CRNN-Refined Spatiotemporal Transformer for Dynamic MRI reconstruction.

Magnetic Resonance Imaging (MRI) plays a pivotal role in modern clinical practice, providing detailed anatomical visualization with exceptional spatial resolution and soft tissue contrast. Dynamic MRI, aiming to capture both spatial and temporal characteristics, faces challenges related to prolonged acquisition times and susceptibility to motion artifacts. Balancing spatial and temporal resolutions becomes crucial in real-world clinical scenarios. In the realm of dynamic MRI reconstruction, while Convolutional Recurrent Neural Networks (CRNNs) struggle with long-range dependencies, CRNNs require extensive iterations, impacting efficiency. Transformers, known for their effectiveness in high-dimensional imaging, are underexplored in dynamic MRI reconstruction. Additionally, prevailing algorithms fall short of achieving superior results in demanding generative reconstructions at high acceleration rates. This research proposes a novel approach for dynamic MRI reconstruction, named CRNN-Refined Spatiotemporal Transformer Network (CST-Net). The spatiotemporal Transformer initiates reconstruction, modeling temporal and spatial correlations, followed by refinement using the CRNN. This integration mitigates inaccuracies caused by damaged frames and reduces CRNN iterations, enhancing computational efficiency without compromising reconstruction quality. Our study compares the performance of the proposed CST-Net at 6 × and 12 × undersampling rates, showcasing its superiority over existing algorithms. Particularly, in challenging 25× generative reconstructions, the CST-Net outperforms current methods. The comparison includes experiments under both radial and Cartesian undersampling patterns. In conclusion, CST-Net successfully addresses the limitations inherent in existing generative reconstruction algorithms, thereby paving the way for further exploration and optimization of Transformer-based approaches in dynamic MRI reconstruction. Code and Datasets can be available: https://github.com/XWangBin/CST-Net.

Dominant activities of fear engram cells in the dorsal dentate gyrus underlie fear generalization in mice.

Over-generalized fear is a maladaptive response to harmless stimuli or situations characteristic of posttraumatic stress disorder (PTSD) and other anxiety disorders. The dorsal dentate gyrus (dDG) contains engram cells that play a crucial role in accurate memory retrieval. However, the coordination mechanism of neuronal subpopulations within the dDG network during fear generalization is not well understood. Here, with the Tet-off system combined with immunostaining and two-photon calcium imaging, we report that dDG fear engram cells labeled in the conditioned context constitutes a significantly higher proportion of dDG neurons activated in a similar context where mice show generalized fear. The activation of these dDG fear engram cells encoding the conditioned context is both sufficient and necessary for inducing fear generalization in the similar context. Activities of mossy cells in the ventral dentate gyrus (vMCs) are significantly suppressed in mice showing fear generalization in a similar context, and activating the vMCs-dDG pathway suppresses generalized but not conditioned fear. Finally, modifying fear memory engrams in the dDG with "safety" signals effectively rescues fear generalization. These findings reveal that the competitive advantage of dDG engram cells underlies fear generalization, which can be rescued by activating the vMCs-dDG pathway or modifying fear memory engrams, and provide novel insights into the dDG network as the neuronal basis of fear generalization.

Impact of ovariectomy on neurotransmitter receptors BDNF/TrkB and endoplasmic reticulum molecular chaperones in rat hypoglossal nucleus.

Currently hypoglossal nerve-genioglossus axis is the major research core of OSA pathogenesis. The pathogenesis of OSA incidence changes before and after menopause needs to be clarified further. Little is known about the influences of ovariectomy on hypoglossal motoneurons. In the research, we utilized a rat ovariectomy model to evaluate the expression changes of 5-HT2A and α1-Adrenergic receptors in the hypoglossal nucleus and to explore the involvement of BDNF/TrkB signaling and endoplasmic reticulum molecular chaperones in the hypoglossal nucleus. Results indicated that the expression of 5-HT2A and α1-Adrenergic receptors reduced dramatically in the hypoglossal nucleus of ovariectomized rats. The apoptosis level of hypoglossal motor neurons increased markedly in the OVX groups. The up-regulated expression of BDNF and down-regulated expression of TrkB were found in the OVX groups. Ovarian insufficiency resulted in the activation of UPR and the loss of CANX-CALR cycle. Estrogen replacement could restore these changes partially. Estrogen level influences the expression of neurotransmitter receptors, and regulates BDNF/TrkB signaling compensation and endoplasmic reticulum homeostasis, which might be one of the pathogenesis of menopausal female OSA. The results reveal a new perspective for studying female OSA from the view of hypoglossal nerve and hormonal changes and attempt to propel 17ß-estradiol toward a feasible therapy for female OSA. Supplementary Information: The online version contains supplementary material available at 10.1007/s41105-024-00520-5.

MSA-Net: A Precise and Robust Model for Predicting the Carbon Content on an As-Received Basis of Coal.

The carbon content as received (Car) of coal is essential for the emission factor method in IPCC methodology. The traditional carbon measurement mechanism relies on detection equipment, resulting in significant detection costs. To reduce detection costs and provide precise predictions of Cars even in the absence of measurements, this paper proposes a neural network combining MLP with an attention mechanism (MSA-Net). In this model, the Attention Module is proposed to extract important and potential features. The Skip-Connections are utilized for feature reuse. The Huber loss is used to reduce the error between predicted Car values and actual values. The experimental results show that when the input includes eight measured parameters, the MAPE of MSA-Net is only 0.83%, which is better than the state-of-the-art Gaussian Process Regression (GPR) method. MSA-Net exhibits better predictive performance compared to MLP, RNN, LSTM, and Transformer. Moreover, this article provides two measurement solutions for thermal power enterprises to reduce detection costs.

Confined Gel-Electromembrane Extraction of Oligonucleotides.

Gel-electromembrane extraction (G-EME) is an increasingly popular green variant of electromembrane extraction (EME). However, the electroendosmosis (EEO) flow associated with G-EME greatly limits the development of this technology. To address this challenge, the current study proposed the concept of confined G-EME (CG-EME), and a three-dimensional-printed modular device was elaborately designed to realize this concept. The device blocked the EEO flow by limiting the volume of the sample compartment. Moreover, the mesh structure at the bottom of the extraction module helps to prepare thin and stable gel films, which enhance the electromigration driving force and shorten the migration path. In addition, polar oligonucleotides, a nucleic acid analyte, were extracted for the first time to prove the concept of CG-EME. After optimization, 62% of the oligonucleotides were extracted at 50 V voltage for 15 min using a 3 mm thick agarose (3%) gel film. Finally, the application capability of CG-EME was further demonstrated by recovering DNA primers and isolating disease biomarkers (miRNA-181b) from real samples. In combination with CG-EME and quantitative polymerase chain reaction (qPCR) analysis, the upregulation of miRNA-181b expression in the peripheral blood of patients with schizophrenia was observed. In conclusion, this study proposes CG-EME to diminish EEO and push EME into the clinical field to isolate nucleic acid biomarkers, which will greatly expand the application scenarios of this emerging technology.

Lung tumor discrimination by deep neural network model CanDo via DNA methylation in bronchial lavage.

Bronchoscopic-assisted discrimination of lung tumors presents challenges, especially in cases with contraindications or inaccessible lesions. Through meta-analysis and validation using the HumanMethylation450 database, this study identified methylation markers for molecular discrimination in lung tumors and designed a sequencing panel. DNA samples from 118 bronchial washing fluid (BWF) specimens underwent enrichment via multiplex PCR before targeted methylation sequencing. The Recursive Feature Elimination Cross-Validation and deep neural network algorithm established the CanDo classification model, which incorporated 11 methylation features (including 8 specific to the TBR1 gene), demonstrating a sensitivity of 98.6% and specificity of 97.8%. In contrast, bronchoscopic rapid on-site evaluation (bronchoscopic-ROSE) had lower sensitivity (87.7%) and specificity (80%). Further validation in 33 individuals confirmed CanDo's discriminatory potential, particularly in challenging cases for bronchoscopic-ROSE due to pathological complexity. CanDo serves as a valuable complement to bronchoscopy for the discriminatory diagnosis and stratified management of lung tumors utilizing BWF specimens.

Correction: Chen et al. HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm. Sensors 2024, 24, 2170.

In the original publication [...].

Design and Implementation of an Ontology for Measurement Terminology in Digital Calibration Certificates.

Digital Calibration Certificates (DCCs) are a key focus in metrology digitalization, necessitating that they satisfy the criteria for machine readability and understandability. Current DCCs are machine-readable, but they are still missing the essential semantic information required for machine understandability. This shortfall is particularly notable in the lack of a dedicated semantic ontology for measurement terminologies. This paper proposes a domain ontology for measurement terminologies named the OMT (Ontology for Measurement Terminology), using a foundation of metrological terms from standards like the International Vocabulary of Metrology (VIM), the Guide to the Expression of Uncertainty in Measurement (GUM), and JJF1001. It also incorporates insights from models such as the SI Reference Point, the Simple Knowledge Organization System (SKOS), and the DCC Schema. The methodology was guided by Stanford's Seven-Step Method, ensuring a systematic development process tailored to the needs of metrological semantics. Through semantic expression capability verification and SPARQL query validations, the OMT has been confirmed to possess essential machine readability and understandability features. It has been successfully integrated into version 3.2.1 of DCCs across ten representative domains. This integration demonstrates an effective method for ensuring that DCCs are machine-readable and capable of interoperating within digital environments, thereby advancing the research in metrology digitization.

A special type of homicide-suicide: A retrospective study of the characteristics of extended suicide.

Extended suicide, a specific type of homicide-suicide event, has severe social consequences yet remains lacking systematic research. This retrospective study investigated 51 cases of extended suicide involving mental disorders in central China with aim of better understanding risk factors for such events and guiding prevention strategies. Over an 8-year period from 2015 to 2022, cases were collected from forensic institutions, and demographic characteristics, case details, and psychiatric data were recorded. The 51 incidents involved 51 perpetrators and 79 victims, with more female perpetrators (58.8%) and more female victims (54.4%). The average age of the perpetrators was 36.1, and most were married (88.2%). Almost all of the victims were family members of the perpetrator, like the most numerous children (64.6%), followed by spouses (24.1%). The most common homicide mode of death was mechanical asphyxia (38.0%), followed by sharp devices (36.7%) and drug poisoning (16.5%). Depressive disorders (76.5%) were the most common diagnosis of mental disorder for perpetrators. The study analyzed the unique characteristics of extended suicide to enrich such data. These findings help strengthen the screening and identification of potential perpetrators and victims to prevent such cases from occurring.

Unsupervised spectral reconstruction from RGB images under two lighting conditions.

Unsupervised spectral reconstruction (SR) aims to recover the hyperspectral image (HSI) from corresponding RGB images without annotations. Existing SR methods achieve it from a single RGB image, hindered by the significant spectral distortion. Although several deep learning-based methods increase the SR accuracy by adding RGB images, their networks are always designed for other image recovery tasks, leaving huge room for improvement. To overcome this problem, we propose a novel, to our knowledge, approach that reconstructs the HSI from a pair of RGB images captured under two illuminations, significantly improving reconstruction accuracy. Specifically, an SR iterative model based on two illuminations is constructed at first. By unfolding the proximal gradient algorithm solving this SR model, an interpretable unsupervised deep network is proposed. All the modules in the proposed network have precise physical meanings, which enable our network to have superior performance and good generalization capability. Experimental results on two public datasets and our real-world images show the proposed method significantly improves both visually and quantitatively as compared with state-of-the-art methods.

HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm.

Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception algorithm. The algorithm utilizes a voxel grid-based statistical filter to effectively denoise point cloud data to obtain clean and reliable data. In addition, we design a feature extraction network based on the fusion of voxels and PointPillars and encode it to generate BEV features, which solves the spatial feature interaction problem lacking in the PointPillars approach and enhances the semantic information of the extracted features. A maximum pooling technique is used to reduce the dimensionality and generate pseudoimages, thereby skipping complex 3D convolutional computation. To facilitate effective feature fusion, we design a feature level-based crossvehicle feature fusion module. Experimental validation is conducted using the OPV2V dataset to assess vehicle coperception performance and compare it with existing mainstream coperception algorithms. Ablation experiments are also carried out to confirm the contributions of this approach. Experimental results show that our architecture achieves lightweighting with a higher average precision (AP) than other existing models.

Genomic structure analysis and construction of DNA fingerprint for four sheep populations.

The Yongdeng Qishan sheep (QS) is a sheep population found locally in China. To gain in-depth knowledge of its population characteristics, three control groups were chosen, comprising the Lanzhou fat-tailed sheep (LFT), TAN sheep (TAN), and Minxian black fur sheep (MBF), inhabiting the nearby environments. This study genotyped a total of 120 individuals from four sheep populations: QS, LFT, TAN, and MBF. Using Specific-Locus Amplified Fragment Sequencing, we conducted genetic diversity, population structure, and selective sweep analysis, and constructed the fingerprint of each population. In total, there were 782 535 single nucleotide polymorphism (SNP) variations identified, with most being situated within regions that are intergenic or intronic. The genetic diversity analysis revealed that the QS population exhibited lower genetic diversity compared to the other three populations. Consistent results were obtained from the principal component, phylogenetic tree, and population structure analysis, indicating significant genetic differences between QS and the other three populations. However, a certain degree of differentiation was observed within the QS population. The linkage disequilibrium (LD) patterns among the four populations showed clear distinctions, with the QS group demonstrating the most rapid LD decline. Kinship analysis supported the findings of population structure, dividing the 90 QS individuals into two subgroups consisting of 23 and 67 individuals. Selective sweep analysis identified a range of genes associated with reproduction, immunity, and adaptation to high-altitude hypoxia. These genes hold potential as candidate genes for marker-assisted selection breeding. Additionally, a total of 86 523 runs of homozygosity (ROHs) were detected, showing non-uniform distribution across chromosomes, with chromosome 1 having the highest coverage percentage and chromosome 26 the lowest. In the high-frequency ROH islands, 79 candidate genes were associated with biological processes such as reproduction and fat digestion and absorption. Furthermore, a DNA fingerprint was constructed for the four populations using 349 highly polymorphic SNPs. In summary, our research delves into the genetic diversity and population structure of QS population. The construction of DNA fingerprint profiles for each population can provide valuable references for the identification of sheep breeds both domestically and internationally.

Sorption of cadmium by layered double hydroxides: Performance, structure-related mechanisms, and sequestration stability assessment.

Layered double hydroxides (LDHs) have been recognized to have great potential for the treatment of heavy metals in wastewater and soil through various mechanisms. Isomorphic substitution is an important mechanism for the sorption of heavy metal cations with LDH reconstruction and highly stable product formation. However, sorption performance, structure-related relationships, and, more importantly, stability are still poorly understood. In this study, a series of LDHs with different structures were synthesized to evaluate their cadmium (Cd) sorption performance and stability concerning the isomorphic substitution mechanism. Divalent cation types in the LDH lattice determined the Cd sorption capacity as well as the isomorphic substitution possibility, following the order of hydroxide solubility of divalent cations (MII): Ca2+>Mg2+>(Cd2+) > Ni2+>Zn2+. In addition, CaAl-LDH exhibited a super-high Cd sorption capacity of 625.0 mg g-1. Cd sorption by LDHs with different interlayer anion types and divalent/trivalent cation molar ratios varied due to crystallite size-related MII release through cation-exchange/isomorphic substitution. Coexisting cations (e.g., Zn2+, Ni2+, Mg2+) influence the sorption performance of MII-LDH mainly through isomorphic substitution mechanism, largely depending on the solubility of MII(OH)2 with a trend of stable product formation. Furthermore, Mg2.9Cd0.1AlCl-LDH was fabricated, and limited Cd dissolution without destruction of the LDH structure was observed under various conditions. For example, only 7.69%, 2.16% and 0.96% of Cd was released from as-prepared Mg2.9Cd0.1AlCl-LDH in NaCl solution (0.02 mol L-1, pH 5), soil extract, and soil matrix, respectively. The very low leaching of Cd from Cd-containing LDHs indicated the high stability of LDH-sorbed Cd via isomorphic substitution and feasible practical application in Cd sequestration in wastewater treatment and soil remediation.

Inverted Placement of Endoscopic One-Way Endobronchial Valve Combined with Gelfoam in the Closure of Bronchopleural Fistula with Empyema in a Mechanically Ventilated Patient: A Case Report.

Bronchopleural fistula (BPF) with empyema caused by severe necrotizing pulmonary infection is a complicated clinical problem that is often associated with poor general condition so surgical interventions cannot be tolerated in most cases. Here, we present the successful management of multiple BPF with empyema in a mechanically ventilated patient with aspiration lung abscess. Occlusion utilizing Gelfoam followed by endobronchial valves (EBVs) implanted inverted via bronchoscope decreased the air leaking significantly and made intrapleural irrigation for empyema achievable and safe. This is the first report of a novel way of EBV placement and the combination use with other occlusive substances in BPF with empyema in a patient on mechanical ventilation. This method may be an option for refractory BPF cases with pleural infection.

Malingering assessment after severe traumatic brain injury in forensic psychology with a potential embedded symptom validity indicator of Symptom Checklist 90.

Objective: Malingering of neuropsychological damage is common among traumatic brain injury patients pursuing disability compensation in forensic contexts. There is an urgent need to explore differences in neuropsychological assessment outcomes with different levels of cooperation. Methods: A total of 420 participants with severe traumatic brain injury were classified into malingering group, partial cooperation group, and complete cooperation group according to the Binomial forced-choice digit memory test. The Wechsler Adult Intelligence Scale, event-related potential component, and Symptom Checklist 90 were applied subsequently to assess the psychological status of participants. Results: Participants in the malingering group presented lower scores in the binomial forced-choice digit memory test and the Wechsler Adult Intelligence Scale, lower P3 amplitude, and simultaneously higher scores in the Symptom Checklist 90 than the other two groups. The actual intelligence quotient of participants with malingering tendencies ranged mostly between normal and marginal damage, and they often reported elevated whole scale scores in the Symptom Checklist 90. The Cooperation Index (defined as the ratio of positive symptom distress index to global severity index, CI) was proposed and validated to function as an embedded validity indicator of the Symptom Checklist 90, and the area under the receiver operating characteristic (ROC) curve was 0.938. When valued at 1.28, CI has the highest classification ability in differentiating malingering from non-malingering. Combined with the CI and P3 amplitude, the area under the ROC curve for malingering diagnosis further reached 0.952. Conclusion: Any non-optimal effort in a forensic context will lead to unexpected deviation in psychology evaluation results. CI is a potential candidate to act as an embedded validity indicator of the Symptom Checklist 90. The combination of CI and P3 amplitude can help to identify malingering in participants after severe traumatic brain injury.

Anterior cingulate cortex projections to the dorsal medial striatum underlie insomnia associated with chronic pain.

Chronic pain often leads to the development of sleep disturbances. However, the precise neural circuit mechanisms responsible for sleep disorders in chronic pain have remained largely unknown. Here, we present compelling evidence that hyperactivity of pyramidal neurons (PNs) in the anterior cingulate cortex (ACC) drives insomnia in a mouse model of nerve-injury-induced chronic pain. After nerve injury, ACC PNs displayed spontaneous hyperactivity selectively in periods of insomnia. We then show that ACC PNs were both necessary for developing chronic-pain-induced insomnia and sufficient to mimic sleep loss in naive mice. Importantly, combining optogenetics and electrophysiological recordings, we found that the ACC projection to the dorsal medial striatum (DMS) underlies chronic-pain-induced insomnia through enhanced activity and plasticity of ACC-DMS dopamine D1R neuron synapses. Our findings shed light on the pivotal role of ACC PNs in developing chronic-pain-induced sleep disorders.

DCT-net: Dual-domain cross-fusion transformer network for MRI reconstruction.

Current challenges in Magnetic Resonance Imaging (MRI) include long acquisition times and motion artifacts. To address these issues, under-sampled k-space acquisition has gained popularity as a fast imaging method. However, recovering fine details from under-sampled data remains challenging. In this study, we introduce a pioneering deep learning approach, namely DCT-Net, designed for dual-domain MRI reconstruction. DCT-Net seamlessly integrates information from the image domain (IRM) and frequency domain (FRM), utilizing a novel Cross Attention Block (CAB) and Fusion Attention Block (FAB). These innovative blocks enable precise feature extraction and adaptive fusion across both domains, resulting in a significant enhancement of the reconstructed image quality. The adaptive interaction and fusion mechanisms of CAB and FAB contribute to the method's effectiveness in capturing distinctive features and optimizing image reconstruction. Comprehensive ablation studies have been conducted to assess the contributions of these modules to reconstruction quality and accuracy. Experimental results on the FastMRI (2023) and Calgary-Campinas datasets (2021) demonstrate the superiority of our MRI reconstruction framework over other typical methods (most are illustrated in 2023 or 2022) in both qualitative and quantitative evaluations. This holds for knee and brain datasets under 4× and 8× accelerated imaging scenarios.

Yeast culture repairs rumen epithelial injury by regulating microbial communities and metabolites in sheep.

This study delves into the impact of yeast culture (YC) on rumen epithelial development, microbiota, and metabolome, with the aim of investigating YC's mechanism in regulating rumen fermentation. Thirty male lambs of Hu sheep with similar age and body weight were selected and randomly divided into three groups with 10 lambs in each group. Lambs were fed a total mixed ration [TMR; rough: concentrate (R:C) ratio ≈ 30:70] to meet their nutritional needs. The experiment adopted completely randomized design (CRD). The control group (CON) was fed the basal diet with high concentrate, to which 20 g/d of YC was added in the low dose YC group (LYC) and 40 g/d of YC in the high dose YC group (HYC). The pretrial period was 14 days, and the experimental trial period was 60 days. At the end of a 60-day trial, ruminal epithelial tissues were collected for histomorphological analysis, and rumen microorganisms were analyzed by 16S rDNA sequencing and rumen metabolites by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics techniques. The results showed that YC improved rumen papilla development and increased rumen papilla length (p < 0.05), while decreased cuticle thickness (p < 0.05). The 16S rDNA sequencing results showed that YC reduced the relative abundance of Prevotella_1 (p < 0.05), while significantly increased the relative abundance of Ruminococcaceae_UCG-005, uncultured_bacterium_f_Lachnospiraceae, and Ruminococcus_1 genus (p < 0.05). Metabolomics analysis showed that YC changed the abundance of metabolites related to amino acid metabolism, lipid metabolism and vitamin metabolism pathways in the rumen. In summary, YC might maintain rumen health under high-concentrate diet conditions by changing rumen microbiota structure and fermentation patterns, thereby affecting rumen metabolic profiles and repairing rumen epithelial injury.

Hyperspectral image super-resolution via spectral matching and correction.

Fusing a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution RGB image (HR-RGB) is an important technique for HR-HSI obtainment. In this paper, we propose a dual-illuminance fusion-based super-resolution method consisting of spectral matching and correction. In the spectral matching stage, an LR-HSI patch is first searched for each HR-RGB pixel; with the minimum color difference as a constraint, the matching spectrum is constructed by linear mixing the spectrum in the HSI patch. In the spectral correlation stage, we establish a polynomial model to correct the matched spectrum with the aid of the HR-RGBs illuminated by two illuminances, and the target spectrum is obtained. All pixels in the HR-RGB are traversed by the spectral matching and correction process, and the target HR-HSI is eventually reconstructed. The effectiveness of our method is evaluated on three public datasets and our real-world dataset. Experimental results demonstrate the effectiveness of our method compared with eight fusion methods.

Ventral pallidal glutamatergic neurons regulate wakefulness and emotion through separated projections.

Insomnia is often comorbid with depression, but the underlying neuronal circuit mechanism remains elusive. Recently, we reported that GABAergic ventral pallidum (VP) neurons control wakefulness associated with motivation. However, whether and how other subtypes of VP neurons regulate arousal and emotion are largely unknown. Here, we report glutamatergic VP (VPVglut2) neurons control wakefulness and depressive-like behaviors. Physiologically, the calcium activity of VPVglut2 neurons was increased during both NREM sleep-to-wake transitions and depressive/anxiety-like behaviors in mice. Functionally, activation of VPVglut2 neurons was sufficient to increase wakefulness and induce anxiety/depressive-like behaviors, whereas inhibition attenuated both. Dissection of the circuit revealed that separated projections of VPVglut2 neurons to the lateral hypothalamus and lateral habenula promote arousal and depressive-like behaviors, respectively. Our results demonstrate a subtype of VP neurons is responsible for wakefulness and emotion through separated projections, and may provide new lines for the intervention of insomnia and depression in patients.