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1.
Proc Natl Acad Sci U S A ; 117(26): 15200-15208, 2020 06 30.
Artigo em Inglês | MEDLINE | ID: mdl-32527855

RESUMO

Do dopaminergic reward structures represent the expected utility of information similarly to a reward? Optimal experimental design models from Bayesian decision theory and statistics have proposed a theoretical framework for quantifying the expected value of information that might result from a query. In particular, this formulation quantifies the value of information before the answer to that query is known, in situations where payoffs are unknown and the goal is purely epistemic: That is, to increase knowledge about the state of the world. Whether and how such a theoretical quantity is represented in the brain is unknown. Here we use an event-related functional MRI (fMRI) task design to disentangle information expectation, information revelation and categorization outcome anticipation, and response-contingent reward processing in a visual probabilistic categorization task. We identify a neural signature corresponding to the expectation of information, involving the left lateral ventral striatum. Moreover, we show a temporal dissociation in the activation of different reward-related regions, including the nucleus accumbens, medial prefrontal cortex, and orbitofrontal cortex, during information expectation versus reward-related processing.


Assuntos
Antecipação Psicológica/fisiologia , Motivação/fisiologia , Recompensa , Estriado Ventral/fisiologia , Adulto , Humanos , Imagem por Ressonância Magnética , Masculino , Estriado Ventral/diagnóstico por imagem , Adulto Jovem
2.
Sci Rep ; 10(1): 4724, 2020 Mar 10.
Artigo em Inglês | MEDLINE | ID: mdl-32152329

RESUMO

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

3.
J Am Chem Soc ; 142(9): 4114-4120, 2020 03 04.
Artigo em Inglês | MEDLINE | ID: mdl-32045230

RESUMO

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

4.
J Nat Prod ; 83(3): 617-625, 2020 03 27.
Artigo em Inglês | MEDLINE | ID: mdl-31916778

RESUMO

A thiazole-containing cyclic depsipeptide with 11 amino acid residues, named pagoamide A (1), was isolated from laboratory cultures of a marine Chlorophyte, Derbesia sp. This green algal sample was collected from America Samoa, and pagoamide A was isolated using guidance by MS/MS-based molecular networking. Cultures were grown in a light- and temperature-controlled environment and harvested after several months of growth. The planar structure of pagoamide A (1) was characterized by detailed 1D and 2D NMR experiments along with MS and UV analysis. The absolute configurations of its amino acid residues were determined by advanced Marfey's analysis following chemical hydrolysis and hydrazinolysis reactions. Two of the residues in pagoamide A (1), phenylalanine and serine, each occurred twice in the molecule, once in the d- and once in the l-configuration. The biosynthetic origin of pagoamide A (1) was considered in light of other natural products investigations with coenocytic green algae.

5.
IEEE Trans Cybern ; 50(12): 4908-4920, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-30990205

RESUMO

Time series with missing values (incomplete time series) are ubiquitous in real life on account of noise or malfunctioning sensors. Time-series imputation (replacing missing data) remains a challenge due to the potential for nonlinear dependence on concurrent and previous values of the time series. In this paper, we propose a novel framework for modeling incomplete time series, called a linear memory vector recurrent neural network (LIME-RNN), a recurrent neural network (RNN) with a learned linear combination of previous history states. The technique bears some similarity to residual networks and graph-based temporal dependency imputation. In particular, we introduce a linear memory vector [called the residual sum vector (RSV)] that integrates over previous hidden states of the RNN, and is used to fill in missing values. A new loss function is developed to train our model with time series in the presence of missing values in an end-to-end way. Our framework can handle imputation of both missing-at-random and consecutive missing inputs. Moreover, when conducting time-series prediction with missing values, LIME-RNN allows imputation and prediction simultaneously. We demonstrate the efficacy of the model via extensive experimental evaluation on univariate and multivariate time series, achieving state-of-the-art performance on synthetic and real-world data. The statistical results show that our model is significantly better than most existing time-series univariate or multivariate imputation methods.

6.
Neural Netw ; 117: 225-239, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31176962

RESUMO

Echo state networks (ESNs) are randomly connected recurrent neural networks (RNNs) that can be used as a temporal kernel for modeling time series data, and have been successfully applied on time series prediction tasks. Recently, ESNs have been applied to time series classification (TSC) tasks. However, previous ESN-based classifiers involve either training the model by predicting the next item of a sequence, or predicting the class label at each time step. The former is essentially a predictive model adapted from time series prediction work, rather than a model designed specifically for the classification task. The latter approach only considers local patterns at each time step and then averages over the classifications. Hence, rather than selecting the most discriminating sections of the time series, this approach will incorporate non-discriminative information into the classification, reducing accuracy. In this paper, we propose a novel end-to-end framework called the Echo Memory Network (EMN) in which the time series dynamics and multi-scale discriminative features are efficiently learned from an unrolled echo memory using multi-scale convolution and max-over-time pooling. First, the time series data are projected into the high dimensional nonlinear space of the reservoir and the echo states are collected into the echo memory matrix, followed by a single multi-scale convolutional layer to extract multi-scale features from the echo memory matrix. Max-over-time pooling is used to maintain temporal invariance and select the most important local patterns. Finally, a fully-connected hidden layer feeds into a softmax layer for classification. This architecture is applied to both time series classification and human action recognition datasets. For the human action recognition datasets, we divide the action data into five different components of the human body, and propose two spatial information fusion strategies to integrate the spatial information over them. With one training-free recurrent layer and only one layer of convolution, the EMN is a very efficient end-to-end model, and ranks first in overall classification ability on 55 TSC benchmark datasets and four 3D skeleton-based human action recognition tasks.


Assuntos
Redes Neurais de Computação , Humanos , Tempo
7.
Sci Rep ; 7(1): 14243, 2017 10 27.
Artigo em Inglês | MEDLINE | ID: mdl-29079836

RESUMO

Various algorithms comparing 2D NMR spectra have been explored for their ability to dereplicate natural products as well as determine molecular structures. However, spectroscopic artefacts, solvent effects, and the interactive effect of functional group(s) on chemical shifts combine to hinder their effectiveness. Here, we leveraged Non-Uniform Sampling (NUS) 2D NMR techniques and deep Convolutional Neural Networks (CNNs) to create a tool, SMART, that can assist in natural products discovery efforts. First, an NUS heteronuclear single quantum coherence (HSQC) NMR pulse sequence was adapted to a state-of-the-art nuclear magnetic resonance (NMR) instrument, and data reconstruction methods were optimized, and second, a deep CNN with contrastive loss was trained on a database containing over 2,054 HSQC spectra as the training set. To demonstrate the utility of SMART, several newly isolated compounds were automatically located with their known analogues in the embedded clustering space, thereby streamlining the discovery pipeline for new natural products.


Assuntos
Produtos Biológicos/química , Análise de Dados , Espectroscopia de Ressonância Magnética/métodos , Redes Neurais de Computação , Cianobactérias/química , Peptídeo Sintases/química
8.
J Vis ; 17(4): 9, 2017 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-28437797

RESUMO

What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is more efficient for scene recognition than peripheral, based on the amount of visual area needed for accurate recognition. In this study, we model and explain the results of Larson and Loschky (2009) using a neurocomputational modeling approach. We show that the advantage of peripheral vision in scene recognition, as well as the efficiency advantage for central vision, can be replicated using state-of-the-art deep neural network models. In addition, we propose and provide support for the hypothesis that the peripheral advantage comes from the inherent usefulness of peripheral features. This result is consistent with data presented by Thibaut, Tran, Szaffarczyk, and Boucart (2014), who showed that patients with central vision loss can still categorize natural scenes efficiently. Furthermore, by using a deep mixture-of-experts model ("The Deep Model," or TDM) that receives central and peripheral visual information on separate channels simultaneously, we show that the peripheral advantage emerges naturally in the learning process: When trained to categorize scenes, the model weights the peripheral pathway more than the central pathway. As we have seen in our previous modeling work, learning creates a transform that spreads different scene categories into different regions in representational space. Finally, we visualize the features for the two pathways, and find that different preferences for scene categories emerge for the two pathways during the training process.


Assuntos
Redes Neurais de Computação , Reconhecimento Visual de Modelos/fisiologia , Percepção Visual/fisiologia , Humanos , Aprendizagem , Estimulação Luminosa/métodos
9.
Vision Res ; 108: 67-76, 2015 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-25641371

RESUMO

Since Yarbus's seminal work, vision scientists have argued that our eye movement patterns differ depending upon our task. This has recently motivated the creation of multi-fixation pattern analysis algorithms that try to infer a person's task (or mental state) from their eye movements alone. Here, we introduce new algorithms for multi-fixation pattern analysis, and we use them to argue that people have scanpath routines for judging faces. We tested our methods on the eye movements of subjects as they made six distinct judgments about faces. We found that our algorithms could detect whether a participant is trying to distinguish angriness, happiness, trustworthiness, tiredness, attractiveness, or age. However, our algorithms were more accurate at inferring a subject's task when only trained on data from that subject than when trained on data gathered from other subjects, and we were able to infer the identity of our subjects using the same algorithms. These results suggest that (1) individuals have scanpath routines for judging faces, and that (2) these are diagnostic of that subject, but that (3) at least for the tasks we used, subjects do not converge on the same "ideal" scanpath pattern. Whether universal scanpath patterns exist for a task, we suggest, depends on the task's constraints and the level of expertise of the subject.


Assuntos
Atenção/fisiologia , Movimentos Oculares/fisiologia , Face , Reconhecimento Facial/fisiologia , Adolescente , Adulto , Algoritmos , Expressão Facial , Feminino , Fixação Ocular/fisiologia , Humanos , Julgamento/fisiologia , Masculino , Reconhecimento Visual de Modelos/fisiologia , Estimulação Luminosa/métodos , Tempo de Reação , Reconhecimento Psicológico/fisiologia , Adulto Jovem
10.
Cereb Cortex ; 25(9): 3144-58, 2015 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-24862848

RESUMO

Previous functional magnetic resonance imaging (fMRI) research on action observation has emphasized the role of putative mirror neuron areas such as Broca's area, ventral premotor cortex, and the inferior parietal lobule. However, recent evidence suggests action observation involves many distributed cortical regions, including dorsal premotor and superior parietal cortex. How these different regions relate to traditional mirror neuron areas, and whether traditional mirror neuron areas play a special role in action representation, is unclear. Here we use multi-voxel pattern analysis (MVPA) to show that action representations, including observation, imagery, and execution of reaching movements: (1) are distributed across both dorsal (superior) and ventral (inferior) premotor and parietal areas; (2) can be decoded from areas that are jointly activated by observation, execution, and imagery of reaching movements, even in cases of equal-amplitude blood oxygen level-dependent (BOLD) responses; and (3) can be equally accurately classified from either posterior parietal or frontal (premotor and inferior frontal) regions. These results challenge the presumed dominance of traditional mirror neuron areas such as Broca's area in action observation and action representation more generally. Unlike traditional univariate fMRI analyses, MVPA was able to discriminate between imagined and observed movements from previously indistinguishable BOLD activations in commonly activated regions, suggesting finer-grained distributed patterns of activation.


Assuntos
Mapeamento Encefálico , Função Executiva/fisiologia , Imaginação/fisiologia , Movimento/fisiologia , Lobo Parietal/fisiologia , Córtex Pré-Frontal/fisiologia , Humanos , Processamento de Imagem Assistida por Computador , Imagem por Ressonância Magnética , Rede Nervosa/irrigação sanguínea , Rede Nervosa/fisiologia , Observação , Oxigênio/sangue , Lobo Parietal/irrigação sanguínea , Córtex Pré-Frontal/irrigação sanguínea , Desempenho Psicomotor
11.
J Cogn Neurosci ; 25(11): 1777-93, 2013 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-23859648

RESUMO

We trained a neurocomputational model on six categories of photographic images that were used in a previous fMRI study of object and face processing. Multivariate pattern analyses of the activations elicited in the object-encoding layer of the model yielded results consistent with two previous, contradictory fMRI studies. Findings from one of the studies [Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425-2430, 2001] were interpreted as evidence for the object-form topography model. Findings from the other study [Spiridon, M., & Kanwisher, N. How distributed is visual category information in human occipito-temporal cortex? An fMRI study. Neuron, 35, 1157-1165, 2002] were interpreted as evidence for neural processing mechanisms in the fusiform face area that are specialized for faces. Because the model contains no special processing mechanism or specialized architecture for faces and yet it can reproduce the fMRI findings used to support the claim that there are specialized face-processing neurons, we argue that these fMRI results do not actually support that claim. Results from our neurocomputational model therefore constitute a cautionary tale for the interpretation of fMRI data.


Assuntos
Face , Imagem por Ressonância Magnética/métodos , Percepção Visual/fisiologia , Algoritmos , Inteligência Artificial , Mapeamento Encefálico , Simulação por Computador , Humanos , Processamento de Imagem Assistida por Computador , Modelos Neurológicos , Redes Neurais de Computação , Estimulação Luminosa , Análise de Componente Principal , Reprodutibilidade dos Testes , Córtex Visual/fisiologia
12.
J Cogn Neurosci ; 25(7): 998-1007, 2013 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-23448523

RESUMO

Hemispheric asymmetry in the processing of local and global features has been argued to originate from differences in frequency filtering in the two hemispheres, with little neurophysiological support. Here we test the hypothesis that this asymmetry takes place at an encoding stage beyond the sensory level, due to asymmetries in anatomical connections within each hemisphere. We use two simple encoding networks with differential connection structures as models of differential encoding in the two hemispheres based on a hypothesized generalization of neuroanatomical evidence from the auditory modality to the visual modality: The connection structure between columns is more distal in the language areas of the left hemisphere and more local in the homotopic regions in the right hemisphere. We show that both processing differences and differential frequency filtering can arise naturally in this neurocomputational model with neuroanatomically inspired differences in connection structures within the two model hemispheres, suggesting that hemispheric asymmetry in the processing of local and global features may be due to hemispheric asymmetry in connection structure rather than in frequency tuning.


Assuntos
Lateralidade Funcional/fisiologia , Modelos Neurológicos , Percepção Visual/fisiologia , Análise de Variância , Simulação por Computador , Humanos , Estimulação Luminosa
13.
PLoS One ; 7(1): e29740, 2012.
Artigo em Inglês | MEDLINE | ID: mdl-22253768

RESUMO

In image recognition it is often assumed the method used to convert color images to grayscale has little impact on recognition performance. We compare thirteen different grayscale algorithms with four types of image descriptors and demonstrate that this assumption is wrong: not all color-to-grayscale algorithms work equally well, even when using descriptors that are robust to changes in illumination. These methods are tested using a modern descriptor-based image recognition framework, on face, object, and texture datasets, with relatively few training instances. We identify a simple method that generally works best for face and object recognition, and two that work well for recognizing textures.


Assuntos
Algoritmos , Interpretação de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos , Animais , Cor , Bases de Dados como Assunto , Humanos
14.
Emotion ; 10(6): 874-93, 2010 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-21171759

RESUMO

Facial expressions are crucial to human social communication, but the extent to which they are innate and universal versus learned and culture dependent is a subject of debate. Two studies explored the effect of culture and learning on facial expression understanding. In Experiment 1, Japanese and U.S. participants interpreted facial expressions of emotion. Each group was better than the other at classifying facial expressions posed by members of the same culture. In Experiment 2, this reciprocal in-group advantage was reproduced by a neurocomputational model trained in either a Japanese cultural context or an American cultural context. The model demonstrates how each of us, interacting with others in a particular cultural context, learns to recognize a culture-specific facial expression dialect.


Assuntos
Características Culturais , Expressão Facial , Reconhecimento Psicológico , Adolescente , Adulto , Feminino , Humanos , Japão , Masculino , Estados Unidos , Adulto Jovem
15.
Psychol Sci ; 21(7): 960-9, 2010 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-20525915

RESUMO

Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information-information gain, Kullback-Liebler distance, probability gain (error minimization), and impact-are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning to convey environmental probabilities, found that probability gain explained subjects' information search better than the other statistical theories or the probability-of-certainty heuristic. Experiments 1 and 2 found that subjects behaved differently when the standard method of verbally presented summary statistics (rather than experience-based learning) was used to convey environmental probabilities. Experiment 3 found that subjects' preference for probability gain is robust, suggesting that the other models contribute little to subjects' search behavior.


Assuntos
Aprendizagem/fisiologia , Probabilidade , Teoria Psicológica , Humanos , Estudantes/psicologia , Análise e Desempenho de Tarefas
16.
Psychol Sci ; 20(4): 455-63, 2009 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-19399974

RESUMO

We examined whether two purportedly face-specific effects, holistic processing and the left-side bias, can also be observed in expert-level processing of Chinese characters, which are logographic and share many properties with faces. Non-Chinese readers (novices) perceived these characters more holistically than Chinese readers (experts). Chinese readers had a better awareness of the components of characters, which were not clearly separable to novices. This finding suggests that holistic processing is not a marker of general visual expertise; rather, holistic processing depends on the features of the stimuli and the tasks typically performed on them. In contrast, results for the left-side bias were similar to those obtained in studies of face perception. Chinese readers exhibited a left-side bias in the perception of mirror-symmetric characters, whereas novices did not; this effect was also reflected in eye fixations. Thus, the left-side bias may be a marker of visual expertise.


Assuntos
Grupo com Ancestrais do Continente Asiático , Idioma , Reconhecimento Visual de Modelos , Percepção Visual , Adulto , Humanos , Campos Visuais
17.
Vis cogn ; 17(6-7): 979-1003, 2009 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-21052485

RESUMO

When people try to find particular objects in natural scenes they make extensive use of knowledge about how and where objects tend to appear in a scene. Although many forms of such "top-down" knowledge have been incorporated into saliency map models of visual search, surprisingly, the role of object appearance has been infrequently investigated. Here we present an appearance-based saliency model derived in a Bayesian framework. We compare our approach with both bottom-up saliency algorithms as well as the state-of-the-art Contextual Guidance model of Torralba et al. (2006) at predicting human fixations. Although both top-down approaches use very different types of information, they achieve similar performance; each substantially better than the purely bottom-up models. Our experiments reveal that a simple model of object appearance can predict human fixations quite well, even making the same mistakes as people.

18.
J Cogn Neurosci ; 20(12): 2298-307, 2008 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-18457514

RESUMO

Anatomical evidence shows that our visual field is initially split along the vertical midline and contralaterally projected to different hemispheres. It remains unclear at which processing stage the split information converges. In the current study, we applied the Double Filtering by Frequency (DFF) theory (Ivry & Robertson, 1998) to modeling the visual field split; the theory assumes a right-hemisphere/low-frequency bias. We compared three cognitive architectures with different timings of convergence and examined their cognitive plausibility to account for the left-side bias effect in face perception observed in human data. We show that the early convergence model failed to show the left-side bias effect. The modeling, hence, suggests that the convergence may take place at an intermediate or late stage, at least after information has been extracted/encoded separately in the two hemispheres, a fact that is often overlooked in computational modeling of cognitive processes. Comparative anatomical data suggest that this separate encoding process that results in differential frequency biases in the two hemispheres may be engaged from V1 up to the level of area V3a and V4v, and converge at least after the lateral occipital region. The left-side bias effect in our model was also observed in Greeble recognition; the modeling, hence, also provides testable predictions about whether the left-side bias effect may also be observed in (expertise-level) object recognition.


Assuntos
Face , Lateralidade Funcional/fisiologia , Reconhecimento Visual de Modelos/fisiologia , Percepção Espacial/fisiologia , Campos Visuais/fisiologia , Expressão Facial , Humanos , Modelos Biológicos , Estimulação Luminosa/métodos , Análise de Componente Principal
19.
Vision Res ; 48(5): 703-15, 2008 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-18226826

RESUMO

Research has shown that inverting faces significantly disrupts the processing of configural information, leading to a face inversion effect. We recently used a contextual priming technique to show that the presence or absence of the face inversion effect can be determined via the top-down activation of face versus non-face processing systems [Ge, L., Wang, Z., McCleery, J., & Lee, K. (2006). Activation of face expertise and the inversion effect. Psychological Science, 17(1), 12-16]. In the current study, we replicate these findings using the same technique but under different conditions. We then extend these findings through the application of a neural network model of face and Chinese character expertise systems. Results provide support for the hypothesis that a specialized face expertise system develops through extensive training of the visual system with upright faces, and that top-down mechanisms are capable of influencing when this face expertise system is engaged.


Assuntos
Face , Modelos Neurológicos , Reconhecimento Visual de Modelos/fisiologia , Adulto , Aprendizagem por Discriminação/fisiologia , Feminino , Humanos , Masculino , Redes Neurais de Computação , Orientação , Prática Psicológica , Psicofísica , Tempo de Reação , Semântica
20.
J Vis ; 8(7): 32.1-20, 2008 Dec 16.
Artigo em Inglês | MEDLINE | ID: mdl-19146264

RESUMO

We propose a definition of saliency by considering what the visual system is trying to optimize when directing attention. The resulting model is a Bayesian framework from which bottom-up saliency emerges naturally as the self-information of visual features, and overall saliency (incorporating top-down information with bottom-up saliency) emerges as the pointwise mutual information between the features and the target when searching for a target. An implementation of our framework demonstrates that our model's bottom-up saliency maps perform as well as or better than existing algorithms in predicting people's fixations in free viewing. Unlike existing saliency measures, which depend on the statistics of the particular image being viewed, our measure of saliency is derived from natural image statistics, obtained in advance from a collection of natural images. For this reason, we call our model SUN (Saliency Using Natural statistics). A measure of saliency based on natural image statistics, rather than based on a single test image, provides a straightforward explanation for many search asymmetries observed in humans; the statistics of a single test image lead to predictions that are not consistent with these asymmetries. In our model, saliency is computed locally, which is consistent with the neuroanatomy of the early visual system and results in an efficient algorithm with few free parameters.


Assuntos
Atenção/fisiologia , Teorema de Bayes , Simulação por Computador , Movimentos Oculares/fisiologia , Percepção Visual/fisiologia , Humanos
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