Adjacent Networks Research Articles

High gamma activity distinguishes frontal cognitive control regions from adjacent cortical networks

Though the lateral frontal cortex is broadly implicated in cognitive control, functional MRI (fMRI) studies suggest fine-grained distinctions within this region. To examine this question electrophysiologically, we placed electrodes on the lateral frontal cortex in patients undergoing awake craniotomy for tumor resection. Patients performed verbal tasks with a manipulation of attentional switching, a canonical control demand. Power in the high gamma range (70–250 Hz) distinguished electrodes based on their location within a high-resolution fMRI network parcellation of the frontal lobe. Electrodes within the canonical fronto-parietal control network showed increased power in the switching condition, a result absent in electrodes within default mode, language and somato-motor networks. High gamma results contrasted with spatially distributed power decreases in the beta range (12–30 Hz). These results confirm the importance of fine-scale functional distinctions within the human frontal lobe, and pave the way for increased precision of functional mapping in tumor surgeries.

Short‐term load forecasting of multi‐scale recurrent neural networks based on residual structure

SummaryAccurate short‐term load forecasting plays an important role in reducing power generation costs, maintaining supply and demand balance, and stabling the power grids operation. In recent years, deep learning models based on recurrent neural networks (RNN) have been widely used in short‐term load forecasting. Nevertheless, RNN cannot extract multi‐scale features of load data, resulting in low forecasting accuracy. A model for short‐term power load forecasting of residual multiscale‐RNN (RM‐RNN) was proposed in this study. RM‐RNN uses the multilayer RNN network structure. Specifically, each layer sets the dilated convolution with different dilated coefficients to extract the multi‐scale features of the load data. Adjacent networks transfer feature information for feature fusion through the residual structures. The experiment used random sampling data training model, and compared RM‐RNN with multiple deep learning models. The experimental results demonstrated that the mean error of RM‐RNN prediction is the lowest, indicating that dilated convolution can effectively extract multi‐scale features of load data. This result verified the effectiveness of residual structure fusion features, and improved the accuracy of short‐term load forecasting.

Consensus-based state of charge dynamic equilibrium strategy in isolated DC microgrid with bus voltage regulation

Due to the tremendous benefits of integrating sustainable energy, the use of DC microgrid is gradually expanding. This paper presents a novel consensus-based State of Charge (SoC) dynamic equilibrium strategy to solve the issues of unbalanced SoC, imprecise load power distribution, and unreasonable bus voltage regulation for the Battery Energy Storage System (BESS) in DC microgrids with Distributed Sustainable Energy Generation (DSEG). In the communication layer, an adjacent communication network is built, which applies the Dynamic Average Consensus Algorithm (DACA) to estimate relevant average values. In addition, the conventional droop control is applied to distribute the load power preliminarily in the main control layer. With the proposed scheme, the dynamic SoC equilibrium and accurate current distribution are realized by the consensus iteration of the designed coupling factor, and the bus voltage recovery is achieved by compensating for the average output voltage of energy storage units (ESUs) in the secondary control layer. Furthermore, the exponential function is introduced to modify DACA, thus accelerating the convergence of DACA. Finally, the simulation and experimental results show that the proposed scheme can realize the control objectives of rapid SoC equilibrium, accurate load power distribution, and reasonable bus voltage regulation.

Functional specialization of parallel distributed networks revealed by analysis of trial-to-trial variation in processing demands.

Multiple large-scale networks populate human association cortex. Here, we explored the functional properties of these networks by exploiting trial-to-trial variation in component-processing demands. In two behavioral studies (n = 136 and n = 238), participants quantified strategies used to solve individual task trials that spanned remembering, imagining future scenarios, and various control trials. These trials were also all scanned in an independent sample of functional MRI participants (n = 10), each with sufficient data to precisely define within-individual networks. Stable latent factors varied across trials and correlated with trial-level functional responses selectively across networks. One network linked to parahippocampal cortex, labeled Default Network A (DN-A), tracked scene construction, including for control trials that possessed minimal episodic memory demands. To the degree, a trial encouraged participants to construct a mental scene with imagery and awareness about spatial locations of objects or places, the response in DN-A increased. The juxtaposed Default Network B (DN-B) showed no such response but varied in relation to social processing demands. Another adjacent network, labeled Frontoparietal Network B (FPN-B), robustly correlated with trial difficulty. These results support that DN-A and DN-B are specialized networks differentially supporting information processing within spatial and social domains. Both networks are dissociable from a closely juxtaposed domain-general control network that tracks cognitive effort.NEW & NOTEWORTHY Tasks shown to differentially recruit parallel association networks are multifaceted, leaving open questions about network processes. Here, examining trial-to-trial network response properties in relation to trial traits reveals new insights into network functions. In particular, processes linked to scene construction selectively recruit a distributed network with links to parahippocampal and retrosplenial cortices, including during trials designed not to rely on the personal past. Adjacent networks show distinct patterns, providing novel evidence of functional specialization.

CONSIDERATION OF THE CRITERION OF THE INFLUENCE OF ASYMMETRIC LOAD ON VOLTAGE DISTORTION IN A 10 kV NETWORK FOR RECOMMENDATIONS FOR REDUCING VOLTAGE ASYMMETRY

To date, there is a fairly large number of electrical equipment that is a source of asymmetric modes of operation in medium and low voltage electrical networks. This electrical equipment can create conductive low-frequency electromagnetic interference in electrical networks by the coefficient of voltage asymmetry in the reverse sequence. Studying the methodological basis of research on the theory of conductive low-frequency interference, it was found that when switching from one adjacent electrical network to another (for example, from a 0.4 kV network to a 10 kV network and vice versa) potential for conductive low-frequency interference decreases, i.e. the interference is partially suppressed by a certain amount. This amount is called the criterion determining the changes of the voltage asymmetry coefficient values in the reverse sequence in the transition of conductive low-frequency electromagnetic interference to the adjacent network. The purpose of this article is to develop a mathematical model for the study of network parameters affecting voltage asymmetry in the reverse sequence, which is reduced to a method for determining the value of the criterion for the effect of an asymmetric load on voltage distortion in a 10 kV network for recommendations for reducing voltage asymmetry. The article describes in detail the method of calculating this value and how it can be used to form recommendations for reducing voltage asymmetry. Scientific novelty of the study consists in the fact that an empirical mathematical model has been obtained that determines the criterion for the influence of an asymmetric load in a low-voltage network, the impact on which makes it possible to improve the electromagnetic situation in a 10 kV electrical network. This parameter can be taken into account when designing elements of electrical networks to determine the electromagnetic environment.

The effect of complex load on the reliable operation of solar photovoltaic and wind power stations integrated into energy systems and into off-grid energy areas

A mass renewable power stations commissioning, first of all solar photovoltaic stations (SPVS) and wind power stations (WPS) shall be associated with the properly implemented complex load modeling while calculating their electric operation modes. This article deals with the basic failures of standard algorithm of SPVS and WPS invertors/converters, Fault Ride Through (FRT), that has to reduce the active power output and to increase that of reactive power, in case of voltage drop. In order to prevent outages of SPVS and WPS equipment during emergency disturbances, the trip set points of SPVS and WPS inventors/converters shall be adjusted to those of the adjacent power network protective relay. In this article, an approach to selecting methods for complex loads modeling, in industrial energy areas, has been developed. A method for calculating the value of critical voltage across the load bars has been considered, and the relevancy to apply the dynamic scale model of electric motor has been substantiated. The specific aspects of the voltage-dependent and frequency-dependent load static characteristics employment, in calculations of electromechanical transient processes, have been analyzed. Recommendations on representing electric motors integrated into complex load circuits, in various software packages designed for operation modes calculations, have been stipulated. Various methods making it possible to take account of electromagnetic transient processes, in rotors of asynchronous motors, have been described. A proper account of complex loads enables to ensure reliable functioning of SPVS and WPS integrated into energy systems and operating in isolated energy areas avoiding their outages including those disturbances in power supply of power consuming equipment of industrial areas.

Adjacent Feature Propagation Network (AFPNet) for Real-Time Semantic Segmentation

With the development of deep learning, semantic segmentation has received considerable attention within the robotics community. For semantic segmentation to be applied to mobile robots or autonomous vehicles, real-time processing is essential. In this article, a new real-time semantic segmentation network, called the adjacent feature propagation network (AFPNet), is proposed to achieve high performance and fast inference. AFPNet executes in real time on a commercial embedded GPU. The network includes two new modules. The local memory module (LMM) is the first; it improves the upsampling accuracy by propagating the high-level features to the adjacent grids. The cascaded pyramid pooling module (CPPM) is the second; it reduces computational time by changing the structure of the pyramid pooling module. Using these two modules, the proposed AFPNet achieved 76.4% mean intersection-over-union on the Cityscapes test dataset, outperforming other real-time semantic segmentation networks. Furthermore, AFPNet was successfully deployed on an embedded board Jetson AGX Xavier and applied to the real-world navigation of a mobile robot, proving that AFPNet can be effectively used in a variety of real-time applications.

Tbx18 Orchestrates Cytostructural Transdifferentiation of Cardiomyocytes to Pacemaker Cells by Recruiting the Epithelial-Mesenchymal Transition Program.

Previously, we reported that heterologous expression of an embryonic transcription factor, Tbx18, reprograms ventricular cardiomyocytes into induced pacemaker cells (Tbx18-iPMs), though the key pathways are unknown. Here, we have used a tandem mass tag proteomic approach to characterize the impact of Tbx18 on neonatal rat ventricular myocytes. Tbx18 expression triggered vast proteome remodeling. Tbx18-iPMs exhibited increased expression of known pacemaker ion channels, including Hcn4 and Cx45 as well as upregulation of the mechanosensitive ion channels Piezo1, Trpp2 (PKD2), and TrpM7. Metabolic pathways were broadly downregulated, as were ion channels associated with ventricular excitation-contraction coupling. Tbx18-iPMs also exhibited extensive intracellular cytoskeletal and extracellular matrix remodeling, including 96 differentially expressed proteins associated with the epithelial-to-mesenchymal transition (EMT). RNAseq extended coverage of low abundance transcription factors, revealing upregulation of EMT-inducing Snai1, Snai2, Twist1, Twist2, and Zeb2. Finally, network diffusion mapping of >200 transcriptional regulators indicates EMT and heart development factors occupy adjacent network neighborhoods downstream of Tbx18 but upstream of metabolic control factors. In conclusion, transdifferentiation of cardiac myocytes into pacemaker cells entails massive electrogenic, metabolic, and cytostructural remodeling. Structural changes exhibit hallmarks of the EMT. The results aid ongoing efforts to maximize the yield and phenotypic stability of engineered biological pacemakers.

Technical Solutions for Preventing the Development of Single-Phase Ground Faults and for Eliminating Them in 6–10 kV Power Plant Switchgears

The severity of the consequences from single-phase ground faults (SPGF) at the combined heat and power plants (CHPPs) operating as branches of PJSC Mosenergo is due to the following two main reasons: prolonged exposure to overvoltage and a significant wear of primary electrical equipment. The operation mode with a compensated neutral assumes setting up conditions for arc self-extinguishing at the SPGF location; however, in the majority of cases this does not happen. The practice of continuing the operation with an SPGF for several hours has a negative impact on the power plant electrical equipment and needs to be revised. To evaluate the influence of an SPGF in a 10 kV network on the reliability of power plant operation, the main electrical connection circuits and the adjacent network at 12 CHPP branches of the Mosenergo company is analyzed. The basic technical solutions aimed at preventing the development of and eliminating SPGFs in the 6–10 kV CHPP network are considered. In carrying out the analysis, the overvoltage modeling results were used with taking into account the feasibility of implementing various solutions at power plants. Based on the analysis results, the possible technical solutions have been divided into more and less efficient ones. For the majority of CHPPs, an efficient solution implies the transition from the compensated neutral grounding mode with holding an SPGF for a long period of time to low-impedance resistive neutral grounding with clearing the fault by the action of relay protection and automatic control devices

Abstract 261: Released exosomes from colon cancer cells regulate formation and progression of perineural invasion

Abstract Colorectal cancer (CRC) is the third most diagnosed cancer and cause of cancer related mortality. CRC that metastasizes to distance sites results in poor prognosis, diminished survival, and reduced effectiveness of treatments. Perineural invasion (PNI) is an emerging field that aims to understand cancer-neuronal crosstalk that enables cancer to spread via nerves. Several studies have indicated that tumor derived exosomes (TDEs) play significant roles in the initiation and spread of cancer, along with driving chemoresistance. We hypothesize that CRC derived exosomes alter nerves within the surrounding environment and result in recruitment of nerves towards tumors for invasion along the nerve. Exosomes from both HCT116 and SW837 colon cancer cells were isolated and purified using a modified iodixanol ultracentrifugation gradient technique. A Proteomic analysis conducted on cancer derived exosomes revealed several potential proteins involved in neuronal growth and migration, including Vinculin (VCL), Periostin (POSTN or OSF-2), DCLK1, and USP9X. In another set of experiments, conditioned media (CM) from HCT116 and SW837 cells were added to both PC12 neuronal-like cells and dorsal root ganglion (DRG) from C57BL/6J mice to understand the effects of excreted cancer factors on neuronal outgrowth and differentiation. CM studies showed how signaling factors excreted from colon cancer cells induce increased nerite outgrowth in both PC12 cells and DRG. These results were not seen in CM from cancer cells that were grown in the presence of an exosome inhibitor. Altogether, these results indicate that CRC utilizes exosomes to alter the adjacent neuronal networks as a means to employ surrounding nerves for invasion. Citation Format: Colewyn Knoblich, Khosrow Rezvani. Released exosomes from colon cancer cells regulate formation and progression of perineural invasion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 261.

Rational design of three Co(II) coordination polymers based on a semirigid tricarboxylate ligand: Syntheses, structural variability, electrochemical behavior, magnetic and photocatalytic properties

Three novel Co(II) based coordination polymers (CPs) are fabricated using the 5-(2-carboxylphenoxy)isophthalic acid (H3L) and different N-donor configuration under the solvothermal conditions, namely, [Co2(L)(1,4-bibt)(μ3OH)]n (1), [Co(HL)(4,4′-bidpe)]n (2), and [Co(HL)(tib)]n (3) (1,4-bibt = 1,4-bis(imidazol-1-yl)butane, 4,4′-bidpe = 4,4′-bis(imidazolyl)diphenylether, tib = 1,3,5-tris(1-imidazolyl)benzene). The configuration of 1–3 are confirmed by various characterization analysis. The CP 1 possesses an unprecedented (3,10)-connected 3D architecture with a unique tetranuclear [Co4(µ3OH)2(COO)4]2+cluster. Interestingly, when the H3L ligands are not calculated, the whole structure of N-donor still shows a 3-fold interpenetrating dia 3D framework (3D + 3D → 3D). Moreover, CPs 2 and 3 are 3D supramolecular structure with significant differences structures. The CP 2 shows a 2D sql uninodal 4-connected 2D network with the Schläfli symbol (44•62), while CP 3 exhibits an unprecedented 2D framework. And both 2 and 3 construct 3D supramolecular architecture through H-bonds between the two adjacent 2D networks. Comparing CPs 1–3 shows that the N-donor ligands play a key role in adjusting the final structures. Furthermore, the electrochemical behavior, magnetic properties and photocatalytic activity of 1–3 are also discussed in details.

High-Throughput Deep Unfolding Network for Compressive Sensing MRI

Deep unfolding network (DUN) has become the mainstream for compressive sensing MRI (CS-MRI) due to its good interpretability and high performance. Different optimization algorithms are usually unfolded into deep networks with different architectures, in which one iteration corresponds to one stage of DUN. However, there are few works discussing the following two questions: Which optimization algorithm is better after being unfolded into a DUN? What are the bottlenecks in existing DUNs? This paper attempts to answer these questions and give a feasible solution. For the first question, our mathematical and empirical analysis verifies the similarity of DUNs unfolded by alternating minimization (AM), alternating iterative shrinkage-thresholding algorithm (ISTA) and alternating direction method of multipliers (ADMM). For the second question, we point out that one major bottleneck of existing DUNs is that the input and output of each stage are just images of one channel, which greatly limits the transmission of network information. To break the information bottleneck, this paper proposes a novel, simple yet powerful high-throughput deep unfolding network (HiTDUN), which is not constrained by any optimization algorithm and can transmit multi-channel information between adjacent network stages. The developed multi-channel fusion strategy can also be easily incorporated into existing DUNs to further boost their performance. Extensive CS-MRI experiments on three benchmark datasets demonstrate that the proposed HiTDUN outperforms existing state-of-the-art DUNs by large margins while maintaining fast computational speed.<xref ref-type="fn" rid="fn1">¹</xref><fn id="fn1"><label>¹</label> For reproducible research, the source codes and training models of our HiTDUN. [Online]. Available: <uri>https://github.com/jianzhangcs/HiTDUN</uri>. </fn>

A deep recursively learning LSTM model to improve cyber security botnet attack intrusion detection

The occurrence of botnets over the network is crucial as it shows advent effect on various applications like finance, cyber-security, and healthcare application. Botnets are refined and more dangerous in their functionality over the network model. Most of the prevailing models and flow- and rule-based models feel challenging to predict the bot functionalities in a preventive manner. Therefore, the modeling of efficient and automated botnet detection approaches is highly essential. This research concentrates on modeling a novel botnet detection approach based on the recursively analyzing the flow of features of the network nodes spatially and temporally where the attack samples are intra-dependent time-series data. The hierarchical structural design of the network helps to integrate various levels of feature information and learns the spatial and temporal information automatically among the adjacent network connection. This process is carried out by the proposed architectural model known as Recursively Learning Long Short-Term Memory over spatial and temporal ([Formula: see text]. Thus, the bot activities are detected by recursively analyzing the limited number of nodes. The [Formula: see text] model is modeled to improve the efficiency of the network by eliminating unnecessary activities. The proposed model is validated using the online accessible CTU-13 dataset and benchmarked against the prevailing classification approaches for botnet detection. The simulation is done in a MATLAB environment, and the outcomes work efficiently and evaluated with prevailing models to project the significance of the [Formula: see text] model.

A high-precision forest fire smoke detection approach based on ARGNet

The occurrence of forest fires can lead to ecological damage, property loss, and human casualties. Current forest fire smoke detection methods do not sufficiently consider the characteristics of smoke with high transparency and no clear edges and have low detection accuracy, which cannot meet the needs of complex aerial forest fire smoke detection tasks. In this paper, we propose Adjacent layer composite network based on a recursive feature pyramid with deconvolution and dilated convolution and global optimal nonmaximum suppression (ARGNet) for high-accuracy detection of forest fire smoke. First, the Adjacent layer composite network is proposed to enhance the extraction of smoke features with high transparency and no clear edges, and SoftPool in it is used to retain more feature information of smoke. Then, a recursive feature pyramid with deconvolution and dilated convolution (RDDFPN) is proposed to fuse shallow visual features and deep semantic information in the channel dimension to improve the accuracy of long-range aerial smoke detection. Finally, global optimal nonmaximum suppression (GO-NMS) sets the objective function to globally optimize the selection of anchor frames to adapt to the aerial photography of multiple smoke locations in forest fire scenes. The experimental results show that the ARGNet parametric number on the UAV-IoT platform is as low as 53.48 M, mAP reaches 79.03%, mAP50 reaches 90.26%, mAP75 reaches 82.35%, FPS reaches 122.5, and GFLOPs reaches 55.78. Compared with other mainstream methods, it has the advantages of real-time detection and high accuracy.

Coordinated investment in wind‐rich regions using dynamic line rating, energy storage and distributed static series compensation to facilitate congestion management

Wind farms installed at wind-rich regions often located in remote areas increase overloads in the weak adjacent transmission network. As a result of this congestion, extra transmission capacity is required to avoid wind energy or load curtailment that can be achieved by applying some modifications. New line construction and line uprating have been recognised as conventional solutions for the transmission capacity increase. Meanwhile, in the last decade, these solutions have brought several serious issues for the network operators, forcing them to evaluate and install different types of assets based on state-of-the-art technology in order to harvest the existing lines at the maximum possible level. An investment model is developed to relieve the congestion, including dynamic line rating (DLR), distributed static series compensation and energy storage systems (ESS), with a simplified unit commitment implementation. The resulting mixed-integer linear model is then solved with the classic Benders decomposition. Installations of DLR and distributed static series compensator (DSSC) on the same line are formulated, with techno-economic analysis performed for different asset combinations on the IEEE RTS 24 bus system, for high wind energy share scenarios. Wind curtailment levels are studied for different DLR/DSSC approaches, including the impact of practical implementation requirements. Based upon additional interlinking constraints, the robustness of investment decisions for independent DLR and DSSC investments is compared against approaches where DLR and DSSC investments are permitted on the same line. The investment based on sequential incorporation of the unit commitment in the study is also investigated to reflect the importance of the integration of the unit scheduling in the model for high renewable systems.

Paying attention for adjacent areas: Learning discriminative features for large-scale 3D scene segmentation

Despite recent improvements in analyzing large-scale 3D point clouds, several problems still exist: (a) segmentation models suffer from intra-class inconsistency and inter-class indistinction; (b) the existing methods ignore the inherent long-tailed class distribution of real-world 3D data. These problems result in unsatisfactory semantic segmentation predictions, especially in object adjacent areas. To handle these problems, this paper proposes a novel Adjacent areas Refinement Network (ARNet). Specifically, an Adjacent areas Refinement (AR) module is designed, which consists of two parallel attention blocks. Besides, our proposed attention blocks can process a large number of points (N∼105) with a slight increase in the computational complexity and time cost. Additionally, to deal with the inherent long-tailed class distribution in real-world 3D data, imbalance adjustment loss and occupancy regression loss are introduced. Based on this, the proposed network can handle the classification of both majority and minority classes, which is essential in distinguishing the ambiguous parts in large-scale 3D scenes. The proposed AR module and the loss functions can be easily integrated into the cutting-edge backbone networks, contributing to better performance in modeling semantic inter-dependencies and significantly improving the accuracy of the state-of-the-art semantic segmentation methods on indoor and outdoor scenes.

Unabridged adjacent modulation for clothing parsing

Clothing parsing has made tremendous progress in the domain of computer vision recently. Most state-of-the-art methods are based on the encoder-decoder architecture. However, the existing methods mainly neglect problems of feature uncalibration within blocks and semantics dilution between blocks. In this work, we propose an unabridged adjacent modulation network (UAM-Net) to aggregate multi-level features for clothing parsing. We first build an unabridged channel attention (UCA) mechanism on feature maps within each block for feature recalibration. We further design a top-down adjacent modulation (TAM) for decoder blocks. By deploying TAM, high-level semantic information and visual contexts can be gradually transferred into lower-level layers without loss. The joint implementation of UCA and TAM ensures that the encoder has an enhanced feature representation ability, and the low-level features of the decoders contain abundant semantic contexts. Quantitative and qualitative experimental results on two challenging benchmarks (i.e., colorful fashion parsing and the modified fashion clothing) declare that our proposed UAM-Net can achieve competitive high-accurate performance with the state-of-the-art methods. The source codes are available at: https://github.com/ctzuo/UAM-Net.

Maximizing Brain Health After Hemorrhagic Stroke: Bugher Foundation Centers of Excellence.

Maximizing Brain Health After Hemorrhagic Stroke: Bugher Foundation Centers of Excellence.

A case of unruptured tectal located AVM presenting with obstructive hydrocephalus

We present a case of arteriovenous malformation with aneurysmatic dilatation of the drainage vein, presenting with obstructive hydrocephalus located in the tectum. A 59-year-old female patient was taken to the emergency department after complaining for 8 days of increasingly worsening headache, gait disturbance, sleepiness, blurred vision, and double vision, as well as gush vomiting. The patient indicated that he had been experiencing from intermittent headaches for almost a year. On examination, there was an ataxic walk, bilateral papilledema, and slight cognitive impairment. CT, MRI and CTA imaging tests were used for the diagnosis, respectively (Figures 1-3). CT scans of the brain revealed triventricular hydrocephalus and a dense space occupying lesion thought to produce aqueductal stenosis in the tectal region of the mesencephalon (Figure 1). Following that, the patient had brain MRI and CTA imaging. This dense lesion in the tectum was found to be a vascular structure connected to the adjacent arteriovenous capillary network and the vein of galena (Figure 2,3). In the differential diagnosis, arteriovenous malformation with aneurysmatic dilatation in the drainage vein was considered primarily.

Collagen triple helix repeat containing-1 promotes functional recovery of sweat glands by inducing adjacent microvascular network reconstruction in vivo.

BackgroundSweat glands (SGs) have low regenerative potential after severe burns or trauma and their regeneration or functional recovery still faces many obstacles. In practice, restoring SG function requires not only the structural integrity of the gland itself, but also its neighboring tissues, especially blood vessels. Collagen triple helix repeat containing-1 (CTHRC1) was first identified in vascular repair, and increasing reports showed a close correlation between cutaneous appendage specification, patterning and regeneration. The purpose of the present study was to clarify the role of CTHRC1 in SGs and their adjacent microvessels and find therapeutic strategies to restore SG function.MethodsThe SGs and their adjacent microvascular network of Cthrc1−/− mice were first investigated using sweat test, laser Doppler imaging, tissue clearing technique and transcriptome analysis. The effects of CTHRC1 on dermal microvascular endothelial cells (DMECs) were further explored with cell proliferation, DiI-labeled acetylated low-density lipoprotein uptake, tube formation and intercellular junction establishment assays. The effects of CTHRC1 on SG function restoration were finally confirmed by replenishing the protein into the paws of Cthrc1−/− mice.ResultsCTHRC1 is a key regulator of SG function in mice. At the tissue level, Cthrc1 deletion resulted in the disorder and reduction of the microvascular network around SGs. At the molecular level, the knockout of Cthrc1 reduced the expression of vascular development genes and functional proteins in the dermal tissues. Furthermore, CTHRC1 administration considerably enhanced SG function by inducing adjacent vascular network reconstruction.ConclusionsCTHRC1 promotes the development, morphogenesis and function execution of SGs and their neighboring vasculature. Our study provides a novel target for the restoration or regeneration of SG function in vivo.