The traditional view on visual processing emphasizes a hierarchy: local line segments are first linked into global contours, which in turn are assembled into more complex forms. Distinct from this bottom-up viewpoint, here we provide evidence for a theoretical framework whereby objects and their parts are processed almost concurrently in a bidirectional cortico-cortical loop. By simultaneous recordings from V1 and V4 in awake monkeys, we found that information about global contours in a cluttered background emerged initially in V4, started ∼40 ms later in V1, and continued to develop in parallel in both areas. Detailed analysis of neuronal response properties implicated contour integration to emerge from both bottom-up and reentrant processes. Our results point to an incremental integration mechanism: feedforward assembling accompanied by feedback disambiguating to define and enhance the global contours and to suppress background noise. The consequence is a parallel accumulation of contour information over multiple cortical areas.

Perceptual grouping of line segments into object contours has been thought to be mediated, in part, by long-range horizontal connectivity intrinsic to the primary visual cortex (V1), with a contribution by top-down feedback projections. To dissect the contributions of intraareal and interareal connections during contour integration, we applied conditional Granger causality analysis to assess directional influences among neural signals simultaneously recorded from visual cortical areas V1 and V4 of monkeys performing a contour detection task. Our results showed that discounting the influences from V4 markedly reduced V1 lateral interactions, indicating dependence on feedback signals of the effective connectivity within V1. On the other hand, the feedback influences were reciprocally dependent on V1 lateral interactions because the modulation strengths from V4 to V1 were greatly reduced after discounting the influences from other V1 neurons. Our findings suggest that feedback and lateral connections closely interact to mediate image grouping and segmentation.perceptual grouping | contour integration | Granger causality | horizontal connection | feedback connection A key step in the visual system's analysis of object shape is to group line segments into global contours and segregate these contours from background features. This process is critical to identifying object boundaries in complex visual scenes, and thus particularly important for performing shape discrimination; image segmentation; and, ultimately, object recognition.Contour integration follows the Gestalt principle of good continuation (1). The underlying neural underpinnings have been characterized as an association field (2), which links contour elements that are part of smooth contours. Neurophysiological studies in monkeys have identified that the primary visual cortex (V1) makes a fundamental contribution to contour integration (3-6), and anatomical studies have shown that the topology of horizontal connections in V1 is well suited for mediating interactions between neurons with a similar orientation preference (7-10). Such intracortical circuitry in V1 has been implemented in many computational models to account for the successful process of contour integration (11-13). Although many lines of converging evidence suggest that V1 is intimately involved in contour integration, circuitbased models have to take into account the findings that contour grouping is more than a bottom-up or hard-wired process, but that it is strongly dependent on top-down feedback influences (5, 14-17). Surface segmentation, another important intermediate stage in processing of visual images, is also mediated by interactions between feedforward and feedback connections (18).We have proposed a model whereby cortical feedback contributes to the effective connectivity of horizontal connections within V1 (13,19). A possible role of higher cortical areas in this process is to disambiguate local image components by creating a template that is fed back to V1, which then can select...

Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time‐to‐event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high‐dimensional data featured by a large number of predictor variables. Our results showed that ML‐based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high‐dimensional data. The prediction performances of ML‐based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML‐based methods provide a powerful tool for time‐to‐event analysis, with a built‐in capacity for high‐dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function.

Inferotemporal (IT) neurons are known to exhibit persistent, stimulus-selective activity during the delay period of object-based working memory tasks. Frontal eye field (FEF) neurons show robust, spatially selective delay period activity during memory-guided saccade tasks. We present a copula regression paradigm to examine neural interaction of these two types of signals between areas IT and FEF of the monkey during a working memory task. This paradigm is based on copula models that can account for both marginal distribution over spiking activity of individual neurons within each area and joint distribution over ensemble activity of neurons between areas. Considering the popular GLMs as marginal models, we developed a general and flexible likelihood framework that uses the copula to integrate separate GLMs into a joint regression analysis. Such joint analysis essentially leads to a multivariate analog of the marginal GLM theory and hence efficient model estimation. In addition, we show that Granger causality between spike trains can be readily assessed via the likelihood ratio statistic. The performance of this method is validated by extensive simulations, and compared favorably to the widely used GLMs. When applied to spiking activity of simultaneously recorded FEF and IT neurons during working memory task, we observed significant Granger causality influence from FEF to IT, but not in the opposite direction, suggesting the role of the FEF in the selection and retention of visual information during working memory. The copula model has the potential to provide unique neurophysiological insights about network properties of the brain.

The outbreak of the novel coronavirus SARS‐CoV‐2, the causative agent of COVID‐19 respiratory disease, leads to a global pandemic with high morbidity and mortality. Despite frenzied efforts in therapeutic development, there are currently no effective drugs for treatment, nor are there vaccines for its prevention. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, is one of the most practical treatment options against the outbreak. In this study, we present a novel strategy for in‐silico molecular modeling screening for potential drugs that may interact with multiple main proteins of SARS‐CoV‐2. Targeting multiple viral proteins is a novel drug discovery concept in that it enables the potential drugs to act on different stages of the virus' life cycle, thereby potentially maximizing the drug potency. We screened 2,631 FDA‐approved small molecules against four key proteins of SARS‐CoV‐2 that are known as attractive targets for anti‐viral drug development. In total, we identified 29 drugs that could actively interact with two or more target proteins, with 5 drugs (Avapritinib, Bictegravir, Ziprasidone, Capmatinib and Pexidartinib) being common candidates for all four key host proteins and 3 of them possessing the desirable molecular properties. By overlaying docked positions of drug candidates onto individual host proteins, it has been further confirmed that the binding site conformations are conserved. The drugs identified in our screening provide potential guidance for experimental confirmation such as in vitro molecular assays, in vivo animal testing as well as incorporation into ongoing clinical studies.