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ABSTRACTMacroautophagy/autophagy is a complex degradation process with a dual role in cell death that is influenced by the cell types that are involved and the stressors they are exposed to. Ferroptosis is an iron-dependent oxidative form of cell death characterized by unrestricted lipid peroxidation in the context of heterogeneous and plastic mechanisms. Recent studies have shed light on the involvement of specific types of autophagy (e.g. ferritinophagy, lipophagy, and clockophagy) in initiating or executing ferroptotic cell death through the selective degradation of anti-injury proteins or organelles. Conversely, other forms of selective autophagy (e.g. reticulophagy and lysophagy) enhance the cellular defense against ferroptotic damage. Dysregulated autophagy-dependent ferroptosis has implications for a diverse range of pathological conditions. This review aims to present an updated definition of autophagy-dependent ferroptosis, discuss influential substrates and receptors, outline experimental methods, and propose guidelines for interpreting the results.Abbreviation: 3-MA:3-methyladenine; 4HNE: 4-hydroxynonenal; ACD: accidentalcell death; ADF: autophagy-dependentferroptosis; ARE: antioxidant response element; BH2:dihydrobiopterin; BH4: tetrahydrobiopterin; BMDMs: bonemarrow-derived macrophages; CMA: chaperone-mediated autophagy; CQ:chloroquine; DAMPs: danger/damage-associated molecular patterns; EMT,epithelial-mesenchymal transition; EPR: electronparamagnetic resonance; ER, endoplasmic reticulum; FRET: Försterresonance energy transfer; GFP: green fluorescent protein;GSH: glutathione;IF: immunofluorescence; IHC: immunohistochemistry; IOP, intraocularpressure; IRI: ischemia-reperfusion injury; LAA: linoleamide alkyne;MDA: malondialdehyde; PGSK: Phen Green™ SK;RCD: regulatedcell death; PUFAs: polyunsaturated fatty acids; RFP: red fluorescentprotein;ROS: reactive oxygen species; TBA: thiobarbituricacid; TBARS: thiobarbituric acid reactive substances; TEM:transmission electron microscopy.

Sporadic or late-onset Alzheimer’s disease (LOAD) accounts for more than 95% of Alzheimer’s disease (AD) cases without any family history. Although genome-wide association studies have identified associated risk genes and loci for LOAD, numerous studies suggest that many adverse environmental factors, such as social isolation, are associated with an increased risk of dementia. However, the underlying mechanisms of social isolation in AD progression remain elusive. In the current study, we found that 7 days of social isolation could trigger pattern separation impairments and presynaptic abnormalities of the mossy fibre-CA3 circuit in AD mice. We also revealed that social isolation disrupted histone acetylation and resulted in the downregulation of 2 dentate gyrus (DG)-enriched miRNAs, which simultaneously target reticulon 3 (RTN3), an endoplasmic reticulum protein that aggregates in presynaptic regions to disturb the formation of functional mossy fibre boutons (MFBs) by recruiting multiple mitochondrial and vesicle-related proteins. Interestingly, the aggregation of RTN3 also recruits the PP2A B subunits to suppress PP2A activity and induce tau hyperphosphorylation, which, in turn, further elevates RTN3 and forms a vicious cycle. Finally, using an artificial intelligence-assisted molecular docking approach, we determined that senktide, a selective agonist of neurokinin3 receptors (NK3R), could reduce the binding of RTN3 with its partners. Moreover, application of senktide in vivoeffectively restored DG circuit disorders in socially isolated AD mice. Taken together, our findings not only demonstrate the epigenetic regulatory mechanism underlying mossy fibre synaptic disorders orchestrated by social isolation and tau pathology but also reveal a novel potential therapeutic strategy for AD.

Low-light image enhancement aims to improve the visual quality of images captured under poor illumination and has caught much attention these years. However, existing low-light enhancement methods encounter many problems, e.g., they may not be robust to diverse low-light conditions or have to sacrifice computational efficiency for enhancement performance, which hinder their practical applications. To solve these problems, this paper proposes a novel enhancement method, called Pixel-Wise Gamma Correction Mapping (PWGCM), which combines our innovative pixel-wise Gamma Correction (GC) and deep learning. Compared with conventional GC, our pixel-wise GC is characterized by a set of gamma correction maps, which have the same size as the input image and are taken to replace the single global GC parameter of conventional GC. These gamma correction maps are generated from the low-light image input by a lightweight convolutional neural network at low computational cost. New no-reference loss functions are provided to train the network, ensuring reliable unsupervised learning. Furthermore, our PWGCM is enhanced by an iterative strategy, under which the low-light input image is iteratively enhanced based on the generated gamma correction maps and can yield visually pleasant results. Extensive experiments are done to compare our PWGCM with several state-of-the-art methods in terms of visual quality, efficiency, and auxiliary effects on high-level tasks. The comparison results confirm the superiority of our PWGCM.

To take full advantage of lab-on-fiber technologies for gas sensing, we propose a reflective Fabry-Perot interferometer (FPI) CO2 gas sensor composed of the functional material [polyhexamethylene biguanide (PHMB)] and a single-mode fiber (SMF)-hollow core capillary (HCC) structure. The theoretical analysis of the FPI cavity mode in the PHMB film is discussed. The experiment results show a maximum sensitivity of 286 pm/ppm in the low-concentration region of 0–1000-ppm detection range and a high sensitivity of 6.88 pm/ppm in the atmosphere CO2 concentration range (200–600 ppm). The response time of the CO2 sensor is about 10 min. Repeated experiments show that the sensor has good sensing stability and a high selectivity for CO2 gas. In addition, the sensitivity of our CO2 sensor is stable to temperature changes. Moreover, the results of monitoring the real in-door atmosphere verify the repeatability and desirable sensing performance of our sensor. This reflective lab-on-fiber CO2 sensor has a concise structure, low cost, and high sensitivity possessing more possibilities in various and complex applications.

For iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially set threshold, plane fitting is carried out, the on-time point cloud to be deployed is separated from the complex background, and statistical analysis is used to calculate the distance between the point cloud and the neighborhood point to quickly remove the invalid point cloud. The surface equation is set to calculate the tangent plane of point cloud normal vector and each normal vector, and the local coordinate system is constructed. The angle between adjacent vectors and the local coordinate system is calculated to determine the feature point set of edge contour. According to the covariance matrix of the feature points set, the principal feature component is obtained, the principal axis direction of the two sets of point clouds is found, and the rotation matrix and the displacement vector are obtained. Finally, GICP precise registration of point cloud is carried out according to initial pose parameters and rigid body transformation matrix obtained by maximum likelihood estimation method. The results show that the optimized algorithm can effectively avoid local convergence. Compared with the traditional ICP algorithm, when the algorithm achieves the same registration accuracy in the public dataset experiment, the registration speed is on average 44.82% faster and the overlap rate is on average 15.26% higher. In the real dataset experiment, the registration speed is on average 59.04% faster, the registration accuracy is on average 30.24% higher and the overlap rate is on average 10.61% higher. This shows that the optimization algorithm is superior to the traditional ICP algorithm in registration accuracy and convergence speed.

An integrated all-fiber sensor based on ring-core fiber (RCF)-embedded long-period fiber grating (LPFG) cascaded RCF-Mach-Zehnder interferometer (MZI) for measuring curvature and temperature is proposed and demonstrated experimentally. The RCF-embedded LPFG section is fabricated by periodically embedding hundreds of micrometers sized RCF into SMF by employing the mature, low-cost cleaving splicing method (CSM). Due to the large core diameter mismatch, the energy within the core is exceeded into the cladding mode more effectively, and the periodic refractive index modulation gives our sensor has a compact size with only three-period SMF-RCF structures and a total length of 2.8 mm. To measure curvature and temperature simultaneously, an RCF-based MZI with an RCF length of 1 mm is cascaded behind LPFG by employing the CSM. Thus, there are two dips corresponding to LPFG and MZI occur in the transmission spectrum. The dips show different responses to curvature and temperature changes. The maximum curvature and temperature sensitivities of the two dips are 26.61, −1.67 nm/ $\text{m}^{-{1}}$ , 0.053 nm/°C, and 0.059 nm/°C, respectively. By utilizing a $2\times2$ matrix, simultaneous measurement of curvature and temperature can be achieved. The integrated all-fiber sensor has a compact size, low cost, and high sensitivity possessing more competitivity in complex, harsh applications.

Astrocytes constitute a major part of the central nervous system and the delineation of their activity patterns is conducive to a better understanding of brain network dynamics. This study aimed to develop a magnetic resonance imaging (MRI)-based method in order to monitor the brain-wide or region-specific astrocytes in live animals. Adeno-associated virus (AAVs) vectors carrying the human glial fibrillary acidic protein (GFAP) promoter driving the EGFP-AQP1 (Aquaporin-1, an MRI reporter) fusion gene were employed. The following steps were included: constructing recombinant AAV vectors for astrocyte-specific expression, detecting MRI reporters in cell culture, brain regions, or whole brain following cell transduction, stereotactic injection, or tail vein injection. The astrocytes were detected by both fluorescent imaging and Diffusion-weighted MRI. The novel AAV mutation (Site-directed mutagenesis of surface-exposed tyrosine (Y) residues on the AAV5 capsid) significantly increased fluorescence intensity (p< 0.01) compared with the AAV5 wild type. Transduction of the rAAV2/5 carrying AQP1 induced the titer-dependent changes in MRI contrast in cell cultures (p< 0.05) and caudate-putamen (CPu) in the brain (p< 0.05). Furthermore, the MRI revealed a good brain-wide alignment between AQP1 levels and ADC signals, which increased over time in most of the transduced brain regions. In addition, the rAAV2/PHP.eB serotype efficiently introduced AOP1 expression in the whole brain via tail vein injection. This study provides an MRI-based approach to detect dynamic changes in astrocytes in live animals. The novel in vivo tool could help us to understand the complexity of neuronal and glial networks in different pathophysiological conditions.

Oxytocin (OXT) is a nine amino acid neuropeptide hormone that has become one of the most intensively studied molecules in the past few decades. The vast majority of OXT is synthesized in the periventricular nucleus and supraoptic nucleus of the hypothalamus, and a few are synthesized in some peripheral organs (such as the uterus, ovaries, adrenal glands, thymus, pancreas, etc.) OXT modulates a series of physiological processes, including lactation, parturition, as well as some social behaviors. In addition, more and more attention has recently been focused on the analgesic effects of oxytocin. It has been reported that OXT can relieve tension and pain without other adverse effects. However, the critical role and detailed mechanism of OXT in analgesia remain unclear. This review aims to summarize the mechanism of OXT in analgesia and some ideas about the mechanism.

In the article, a novel reflective all-fiber integrated sensor for simultaneous gas pressure and temperature sensing has been proposed and demonstrated in experiment. The proposed sensor is fabricated by combining seven core fibers (SCFs), a twin-hole and dual-core fiber (THDCF), a single-mode fiber (SMF) extrinsic Fabry-Perot interferometer (FPI) and a fiber Bragg grating (FBG). The FPI section and FBG section act as a pressure and temperature sensing unit. Especially, the MZI has the effect of filtering the FPI spectrum and is more conducive to the demodulation of the FPI reflected signal. In FPI section, the two SMFs connects with a hollow core capillary (HCC) by the ultraviolet (UV) curing glue. The sensor exhibits a high pressure and temperature sensitivity of 13.10 nm/MPa and 0.20 nm/°C in the range of 0.1-1.2 MPa and 30-90 °C, respectively. The FBG works as a temperature sensor without pressure responding, is fabricated by using the UV laser-based phase mask technology in our lab. By utilizing the dual-wavelength matrix method, both gas pressure and temperature can be measured simultaneously. In sum, with the advantage of compact size, easy fabricated, low cost, high sensitivity, reflection-type and the ability of multi-parameter measurement, our sensor is more competitive in harsh environments.

This paper investigates the dynamic event-triggered control problem for a networked Markovian jump system under hybrid attacks. In the concerned system, both the sensor-controller (S-C) network and the controller-actuator (C-A) network suffer from hybrid attacks, including denial-of-service (DoS) attacks and deception attacks. Two dynamic event-triggers at the controller and the sensor are designed to handle these dual-network hybrid attacks. Moreover, a unified framework is constructed to design an observer-based output feedback controller (OBOFC) which simultaneously considers hybrid attacks and dynamic event-triggering strategies (DETSs). Sufficient conditions to ensure the input-to-state stability (ISS) of the concerned system are derived. Furthermore, a set of linear matrix inequalities (LMIs) are proposed to obtain the desired observer-based feedback control gains and event-triggering parameters. A simulation example is given to further verify the effectiveness of the proposed control and event-triggered schemes. • A general model whose S-C and C-A networks suffer from both deception and DoS attacks • Two dynamic event-triggers to reduce the frequency of transmissions • A general ISS analysis framework based on a set of LMIs [ABSTRACT FROM AUTHOR]

This article takes into account general repeated security games with no prior knowledge, i.e., the game payoffs and the attacker’s behavior model are unknown and limited observability. Besides the traditional “regret” criterion, “reallocation times” is introduced as an additional criterion that provides a more comprehensive evaluation of the defense strategies. For such games, a novel random-walk perturbations with uniform exploration (RWP-UE) algorithm is proposed and we deduce the corresponding upper bound of the expected regret and expected reallocation times. Theoretical analysis shows that the RWP-UE algorithm achieves not only low regret with the same magnitude as existing achievements but also fewer reallocation times. Experiments are carried out against four types of attackers, and the results illustrate that the RWP-UE algorithm achieves superior performance.

We have proposed a fiber-optic bending sensor based on cascading seven-core fiber (SCF)–twin-hole dual-core fiber (THDCF)–SCF structure. Several dips are generated by the Mach–Zehnder interference. The THDCF Section acts as a bending unit, exhibiting high bending sensitivity. As the applied bending increases from 0 to 8.51 $\text{m}^{-{1}}$ , the dip wavelength exhibits the highest sensitivity of 12.15 nm/ $\text{m}^{-{1}}$ . With the benefit of the asymmetric structure of the THDCF, the dip shows different obvious responses for different bending directions. Besides, through the temperature and the surrounding refractive index (SRI) sensing experiments, it is proved that our sensor shows low-temperature sensitivity (0.01 nm/°C) and low SRI sensitivity (2.38 nm/RIU). Simple structure, low cost, and high sensitivity make the sensor more competitive in the applications of structural conventional health monitoring and human posture detection.

Cancer stem cells (CSCs), a rare subset of tumor cells, have been recognized as promotive role on tumor initiation and propagation. Among, aerobic glycolysis, widely clarified in multiple tumor cells, is the key for maintaining cancer stemness. Regrettably, it is largely unknown about the connection of cellular metabolic reprogramming and stemness in gastric carcinoma (GC). Two GC parental cells lines PAMC‐82 and SNU‐16 and their spheroids were obtained to determine the expression status of POU1F1 using quantitative real‐time PCR (qRT‐PCR) and western blotting analysis, respectively. Gain or loss‐of‐function assay was employed to assess its biological effects. Sphere formation and transwell assays were performed to evaluate the stem cell‐like traits, including self‐renewal capacity, migration and invasion. Chromatin immunoprecipitation (ChIP) and luciferase reporter assays were conducted for determining the binding relationship of POU1F1 on ENO1 promoter region. Herein, aberrantly upregulated POU1F1 was observed in spheroids, compared with the parental PAMC‐82 and SNU‐16 cells, which promoted stem cell‐like traits, as representing increasing sphere formation, enhanced cell migration and invasion. Additionally, POU1F1 expression was positively with glycolytic signaling, as displaying increasing glucose consumption, lactic acid production, and extracellular acid ratio (ECAR). Furthermore, POU1F1 was identified to be a transcriptional activator of ENO1, of which overexpression remarkably abolished POU1F1 knockdown‐mediated blocking effects. Taken together, we draw a conclusion that POU1F1 facilitated the stem cell‐like properties of GC cells through transcriptionally augmenting ENO1 to enhance glycolysis.

Neuronal death is one of the most common pathological hallmarks of diverse neurological diseases, which manifest varying degrees of cognitive or motor dysfunction. Neuronal death can be classified into multiple forms with complicated and unique regulatory signaling pathways. Tau is a key microtubule-associated protein that is predominantly expressed in neurons to stabilize microtubules under physiological conditions. In contrast, pathological tau always detaches from microtubules and is implicated in a series of neurological disorders that are characterized by irreversible neuronal death, such as necrosis, apoptosis, necroptosis, pyroptosis, ferroptosis, autophagy-dependent neuronal death and phagocytosis by microglia. However, recent studies have also revealed that pathological tau can facilitate neuron escape from acute apoptosis, delay necroptosis through its action on granulovacuolar degeneration bodies (GVBs), and facilitate iron export from neurons to block ferroptosis. In this review, we briefly describe the current understanding of how pathological tau exerts dual effects on neuronal death by acting as a double-edged sword in different neurological diseases. We propose that elucidating the mechanism by which pathological tau affects neuronal death is critical for exploring novel and precise therapeutic strategies for neurological disorders.

Recently online intelligent education has caught more and more attention, especially due to the global influence of Covid-19. A major task of intelligent education is Knowledge Tracing (KT) which aims to capture students' dynamic status based on their historical interaction records and predict their responses to new questions. However, most existing KT methods suffer from the record data sparsity problem. In reality, there are a huge number of questions in the online database and students can only interact with a very small set of these questions. The records of some questions could be extremely sparse, which may significantly degrade the performance of traditional KT methods. Although recent graph neural network (GNN) based KT methods can fuse graph-structured information and improve the representation of questions to some extent, the pairwise structure of GNN neglects the complex high-order and heterogeneous relations among questions. To resolve the above issues, we develop a novel KT model with the heterogeneous hypergraph network (HHN) and propose an attentive mechanism, including intra- and inter-graph attentions, to aggregate neighbors' information upon HHN. To further enhance the question representation, we supplement the supervised prediction task of KT with an auxiliary self-supervised task, i.e., we additionally generate an augmented view with adaptive data augmentation to implement contrastive learning and exploit the unobserved relations among questions. We conduct extensive experiments on several real-world datasets. Experimental results demonstrate that our proposed method achieves significant performance improvement compared to some state-of-the-art KT methods. [ABSTRACT FROM AUTHOR]

This paper investigates the control-based event-triggered sliding mode control for a networked linear system whose feedback information is transmitted over a digital communication network. In this paper, a novel event-triggered mechanism based on control value is proposed. Different from traditional event-triggered mechanisms that are normally based on states, our mechanism pays more attention to the desired control input value of the system. When the deviation between the current control input and the control input being calculated on the basis of the previous system state exceeds a given threshold, an event is triggered. For the sake of reducing the information to be transmitted, a quantization policy is executed and only a few bits are needed to transmit the feedback symbol of each sample. The combination of the control-based event-triggered mechanism and the quantization policy can significantly reduce both the transmission frequency and the number of bits of each feedback packet. For the concerned system, sliding mode control is implemented. The reachability of the sliding mode surface and the robust stability of the system are analyzed by fully taking quantization effects into account. Moreover, the effects of transmission delay of feedback packets on the event-triggering mechanism are considered. Under the proposed mechanism, the lower bound of event intervals is proven to be non-zero, i.e., the Zeno behavior is excluded. Simulations of a mechanical system are done to further verify the superiority of the proposed mechanism. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting parameters or retraining on new test scenes as required by most existing methods. Employing an image-level training paradigm, we achieve a general anomaly enhancement network for hyperspectral AD that only needs to be trained once. Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and backgrounds in an unsupervised way. In addition, a plug-and-play model selection module is proposed to search for a spatial–spectral transform domain that is more suitable for AD tasks than the original data. To establish a unified benchmark to comprehensively evaluate our method and existing methods, we develop a large-scale hyperspectral AD dataset (HAD100) that includes 100 real test scenes with diverse anomaly targets. In comparison experiments, we combine our network with a parameter-free detector and achieve the optimal balance between detection accuracy and inference speed among state-of-the-art AD methods. Experimental results also show that our method still achieves competitive performance when the training and test sets are captured by different sensor devices. Our code is available at https://github.com/ZhaoxuLi123/AETNet.

Reducing coke use is an effective measure to reduce carbon emission and energy consumption in the blast furnace (BF) ironmaking. Essentially, BF is a high-temperature moving bed reactor, where complex physical transformations coupled with complicated reactions occur. This makes it challenging to investigate the factors determining BF performance with the conventional method. A multi-physical field coupling mathematical model of BF was thus developed to describe its mass and heat transfer as well as its intrinsic reactions. Then, the proposed model was validated with the production data. Under coupling conditions, influences of dominating reactions on BF performance (temperature distribution, gas distribution, iron formation reaction, and direct reduction degree) were revealed. The results indicated that coke combustion, indirect reduction, and direct reduction of iron ore mainly took place nearby the shaft tuyere, cohesive zone, and dripping zone, respectively. Besides, the rate of coke solution loss reaction was increased with the rising coke porosity in the cohesive zone. Considering the effect of coke porosity on the efficiency and stability of BF, the coke porosity of 0.42 was regarded as a reasonable value.

Astrocytes, an integral component of the central nervous system (CNS), contribute to the maintenance of physiological homeostasis through their roles in synaptic function, K+buffering, blood-brain barrier (BBB) maintenance, and neuronal metabolism. Reactive astrocytes refer to astrocytes undergoing morphological, molecular and functional remodelling in response to pathological stimuli. The activation and differentiation of astrocytes are implicated in the pathogenesis of multiple neurodegenerative diseases. However, there are still controversies regarding their subset identification, function and nomenclature in neurodegeneration. In this review, we revisit the multidimensional roles of reactive astrocytes in Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS). Furthermore, we propose a precise linkage between astrocyte subsets and their functions based on single-cell sequencing analyses.

AbstractPurposeTo identify consistently expressed lncRNAs and suitable lncRNAs with high sensitivity and specificity from multiple independent studies as potential biomarkers for PCa diagnostics.MethodsWe searched multiple electronic databases including PubMed, Web of Science, EMBASE, Cochrane Library, CNKI, CQVIP, Wanfang, and CBMdisc for studies published up to July 2022. The quality of the included studies was assessed by two independent reviewers based on the QUADAS-2 tool using Review Manager 5.3. A vote-counting method was used based on the ranking of potential molecular biomarkers. The top-ranked lncRNAs were further assessed for diagnostic value using Meta-disc version 1.4 software.ResultsAmong the 26 included studies, 2 circulating lncRNAs (PCA3 and MALAT-1) were reported 3 or more times in PCa patients versus non-PCa patients. In further analysis, the areas under the curve of the summary receiver operating characteristic curves for PCA3 and MALAT-1 distinguishing PCa patients were 0.775 and 0.771, respectively.ConclusionsBased on the current evidence, PCA3 and MALAT-1 are reliable lncRNAs for the diagnosis of PCa.

Hyperspectral anomaly detection (HAD) is regarded as an indispensable, pivotal technology in remote sensing and Earth science domains. Nevertheless, most existing detection approaches for anomaly targets flatten 3-D hyperspectral images (HSIs) with spatial and spectral information into 2-D spectral vector data, which virtually breaks up the internal spatial structure in HSIs and degenerates the detection performance. To this end, we directly consider the HSI data cube as a 3-D tensor and develop a novel tensor low-rank approximation (TLRA) detection algorithm to separate the sparse anomalous component from the background with low-rank characteristics. Then, in light of the multisubspace structure in heterogeneous backgrounds, we utilize multiple subspace learning (MSL) theory to encode the background tensor with a coefficient tensor and corresponding dictionary tensor. In addition, considering that different singular values indicate different information quantities and should be penalized to different extents, we introduce a tighter tensor rank surrogate named the $\epsilon $ -shrinkage tensor nuclear norm ( $\epsilon $ -TNN) to recover the low-rank component more accurately. Meanwhile, concerning the sparse anomaly target, the ${l_{2,1}}$ constraint is incorporated to represent the group sparsity of the abnormal component. Finally, an effective iterative optimization algorithm based on the alternating direction method of multipliers (ADMM) is devised to solve the proposed TLRA-MSL model. We conduct extensive experiments on six hyperspectral datasets to prove the effectiveness and robustness of our method. The experimental results illustrate that better detection performance is obtained using the proposed model compared with other state-of-the-art algorithms.

In this article, we investigate the event-triggered consensus problem for general linear multiagent systems with bounded communication network delay. To reduce the communication frequency, we propose some dynamic periodic event-triggering strategies, under which an auxiliary dynamic variable and a weighting parameter are introduced for each agent to build its event-triggering condition. Then, a distributed adaptive consensus protocol, which includes a time-varying coupling weight for each edge, is designed based on the proposed event-triggering strategies. With the well-designed updating laws of dynamic variables, coupling weights and weighting parameters, we can theoretically guarantee the consensus of the concerned multiagent systems. Our event-triggering strategies do not require agents to continuously verify the event-triggering conditions or read their neighbors’ real-time state information. Moreover, Zeno behavior is inherently excluded as the interevent intervals are bounded from below by one period in the periodic event-triggering framework. Two numerical examples are provided to verify the effectiveness of the proposed event-triggered consensus protocols.

Hyperspectral anomaly detection (HAD) aims at distinguishing anomalies from background in an unsupervised manner. Autoencoder (AE) and its variant-based methods have achieved promising detection performance in HAD. However, most existing methods neglect to exploit the local structure information of hyperspectral images (HSIs) that reflects the underlying relationships between each pixel and its surroundings. Hence, the representation capabilities of the networks are restricted. Moreover, reconstruction of anomalies during training compels the networks to learn abnormal patterns and, thus, reduces the spectral differences between background and anomalies. To address these problems, a spectral difference guided graph attention autoencoder (SDGATA) network is proposed for HAD. Specifically, the relationships among samples are modeled by a GAT encoder, where a spectral sharpening constraint is introduced to guide the attention coefficient learning. In this way, the encoder can also represent the spectral differences between central nodes and their neighbors. Then, a learnable GAT decoder is constructed to reconstruct node attributes and obtain the node reconstruction errors for HAD. Besides, a background purification method is proposed to generate superpixel-level samples to suppress anomaly reconstruction during training. Extensive quantitative and qualitative evaluations on six real datasets and one synthetic dataset show that the proposed method achieves competitive detection performance as compared with the state-of-the-art HAD methods.

Large-scale point cloud semantic segmentation is a challenging task due to the complexity and diversity of real-world 3-D scenes. Most existing methods primarily rely on spatial coordinates to learn geometric representations without fully exploring local structural relationships. Additionally, the semantic gap between the encoder and decoder in segmentation networks is an important factor that constrains model performance. To address these challenges, we propose a novel network architecture called GAF-Net, which comprises a geometric contextual feature aggregation (GCFA) module and a multiscale feature adaptive fusion (MFAF) module. The GCFA module consists of three primary blocks: 1) a geometric edge representation (GER) block, designed to leverage spatial relative position and orientation information between the center point and its neighbors to capture detailed local geometric structural relations; 2) a point geometry prior (PGP) block, aimed at extracting explicit geometric priors for each point from raw point clouds. This block is lightweight and parameter-free; and 3) a geometry-aware attentive pooling (GAAP) block, which combines semantic features with learned geometric representations, enabling the learning and aggregation of informative local contextual features. Our proposed MFAF module integrates multiscale features by introducing an adaptive fusion approach. It effectively bridges the semantic gap between the encoder and decoder and mitigates the information loss caused by random sampling. Extensive experimental results on three large-scale benchmark datasets.

This article is devoted to a specific kind of discrete-time switched linear systems, where the switching signal is governed by a Markov chain, i.e., Markovian jump linear systems. For such systems, a mode feedback control mechanism is adopted to adjust the mode transition probability matrix, which is referred to as the switching law design, and the optimal mode feedback controller is sought to minimize a quadratic performance index containing both the system state and the mode feedback control input. First, the admissible set of the mode feedback control is investigated, and a sufficient condition is derived to ensure the desired stochastic stability. Second, under the assumption that the concerned system is mode-cost sequential, the original performance index is approximated into a new tractable one and a suboptimal mode feedback controller is then derived within the admissible set. Third, we consider a general situation where no specific requirements are imposed on the Markovian jump linear systems’ dynamics, derive the optimal mode feedback controller via a value-iteration-based algorithm, and prove the convergence of the obtained optimal controller. Finally, simulation results are provided to illustrate the validity of the proposed mode feedback control mechanism.

Aging is characterized with a progressive decline in many cognitive functions, including behavioral flexibility, an important ability to respond appropriately to changing environmental contingencies. However, the underlying mechanisms of impaired behavioral flexibility in aging are not clear. In this study, we reported that necroptosis-induced reduction of neuronal activity in the basolateral amygdala (BLA) plays an important role in behavioral inflexibility in 5-month-old mice of the senescence-accelerated mice prone-8 (SAMP8) line, a well-established model with age-related phenotypes. Application of Nec-1s, a specific inhibitor of necroptosis, reversed the impairment of behavioral flexibility in SAMP8 mice. We further observed that the loss of glycogen synthase kinase 3α (GSK-3α) was strongly correlated with necroptosis in the BLA of aged mice and the amygdala of aged cynomolgus monkeys (Macaca fascicularis). Moreover, genetic deletion or knockdown of GSK-3α led to the activation of necroptosis and impaired behavioral flexibility in wild-type mice, while the restoration of GSK-3α expression in the BLA arrested necroptosis and behavioral inflexibility in aged mice. We further observed that GSK-3α loss resulted in the activation of mTORC1 signaling to promote RIPK3-dependent necroptosis. Importantly, we discovered that social isolation, a prevalent phenomenon in aged people, facilitated necroptosis and behavioral inflexibility in 4-month-old SAMP8 mice. Overall, our study not only revealed the molecular mechanisms of the dysfunction of behavioral flexibility in aged people but also identified a critical lifestyle risk factor and a possible intervention strategy.

The raphe nucleus is critical for feeding, rewarding and memory. However, how the heterogenous raphe neurons are molecularly and structurally organized to engage their divergent functions remains unknown. Here, we genetically target a subset of neurons expressing VGLUT3. VGLUT3 neurons control the efficacy of spatial memory retrieval by synapsing directly with parvalbumin-expressing GABA interneurons (PGIs) in the dentate gyrus. In a mouse model of Alzheimer’s disease (AD mice), VGLUT3→PGIs synaptic transmission is impaired by ETV4 inhibition of VGLUT3 transcription. ETV4 binds to a promoter region of VGLUT3 and activates VGLUT3 transcription in VGLUT3 neurons. Strengthening VGLUT3→PGIs synaptic transmission by ETV4 activation of VGLUT3 transcription upscales the efficacy of spatial memory retrieval in AD mice. This study reports a novel circuit and molecular mechanism underlying the efficacy of spatial memory retrieval via ETV4 inhibition of VGLUT3 transcription and hence provides a promising target for therapeutic intervention of the disease progression.

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In this paper, the sensing characteristics of a double cladding fiber (DCF) long-period fiber grating (LPFG) to the surrounding refractive index (SRI) are studied. The outer cladding of the DCF plays the role of the overlay, thus, the mode transition (MT) phenomenon of DCF can be induced by etching the outer cladding radius instead of coating overlays. The response characteristics of the effective refractive index (ERI) of the cladding mode to the outer cladding radius are analyzed. It is found that in the MT range, the change rate of ERIs of cladding modes is relatively larger than that for other ranges. Further, based on the features of the mode transition region (MTR), the phase-matching curve of the 11th cladding mode is investigated, and the response of the DCF-LPFG to the SRI is characterized by the change of wavelength intervals between the dual peaks under different outer cladding radii. The numerical simulation results show that the SRI sensitivity is greatly improved, which is available to 3484.0 nm/RIU with the fitting degree 0.998 in the SRI range of 1.33–1.37. The proposed DCF-LPFG can provide new theoretical support for designing the DCF-LPFG refractive index sensor with excellent performances of sensitivity, linearity and structure.

Iron homeostasis disturbance has been implicated in Alzheimer’s disease (AD), and excess iron exacerbates oxidative damage and cognitive defects. Ferroptosis is a nonapoptotic form of cell death dependent upon intracellular iron. However, the involvement of ferroptosis in the pathogenesis of AD remains elusive. Here, we report that ferroportin1 (Fpn), the only identified mammalian nonheme iron exporter, was downregulated in the brains of APPswe/PS1dE9 mice as an Alzheimer’s mouse model and Alzheimer’s patients. Genetic deletion of Fpn in principal neurons of the neocortex and hippocampus by breeding Fpnfl/flmice with NEX-Cre mice led to AD-like hippocampal atrophy and memory deficits. Interestingly, the canonical morphological and molecular characteristics of ferroptosis were observed in both Fpnfl/fl/NEXcreand AD mice. Gene set enrichment analysis (GSEA) of ferroptosis-related RNA-seq data showed that the differentially expressed genes were highly enriched in gene sets associated with AD. Furthermore, administration of specific inhibitors of ferroptosis effectively reduced the neuronal death and memory impairments induced by Aβ aggregation in vitro and in vivo. In addition, restoring Fpn ameliorated ferroptosis and memory impairment in APPswe/PS1dE9 mice. Our study demonstrates the critical role of Fpn and ferroptosis in the progression of AD, thus provides promising therapeutic approaches for this disease.

The Chinese standard method of GB/T 4000-2017 was unable to accurately measure the coke thermal property in the large blast furnace. Therefore, the coke compression strength (CCS) test at a high temperature was designed to examine the coke thermal property. Then, the large-scale coke model (sp2C17421sp3C6579) was established. After, the ReaxFF molecular dynamics simulations were implemented to mimic the coke solution loss (CSL) and the CCS at the high temperature. It was found that the adsorption energy and the diffusion energy of micropore were greater than mesopore and macropore, indicating that the CSL reaction mainly happened in the coke micropore. It was discovered that the CSL reaction mechanism was the sp3C oxidization mechanism with the transient state of ketene structure. And, it was detected that the CCS process was divided into the plastic deformation, the instantaneous fracture and the elastic deformation & yield, which was caused by the local reconstruction, the overall folding and the center stretching of carbon layer, respectively. By comparing simulated results with experiments, it was proved that obtained mechanisms were valid. The proposed experimental and simulated methods provided the novel method to measure and understand the coke thermal property.

The intricate spatial and temporal complexities inherent in wind speed data necessitate the utilization of spatio-temporal networks for accurate wind power generation forecasting. But these complexities are made worse by the constant presence of high-frequency disturbances and clear outliers in the spatial and temporal correlations of wind speed, which makes forecasting wind power very difficult. Additionally, the intricate topological architectures that underpin current models often result in superfluous computational operations and susceptibility to overfitting. In response to these formidable challenges, we introduce an innovative solution in the form of the dual spatio-temporal (DST) network. This paradigm, characterized by meticulous design, excels in acquiring precise and resilient spatio-temporal representations essential for wind power forecasting. This is possible because of the careful development of two types of spatial correlation layers: synchronous layers and asynchronous layers. Each type is fine-tuned to recognize different spatial patterns. Recognizing that spatial and temporal correlations change over time, our method explores spatio-temporal representations at many different scales. This makes the system more adaptable and robust. Notably, for effective noise mitigation over extended forecasting horizons, we judiciously integrate wavelet decomposition. Empirical validation, rigorously conducted across twelve wind power stations in Anhui Province, China, firmly attests to the efficacy of our proposed DST network. In summary, our innovation presents a promising avenue for advancing wind power forecasting, characterized by adept noise reduction and improved precision while concurrently optimizing computational efficiency in modeling intricate spatial–temporal dependencies.

Glutamate receptor, ionotropic, N-methyl-D-aspartate associated protein 1 (GRINA) is a member of the NMDA receptors (NMDARs) and is involved in several neurological diseases, which governs the key processes of neuronal cell death or the release of neurotransmitters. Upregulation of GRINA has been reported in multiple diseases in human beings, such as major depressive disorder (MDD) and schizophrenia (SCZ), with which the underlying mechanisms remain elusive. In this review, we provide a general overview of the expression and physiological function of GRINA in the central nervous system (CNS) diseases, including stroke, depression ,epilepsy, SCZ, and Alzheimer’s disease (AD).

This paper addresses the problem of distributed simultaneous fault detection and control (SFDC) of multi-weighted and multi-delayed (MWMD) large-scale interconnected systems which are subjected to event-triggered communication, nonlinear perturbations, measured output quantization, redundant channels, and stochastic deception attacks. The large-scale systems under consideration have multiple coupling links between neighboring subsystems, and all the links are considered to have different coupling weights and delays. A distributed fault detector and controller (FDC) module is designed to guarantee the exponential mean square stability of the overall closed-loop system along with a prescribed extended dissipative control performance and H ∞ fault detection performance. The gain parameters of the distributed fault detector and controller module are determined by using the cone complementarity linearization (CCL) algorithm. In the end, a numerical example involving a continuous stirred tank reactor (CSTR) system is described to prove the validity of the presented results. [ABSTRACT FROM AUTHOR]

In our paper, a superimposed long-period fiber grating (SLPFG) sensor based on the dual-peak resonance near the phase-matching turning point (PMTP) for simultaneously detecting surrounding refractive index (SRI) and temperature is proposed. For the SLPFG, two fiber gratings with different periods are overlapped and written in the same fiber core. There are two resonant peaks in the transmission spectrum corresponding to the above gratings and different cladding modes. By adjusting the grating periods, one resonant peak corresponds to a little off-resonant state near the PMTP, while the other one corresponds to the single peak state. When the SRI and temperature change, the transmissivity of the resonant peak in the little off-resonant state changes accordingly at fixed wavelength, and the resonant peak in the single peak state shifts. Results of numerical simulations demonstrate that SRI and temperature simultaneous measurement can be achieved by monitoring the wavelength shift of the left peak and the transmissivity of right peak. For the left peak, the SRI and temperature sensitivities are ${-}628.57\;{{\rm nm} / {\rm RIU}}$−628.57nm/RIU and ${-}0.20\;{{\rm nm} / {^\circ {\rm C}}}$−0.20nm/^∘C, respectively. For the right peak, the SRI sensitivity is from ${-}217.73\;{{\rm dB} / {\rm RIU}}$−217.73dB/RIU to ${-}682.47\;{{\rm dB} / {\rm RIU}}$−682.47dB/RIU when the SRI varies from 1.330 to 1.360. The temperature sensitivity is from ${-}0.044\;{{\rm dB} / {^\circ {\rm C}}}$−0.044dB/^∘C to ${-}0.101\;{{\rm dB} / {^\circ {\rm C}}}$−0.101dB/^∘C when the temperature varies from 20°C to 120°C. The SRI and temperature sensitivities of such sensors are 7 times and 20 times higher than those of other sensors with dual-parameter modulation of intensity and wavelength. The proposed sensor with high sensitivity and simplified structure can be extended to multi-parameter measurement.

This paper considers a scalar continuous-time linear time-invariant system, whose feedback signal is transmitted through a communication network. Such a network has only finite bit rate and suffers from transmission delay which is characterized by both lower and upper delay bounds. The concerned system implements event-triggering strategies, i.e., only when certain events are triggered, the system samples and transmits feedback signals. This paper first derives some lower bounds on the feedback bit rate required to achieve the input-to-state stability under arbitrary event-triggering strategies. Then this paper proposes some constructive methods to design the event-triggering strategy and the controller, and can achieve the input-to-state stability at a bit rate being arbitrarily close to these obtained lower bit rate bounds. Moreover, this paper proves that the stabilizing bit rates under the proposed event-triggering strategies can be strictly lower than the stabilizing bit rate required by any time-triggering strategy. This bit rate superiority comes from the fact that under event triggering, the state information can be freely extracted from the receive time instants of feedback packets without consuming any bit rate. Simulations are done to demonstrate the bit rate superiority of the proposed event-triggering strategies. [ABSTRACT FROM AUTHOR]

Depression is one of the prevalent psychiatric illnesses with a comprehensive performance such as low self-esteem, lack of motivation, anhedonia, poor appetite, low energy, and uncomfortableness without a specific cause. So far, the cause of depression is not very clear, but it is certain that many aspects of biological psychological and social environment are involved in the pathogenesis of depression. Recently, the prefrontal cortex (PFC) has been indicated to be a pivotal brain region in the pathogenesis of depression. And increasing evidence showed that the abnormal activity of the PFC neurons is linked with depressive symptoms. Unveiling the molecular and cellular, as well as the circuit properties of the PFC neurons will help to find out how abnormalities in PFC neuronal activity are associated with depressive disorders. In addition, concerning many antidepressant drugs, in this review, we concluded the effect of several antidepressants on PFC neuronal activity to better understand its association with depression.

Dominance hierarchy is a fundamental phenomenon in grouped animals and human beings, however, the underlying regulatory mechanisms remain elusive. Here, we report that an antisense long non-coding RNA (lncRNA) of synapsin II, named as AtLAS, plays a crucial role in the regulation of social hierarchy. AtLAS is decreased in the prefrontal cortical excitatory pyramidal neurons of dominant mice; consistently, silencing or overexpression of AtLAS increases or decreases the social rank, respectively. Mechanistically, we show that AtLAS regulates alternative polyadenylation of synapsin II gene and increases synapsin 2b (syn2b) expression. Syn2b reduces AMPA receptor (AMPAR)-mediated excitatory synaptic transmission through a direct binding with AMPAR at the postsynaptic site via its unique C-terminal sequence. Moreover, a peptide disrupting the binding of syn2b with AMPARs enhances the synaptic strength and social ranks. These findings reveal a novel role for lncRNA AtLAS and its target syn2b in the regulation of social behaviors by controlling postsynaptic AMPAR trafficking.

DNA double-strand breaks (DSBs) are common events that were recognized as one of the most toxic lesions in eukaryotic cells. DSBs are widely involved in many physiological processes such as V(D)J recombination, meiotic recombination, DNA replication and transcription. Deregulation of DSBs has been reported in multiple diseases in human beings, such as the neurodegenerative diseases, with which the underlying mechanisms are needed to be illustrated. Here, we reviewed the recent insights into the dysfunction of DSB formation and repair, contributing to the pathogenesis of neurodegenerative disorders including Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD) and ataxia telangiectasia (A-T).

The movements of articulators, such as lips, tongue and teeth, play an important role in increasing the language expression capability by unmasking the information hid in text or speech. Hence, it is necessary to deeply mine and visualize the relationship between text, speech and articulatory movements for understanding language in multi-modality and multi-level. As a case study, given text and audio of President Donald John Trump, this paper synthesizes a high quality 3D animation of him speaking with accurate synchronicity between speech and articulators. First, visual co-articulation is modeled by predicting the mapping from text/speech to articulatory movements. Then, based on a reconstructed 3D head model, physiological characteristics and statistical learning are combined to visualize each phoneme. Finally, the visualization results of consecutive phonemes are fused by visual co-articulation model to generate synchronized articulatory animations. Experiments show that the system can not only produce photo-realistic results in front but also distinguish the visual differences among phonemes from unconstrained views.

This work presents a design of a cascaded few-mode long-period fiber grating (FM-CLPFG) refractive index sensor with a narrow bandwidth that is based on the feature of narrow-bandwidth loss peak of few-mode long-period fiber grating (FM-LPFG) and that further reduces the loss-peak bandwidth of a single FM-LPFG by cascading. On the basis of the coupled-mode theory of FM-LPFG, the mutual interference between the loss peaks of each mode is reduced, and the loss-peak coupling intensity is ensured by selecting the appropriate grating period and grating length. Furthermore, the influence of the cascaded fiber length and the number of cascaded grating segments on the loss-peak bandwidth are analyzed. Based on the above designed parameters, the FM-CLPFG narrow-bandwidth sensor with a bandwidth of about 1 nm is designed. The results show that the sensitivity of this sensor is available to 2410 nm/RIU, with the surrounding refractive index changing from 1.4445 to 1.4475.

Shenmu (SM) subbituminous coal without caking property was treated by low-temperature rapid pyrolysis (LTRP) to modify its caking and coking properties. The treated samples were characterized by Fourier transform infrared spectrometry, vitrinite reflectance, and X-ray diffraction to determine the modification mechanism. Moreover, caking index (G) and coking indices (mechanical strength, coke reactivity, and coke strength after reaction) were employed to evaluate caking and coking properties, respectively. The results showed that SM coal was gradually upgraded with increasing processing temperature. Furthermore, the Gvalues for the treated samples were significantly higher than that for SM coal, and Greached the maximum value at 450 °C, implying the modification of caking property and the existence of an optimum temperature (450 °C). Additionally, laboratory coking determinations showed that LTRP increased the mechanical strength of coke and coke strength after reaction and decreased coke reactivity when the treated coals were used in the coal blends instead of raw SM coal. Overall, LTRP treatment is effective to improve the caking and coking properties of SM coal. A mechanism was proposed for the modification. Suitable upgrading degree with suitable molecular masses and some releasable hydrogen-rich donor species present within the coal, which dominate the development of caking property, is important.

A novel few-mode long-period fiber grating (FM-LPFG) sensor based on dual-peak resonance near the phase-match turning point (PMTP) for simultaneous detection of surrounding refractive index (SRI) and temperature is proposed. The dual-peak resonance near the PMTP of the FM-LPFG is analyzed based on the coupled-mode theory. The cladding modes of adjacent order exhibit dual-peak and single-peak resonances in the wavelength band 1450–1850 nm, respectively. And the response characteristics of different resonant peaks to temperature and SRI are discussed. Results of numerical simulations demonstrate that the SRI sensitivities corresponding to the distance between the dual peaks and single-peak transmissivity are 1114.28 nm/RIU and -685.42  dB/RIU, respectively, and the temperature sensitivities corresponding to the distance between the dual peaks and single-peak transmissivity are −0.27  nm/°C and 0.072 dB/°C. The different SRI and temperature sensitivities of those peaks help to sense multi-parameters.

A 65-year-old man had intermittent abdominal pain for the previous 2 years. This pain suddenly became worse with a fever and elevated inflammatory markers. We took a while to diagnose the patient with mesenteric panniculitis (MP). Although imaging findings suggested MP, we needed to rule out other diseases. Choosing a treatment for the patient also took some time and we finally used glucocorticoid to cure the patient.

In this paper, a new method for measuring the beam quality (M^2) of lasers based on phase retrieval with a liquid lens is proposed. With intensity profiles obtained under different focal lengths in a certain position, a variable-focus iterative retrieval algorithm is established for the reconstruction of the complex amplitude. Then M^2 can be calculated with the angular spectrum theory. Feasibility of the proposed method is demonstrated with single- and multimode lasers through both simulations and experiments. Compared with the traditional liquid lens method, the M^2 of lasers can be measured faster with the proposed method.

Abstract In this paper, we consider the quantized consensus problem of multiple discrete-time integrator agents which suffer from input saturation. As agents transmit state information through communication networks with limited bandwidth, the states of agents have to be quantized into a finite number of bits before transmission. To handle this quantized consensus problem, we introduce an internal time-varying saturation function into the controllers of all agents and ensure that the range of the state of each agent can be known in advance by its neighboring agents. Based on such shared state range information, we construct a quantized consensus protocol which implements a finite-bit quantization strategy to all states of agents and can guarantee the achievement of the asymptotic consensus under any given input saturation threshold. Such desired consensus can be guaranteed at as low bit rate as 1 bit per time step for each agent. Moreover, we can place an upper bound on the convergence rate of the consensus error of agents. We further improve that quantized consensus protocol to a robust version whose parameters are determined with only an upper bound on the number of agents and does not require any more global information of the inter-agent network. Simulations are done to confirm the effectiveness of our quantized consensus protocols. [ABSTRACT FROM AUTHOR]

This technical note proposes an event-triggered quantization policy design method to stabilize a scalar continuous-time linear time-invariant (LTI) system, whose feedback loop is closed over an unreliable digital communication network. The feedback packets suffer from network delay and may be dropped in an independent and identically distributed (i.i.d.) way. In order to save quantization bit rate, we implement a periodic event-triggering strategy which periodically samples the state and only quantizes and transmits the sampled state when a predefined event is triggered. By making good use of both the information bits and the sampling time instants of the feedback packets, our policy can obtain the desired mean-square stability at a lower bit rate than the conventional time-triggered quantization policies which utilize only the information bits inside the feedback packets. Under our quantization policy, the stabilizing bit rate condition is up to the dropout rate, the processing and network delays and the unstable eigenvalue of the system, and is independent of the bounded process noise. [ABSTRACT FROM AUTHOR]

The hippocampus is divided into dorsal and ventral zones along its principal axis. The dorsal hippocampus is critical for learning and memory, yet the basic function of the ventral hippocampus remains elusive. Here we genetically manipulate a subset of excitatory neurons expressing the serotonin receptor 2c (Htr2c) in the ventral hippocampus. Genetically modified virus tracing reveals that these Htr2c cells establish monosynaptic excitatory connections with newly identified neurons in the Edinger–Westphal nucleus (EW), which directly innervate the medial prefrontal cortex. Inactivation of Htr2c cells impairs behavioral performance in a visual-detection task that demands attention, without affecting novel-object recognition, learning, or memory. This attention deficit was recapitulated by inhibition of EW cells and rescued by activation of EW cells or synaptic projections from Htr2c cells onto EW cells. This study uncovers a synaptic pathway for control of attention. This study shows that excitatory neurons in the ventral hippocampus that express the serotonin receptor 2c gene control attention through direct innervation of the mouse equivalent of the Edinger–Westphal nucleus.