Semantic segmentation of pyramidal neuron skeletons using geometric deep learning

Neurons can be abstractly represented as skeletons due to the filament nature of neurites.With the rapid development of imaging and image analysis techniques,an increasing amount of neuron skeleton data is being produced.In some scienti fic studies,it is necessary to dissect the axons and dendrites,which is typically done manually and is both tedious and time-consuming.To automate this process,we have developed a method that relies solely on neuronal skeletons using Geometric Deep Learning(GDL).We demonstrate the effectiveness of this method using pyramidal neurons in mammalian brains,and the results are promising for its application in neuroscience studies.

Loading direction dependence of asymmetric response of pyramidal slip in rolled AZ31 magnesium alloy

Textured magnesium alloys usually exhibit anisotropic mechanical behavior due to the asymmetric activation of different twinning and slipping modes.This work focuses on the pyramidal slip responses of rolled AZ31 magnesium alloy under two loading conditions,compressive and tensile loading along the normal direction.Under the condition where the compressive loading direction is closely parallel to the c-axis of the unit cell,tensile twinning and basal slips are prohibited, dislocations then active and tend to accumulate at grain boundaries and form dislocation walls.Meanwhile,these dislocations exhibit zigzag morphologies,which result from the cross-slip from {10■1} first-order pyramidal plane to {11■2} second-order pyramidal plane,then back to {10■1} first-order pyramidal plane.Under the condition where tensile twins are prevalent,{10■1} first-order and {11■2} second-order pyramidal dislocations are favorable to be activated.Both types of dislocations behave climb-like dissociations onto the basal plane,forming zigzag dislocations.

Dissociation of edge and screw pyramidal Ⅰ and Ⅱ dislocations in magnesium

Pyramidal dislocations in magnesium (Mg) and other hexagonal close-packed metals play an important role in accommodating plastic strains along the c-axis.Bulk single crystal Mg only presents very limited plasticity in c-axis compression,and this behavior was attributed to out-of-plane dissociation of pyramidal dislocations onto the basal plane and resulted in an immobile dislocation configuration.In contrast,other simulations and experiments reported in-plane dissociation of pyramidal dislocations on their slip planes.Thus,the core structure and mode of dissociation of pyramidal dislocations are still not well understood.To better understand the dissociation behavior of pyramidal dislocations in Mg at room temperature,in this work,atomistic simulations were conducted to investigate four types of pyramidal dislocations at 300 K:edge and screw Py-Ⅰ on{1011},edge and screw Py-Ⅱ on{1122}by using a modified embedded atom method (MEAM) potential for Mg and anisotropic elasticity dislocation model.The results show that when energy minimization was performed before relaxation,in-plane dissociation of edge dislocations on respective pyramidal plane could be obtained at room temperature for all four types of dislocation.Without energy minimization,the edge dislocations dissociated out-of-plane onto the basal plane.Calculations of potential energy and hydrostatic stress of individual atoms at the edge dislocation core show that the extraordinarily high energy and atomic stresses in the as-constructed dislocation structures caused the out-of-plane dissociation onto the basal plane.The core structures of all four types of pyramidal dislocation after in-plane dissociation were analyzed by computing the distribution of the Burgers vector.

Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical impli-cations.Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments.Consequently,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts.This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection capability.The proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body parts.The experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor activity.Moreover,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and F1-score.Furthermore,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.

Potential Power of the Pyramidal Structure VII: Effects of Pyramid Power and Bio-Entanglement on the Circadian Rhythm of Biosensors

We have demonstrated the existence of a pyramid power and have revealed its characteristics by strictly scientific experiments using biosensors. We also revealed the existence of a Bio-Entanglement, an entangled relationship between biosensors. A parallel study of biosensors (edible cucumber slices) had also been conducted, and we found that the circadian rhythm of gas concentrations emitted from biosensors changes seasonally. The pyramid power and Bio-Entanglement did not change the number of cycles in the periodic approximation curve representing circadian rhythm. Therefore, in this paper we analyzed the influence of the pyramid power and Bio-Entanglement, i.e., their influence on the phase, amplitude, and correlation coefficient of the periodic approximation curve representing the circadian rhythm of emitted gas concentrations. The main results are as follows. 1) The pyramid power shifted the phase of the periodic approximation curve representing the circadian rhythm by 43 minutes. 2) The amplitude of the periodic approximation curve changed with the pyramid power and the Bio-Entanglement. The effect on the lower and upper sections of the biosensors stacked in two layers was different, with a tendency to increase the amplitude of the lower layer and decrease the amplitude of the upper layer. 3) The pyramid power and the Bio-Entanglement affected the correlation coefficient between gas concentration and the periodic approximation curve representing the circadian rhythm of gas concentration. The effect on the lower and upper layers of the biosensors was different, with a tendency for the lower layer correlation coefficient to be larger and the upper layer correlation coefficient to be smaller. Previously we demonstrated that the pyramid power and the Bio-Entanglement affect the ratio of gas concentration, i.e., psi index Ψ. In this paper we demonstrate for the first time that the pyramid power and the Bio-Entanglement affect time, i.e., phase difference.

Potential Power of the Pyramidal Structure VIII: Exploration of Periodic Diurnal Oscillation of Pyramid Power and Bio-Entanglement

To date, numerous books have been published on so-called “pyramid power” but there have been few academic papers on this subject other than our own. Since 2007, to demonstrate the pyramid power, we have undertaken strictly scientific experiments using a pyramidal structure (PS) that we have carefully constructed. In previous reports, we used the edible cucumber, Cucumis sativus as an effective and practical biosensor. Through measurement and analysis of volatile components (gas concentrations) emitted from the biosensor, we were able to demonstrate the existence of the pyramid power and revealed some of its characteristics. In a paper published in 2022, we showed that gas concentration release from this biosensor displayed a circadian rhythm and that this rhythm changed with the season. Based on the result that the biosensor had a periodic diurnal oscillation called a circadian rhythm, we questioned whether or not pyramid power and Bio-Entanglement also had periodic diurnal oscillations. In this paper, we investigated that possibility. Our results have shown that pyramid power and Bio-Entanglement do not exhibit significant periodic diurnal oscillations. Thus we have revealed for the first time that the field associated with pyramid power is a type of static field that always exerts a constant influence. We expect that our research results will be widely accepted in the future and will become the foundation for a new research field in science, with a wide range of applications.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Regulation of specific abnormal calcium signals in the hippocampal CA1 and primary cortex M1 alleviates the progression of temporal lobe epilepsy

Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

An improved deep dilated convolutional neural network for seismic facies interpretation

With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.

Control of GaN inverted pyramids growth on c-plane patterned sapphire substrates

Growth of gallium nitride(GaN)inverted pyramids on c-plane sapphire substrates is benefit for fabricating novel devices as it forms the semipolar facets.In this work,GaN inverted pyramids are directly grown on c-plane patterned sapphire substrates(PSS)by metal organic vapor phase epitaxy(MOVPE).The influences of growth conditions on the surface morphol-ogy are experimentally studied and explained by Wulff constructions.The competition of growth rate among{0001},{1011},and{1122}facets results in the various surface morphologies of GaN.A higher growth temperature of 985 ℃ and a lowerⅤ/Ⅲratio of 25 can expand the area of{}facets in GaN inverted pyramids.On the other hand,GaN inverted pyramids with almost pure{}facets are obtained by using a lower growth temperature of 930℃,a higherⅤ/Ⅲratio of 100,and PSS with pattern arrangement perpendicular to the substrate primary flat.

Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.

An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

In cornfields,factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation.In addition,remote areas such as farmland are usually constrained by limited computational resources and limited collected data.Therefore,it becomes necessary to lighten the model to better adapt to complex cornfield scene,and make full use of the limited data information.In this paper,we propose an improved image segmentation algorithm based on unet.Firstly,the inverted residual structure is introduced into the contraction path to reduce the number of parameters in the training process and improve the feature extraction ability;secondly,the pyramid pooling module is introduced to enhance the network’s ability of acquiring contextual information as well as the ability of dealing with the small target loss problem;and lastly,Finally,to further enhance the segmentation capability of the model,the squeeze and excitation mechanism is introduced in the expansion path.We used images of corn seedlings collected in the field and publicly available corn weed datasets to evaluate the improved model.The improved model has a total parameter of 3.79 M and miou can achieve 87.9%.The fps on a single 3050 ti video card is about 58.9.The experimental results show that the network proposed in this paper can quickly segment corn weeds in a cornfield scenario with good segmentation accuracy.

NFHP-RN:AMethod of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective detection.With the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has progressed.However,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract representation.To address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is proposed.Specifically,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in NFHP.In addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background traffic.Finally,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors.Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities.Comparedwith state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.

IMTNet:Improved Multi-Task Copy-Move Forgery Detection Network with Feature Decoupling and Multi-Feature Pyramid

Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality and quantity,and a narrow range of applicable tasks.These limitations significantly restrict the capacity and applicability of CMFD.To overcome the limitations of existing methods,a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach.Firstly,this study formulates the objective task and network relationship as an optimization problem using transfer learning.Furthermore,it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase.Secondly,a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50.Finally,suspicious regions are localized using a feature pyramid network with bottom-up path augmentation.Experimental results demonstrate that IMTNet achieves faster convergence,shorter training times,and favorable generalization performance compared to existingmethods.Moreover,it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F_(1).

Emerging technological developments to address pest resistance in Bt cotton

Cotton plays a crucial role in shaping Indian economy and rural livelihoods.The cotton crop is prone to numerous insect pests,necessitating insecticidal application,which increases production costs.The advent of the expression of Bacillus thuringiensis(Bt)insecticidal protein in cotton has significantly reduced the burden of pest without compromising environmental or human health.After the introduction of transgenic cotton,the cultivated area expanded to 22 million hectares,with a 64% increase in adoption by farmers worldwide.Currently,Bt cotton accounts for 93% of the cultivated cotton area in India.However,extensive use of Bt cotton has accelerated resistance development in pests like the pink bollworm.Furthermore,the overreliance on Bt cotton has reduced the use of broad-spectrum pesticides,favouring the emergence of secondary pests with significant challenges.This emphasizes the urgent necessity for developing novel pest management strategies.The high-dose and refuge strategy was initially effective for managing pest resistance in Bt cotton,but its implementation in India faced challenges due to misunderstandings about the use of non-Bt refuge crops.Although gene pyramiding was introduced as a solution,combining mono toxin also led to instances of cross-resistance.Therefore,there is a need for further exploration of biotechnological approaches to manage insect resistance in Bt cotton.Advanced biotechnological strategies,such as sterile insect release,RNA interference(RNAi)-mediated gene silencing,stacking Bt with RNAi,and genome editing using clustered regularly interspaced short palindromic repeats/CRISPR-associated protein(CRISPR-Cas),offer promising tools for identifying and managing resistance genes in insects.Additionally,CRISPR-mediated gene drives and the development of novel biopesticides present potential avenues for effective pest management in cotton cultivation.These innovative approaches could significantly enhance the sustainability and efficacy of pest resistance management in Bt cotton.

Two-Layer Attention Feature Pyramid Network for Small Object Detection

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

Friendship Through Football

Lin Yuexin,a student at Beijing No.12 High School,stands in front of a long roll of paper and looks closely at it.In the centre of the scroll are silhouettes of young Chinese and Africans playing football.In the background,cultural symbols of China,such as the Temple of Heaven and the Great Wall,stand alongside those of Africa,represented by the pyramids of Egypt and other emblematic images.On the left is an inscription in Chinese:“We are family,”symbolising the continued commitment and development of cultural exchanges between the two sides.

基于CSSE模型的公路路面裂缝检测方法研究

为解决目前现有的裂缝识别方法存在的识别效果不佳、识别精度低等弊端,提出一种基于CSSE模型的路面裂缝检测识别方法。该方法以CNN卷积网络为基础,通过融合SPPFCSPC空间金字塔池化结构与SE注意力机制,从而实现裂缝的准确快速的定位。首先,并通过lableimg图像标注软件对裂缝图像进行标注,建立本文的裂缝图像数据集,然后使用CSSE模型以及Yolov5-s和Yolov5-mobileone目标检测模型对裂缝数据集进行训练和测试对比,检测结果表明,CSSE模型识别效果优于yolov5裂缝检测模型。该模型能够满足裂缝数据准确、快速的检测需求,实现高阈值检测的裂缝精准定位。为高质量道路裂缝数据集的构建以及复杂裂缝损害智能识别奠定基础。

Takeda G protein-coupled receptor 5 modu⁃lates depression-like behaviors via hippocam⁃pal CA3 pyramidal neurons afferent to dorso⁃lateral septum

OBJECTIVE Takeda G protein-coupled receptor 5(TGR5)is recognized as a promising target for type 2 diabetes and metabolic syndrome;its expression has been demonstrat⁃ed in the brain and is thought to be neuroprotec⁃tive.Here,we hypothesize that dysfunction of central TGR5 may contribute to the pathogene⁃sis of depression.METHODS In well-established chronic social defeat stress(CSDS)and chronic restraint stress(CRS)models of depression,we investigated the functional roles of TGR5 in CA3 pyramidal neurons(PyNs)and underlying mech⁃anisms of the neuronal circuit in depression(for in vivo studies,n=10;for in vitro studies,n=5-10)using fiber photometry;optogenetic,chemoge⁃netic,pharmacological,and molecular profiling techniques;and behavioral tests.RESULTS Both CSDS and CRS most significantly reduced TGR5 expression of hippocampal CA3 PyNs.Genetic overexpression of TGR5 in CA3 PyNs or intra-CA3 infusion of INT-777,a specific agonist,protected against CSDS and CRS,exerting sig⁃nificant antidepressant-like effects that were mediated via CA3 PyN activation.Conversely,genetic knockout or TGR5 knockdown in CA3 facilitated stress-induced depression-like behav⁃iors.Re-expression of TGR5 in CA3 PyNs rather than infusion of INT-777 significantly improved depression-like behaviors in Tgr5 knockout mice exposed to CSDS or CRS.Silencing and stimula⁃tion of CA3 PyNs→somatostatin-GABAergic(gamma-aminobutyric acidergic)neurons of the dorsolateral septum circuit bidirectionally regulat⁃ed depression-like behaviors,and blockade of this circuit abrogated the antidepressant-like effects from TGR5 activation of CA3 PyNs.CON⁃CLUSION TGR5 can regulate depression via CA3 PyNs→somatostatin-GABAergic neurons of dorsolateral septum transmission,suggesting that TGR5 could be a novel target for developing antidepressants.