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.

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.

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.

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.

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.

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 [...].

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.

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.

Optical trapping of two different microparticles by a double-tapered fiber probe.

We theoretically and experimentally study the optical trapping of two different microparticles by a double-tapered optical fiber probe (DOFP) which is fabricated by the interfacial etching method. A SiO2 microsphere and a yeast, or two SiO2 microspheres with different diameters, are trapped. We calculate and measure the trapping forces on the two microparticles, discuss the impacts of the geometrical size and refractive index on the trapping forces. Both the theoretical calculation and experimental measurements indicate that if the two particles have the same refractive index, the larger the second particle is, the larger the trapping force is. Whereas, if the two particles have the same geometrical size, the smaller the refractive index is, the lager trapping force is. Trapping and manipulation of different multiple microparticles by a DOFP enhance the application of optical tweezers, especially in biomedical engineering and material science.

Superior Selective CO2 Adsorption and Separation over N2 and CH4 of Porous Carbon Nitride Nanosheets: Insights from GCMC and DFT Simulations.

Development of high-performance materials for the capture and separation of CO2 from the gas mixture is significant to alleviate carbon emission and mitigate the greenhouse effect. In this work, a novel structure of C9N7 slit was developed to explore its CO2 adsorption capacity and selectivity using Grand Canonical Monte Carlo (GCMC) and Density Functional Theory (DFT) calculations. Among varying slit widths, C9N7 with the slit width of 0.7 nm exhibited remarkable CO2 uptake with superior CO2/N2 and CO2/CH4 selectivity. At 1 bar and 298 K, a maximum CO2 adsorption capacity can be obtained as high as 7.06 mmol/g, and the selectivity of CO2/N2 and CO2/CH4 was 41.43 and 18.67, respectively. In the presence of H2O, the CO2 uptake of C9N7 slit decreased slightly as the water content increased, showing better water tolerance. Furthermore, the underlying mechanism of highly selective CO2 adsorption and separation on the C9N7 surface was revealed. The closer the adsorption distance, the stronger the interaction energy between the gas molecule and the C9N7 surface. The strong interaction between the C9N7 nanosheet and the CO2 molecule contributes to its impressive CO2 uptake and selectivity performance, suggesting that the C9N7 slit could be a promising candidate for CO2 capture and separation.

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.

High-performance green-emitting AIE nanoparticles for lateral flow immunoassay applications.

Ultrabright green-emissive AIE nanoparticles (AIENPs) were used as signal-amplification probes to enhance the detectability of lateral flow immunoassay (LFIA). The detection performances of the green-emissive AIENP probes in both sandwich and competitive LFIA formats were systematically evaluated. Benefiting from its remarkable fluorescent brightness, the developed AIENP-LFIA showed versatile applicability for the detection of small molecules and macromolecules by using ochratoxin A (OTA) and procalcitonin (PCT) as model analytes, respectively. Under the optimum conditions, the detection limits (LODs) of the fabricated AIENP-LFIA for OTA and PCT were 0.043 ng mL-1 and 0.019 ng mL-1, respectively. These LOD values are significantly lower than those of conventional LFIA methods using gold nanoparticles as signal reporters. In addition, we demonstrated the practical application potential of AIENP-LFIA for the detection of OTA in real maize samples and PCT in real serum samples. These results indicated that the ultrabright green-emissive AIENPs were promising as signal output materials for building high-performance LFIA platform and broadening the application scenarios of LFIA.

Immunosuppressive TREM2(+) macrophages are associated with undesirable prognosis and responses to anti-PD-1 immunotherapy in non-small cell lung cancer.

BACKGROUND: Immune checkpoint blockade (ICB) has been improving patient outcomes of non-small cell lung cancer (NSCLC), but its effectiveness is highly subjective to individual tumor microenvironment. As dominant immune cells in NSCLC, tumor-associated macrophages (TAMs) display high diversity and plasticity. This study aims to find crucial TAM subtypes associated with ICB response in NSCLC. MATERIALS AND METHODS: Large cohorts of NSCLC patients from The Cancer Genome Atlas and a single-cell sequencing dataset were integrated to illustrate immunosuppressive phenotypes of TAMs, followed by experimental verification. 341 NSCLC surgical samples and 40 tissue samples of NSCLC patients who received ICB treatment were collected to state the clinical importance of TAMs. RESULTS: We identified a TREM2 positive (+) TAM subtype in NSCLC stratifying patient responses to immunotherapy. NSCLC patients with high TREM2+ TAM infiltration exhibited advanced tumor progression, inferior prognosis and unique NSCLC molecular characteristics, especially mutations of EGFR. TREM2+ TAMs were induced in TME, but not existed in peripheral blood. TREM2+ TAMs were enriched with multiple anti-inflammatory cytokines, exhibiting a M2-like immunosuppressive phenotype, and potentiate T cell dysfunction including impaired anti-tumor activity of CD8+ T cells and enhanced differentiation towards FOXP3+ Tregs, thus facilitating immune evasion of NSCLC. Response rates to PD-1-based ICB were higher in patients with low TREM2+ TAMs (31.58%) compared to high TREM2+ TAMs (14.29%). CONCLUSIONS: Our findings implicated the immunosuppressive role of TREM2+ TAMs in NSCLC, and systematically reveal the clinical significance of TREM2+ TAMs as predictive and prognostic markers for NSCLC patients with ICB treatment.

Hyperspectral image super-resolution via a multi-stage scheme without employing spatial degradation.

Recently, it has become popular to obtain a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution RGB image (HR-RGB). Existing HSI super-resolution methods are designed based on a known spatial degeneration. In practice, it is difficult to obtain correct spatial degradation, which restricts the performance of existing methods. Therefore, we propose a multi-stage scheme without employing the spatial degradation model. The multi-stage scheme consists of three stages: initialization, modification, and refinement. According to the angle similarity between the HR-RGB pixel and LR-HSI spectra, we first initialize a spectrum for each HR-RGB pixel. Then, we propose a polynomial function to modify the initialized spectrum so that the RGB color values of the modified spectrum are the same as the HR-RGB. Finally, the modified HR-HSI is refined by a proposed optimization model, in which a novel, to the best of our knowledge, spectral-spatial total variation (SSTV) regularizer is investigated to keep the spectral and spatial structure of the reconstructed HR-HSI. The experimental results on two public datasets and our real-world images demonstrate our method outperforms eight state-of-the-art existing methods in terms of both reconstruction accuracy and computational efficiency.

Hyperspectral image super-resolution based on the transfer of both spectra and multi-level features.

Existing hyperspectral image (HSI) super-resolution methods fusing a high-resolution RGB image (HR-RGB) and a low-resolution HSI (LR-HSI) always rely on spatial degradation and handcrafted priors, which hinders their practicality. To address these problems, we propose a novel, to the best of our knowledge, method with two transfer models: a window-based linear mixing (W-LM) model and a feature transfer model. Specifically, W-LM initializes a high-resolution HSI (HR-HSI) by transferring the spectra from the LR-HSI to the HR-RGB. By using the proposed feature transfer model, the HR-RGB multi-level features extracted by a pre-trained convolutional neural network (CNN) are then transferred to the initialized HR-HSI. The proposed method fully exploits spectra of LR-HSI and multi-level features of HR-RGB and achieves super-resolution without requiring the spatial degradation model and any handcrafted priors. The experimental results for 32 × super-resolution on two public datasets and our real image set demonstrate the proposed method outperforms eight state-of-the-art existing methods.

Microparticles from Hyperphosphatemia-Stimulated Endothelial Cells Promote Vascular Calcification Through Astrocyte-Elevated Gene-1.

Endothelial microparticles (EMPs) can be released in chronic kidney disease (CKD). Plasma concentration of high inorganic phosphate (HP) is considered as a decisive determinant of vascular calcification in CKD. We therefore explored the role of HP-induced EMPs (HP-EMPs) in the vascular calcification and its potential mechanism. We observed the shape of HP-EMPs captured by vascular smooth muscle cells (VSMCs) dynamically changed from rare dots, rosettes, to semicircle or circle. Our results demonstrated that HP-EMPs could directly promote VSMC calcification, or accelerate HP-induced calcification through signal transducers and activators of transcription 3 (STAT3)/bone morphogenetic protein-2 (BMP2) signaling pathway. AEG-1 activity was increased through HP-EMPs-induced VSMC calcification, in arteries from uremic rats, or from uremic rats treated with HP-EMPs. AEG-1 deficiency blocked, whereas AEG-1 overexpression exacerbated, the calcium deposition of VSMCs. AEG-1, a target of miR-153-3p, could be suppressed by agomiR-153-3p. Notably, VSMC-specific enhance of miR-153-3p by tail vein injection of aptamer-agomiR-153-3p decreased calcium deposition in both uremia rats treated with HP-EMPs or not. HP-EMPs could directly induce VSMCs calcification and accelerate Pi-induced calcification, and AEG-1 may act as crucial regulator of HP-EMPs-induced vascular calcification. This study sheds light on the therapeutic agents that influence HP-EMPs production or AEG-1 activity, which may be of benefit to treat vascular calcification.

The Common and Different Neural Bases of Distraction and Reinterpretation: A Meta-Analysis of fMRI Studies.

Distraction and reinterpretation have been recognized as two different tactics of emotion regulation. As a tactic of attention deployment, distraction involves shifting attention to neutral information or performing a secondary task to distract attention from emotion stimuli of the primary task. Reinterpretation, a representative tactic of cognitive change, was defined as changing the meaning of a situation to enhance or reduce its emotional impact. Thus, there are significant differences between the two processes. We wondered if the neural mechanisms underlying distraction and reinterpretation are different. Even though their neural correlates have been widely studied with functional magnetic resonance imaging (fMRI), few studies were conducted to compare the two tactics directly. Here we conducted an activation likelihood estimation (ALE) meta-analyses to investigate the common or different neural bases of distraction and reinterpretation. Moreover, we also used the meta-analytic connectivity modeling (MACM) to identify the emotion regulation network of distraction and reinterpretation. Overall, we found that the left dorsal lateral prefrontal cortex (DLPFC) was consistently activated during distraction and reinterpretation, whereas the left amygdala and inferior frontal gyrus/ventrolateral prefrontal cortex (VLPFC) were specifically activated during reinterpretation alone. The results indicate that the neural basis of distraction and reinterpretation are similar but not identical. The MACM results showed that distraction and reinterpretation share a common emotion regulation network, including the bilateral DLPFC, the dorsal medial prefrontal cortex, the inferior parietal lobule, the insula, the left (pre) supplementary motor area, the left middle temporal gyrus, and the right superior temporal gyrus. However, that network may subserve different functions when adopting various emotion regulation strategies. In addition, we suggest that the emotion regulation network of the left VLPFC may be a specific regulatory network for reinterpretation.

[Electrocardiogram data recognition algorithm based on variable scale fusion network model].

The judgment of the type of arrhythmia is the key to the prevention and diagnosis of early cardiovascular disease. Therefore, electrocardiogram (ECG) analysis has been widely used as an important basis for doctors to diagnose. However, due to the large differences in ECG signal morphology among different patients and the unbalanced distribution of categories, the existing automatic detection algorithms for arrhythmias have certain difficulties in the identification process. This paper designs a variable scale fusion network model for automatic recognition of heart rhythm types. In this study, a variable-scale fusion network model was proposed for automatic identification of heart rhythm types. The improved ECG generation network (EGAN) module was used to solve the imbalance of ECG data, and the ECG signal was reproduced in two dimensions in the form of gray recurrence plot (GRP) and spectrogram. Combined with the branching structure of the model, the automatic classification of variable-length heart beats was realized. The results of the study were verified by the Massachusetts institute of technology and Beth Israel hospital (MIT-BIH) arrhythmia database, which distinguished eight heart rhythm types. The average accuracy rate reached 99.36%, and the sensitivity and specificity were 96.11% and 99.84%, respectively. In conclusion, it is expected that this method can be used for clinical auxiliary diagnosis and smart wearable devices in the future.

Control of wakefulness by lateral hypothalamic glutamatergic neurons in male mice.

The lateral hypothalamus (LH) plays a key role in the maintenance of cortical activation and wakefulness. In the LH, the two main neuronal cell populations consist of excitatory glutamatergic neurons and inhibitory GABAergic neurons. Recent studies have shown that inhibitory LH GABAergic neurons are wake-promoting. However, the mechanism by which excitatory LH glutamatergic neurons contribute to sleep-wake regulation remains unclear. Using fiber photometry in male mice, we demonstrated that LH glutamatergic neurons exhibited high activities during both wakefulness and rapid eye movement sleep. Chemogenetic activation of LH glutamatergic neurons induced an increase in wakefulness that lasted for 6 hr, whereas suppression of LH glutamatergic neuronal activity caused a reduction in wakefulness. Brief optogenetic activation of LH glutamatergic neurons induced an immediate transition from slow-wave sleep to wakefulness, and long-lasting optogenetic stimulation of these neurons maintained wakefulness. Moreover, we found that LH-locus coeruleus/parabrachial nucleus and LH-basal forebrain projections mediated the wake-promoting effects of LH glutamatergic neurons. Taken together, our data indicate that LH glutamatergic neurons are essential for the induction and maintenance of wakefulness. The results presented here may advance our understanding of the role of LH in the control of wakefulness.

BTB and CNC homology 1 (Bach1) induces lung cancer stem cell phenotypes by stimulating CD44 expression.

BACKGROUND: Growing evidence suggests that cancer stem cells (CSCs) are responsible for cancer initiation in tumors. Bach1 has been identified to contribute to several tumor progression, including lung cancer. The role of Bach1 in CSCs remains poorly known. Therefore, the function of Bach1 on lung CSCs was focused currently. METHODS: The expression of Bach1, CD133, CD44, Sox2, Nanog and Oct4 mRNA was assessed using Real-Time Quantitative Reverse Transcription PCR (RT-qPCR). Protein expression of Bach1, CD133, CD44, Sox2, Nanog, Oct4, p53, BCL2, BAX, p-p38, p-AKT1, c-Fos and c-Jun protein was analyzed by western blotting. 5-ethynyl-29-deoxyuridine (EdU), colony formation, Flow cytometry analysis and transwell invasion assay were carried out to analyze lung cancer cell proliferation, apoptosis and invasion respectively. Tumor sphere formation assay was utilized to evaluate spheroid capacity. Flow cytometry analysis was carried out to isolate CD133 or CD44 positive lung cancer cells. The relationship between Bach1 and CD44 was verified using ChIP-qPCR and dual-luciferase reporter assay. Xenograft tumor tissues were collected for hematoxylin and eosin (HE) staining and IHC analysis to evaluate histology and Ki-67. RESULTS: The ratio of CD44 + CSCs from A549 and SPC-A1 cells were significantly enriched. Tumor growth of CD44 + CSCs was obviously suppressed in vivo compared to CD44- CSCs. Bach1 expression was obviously increased in CD44 + CSCs. Then, via using the in vitro experiment, it was observed that CSCs proliferation and invasion were greatly reduced by the down-regulation of Bach1 while cell apoptosis was triggered by knockdown of Bach1. Loss of Bach1 was able to repress tumor-sphere formation and tumor-initiating CSC markers. A repression of CSCs growth and metastasis of shRNA-Bach1 was confirmed using xenograft models and caudal vein injection. The direct interaction between Bach1 and CD44 was confirmed by ChIP-qPCR and dual-luciferase reporter assay. Furthermore, mitogen-activated protein kinases (MAPK) signaling pathway was selected and we proved the effects of Bach1 on lung CSCs were associated with the activation of the MAPK pathway. As manifested, loss of Bach1 was able to repress p-p38, p-AKT1, c-Fos, c-Jun protein levels in lung CSCs. Inhibition of MAPK signaling remarkably restrained lung CSCs growth and CSCs properties induced by Bach1 overexpression. CONCLUSION: In summary, we imply that Bach1 demonstrates great potential for the treatment of lung cancer metastasis and recurrence via activating CD44 and MPAK signaling.