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Biological specimens suffer radiation damage when imaged in an electron microscope, ultimately limiting the attainable resolution. At a given resolution, an optimal exposure can be defined that maximizes the signal-to-noise ratio in the image. Using a 2.6 Å resolution single particle cryo-EM reconstruction of rotavirus VP6, determined from movies recorded with a total exposure of 100 electrons/Å2, we obtained accurate measurements of optimal exposure values over a wide range of resolutions. At low and intermediate resolutions, our measured values are considerably higher than obtained previously for crystalline specimens, indicating that both images and movies should be collected with higher exposures than are generally used. We demonstrate a method of using our optimal exposure values to filter movie frames, yielding images with improved contrast that lead to higher resolution reconstructions. This ‘high-exposure’ technique should benefit cryo-EM work on all types of samples, especially those of relatively low-molecular mass. Microscopes allow us to visualize objects that are invisible to the naked eye. One type of microscope—called the electron microscope—produces images using beams of particles known as electrons, which enables them to produce more detailed images than microscopes that use light. There are several ways to prepare samples for electron microscopy. For example, in ‘electron cryo-microscopy’—or cryo-EM for short—a sample is rapidly frozen to preserve its features before it is examined under the microscope. This technique generates images that can be analyzed by computers to produce three-dimensional models of individual viruses, proteins, and other tiny objects. Unfortunately, the samples need to be exposed to high-energy beams of electrons that will damage the sample while the images are gathered, which results in sample movement and blurry images that lack the finer details. The contrast between the sample and its background is one of the factors that determine the final quality of an image. The higher the contrast, the greater the level of structural information that can be obtained, but this requires the use of longer exposures to the electron beam. To overcome this issue, researchers found that instead of recording a single image, it is possible to record movies in which the movement of the sample under the electron beam can be tracked. After the movies are gathered, the movie frames are aligned using computer software to reduce the blurring caused by the sample moving and can then be used to make three-dimensional models. Grant and Grigorieff improved this method further by studying how quickly a large virus-like particle called ‘rotavirus double-layered particle’ is damaged under the electron beam. These experiments identified an optimum range of exposure to electrons that provides the highest image contrast at any given level of detail. These findings were used to design an exposure filter that can be applied to the movie frames, allowing Grant and Grigorieff to visualize features of the virus that had not previously been observed by cryo-EM. This method was also used to study an assembly of proteins known as the proteasome, which is responsible for destroying old proteins. Grant and Grigorieff's findings should be useful for cryo-EM studies on many kinds of samples.

Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.

Neighborhood filters are nonlocal image and movie filters which reduce the noise by averaging similar pixels. The first object of the paper is to present a unified theory of these filters and reliable criteria to compare them to other filter classes. A CCD noise model will be presented justifying the involvement of neighborhood filters. A classification of neighborhood filters will be proposed, including classical image and movie denoising methods and discussing further a recently introduced neighborhood filter, NL-means. In order to compare denoising methods three principles will be discussed. The first principle, “method noise”, specifies that only noise must be removed from an image. A second principle will be introduced, “noise to noise”, according to which a denoising method must transform a white noise into a white noise. Contrarily to “method noise”, this principle, which characterizes artifact-free methods, eliminates any subjectivity and can be checked by mathematical arguments and Fourier analysis. “Noise to noise” will be proven to rule out most denoising methods, with the exception of neighborhood filters. This is why a third and new comparison principle, the “statistical optimality”, is needed and will be introduced to compare the performance of all neighborhood filters. The three principles will be applied to compare ten different image and movie denoising methods. It will be first shown that only wavelet thresholding methods and NL-means give an acceptable method noise. Second, that neighborhood filters are the only ones to satisfy the “noise to noise” principle. Third, that among them NL-means is closest to statistical optimality. A particular attention will be paid to the application of the statistical optimality criterion for movie denoising methods. It will be pointed out that current movie denoising methods are motion compensated neighborhood filters. This amounts to say that they are neighborhood filters and that the ideal neighborhood of a pixel is its trajectory. Unfortunately the aperture problem makes it impossible to estimate ground true trajectories. It will be demonstrated that computing trajectories and restricting the neighborhood to them is harmful for denoising purposes and that space-time NL-means preserves more movie details.

Several research groups have shown how to map fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBCs Sherlock (Chen et al., 2017), and learn bidirectional mappings between fMRI responses and natural language representations. By leveraging data from multiple subjects watching the same movie, we were able to perform scene classification with 72% accuracy (random guessing would give 4%) and scene ranking with average rank in the top 4% (random guessing would give 50%). The key ingredients underlying this high level of performance are (a) the use of the Shared Response Model (SRM) and its variant SRM-ICA (Chen et al., 2015; Zhang et al., 2016) to aggregate fMRI data from multiple subjects, both of which are shown to be superior to standard PCA in producing low-dimensional representations for the tasks in this paper; (b) a sentence embedding technique adapted from the natural language processing (NLP) literature (Arora et al., 2017) that produces semantic vector representation of the annotations; (c) using previous timestep information in the featurization of the predictor data. These optimizations in how we featurize the fMRI data and text annotations provide a substantial improvement in classification performance, relative to standard approaches. •We learn maps between fMRI data and fine-grained text annotations.•The Shared Response Model highlights movie-related variance in the fMRI response.•Semantic annotations can be featurized with weighted sums of word embeddings.•Using previous timepoints helps with fMRI to Text, but hurts Text to fMRI.•Our methods attain high performance on scene classification and ranking tasks.

Use of socially generated \"big data\" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between \"real time monitoring\" and \"early predicting\" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

Abstract Research on anxiety focuses on clinically relevant behaviours and neurophysiological responses, particularly emphasizing recruitment of amygdala, insula, and cingulate cortex. Whether these same circuits instantiate subjective experience of anxiety remains unclear, a vital hurdle for clinical neuroscience. We used a semi-naturalistic, anxiogenic stimulus (animated movie) to evoke anxiety during fMRI in a pediatric sample with and without anxiety disorders (N = 84, before exclusion). After, participants provided verbal responses to interview questions about the stimulus. We quantified semantic content and valence of responses via natural language processing algorithms. Preregistered analyses found that wide-spread brain activity during the movie—including in the anterior insula cortex—related to participants’ descriptions of the movie’s narrative. Secondary analyses indicated anxiety symptoms were associated with insula responses, participants’ descriptions of the movie’s narrative, and appraisals. This study provides preliminary evidence that anxiety symptoms may shape patterns of insula activity during movie-watching, influencing the type of notable details later recalled. These findings underscore the utility of movie viewing paradigms in clinical neuroscience research on subjective emotional experiences in anxiety.

Social processing occurs within dynamic, complex, and multimodal contexts, but the study of social cognition typically involves static, artificial stimuli. Naturalistic approaches (e.g., movie viewing) can recapture the richness and complexity of real-world interactions. Novel analytic approaches allow for the investigation of functional brain organization in response to contextually embedded and extended events with a complex temporal structure during movie viewing or narrative processing. In addition to these within-brain measures, movies afford between-brain analyses such as inter-subject correlation, which allows for identification of stimulus-specific brain response through the correlation of brain activity between participants’ brains. Research using these approaches offers both practical and theoretical advantages in understanding how we navigate our social world. Practically, movies are engaging stimuli that allow for more rapid presentation of multiple event types and improve compliance even in very young populations. Theoretically, studies have validated the use of these measures by demonstrating functional selectivity to contextually embedded stimuli. Naturalistic approaches also allow for novel insights. For example, regions associated with social cognition have longer temporal receptive windows, making them well suited to social-cognitive processes that require integration of information over longer timescales. Furthermore, the similarity in the temporal and spatial brain response between individuals during naturalistic viewing is related to age, predictive of friendships, and reduced in autism spectrum disorder. These findings offer first glimpses into the power of using these naturalistic, dynamic approaches to understand how we perceive, reason about, and interact with others. •Novel approaches allow for neuroimaging of naturalistic social cognition.•Use of movie and narrative stimuli provide novel insights into social cognition.•Long temporal receptive windows are important for social cognition.•Neural similarity to complex, dynamic stimuli is important for social behavior.

•First study to decouple content and duration effects on movie-evoked brain signals.•Frame-based design reproduces established neural patterns of natural movies.•Content complexity drives engagement in visually-attuned posterior scene network.•Temporal duration drives engagement in memory-attuned anterior scene network.•Content and duration together shift ISC in ROIs along the visual cortical hierarchy. Movie-watching studies have shown that specific cortical areas are tuned to stimulus segments of certain durations. However, increases in stimulus duration naturally co-occur with increases in content complexity. This study aimed to disentangle the effects of stimulus content and duration to determine whether hierarchically nested, complex, naturalistic stimuli, like movies, are processed primarily on the basis of their underlying temporal or content structure. To this end, 48 participants watched six equal-length blocks of movie frames presented at a constant frame rate in an fMRI experiment. Frames were extracted from either movie scenes or movie shots (Content Level) and displayed as continuous segments for 4, 12 or 36 s (Duration). We applied inter-subject correlation and three-dimensional linear mixed-effects modeling with crossed random effects to identify cortical areas selectively modulated by Content Level and Duration. Effects along the visual processing hierarchy were additionally assessed in a ROI analysis. Whole-brain results were located predominately within distinct subnetworks of the scene network: Movie scenes, compared to shots, elicited stronger engagement in the visually-attuned posterior subnetwork containing the occipital and posterior parahippocampal place areas. Longer stimuli whereas additionally engaged the memory-related anterior scene network including the anterior parahippocampal place area, retrosplenial cortex, and caudal inferior parietal lobe. ROI analyses confirmed that temporally extended, content-rich stimuli preferentially engaged hierarchically higher areas. Overall, these findings support a functional differentiation within the scene network while expanding on its relation to temporal receptive windows, demonstrating that Content Level and Duration interact in shaping the cortical processing of naturalistic movie stimuli.

The exposure of frozen biological samples to the high-energy electron beam in a cryo-electron microscope commonly leads to beam-induced sample motion and distortions. Previously, we described Unblur , software to correct for beam-induced motion based on the alignment of full frames in a movie collected during the beam exposure (Grant and Grigorieff, 2015). Here, we present Unbend , extending Unblur by accommodating more localized sample bending and distortions using a 3D cubic B-spline model. Unbend is integrated into our cis TEM software with a new local motion visualization panel. We processed movie frames from various in situ sample types, including whole cells, lamellae, and cell lysates, to analyze motion behavior across different specimen types. To quantify the improvement in high-resolution signal, we utilized the 2D template matching method to search large ribosomal subunits from the motion-corrected micrographs. Overall, the signal-to-noise ratio of detected particles improved by 3–8% across different samples compared with full-frame aligned micrographs, while the number of detected target particles increased by up to ~300%. Furthermore, we processed micrograph montages to study motion patterns across an entire sample, revealing considerable variance in distortion scale within the same sample, suggesting a complex underlying mechanism.

•Movie-fMRI reveals novel, more granular principles of hierarchical cortical organization.•Top movie gradients delineate three separate perception-to-cognition hierarchies.•A distinctive third gradient in movie-watching is anchored by auditory/language regions.•Gradient scores demonstrate good reliability even across different movie stimuli.•Movie gradients yield stronger correlations with behavior relative to resting state gradients. Understanding cortical topographic organization and how it supports complex perceptual and cognitive processes is a fundamental question in neuroscience. Previous work has characterized functional gradients that demonstrate large-scale principles of cortical organization. How these gradients are modulated by rich ecological stimuli remains unknown. Here, we utilize naturalistic stimuli via movie-fMRI to assess macroscale functional organization. We identify principal movie gradients that delineate separate hierarchies anchored in sensorimotor, visual, and auditory/language areas. At the opposite/heteromodal end of these perception-to-cognition axes, we find a more central role for the frontoparietal network along with the default network. Even across different movie stimuli, movie gradients demonstrated good reliability, suggesting that these hierarchies reflect a brain state common across different naturalistic conditions. The relative position of brain areas within movie gradients showed stronger and more numerous correlations with cognitive behavioral scores compared to resting state gradients. Together, these findings provide an ecologically valid representation of the principles underlying cortical organization while the brain is active and engaged in multimodal, dynamic perceptual and cognitive processing.