Larvae-Inspired Soft Crawling Robot with Multimodal Locomotion and Versatile Applications.

Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible movement. However, most existing soft crawling robots typically exhibit a single-motion mode and lack diverse capabilities. Inspired by larvae, this paper proposes a compact soft crawling robot (weight, 13 g; length, 165 mm; diameter, 35 mm) with multimodal locomotion (forward, turning, rolling, and twisting).

High-Speed Automated Reconstruction of Drosophila Larval Brain from Volumetric EM Data.

To uncover the relationship between neural activity and behavior, it is essential to reconstruct neural circuits. However, methods typically used for neuron reconstruction from volumetric electron microscopy (EM) dataset are often time-consuming and require extensive manual proofreading, making it difficult to reproduce in a typical laboratory setting. To address this challenge, we have developed a set of acceleration techniques that build upon the Flood Filling Network (FFN), significantly reducing the time required for this task.

Parameter-Efficient Person Re-Identification in the 3D Space.

People live in a 3D world. However, existing works on person re-identification (re-id) mostly consider the semantic representation learning in a 2D space, intrinsically limiting the understanding of people. In this work, we address this limitation by exploring the prior knowledge of the 3D body structure.

An Ultra-Low-Noise, Low Power and Miniaturized Dual-Channel Wireless Neural Recording Microsystem.

As the basic tools for neuroscience research, invasive neural recording devices can obtain high-resolution neuronal activity signals through electrodes connected to the subject's brain. Existing wireless neural recording devices are large in size or need external large-scale equipment for wireless power supply, which limits their application. Here, we developed an ultra-low-noise, low power and miniaturized dual-channel wireless neural recording microsystem.

Using Simulated Training Data of Voxel-Level Generative Models to Improve 3D Neuron Reconstruction.

Reconstructing neuron morphologies from fluorescence microscope images plays a critical role in neuroscience studies. It relies on image segmentation to produce initial masks either for further processing or final results to represent neuronal morphologies. This has been a challenging step due to the variation and complexity of noisy intensity patterns in neuron images acquired from microscopes.

Generative Adversarial Networks in Medical Image augmentation: A review.

Object: With the development of deep learning, the number of training samples for medical image-based diagnosis and treatment models is increasing. Generative Adversarial Networks (GANs) have attracted attention in medical image processing due to their excellent image generation capabilities and have been widely used in data augmentation. In this paper, a comprehensive and systematic review and analysis of medical image augmentation work are carried out, and its research status and development prospects are reviewed.

PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance.

Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of user-friendly software for calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-source toolbox designed to evaluate reconstruction results conveniently as far as we know. The toolbox supports popular metrics in two major categories, geometrical metrics and topological metrics, with an easy way to configure custom parameters for each metric.

Spatially-Aware Context Neural Networks.

A variety of computer vision tasks benefit significantly from increasingly powerful deep convolutional neural networks. However, the inherently local property of convolution operations prevents most existing models from capturing long-range feature interactions for improved performances. In this paper, we propose a novel module, called Spatially-Aware Context (SAC) block, to learn spatially-aware contexts by capturing multi-mode global contextual semantics for sophisticated long-range dependencies modeling.

HTsort: Enabling Fast and Accurate Spike Sorting on Multi-Electrode Arrays.

Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty.

An ultrasound image-based deep multi-scale texture network for liver fibrosis grading in patients with chronic HBV infection.

Background & Aims: The evaluation of the stage of liver fibrosis is essential in patients with chronic liver disease. However, due to the low quality of ultrasound images, the non-invasive diagnosis of liver fibrosis based on ultrasound images is still an outstanding question. This study aimed to investigate the diagnostic accuracy of a deep learning-based method in ultrasound images for liver fibrosis staging in multicentre patients.

Brain-Machine Interface-Based Rat-Robot Behavior Control.

Brain-machine interface (BMI) provides a bidirectional pathway between the brain and external facilities. The machine-to-brain pathway makes it possible to send artificial information back into the biological brain, interfering neural activities and generating sensations. The idea of the BMI-assisted bio-robotic animal system is accomplished by stimulations on specific sites of the nervous system.

A Neuronal Pathway that Commands Deceleration in Drosophila Larval Light-Avoidance.

When facing a sudden danger or aversive condition while engaged in on-going forward motion, animals transiently slow down and make a turn to escape. The neural mechanisms underlying stimulation-induced deceleration in avoidance behavior are largely unknown. Here, we report that in Drosophila larvae, light-induced deceleration was commanded by a continuous neural pathway that included prothoracicotropic hormone neurons, eclosion hormone neurons, and tyrosine decarboxylase 2 motor neurons (the PET pathway).

Author Correction: A disinhibitory mechanism biases Drosophila innate light preference.

Affiliation 2 incorrectly read 'Department of Neurology of the Second Affiliated Hospital, Department of Neurobiology, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Key Laboratory of Neurobiology, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310007, China' and affiliation 3 incorrectly read 'Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, Zhejiang 310058, China.'

A disinhibitory mechanism biases Drosophila innate light preference.

Innate preference toward environmental conditions is crucial for animal survival. Although much is known about the neural processing of sensory information, how the aversive or attractive sensory stimulus is transformed through central brain neurons into avoidance or approaching behavior is largely unclear. Here we show that Drosophila larval light preference behavior is regulated by a disinhibitory mechanism.

Abdominal-Waving Control of Tethered Bumblebees Based on Sarsa With Transformed Reward.

Cyborg insects have attracted great attention as the flight performance they have is incomparable by micro aerial vehicles and play a critical role in supporting extensive applications. Approaches to construct cyborg insects consist of two major issues: 1) the stimulating paradigm and 2) the control policy. At present, most cyborg insects are constructed based on invasive methods, requiring the implantation of electrodes into neural or muscle systems, which would harm the insects.

Identification of genes related to high royal jelly production in the honey bee (Apis mellifera) using microarray analysis.

China is the largest royal jelly producer and exporter in the world, and high royal jelly-yielding strains have been bred in the country for approximately three decades. However, information on the molecular mechanism underlying high royal jelly production is scarce. Here, a cDNA microarray was used to screen and identify differentially expressed genes (DEGs) to obtain an overview on the changes in gene expression levels between high and low royal jelly producing bees.

Turns with multiple and single head cast mediate Drosophila larval light avoidance.

Drosophila larvae exhibit klinotaxis when placed in a gradient of temperature, chemicals, or light. The larva samples environmental stimuli by casting its head from side to side. By comparing the results of two consecutive samples, it decides the direction of movement, appearing as a turn proceeded by one or more head casts.

Locally Linear Approximation Approach for Incomplete Data.

The matrix completion problem is restoring a given matrix with missing entries when handling incomplete data. In many existing researches, rank minimization plays a central role in matrix completion. In this paper, noticing that the locally linear reconstruction can be used to approximate the missing entries, we view the problem from a new perspective and propose an algorithm called locally linear approximation (LLA).

Attribute Selection for Partially Labeled Categorical Data By Rough Set Approach.

Attribute selection is considered as the most characteristic result in rough set theory to distinguish itself to other theories. However, existing attribute selection approaches can not handle partially labeled data. So far, few studies on attribute selection in partially labeled data have been conducted.

Maze learning by a hybrid brain-computer system.

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.

Automatic Training of Rat Cyborgs for Navigation.

A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli.