Role of 5-hydroxytryptamine type 3 receptors in the regulation of anxiety reactions

5-Hydroxytryptamine(5-HT)type 3 receptor(5-HT_(3)R)is the only type of ligand-gated ion channel in the 5-HT receptor family.Through the high permeability of Na+,K+,and Ca2+and activation of subsequent voltage-gated calcium channels(VGCCs),5-HT_(3)R induces a rapid increase of neuronal excitability or the release of neurotransmitters from axon terminals in the central nervous system(CNS).5-HT_(3)Rs are widely expressed in the medial prefrontal cortex(mPFC),amygdala(AMYG),hippocampus(HIP),periaqueductal gray(PAG),and other brain regions closely associated with anxiety reactions.They have a bidirectional regulatory effect on anxiety reactions by acting on different types of cells in different brain regions.5-HT_(3)Rs mediate the activation of the cholecystokinin(CCK)system in the AMYG,and theγ-aminobutyric acid(GABA)“disinhibition”mechanism in the prelimbic area of the mPFC promotes anxiety by the activation of GABAergic intermediate inhibitory neurons(IINs).In contrast,a 5-HT_(3)R-induced GABA“disinhibition”mechanism in the infralimbic area of the mPFC and the ventral HIP produces anxiolytic effects.5-HT_(2)R-mediated regulation of anxiety reactions are also activated by 5-HT_(3)R-activated 5-HT release in the HIP and PAG.This provides a theoretical basis for the treatment of anxiety disorders or the production of anxiolytic drugs by targeting 5-HT_(3)Rs.However,given the circuit specific modulation of 5-HT_(3)Rs on emotion,systemic use of 5-HT_(3)R agonism or antagonism alone seems unlikely to remedy anxiety,which deeply hinders the current clinical application of 5-HT_(3)R drugs.Therefore,the exploitation of circuit targeting methods or a combined drug strategy might be a useful developmental approach in the future.

Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

Deep neural networks often outperform classical machine learning algorithms in solving real-world problems.However,designing better networks usually requires domain expertise and consumes significant time and com-puting resources.Moreover,when the task changes,the original network architecture becomes outdated and requires redesigning.Thus,Neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network architectures.Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively correlated.Nevertheless,complex network structures will bring enormous computing resources.To cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters simultaneously.And then a decomposition-based multi-objective stochastic fractal search method is proposed to solve it.In view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more effectively.Additionally,two distinct update methods are employed in step size update stage to enhance the global and local search abilities adaptively.Furthermore,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the algorithm.Experimental studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.

Image analysis and machine learning-based malaria assessment system

Malaria is an important and worldwide fatal disease that has been widely reported by the World Health Organization(WHO),and it has about 219 million cases worldwide,with 435,000 of those mortal.The common malaria diagnosis approach is heavily reliant on highly trained experts,who use a microscope to examine the samples.Therefore,there is a need to create an automated solution for the diagnosis of malaria.One of the main objectives of this work is to create a design tool that could be used to diagnose malaria from the image of a blood sample.In this paper,we firstly developed a graphical user interface that could be used to help segment red blood cells and infected cells and allow the users to analyze the blood samples.Secondly,a Feed-forward Neural Network(FNN)is designed to classify the cells into two classes.The achieved results show that the proposed techniques can be used to detect malaria,as it has achieved 92%accuracy with a database that contains 27,560 benchmark images.

Endocannabinoids regulate fear memory generalization via hippocampal local circuits

Generalization is widely accepted as adaptive behavioral conditions that allow individuals to quickly respond to similar circumstances.But once overgeneralization occurs,e.g.due to the inability to suppress generalized fear,it could result in anxiety,depression and related mental disorders.Endocannabinoids(eCB)are important endogenous substance,known to play a role in contextual fear memory generalization.However,less is known in terms of the precise neural mechanism and the regulation of overgeneralization,in particular,for the eCB/CB1R signaling.Using fear memory generalization task,we show that type 1 cannabinoid receptors(CB1R)in hippocampal GABAergic neurons are necessary and sufficient for avoiding overgeneralization.Suppression or deletion of CB1R in hippocampal GABAergic neurons produces overgeneralized contextual fear memory.

Single Exposure to Cocaine Impairs Reinforcement Learning by Potentiating the Activity of Neurons in the Direct Striatal Pathway in Mice

Plasticity in the glutamatergic synapses on striatal medium spiny neurons(MSNs)is not only essential for behavioral adaptation but also extremely vulnerable to drugs of abuse.Modulation on these synapses by even a single exposure to an addictive drug may interfere with the plasticity required by behavioral learning and thus produce impairment.In the present work,we found that the negative reinforcement learning,escaping mild foot-shocks by correct nose-poking,was impaired by a single in vivo exposure to 20 mg/kg cocaine 24 h before the learning in mice.Either a single exposure to cocaine or reinforcement learning potentiates the glutamatergic synapses on MSNs expressing the striatal dopamine 1(D1)receptor(D1-MSNs).However,24 h after the cocaine exposure,the potentiation required for reinforcement learning was disrupted.Specific manipulation of the activity of striatal D1-MSNs in D1-cre mice demonstrated that activation of these MSNs impaired reinforcement learning in normal D1-cre mice,but inhibition of these neurons reversed the reinforcement learning impairment induced by cocaine.The results suggest that cocaine potentiates the activity of direct pathway neurons in the dorsomedial striatum and this potentiation might disrupt the potentiation produced during and required for reinforcement learning.

Effects of In-video Questions and Feedback on Learning Performance

The eye-tracking technology was used in this study to investigate the effects of embedded questions and feedback in instructional videos on learning performance and attention allocation and whether an expertise reversal effect existed.The experiment involved 49 learners with high-level prior knowledge and 45 ones with low-level prior knowledge from a university.Meanwhile,they learned instructional videos with no embedded feedback,embedded questions without feedback and embedded questions with feedback.Findings from the experiment showed that the instructional videos with embedded questions but without feedback not only improved the participants’attention but also enhanced their learning performance.Furthermore,there was an expertise reversal effect on the learning performance whereby instructional videos with embedded questions but without feedback improved the learning performance of learners with low-level prior knowledge,but not those with high-level prior knowledge.

A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain

We demonstrate a heuristic approach for optimizing the posterior density of the data association tracking algorithm via the random finite set(RFS)theory.Specifically,we propose an adjusted version of the joint probabilistic data association(JPDA)filter,known as the nearest-neighbor set JPDA(NNSJPDA).The target labels in all possible data association events are switched using a novel nearest-neighbor method based on the Kullback-Leibler divergence,with the goal of improving the accuracy of the marginalization.Next,the distribution of the target-label vector is considered.The transition matrix of the target-label vector can be obtained after the switching of the posterior density.This transition matrix varies with time,causing the propagation of the distribution of the target-label vector to follow a non-homogeneous Markov chain.We show that the chain is inherently doubly stochastic and deduce corresponding theorems.Through examples and simulations,the effectiveness of NNSJPDA is verified.The results can be easily generalized to other data association approaches under the same RFS framework.

Sorafenib sensitization in tumor therapy by iron overload and AMPK activation

Sorafenib,as a first-line drug for advanced hepatocellular carcinoma(HCC),could trigger ferroptosis by inhibiting cystine/glutamate transporter.However,low-level intracellular iron and insufficient activation of adenosine monophosphate(AMP)-activated protein kinase(AMPK)confer impaired response to sorafenib.In this study,a unique sorafenib nanocomposite dexterously modified with Fe-Material of Institut Lavoisier(sora@Fe-MIL)was synthesized to escalate intracellular iron level and activate AMPK,further potentiating the ferroptotic effect of sorafenib.Remarkably,this strategic deployment of sora@Fe-MIL triggered an extensive demise of cancer cells,while manifesting negligible deleterious impact on normal cells.Two prominent ferroptosis biomarkers,glutathione peroxidase 4(GPX4)and solute carrier family 7 member 11(SLC7A11),underwent pronounced downregulation,underscoring the efficacy of this strategy in inducing ferroptosis.Furthermore,the bioactivity of AMPK was considerably elevated,and its downstream targets were conspicuously inhibited by the treatment with sora@Fe-MIL.Using orthotopic HCC animal models,we observed a substantial suppression of primary in situ tumor growth,and ribonucleic acid(RNA)sequencing elucidated an elevated degree of ferroptosis and AMPK activation with the treatment of sora@Fe-MIL.In conclusion,we proposed that the meticulously designed strategy for secure and efficacious iron release and AMPK activation could significantly potentiate the ferroptotic impact of sorafenib,thus resuscitating its therapeutic response in HCC patients.