CFA-treated Mice Induce Hyperalgesia in Healthy mice via an olfactory mechanism

Background Social interactions with subjects experiencing pain can increase nociceptive sensitivity in observers, even without direct physical contact. In previous experiments, extended indirect exposure to soiled bedding from mice with alcohol withdrawal-related hyperalgesia enhanced nociception in their conspecifics. This finding suggested that olfactory cues could be sufficient for nociceptive hypersensitivity in otherwise untreated animals (also known as “bystanders”). Aim The current study addressed this possibility using an inflammation-based hyperalgesia model and long- and short-term exposure paradigms in C57BL/6J mice. Materials & Method Adult male and female mice received intraplantar injection of complete Freund\u27s adjuvant (CFA) and were used as stimulus animals to otherwise naïve same-sex bystander mice (BS). Another group of untreated mice (OLF) was simultaneously exposed to the bedding of the stimulus mice. Results In the long-term, 15-day exposure paradigm, the presence of CFA mice or their bedding resulted in reduced von Frey threshold but not Hargreaves paw withdrawal latency in BS or OLF mice. In the short-term paradigm, 1-hr interaction with CFA conspecifics or 1-hr exposure to their bedding induced mechanical hypersensitivity in BS and OLF mice lasting for 3 hrs. Chemical ablation of the main olfactory epithelium prevented bedding-induced and stimulus mice-induced mechanical hypersensitivity. Gas chromatography-mass spectrometry (GC-MS) analysis of the volatile compounds in the bedding of experimental mice revealed that CFA-treated mice released an increased number of compounds indicative of disease states

SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting

RNN-based methods have faced challenges in the Long-term Time Series Forecasting (LTSF) domain when dealing with excessively long look-back windows and forecast horizons. Consequently, the dominance in this domain has shifted towards Transformer, MLP, and CNN approaches. The substantial number of recurrent iterations are the fundamental reasons behind the limitations of RNNs in LTSF. To address these issues, we propose two novel strategies to reduce the number of iterations in RNNs for LTSF tasks: Segment-wise Iterations and Parallel Multi-step Forecasting (PMF). RNNs that combine these strategies, namely SegRNN, significantly reduce the required recurrent iterations for LTSF, resulting in notable improvements in forecast accuracy and inference speed. Extensive experiments demonstrate that SegRNN not only outperforms SOTA Transformer-based models but also reduces runtime and memory usage by more than 78%. These achievements provide strong evidence that RNNs continue to excel in LTSF tasks and encourage further exploration of this domain with more RNN-based approaches. The source code is coming soon

Transient stability of a hydro-turbine governing system with different tailrace tunnels

This paper focuses on the transient stability of a hydro-turbine governing system with three kinds of tailrace tunnels. As the transfer coefficients change with operation conditions, the dynamic transfer coefficients which can describe the transient characteristics of the hydro-turbine governing system are introduced. Then, the transient stability of the hydro-turbine governing system is analysed. The global bifurcation diagrams for the three kinds of tailrace tunnels are respectively presented to investigate the effects of the tailrace tunnels on the stability of the hydro-turbine governing system. Theoretical analysis which proves the validity of simulation results is provided to explain the effects of the flow inertia and water level fluctuation. The influence of the tailrace tunnel gradient on the transient stability is also studied. More importantly, these methods and research results provide theoretical guidance for the arrangement of the tailrace tunnel and the operation of the hydropower station

Mussel-Inspired and Bioclickable Peptide Engineered Surface to Combat Thrombosis and Infection

Thrombosis and infections are the two major complications associated with extracorporeal circuits and indwelling medical devices, leading to significant mortality in clinic. To address this issue, here, we report a biomimetic surface engineering strategy by the integration of mussel-inspired adhesive peptide, with bio-orthogonal click chemistry, to tailor the surface functionalities of tubing and catheters. Inspired by mussel adhesive foot protein, a bioclickable peptide mimic (DOPA)(4)-azide-based structure is designed and grafted on an aminated tubing robustly based on catechol-amine chemistry. Then, the dibenzylcyclooctyne (DBCO) modified nitric oxide generating species of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) chelated copper ions and the DBCO-modified antimicrobial peptide (DBCO-AMP) are clicked onto the grafted surfaces via bio-orthogonal reaction. The combination of the robustly grafted AMP and Cu-DOTA endows the modified tubing with durable antimicrobial properties and ability in long-term catalytically generating NO from endogenous s-nitrosothiols to resist adhesion/activation of platelets, thus preventing the formation of thrombosis. Overall, this biomimetic surface engineering technology provides a promising solution for multicomponent surface functionalization and the surface bioengineering of biomedical devices with enhanced clinical performance.Peer reviewe

Component Segmentation of Engineering Drawings Using Graph Convolutional Networks

We present a data-driven framework to automate the vectorization and machine interpretation of 2D engineering part drawings. In industrial settings, most manufacturing engineers still rely on manual reads to identify the topological and manufacturing requirements from drawings submitted by designers. The interpretation process is laborious and time-consuming, which severely inhibits the efficiency of part quotation and manufacturing tasks. While recent advances in image-based computer vision methods have demonstrated great potential in interpreting natural images through semantic segmentation approaches, the application of such methods in parsing engineering technical drawings into semantically accurate components remains a significant challenge. The severe pixel sparsity in engineering drawings also restricts the effective featurization of image-based data-driven methods. To overcome these challenges, we propose a deep learning based framework that predicts the semantic type of each vectorized component. Taking a raster image as input, we vectorize all components through thinning, stroke tracing, and cubic bezier fitting. Then a graph of such components is generated based on the connectivity between the components. Finally, a graph convolutional neural network is trained on this graph data to identify the semantic type of each component. We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings. Results show that our method yields the best performance compared to recent image, and graph-based segmentation methods.Comment: Preprint accepted to Computers in Industr