Chronic pain often develops severe mood changes such as depression.However,how chronic pain leads to depression remains elusive and the mechanisms determining individuals’responses to depression are largely unexplore...Chronic pain often develops severe mood changes such as depression.However,how chronic pain leads to depression remains elusive and the mechanisms determining individuals’responses to depression are largely unexplored.Here we found that depression-like behaviors could only be observed in 67.9%of mice with chronic neuropathic pain,leaving 32.1%of mice with depression resilience.We determined that the spike discharges of the ventral tegmental area(VTA)-projecting lateral habenula(LHb)glutamatergic(Glu)neurons were sequentially increased in sham,resilient and susceptible mice,which consequently inhibited VTA dopaminergic(DA)neurons through a LHbGlu-VTAGABA-VTADA circuit.Furthermore,the LHbGlu-VTADA excitatory inputs were dampened via GABAB receptors in a pre-synaptic manner.Regulation of LHb-VTA pathway largely affected the development of depressive symptoms caused by chronic pain.Our study thus identifies a pivotal role of the LHb-VTA pathway in coupling chronic pain with depression and highlights the activity-dependent contribution of LHbGlu-to-VTADA inhibition in depressive behavioral regulation.展开更多
Recently,deep learning has achieved great success in visual tracking tasks,particularly in single-object tracking.This paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based ...Recently,deep learning has achieved great success in visual tracking tasks,particularly in single-object tracking.This paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based on deep learning.First,we introduce basic knowledge of deep visual tracking,including fundamental concepts,existing algorithms,and previous reviews.Second,we briefly review existing deep learning methods by categorizing them into data-invariant and data-adaptive methods based on whether they can dynamically change their model parameters or architectures.Then,we conclude with the general components of deep trackers.In this way,we systematically analyze the novelties of several recently proposed deep trackers.Thereafter,popular datasets such as Object Tracking Benchmark(OTB)and Visual Object Tracking(VOT)are discussed,along with the performances of several deep trackers.Finally,based on observations and experimental results,we discuss three different characteristics of deep trackers,i.e.,the relationships between their general components,exploration of more effective tracking frameworks,and interpretability of their motion estimation components.展开更多
基金This work was supported by the National Natural Science Foundation of China(32192410,32071000,81870866,81571074,82230037,81971226,81620108008,82130034)the Foundation for Distinguished Young Scholars of ShaanXi(2019JC-21,2021JC-33)+1 种基金Young Scholar Project of the First Affiliated Hospital of Nanchang University(YFYPY202109)the Boost Plan of Xijing Hospital(XJZT21J01).
文摘Chronic pain often develops severe mood changes such as depression.However,how chronic pain leads to depression remains elusive and the mechanisms determining individuals’responses to depression are largely unexplored.Here we found that depression-like behaviors could only be observed in 67.9%of mice with chronic neuropathic pain,leaving 32.1%of mice with depression resilience.We determined that the spike discharges of the ventral tegmental area(VTA)-projecting lateral habenula(LHb)glutamatergic(Glu)neurons were sequentially increased in sham,resilient and susceptible mice,which consequently inhibited VTA dopaminergic(DA)neurons through a LHbGlu-VTAGABA-VTADA circuit.Furthermore,the LHbGlu-VTADA excitatory inputs were dampened via GABAB receptors in a pre-synaptic manner.Regulation of LHb-VTA pathway largely affected the development of depressive symptoms caused by chronic pain.Our study thus identifies a pivotal role of the LHb-VTA pathway in coupling chronic pain with depression and highlights the activity-dependent contribution of LHbGlu-to-VTADA inhibition in depressive behavioral regulation.
基金supported by National Natural Science Foundation of China(Nos.61922064 and U2033210)Zhejiang Provincial Natural Science Foundation(Nos.LR17F030001 and LQ19F020005)the Project of Science and Technology Plans of Wenzhou City(Nos.C20170008 and ZG2017016)。
文摘Recently,deep learning has achieved great success in visual tracking tasks,particularly in single-object tracking.This paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based on deep learning.First,we introduce basic knowledge of deep visual tracking,including fundamental concepts,existing algorithms,and previous reviews.Second,we briefly review existing deep learning methods by categorizing them into data-invariant and data-adaptive methods based on whether they can dynamically change their model parameters or architectures.Then,we conclude with the general components of deep trackers.In this way,we systematically analyze the novelties of several recently proposed deep trackers.Thereafter,popular datasets such as Object Tracking Benchmark(OTB)and Visual Object Tracking(VOT)are discussed,along with the performances of several deep trackers.Finally,based on observations and experimental results,we discuss three different characteristics of deep trackers,i.e.,the relationships between their general components,exploration of more effective tracking frameworks,and interpretability of their motion estimation components.
