a small device resulted in substantial benefits, improving reading speeds by 27%, reducing the number of fixations by 11%, and importantly, reducing the number of regressive saccades by more than a factor of 2, with no cost to comprehension. Given that an expected trade-off between horizontal and vertical regression was not observed when line lengths were altered, we speculate that these effects occur because sluggish attention spreads perception to the left as the gaze shifts during reading. Short lines eliminate crowded text to the left, reducing regression. The effects of attention modulation by the hand, and of increased letter spacing to reduce crowding, were also found to modulate the oculomotor dynamics in reading, but whether these factors resulted in benefits or costs depended on characteristics, such as visual attention span, that varied within our sample. [ABSTRACT FROM AUTHOR]

People with dyslexia, who face lifelong struggles with reading, exhibit numerous associated low-level sensory deficits including deficits in focal attention. Countering this, studies have shown that struggling readers outperform typical readers in some visual tasks that integrate distributed information across an expanse. Though such abilities would be expected to facilitate scene memory, prior investigations using the contextual cueing paradigm failed to find corresponding advantages in dyslexia. We suggest that these studies were confounded by task-dependent effects exaggerating known focal attention deficits in dyslexia, and that, if natural scenes were used as the context, advantages would emerge. Here, we investigate this hypothesis by comparing college students with histories of severe lifelong reading difficulties (SR) and typical readers (TR) in contexts that vary attention load. We find no differences in contextual-cueing when spatial contexts are letter-like objects, or when contexts are natural scenes. However, the SR group significantly outperforms the TR group when contexts are lowpass filtered natural scenes [F(3, 39) = 3.15, p<.05]. These findings suggest that perception or memory for low spatial frequency components in scenes is enhanced in dyslexia. These findings are important because they suggest strengths for spatial learning in a population otherwise impaired, carrying implications for the education and support of students who face challenges in school. [ABSTRACT FROM AUTHOR]

Color coding is used to guide attention in computer displays for such critical tasks as baggage screening or air traffic control. It has been shown that a display object attracts more attention if its color is more similar to the color for which one is searching. However, what does similar precisely mean? Can we predict the amount of attention that a display color will receive during a search for a given target color? To tackle this question, two color-search experiments measuring the selectivity of saccadic eye movements and mapping out its underlying color space were conducted. A variety of mathematical models, predicting saccadic selectivity for given target and display colors, were devised and evaluated. The results suggest that applying a Gaussian function to a weighted Euclidean distance in a slightly modified HSI color space is the best predictor of saccadic selectivity in the chosen paradigm. Hue and intensity information by itself provides a basis for useful predictors, spanning a possibly spherical color space of saccadic selectivity. Although the current models cannot predict saccadic selectivity values for a wide variety of visual search tasks, they reveal some characteristics of color search that are of both theoretical and applied interest, such as for the design of human–computer interfaces. [ABSTRACT FROM AUTHOR]

The reported research extends classic findings that after briefly viewing structured, but not random, chess positions, chess masters reproduce these positions much more accurately than less-skilled players. Using a combination of the gaze-contingent window paradigm and the change blindness flicker paradigm, we documented dramatically larger visual spans for experts while processing structured, but not random, chess positions. In addition, in a check-detection task, a minimized 3 × 3 chessboard containing a King and potentially checking pieces was displayed. In this task, experts made fewer fixations per trial than less-skilled players, and had a greater proportion of fixations between individual pieces, rather than on pieces. Our results provide strong evidence for a perceptual encoding advantage for experts attributable to chess experience, rather than to a general perceptual or memory superiority. [ABSTRACT FROM AUTHOR]