Interneurons control brain arousal states: The underlying circuit mechanisms coordinating brain arousal and motor activity are poorly understood. Liu et al. found that glutamic acid decarboxylase 2 (GAD2)–expressing, but not parvalbumin-expressing, interneurons in a part of the brain known as the substantia nigra promote sleep (see the Perspective by Wisden and Franks). Parvalbuminergic neurons fire at higher rates in states of high motor activity, and their activation increases movement termination consistent with the function of the substantia nigra in suppressing unwanted movements during action selection. By contrast, GAD2 neurons are preferentially active in states of low motor activity. In addition to motor suppression, their activation powerfully enhances the transition from quiet wakefulness to sleep, which differ mainly in the arousal level rather than motor behavior. GAD2 interneurons thus provide general suppression of both motor activity and brain arousal to promote states of quiescence. Science , this issue p. 440 ; see also p. 366

Some of the most conspicuous aging phenotypes of C. elegans are related to post‐reproductive production of vitellogenins (Vtg), which form yolk protein (YP) complexes after processing and lipid loading. Vtg/YP levels show huge increases with age, and inhibition of this extends lifespan, but how subcellular and organism‐wide distribution of these proteins changes with age has not been systematically explored. Here, this has been done to understand how vitellogenesis promotes aging. The age‐associated changes of intestinal vitellogenin vesicles (VVs), pseudocoelomic yolk patches (PYPs), and gonadal yolk organelles (YOs) have been characterized by immuno‐electron microscopy. We find that from reproductive adult day 2 (AD 2) to post‐reproductive AD 6 and AD 9, intestinal VVs expand from 0.2 to 3–4 μm in diameter or by >3000 times in volume, PYPs increase by >3 times in YP concentration and volume, while YOs in oocytes shrink slightly from 0.5 to 0.4 μm in diameter or by 49% in volume. In AD 6 and AD 9 worms, mislocalized YOs found in the hypodermis, uterine cells, and the somatic gonadal sheath can reach a size of 10 μm across in the former two tissues. This remarkable size increase of VVs and that of mislocalized YOs in post‐reproductive worms are accompanied by extensive fusion between these Vtg/YP‐containing vesicular structures in somatic cells. In contrast, no fusion is seen between YOs in oocytes. We propose that in addition to the continued production of Vtg, excessive fusion between VVs and mislocalized YOs in the soma worsen the aging pathologies seen in C. elegans.

The locations and shapes of synapses are important in reconstructing connectomes and analyzing synaptic plasticity. However, current synapse detection and segmentation methods are still not adequate for accurately acquiring the synaptic connectivity, and they cannot effectively alleviate the burden of synapse validation. We propose a fully automated method that relies on deep learning to realize the 3D reconstruction of synapses in electron microscopy (EM) images. The proposed method consists of three main parts: (1) training and employing the faster region convolutional neural networks (R-CNN) algorithm to detect synapses, (2) using the z-continuity of synapses to reduce false positives, and (3) combining the Dijkstra algorithm with the GrabCut algorithm to obtain the segmentation of synaptic clefts. Experimental results were validated by manual tracking, and the effectiveness of our proposed method was demonstrated. The experimental results in anisotropic and isotropic EM volumes demonstrate the effectiveness of our algorithm, and the average precision of our detection (92.8% in anisotropy, 93.5% in isotropy) and segmentation (88.6% in anisotropy, 93.0% in isotropy) suggests that our method achieves state-of-the-art results. Our fully automated approach contributes to the development of neuroscience, providing neurologists with a rapid approach for obtaining rich synaptic statistics.

Feature phones or low-end smart phones are an important part of our daily life. While lacking the powerful embedded camera and processing capability of the high-end smart phone, the feature phones with the low-end camera and processing capability are still widely used owing to its lower cost. Consequently, image and video processing algorithms are needed for many intelligent applications on the low-end smart phones. In this article, we propose a novel robust and efficient video anti-shaking algorithm for the low-end smartphone platform. It provides an integrated application for the low-end smartphone platform, which includes a novel global motion estimation method, and a rapid and efficient local motion detection method. The overall framework is proposed considering the limited camera perception quality and limited mobile processing capability of the feature phone. The proposed algorithm can not only obtain a good visual effect, but also be implemented in real-time for the low-end mobile platforms with very limited computational resource. Experimental results demonstrate that the proposed method can achieve a similar video stabilization effect to the Optical Image Stabilization hardware in high-end smartphones.

Motivation: The registration of serial section electron microscope images is a critical step in reconstructing biological tissue volumes, and it aims to eliminate complex nonlinear deformations from sectioning and replicate the correct neurite structure. However, due to the inherent properties of biological structures and the challenges posed by section preparation of biological tissues, achieving an accurate registration of serial sections remains a significant challenge. Conventional nonlinear registration techniques, which are effective in eliminating nonlinear deformation, can also eliminate the natural morphological variation of neurites across sections. Additionally, accumulation of registration errors alters the neurite structure. Results: This article proposes a novel method for serial section registration that utilizes an unsupervised optical flow network to measure feature similarity rather than pixel similarity to eliminate nonlinear deformation and achieve pairwise registration between sections. The optical flow network is then employed to estimate and compensate for cumulative registration error, thereby allowing for the reconstruction of the structure of biological tissues. Based on the novel serial section registration method, a serial split technique is proposed for long-serial sections. Experimental results demonstrate that the state-of-the-art method proposed here effectively improves the spatial continuity of serial sections, leading to more accurate registration and improved reconstruction of the structure of biological tissues. Availability and implementation: The source code and data are available at https://github.com/TongXin-CASIA/EFSR.

Superpixel aggregation is a powerful tool for automated neuron segmentation from electron microscopy (EM) volume. However, existing graph partitioning methods for superpixel aggregation still involve two separate stages—model estimation and model solving, and therefore model error is inherent. To address this issue, we integrate the two stages and propose an end-to-end aggregation framework based on deep learning of the minimum cost multicut problem called DeepMulticut. The core challenge lies in differentiating the NPhard multicut problem, whose constraint number is exponential in the problem size. With this in mind, we resort to relaxing the combinatorial solver—the greedy additive edge contraction (GAEC)—to a continuous Soft-GAEC algorithm, whose limit is shown to be the vanilla GAEC. Such relaxation thus allows the DeepMulticut to integrate edge cost estimators, Edge-CNNs, into a differentiable multicut optimization system and allows a decision-oriented loss to feed decision quality back to the Edge-CNNs for adaptive discriminative feature learning. Hence, the model estimators, Edge-CNNs, can be trained to improve partitioning decisions directly while beyond the NP-hardness. Also, we explain the rationale behind the DeepMulticut framework from the perspective of bi-level optimization. Extensive experiments on three public EM datasets demonstrate the effectiveness of the proposed DeepMulticut.