Imaging Neural Activity in the Primary Somatosensory Cortex Using Thy1-GCaMP6s Transgenic Mice

The mammalian brain exhibits marked symmetry across the sagittal plane. However, detailed description of neural dynamics in symmetric brain regions in adult mammalian animals remains elusive. In this study, we describe an experimental procedure for measuring calcium dynamics through dual optical windows above bilateral primary somatosensory corticies (S1) in Thy1-GCaMP6s transgenic mice using 2-photon (2P) microscopy. This method enables recordings and quantifications of neural activity in bilateral mouse brain regions one at a time in the same experiment for a prolonged period in vivo. Key aspects of this method, which can be completed within an hour, include minimally invasive surgery procedures for creating dual optical windows, and the use of 2P imaging. Although we only demonstrate the technique in the S1 area, the method can be applied to other regions of the living brain facilitating the elucidation of structural and functional complexities of brain neural networks

Longitudinal Optogenetic Motor Mapping Revealed Structural and Functional Impairments and Enhanced Corticorubral Projection after Contusive Spinal Cord Injury in Mice

Current evaluation of impairment and repair after spinal cord injury (SCI) is largely dependent on behavioral assessment and histological analysis of injured tissue and pathways. Here, we evaluated whether transcranial optogenetic mapping of motor cortex could reflect longitudinal structural and functional damage and recovery after SCI. In Thy1-Channelrhodopsin2 transgenic mice, repeated motor mappings were made by recording optogenetically evoked electromyograms (EMGs) of a hindlimb at baseline and 1 day and 2, 4, and 6 weeks after mild, moderate, and severe spinal cord contusion. Injuries caused initial decreases in EMG amplitude, losses of motor map, and subsequent partial recoveries, all of which corresponded to injury severity. Reductions in map size were positively correlated with motor performance, as measured by Basso Mouse Scale, rota-rod, and grid walk tests, at different time points, as well as with lesion area at spinal cord epicenter at 6 weeks post-SCI. Retrograde tracing with Fluoro-Gold showed decreased numbers of cortico- and rubrospinal neurons, with the latter being negatively correlated with motor map size. Combined retro- and anterograde tracing and immunostaining revealed more neurons activated in red nucleus by cortical stimulation and enhanced corticorubral axons and synapses in red nucleus after SCI. Electrophysiological recordings showed lower threshold and higher amplitude of corticorubral synaptic response after SCI. We conclude that transcranial optogenetic motor mapping is sensitive and efficient for longitudinal evaluation of impairment and plasticity of SCI, and that spinal cord contusion induces stronger anatomical and functional corticorubral connection that may contribute to spontaneous recovery of motor function

Estimation of control area in badminton doubles with pose information from top and back view drone videos

The application of visual tracking to the performance analysis of sports players in dynamic competitions is vital for effective coaching. In doubles matches, coordinated positioning is crucial for maintaining control of the court and minimizing opponents' scoring opportunities. The analysis of such teamwork plays a vital role in understanding the dynamics of the game. However, previous studies have primarily focused on analyzing and assessing singles players without considering occlusion in broadcast videos. These studies have relied on discrete representations, which involve the analysis and representation of specific actions (e.g., strokes) or events that occur during the game while overlooking the meaningful spatial distribution. In this work, we present the first annotated drone dataset from top and back views in badminton doubles and propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance. We present an efficient framework of deep neural networks that enables the calculation of full probability surfaces. This framework utilizes the embedding of a Gaussian mixture map of players' positions and employs graph convolution on their poses. In the experiment, we verify our approach by comparing various baselines and discovering the correlations between the score and control area. Additionally, we propose a practical application for assessing optimal positioning to provide instructions during a game. Our approach offers both visual and quantitative evaluations of players' movements, thereby providing valuable insights into doubles teamwork. The dataset and related project code is available at https://github.com/Ning-D/Drone_BD_ControlAreaComment: 15 pages, 10 figures, to appear in Multimedia Tools and Application

Electrospun 1D and 2D Carbon and Polyvinylidene Fluoride (PVDF) Piezoelectric Nanocomposites

Piezoelectric nanocomposite fibrous membranes consisting of polymer polyvinylidene fluoride (PVDF) as matrix and incorporating 1D carbon nanotubes (CNTs) and 2D graphene oxide (GO) were prepared using an electrospinning process. The influence of the filler type, loading, and dispersion status on the total PVDF crystallinity (X_{c}); Piezoelectric nanocomposite fibrous membranes consisting of polymer polyvinylidene fluoride (PVDF) as matrix and incorporating 1D carbon nanotubes (CNTs) and 2D graphene oxide (GO) were prepared using an electrospinning process. The influence of the filler type, loading, and dispersion status on the total PVDF crystallinity (F_{β}); the volume fraction of β phase in the samples (v_{β}); and the piezoelectric coefficient d_{33} were investigated. The V_{β} is used to assess the formation of β phase for the first time, which considered the combined influence of fillers on X_{c} and F_{β}, and is more practical than other investigations using only F_{β} for the assessment. The inclusion of all types of carbon fillers had resulted in a considerable reduction in the X_{c} compared with the neat PVDF, and the X_{c} decreased with the CNT loading while increased with the GO loading. The addition of CNT and GO had also reduced the F_{β} compared with the neat PVDF, and F_{β} increased with CNT loading while decreased as GO loading increased. The v_{β} is significantly reduced by the addition of CNT and GO, while v_{β} decreases with CNT and GO loading increases. Since the calculation of V_{β} has considered the combined influence of fillers on X_{c} and F_{β}, both of which were reduced by incorporating CNT and GO, the reduction of v_{β} was expected. The v_{β} of the PVDF/CNT composites were higher than that of the PVDF/GO composites. Although it is generally anticipated that d_{33} increases with v_{β}, it is observed that in the presence of CNT, d_{33} is dominated by the increase in electric conductivity of the composites during and after the electrospinning process, giving rise to transport of charges, produced by β crystals within the fiber to the surface of the sample. In addition, the 1D CNTs may have promoted the orientation of β crystals in the d_{33} direction, therefore, enhancing the d_{33} of the composites despite the hindrance of the β-phase formation (i.e., the reduction of v_{β}). Adding CNTs can also improve piezoelectricity through interfacial polarization, which increases the dielectric constant of composite (mobile charges within CNTs facilitate composite polarization). CNT loadings higher than 0.01 wt.% are sufficient to outperform the neat PVDF, and d_{33} becomes 59.7% higher than the neat PVDF at 0.03 wt.% loading, but only GO loadings of 0.5 wt.% achieved comparable d_{33} to the neat PVDF; further increase in GO loading had resulted in a decline in d_{33}. The low conductivity of GO, the influence of flocculation, and the lower aspect ratio compared with CNT may result in lower electron transfer and less orientation of the β-phase polycrystalline. The d_{33} of the PVDF/CNT composites is higher than that of the PVDF/GO composites despite much higher loading of GO. This study aims to contribute to the development of PVDF nanocomposites in piezoelectric energy harvesting applications (e.g., self-powered biosensors and wireless sensor networks)

Pave the Way to Grasp Anything: Transferring Foundation Models for Universal Pick-Place Robots

Improving the generalization capabilities of general-purpose robotic agents has long been a significant challenge actively pursued by research communities. Existing approaches often rely on collecting large-scale real-world robotic data, such as the RT-1 dataset. However, these approaches typically suffer from low efficiency, limiting their capability in open-domain scenarios with new objects, and diverse backgrounds. In this paper, we propose a novel paradigm that effectively leverages language-grounded segmentation masks generated by state-of-the-art foundation models, to address a wide range of pick-and-place robot manipulation tasks in everyday scenarios. By integrating precise semantics and geometries conveyed from masks into our multi-view policy model, our approach can perceive accurate object poses and enable sample-efficient learning. Besides, such design facilitates effective generalization for grasping new objects with similar shapes observed during training. Our approach consists of two distinct steps. First, we introduce a series of foundation models to accurately ground natural language demands across multiple tasks. Second, we develop a Multi-modal Multi-view Policy Model that incorporates inputs such as RGB images, semantic masks, and robot proprioception states to jointly predict precise and executable robot actions. Extensive real-world experiments conducted on a Franka Emika robot arm validate the effectiveness of our proposed paradigm. Real-world demos are shown in YouTube (https://www.youtube.com/watch?v=1m9wNzfp_4E ) and Bilibili (https://www.bilibili.com/video/BV178411Z7H2/ )

AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation

We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks. These tasks often involve complex multi-step reasoning, presenting significant challenges due to the limited paired data connecting human instructions (e.g., making a smiley face) and robot actions (e.g., end-effector movement). Existing approaches relieve this challenge by adopting an open-loop paradigm decomposing high-level instructions into simple sub-task plans, and executing them step-by-step using low-level control models. However, these approaches are short of instant observations in multi-step reasoning, leading to sub-optimal results. To address this issue, we propose to automatically collect a cognitive robot dataset by Large Language Models (LLMs). The resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of multi-step text plans and paired observation sequences. To enable efficient data acquisition, we employ elaborated multi-round prompt designs that effectively reduce the burden of extensive human involvement. We further propose a closed-loop multi-modal embodied planning model that autoregressively generates plans by taking image observations as input. To facilitate effective learning, we leverage MiniGPT-4 with a frozen visual encoder and LLM, and finetune additional vision adapter and Q-former to enable fine-grained spatial perception for manipulation tasks. We conduct experiments to verify the superiority over existing open and closed-loop methods, and achieve a significant increase in success rate by 21.4% and 14.5% over ChatGPT and GPT-4 based robot tasks. Real-world demos are shown in https://www.youtube.com/watch?v=ayAzID1_qQk

A Novel Maximum Power Point Tracking Algorithm Based on Glowworm Swarm Optimization for Photovoltaic Systems

In order to extract the maximum power from PV system, the maximum power point tracking (MPPT) technology has always been applied in PV system. At present, various MPPT control methods have been presented. The perturb and observe (P&O) and conductance increment methods are the most popular and widely used under the constant irradiance. However, these methods exhibit fluctuations among the maximum power point (MPP). In addition, the changes of the environmental parameters, such as cloud cover, plant shelter, and the building block, will lead to the radiation change and then have a direct effect on the location of MPP. In this paper, a feasible MPPT method is proposed to adapt to the variation of the irradiance. This work applies the glowworm swarm optimization (GSO) algorithm to determine the optimal value of a reference voltage in the PV system. The performance of the proposed GSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy by utilizing MATLAB/SIMULINK. The simulation results demonstrate that the tracking capability of the GSO algorithm is superior to that of the traditional P&O algorithm, particularly under low radiance and sudden mutation irradiance conditions

Novel Implications of Exosomes and lncRNAs in the Diagnosis and Treatment of Pancreatic Cancer

Pancreatic cancer remains a leading cause of cancer-related deaths. Most patients are present with advanced stages of the disease at the time of diagnosis; thus, surgery, which is the best curative option for this malignancy, is no longer an effective treatment modality for affected individuals. As a likely source of “liquid biopsies,” exosomes, which are secreted by fusing intracellular multivesicular bodies with cell membranes, have relative stability and composition, allowing them to cover the entire range of cancer-related biomarkers, including cellular proteins, lipids, DNA, RNA, miRNA, and long non-coding RNAs (lncRNAs). To explore the early detection biomarkers of pancreatic cancer and to develop successful therapeutic intervention for this disease, assessing the implications of exosomes in pancreatic cancer patients is essential. In this chapter, we wish to focus on the possibility of using exosomes and lncRNAs in the clinical management of patients with pancreatic cancer. We will discuss the mechanisms of tumor formation under the exosomal action, demonstrate how circulating exosomes and lncRNAs have come into the research spotlight as likely biomarkers of pancreatic cancer, and discuss the applications of exosomes as transfer vectors in tumor therapeutics