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Abstract Road damage is a great threat to the service life and safety of roads, and the early detection of pavement damage can facilitate maintenance and repair. Street view images serve as a new solution for the monitoring of pavement damage due to their wide coverage and regular updates. In this study, a road pavement damage dataset, the Street View Image Dataset for Automated Road Damage Detection (SVRDD), was developed using 8000 street view images acquired from Baidu Maps. Based on these images, over 20,000 damage instances were visually recognized and annotated. These instances were distributed in five administrative districts of Beijing City. Ten well-established object detection algorithms were trained and assessed using the SVRDD dataset. The results have demonstrated the performances of these algorithms in the detection of pavement damages. To the best of our knowledge, SVRDD is the first public dataset based on street view images for pavement damages detection. It can provide reliable data support for future development of deep learning algorithms based on street view images.

Transformer-based deep networks have been widely employed for fusing multispectral and hyperspectral images because the global self-attention mechanism ensures that more pixel information is used in the fusion. However, the unreasonable information interactions between local windows in window self-attention-based Transformer can adversely affect the network’s modeling of the global relationship, resulting in low-quality fusion results. This study proposes a novel N-Gram based Swin Transformer Network (NGST-Net). It utilizes more pixels for modeling the global relationship, mining more spectral information of similar pixels. A N-Gram strategy is proposed to learn the spectral feature relationship between the local window and previous and subsequent windows to guide the information interactions between the local windows. A maskless shifted window strategy enables the efficient modeling of the global relationship. Experimental results show that the proposed NGST-Net has fewer parameters, higher inference speed, and smaller memory footprint than several recently published Transformer methods. The improvements in the SAM, ERGAS, and PSNR over the second-best method are 9.9%, 6.5%, and 1%, respectively, on the CAVE dataset. The inference speed of NGST-Net is 10.7 times faster than that of Fusformer. The proposed NGST-Net is a novel deep network model for the effective fusion of multispectral and hyperspectral images.

Deep learning has been developed to generate promising super resolution hyperspectral imagery by fusing hyperspectral imagery with the panchromatic band. However, it is still challenging to maintain edge spectral information in the necessary upsampling processes of these approaches, and difficult to guarantee effective feature extraction. This study proposes a pansharpening network denoted as HyperRefiner that consists of, (1) a well performing upsampling network SRNet, in which the dual attention block and refined attention block are cascaded to accomplish the extraction and fusion of features; (2) a spectral autoencoder that is embedded to perform dimensionality reduction under constrained feature extraction; and (3) the optimization module which performs self-attention at the pixel and feature levels. A comparison with several state-of-the-art models reveals that HyperRefiner can improve the quality of the fused image. Specifically, compared to the single-head HyperTransformer and with the Chikusei dataset, our network improved the Peak Signal-to-Noise Ratio, Erreur Relative Globale Adimensionnelle de Synthèse and Spectral Angle Mapper by 0.86%, 3.62%, and 2.09%, and reduce the total memory, floating point operations, model parameters and computation time by 41%, 75%, 86% and 46%, respectively. The experimental results show that HyperRefiner outperforms several networks and demonstrates its usefulness in hyperspectral image fusion. The code is publicly available at https://github.com/zsspo/Fusion_HyperRefiner.

Adding occupants to an enclosed space often leads to perceptible changes in the sound field and, therefore, speech intelligibility; however, this issue has not yet been examined in automotive cabins. This study investigated the effect of occupants in an automotive cabin on SI. Binaural room impulse responses (BRIRs) were measured in an automotive cabin with an artificial mouth and dummy head under different speaker–listener position configurations and occupancy modes. Based on the measured BRIRs, the speech transmission index (STI) was determined, and subjective speech–reception thresholds (SRTs) in Mandarin Chinese were assessed. The results indicate that speech intelligibility mostly decreased slightly after adding additional occupants. In most cases, the occupants did not significantly affect speech intelligibility, with STI variations of no more than the just-noticeable difference and SRT variation within 1 dB. When the listener was in the back-right seat, the effect of the occupants on speech intelligibility could not be ignored, with STI variations of up to 0.07 and an SRT variation of 2 dB under different occupancy modes. In addition, the influence of front-row passengers on the speech intelligibility of rear-row listeners was extremely small, and vice versa. Furthermore, altering the distribution of occupants had an effect comparable to changing the number of occupants.

Road pavement cracks are a critical factor affecting the health conditions of road pavements. Accurate crack detection contributes to providing data support for road maintenance measures. Compared to conventional crack detection algorithms, deep learning based crack segmentation methods have practical significance for road maintenance and traffic safety management due to their high precision and automation. However, existing deep learning methods often suffer from segmentation accuracy loss due to the varying crack sizes and the presence of stains on the pavement with spatial characteristics similar to cracks, leading to the misclassification of cracks. Therefore, this study proposed a multiscale enhanced road pavement crack segmentation network (MS-CrackSeg) by coupling spectral and spatial information to detect pavement cracks from unmanned aerial vehicle (UAV) hyperspectral imagery. MS-CrackSeg can simultaneously learn the spatial and rich spectral features of cracks in the hyperspectral imagery, improving the discrimination of those targets with similar spatial features to cracks compared to previous approaches. Moreover, the Multiscale Self-Attention-like Feature Extraction Module (MSSA) is introduced to extract and fuse multiscale crack features to enhance the crack detection. Experiments on a dataset consisting of 1031 hyperspectral images demonstrated the superior crack segmentation of the proposed method compared to the comparative methods. In particular, the proposed method achieved the highest F1-score of 0.74 and mean Intersection over Union of 0.79, indicating an exceptional performance. The developed approach offers improved data support for road maintenance measures and has validated the advantages of UAV hyperspectral imagery in road crack segmentation. The annotated hyperspectral dataset and the code for MS-CrackSeg network are available at https://github.com/williamchen-x/MS-CrackSeg.

Abstract RIG-I-MAVS signaling pathway plays a crucial role in defending against pathogen infection and maintaining immune balance. Upon detecting viral RNA, RIG-I triggers the formation of prion-like aggregates of the adaptor protein MAVS, which then activates the innate antiviral immune response. However, the mechanisms that regulate the aggregation of MAVS are not yet fully understood. Here, we identified WDR77 as a MAVS-associated protein, which negatively regulates MAVS aggregation. WDR77 binds to MAVS proline-rich region through its WD2-WD3-WD4 domain and inhibits the formation of prion-like filament of recombinant MAVS in vitro. In response to virus infection, WDR77 is recruited to MAVS to prevent the formation of its prion-like aggregates and thus downregulate RIG-I-MAVS signaling in cells. WDR77 deficiency significantly potentiates the induction of antiviral genes upon negative-strand RNA virus infections, and myeloid-specific Wdr77-deficient mice are more resistant to RNA virus infection. Our findings reveal that WDR77 acts as a negative regulator of the RIG-I-MAVS signaling pathway by inhibiting the prion-like aggregation of MAVS to prevent harmful inflammation.

Understanding the developmental changes that affect neurons is a key step in exploring the assembly and maturation of neural circuits in the brain. For decades, researchers have used a number of labeling techniques to visualize neuronal morphology at different stages of development. However, the efficiency and accuracy of neuronal labeling technologies are limited by the complexity and fragility of neonatal brains. In this review, we illustrate the various labeling techniques utilized for examining the neurogenesis and morphological changes occurring during the early stages of development. We compare the advantages and limitations of each technique from different aspects. Then, we highlight the gaps remaining in our understanding of the structure of neurons in the neonatal mouse brain.

Summary: Metastasis is the leading cause of high ovarian-cancer-related mortality worldwide. Three major processes constitute the whole metastatic cascade: invasion, intravasation, and extravasation. Tumor cells often reprogram their metabolism to gain advantages in proliferation and survival. However, whether and how those metabolic alterations contribute to the invasiveness of tumor cells has yet to be fully understood. Here we performed a genome-wide CRISPR-Cas9 screening to identify genes participating in tumor cell dissemination and revealed that PTGES3 acts as an invasion suppressor in ovarian cancer. Mechanistically, PTGES3 binds to phosphofructokinase, liver type (PFKL) and generates a local source of prostaglandin E2 (PGE2) to allosterically inhibit the enzymatic activity of PFKL. Repressed PFKL leads to downgraded glycolysis and the subsequent TCA cycle for glucose metabolism. However, ovarian cancer suppresses the expression of PTGES3 and disrupts the PTGES3-PGE2-PFKL inhibitory axis, leading to hyperactivation of glucose oxidation, eventually facilitating ovarian cancer cell motility and invasiveness.

The autoencoder (AE) framework is usually adopted as a baseline network for hyperspectral unmixing. Totally an AE performs well in hyperspectral unmixing through automatically learning low-dimensional embedding and reconstructing data. However, most available AE-based hyperspectral unmixing networks do not fully consider the spatial and spectral information of different ground features in hyperspectral images and output relatively fixed ratios of linear and nonlinear photon scattering effects under different scenarios. Therefore, these methods have poor generalization abilities across different ground features and scenarios. Here, inspired by the two-stream network structure, we propose a spatial–spectral adaptive nonlinear unmixing network (SSANU-Net) in which the spatial–spectral information of hyperspectral imagery is effectively learned using the two-stream encoder, followed by the simulation of the linear–nonlinear scattering component of photons using a two-stream decoder. Additionally, we adopt a combination of spatial–spectral and linear–nonlinear components using the optimized adaptive weighting strategy of learnable parameters. Experiments with several hyperspectral image datasets (i.e., Samson, Jasper Ridge, and Urban) showed that the proposed SSANU-Net network had higher unmixing accuracy and generalization performance compared with several conventional methods. This demonstrates that SSANU-Net represents a novel method for hyperspectral unmixing analysis.

Road pavement damage affects driving comfort markedly, threatens driving safety, and may even cause traffic accidents. The traffic management department conventionally captures pavement damage information mainly using manual and vehicle-mounted equipment, which is not conducive to the detection of large-scale road pavement distress. Street-view images can provide full-view images of urban roads where the data is updated regularly by navigation map service companies, making it possible to rapidly detect pavement damage in urban areas. This paper presents a new pavement damage detection approach that is built upon an improved YOLOv5 network and street-view images. The proposed model can deal with a large-scale detection layer to improve the detection precision of large distress targets, achieving thus both cross-layer and cross-scale feature fusion by using the Generalized Feature Pyramid Network (Generalized-FPN) structure. The improved network also involves a diagonal Intersection over Union loss for regression calculation of the boundary box and builds the decoupled Head structure to achieve the decoupling detection of prediction and regression. As a result, the fusion of the weak feature information in feature layers is enhanced at different spatial scales, a more suitable method is achieved for pavement damage detection in the complex context of multi-scale street-view images, and the accuracy of the modified network is much improved in the detection of pavement distress from street-view imagery. Furthermore, We created a large image sample set for model training and testing, and a total of 156,304 street-view images, obtained from Fengtai District, Beijing, China was used for demonstrating the usefulness of the proposed network. The findings indicated that the proposed approach could effectively achieve pavement damage detection of urban roads from street-view images, with a precision average of 79.8% on the test samples. Moreover, the developed model was applied for pavement damage detection for all the roads in Fengtai District, Beijing, indicating that our method can offer viable damage data for road maintenance planning.

Stimulator of interferon genes (STING) plays a pivotal role in type I interferon-mediated innate immune response to the cytoplasmic detection of aberrant DNA. STING is a membrane protein localized in endoplasmic reticulum (ER), which upon stimulation translocates to Golgi apparatus and activates downstream signaling cascades. However, the mechanism regulating STING activity and significance of its intracellular traffic are not completely understood. Here we identify a novel region of human STING comprising thirteen residues within its C-terminal tail (CTT) for downstream nuclear factor kappa-light-chain-enhancer of activated B cell (NF-κB) activation. We also discover that STING CTT fragment can activate downstream signaling regardless of its ER localization. In addition, we reveal that ligand-binding domain (LBD) in the middle of STING binds and confers autoinhibition to its CTT for both NF-κB- and interferon regulatory factor 3-activation. Furthermore, STING LBD can inhibit the interferon-stimulating activity of STING CTT in trans and demonstrate a dominant negative effect on endogenous STING for interferon induction. We thus uncover an important autoinhibitory mechanism modulating STING activity.

Liposomes are the most widely used nanocarrier platform for the delivery of therapeutic and diagnostic agents, and a number of liposomes have been approved for use in clinical practice. After systemic administration, most liposomes are cleared by macrophages in the mononuclear phagocyte system, such as the liver and bone marrow (BM). However, the majority of studies have focused on investigating the therapeutic results of liposomal drugs, and too few studies have evaluated the potential side effects of empty nanocarriers on the functions of macrophages in the mononuclear phagocyte system. Here, we evaluate the potential effects of empty liposomes on the functions of BM niche macrophages. Following liposome administration, we observed lipid droplet (LD) accumulation in cultured primary macrophages and BM niche macrophages. We found that these LD-accumulating macrophages, similar to foam cells, exhibited increased expression of inflammatory cytokines, such as IL-1β and IL-6. We further provided evidence that liposome deposition and degradation induced LD biogenesis on the endoplasmic reticulum membrane and subsequently disturbed endoplasmic reticulum homeostasis and activated the inositol-requiring transmembrane kinase/endoribonuclease 1α/NF-κB signaling pathway, which is responsible for the inflammatory activation of macrophages after liposome engulfment. Finally, we also showed the side effects of dysfunctional BM niche macrophages on hematopoiesis in mice, such as the promotion of myeloid-biased output and impairment of erythropoiesis. This study not only draws attention to the safety of liposomal drugs in clinical practice but also provides new directions for the design of lipid-based drug carriers in preclinical studies.

Detailed snow cover maps are essential for estimating the earth’s energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data offers a potential solution. This paper aims to map snow cover distribution at 750 m resolution across the diverse 1,664,900 km2 of Xinjiang, China, based on SNPP-VIIRS DNB radiance. We implemented a swarm intelligent optimization technique Krill Herd algorithm, which finds the optimal threshold value by taking Otsu’s method as the objective function. We derived SNPP-VIIRS DNB snow maps of 14 consecutive scenes in December 2021, compared our snow-covered area estimations with those from MODIS and AMSR2 standard snow cover products, and generated composite snow maps by merging MODIS and SNPP-VIIRS DNB data. Results show that SNPP-VIIRS DNB snow maps are capable of providing reliable snow cover maps superior to MODIS and AMSR2, with an overall accuracy level of 84.66%. The composite snow maps at 500 m spatial resolution provided 55.85% more information on snow cover distribution than standard MODIS products and achieved an overall accuracy of 84.69%. Our study demonstrated the feasibility of snow cover detection in Xinjiang based on SNPP-VIIRS DNB, which can serve as a supplementary dataset for MODIS estimations where clouded pixels are present.

Abstract Two-dimensional (2D) perovskites are known as one of the most promising luminescent materials due to their structural diversity and outstanding optoelectronic properties. Compared with 3D perovskites, 2D perovskites have natural quantum well structures, large exciton binding energy (E b ) and outstanding thermal stability, which shows great potential in the next-generation displays and solid-state lighting. In this review, the fundamental structure, photophysical and electrical properties of 2D perovskite films were illustrated systematically. Based on the advantages of 2D perovskites, such as special energy funnel process, ultra-fast energy transfer, dense film and low efficiency roll-off, the remarkable achievements of 2D perovskite light-emitting diodes (PeLEDs) are summarized, and exciting challenges of 2D perovskite are also discussed. An outlook on further improving the efficiency of pure-blue PeLEDs, enhancing the operational stability of PeLEDs and reducing the toxicity to push this field forward was also provided. This review provides an overview of the recent developments of 2D perovskite materials and LED applications, and outlining challenges for achieving the high-performance devices.

Copper-based zeolite catalysts are widely used in methanol synthesis from methane oxidation, but their photothermal catalytic properties have seldom been explored. This study examines the effect of the preparation method on Cu-based zeolite composite graphite-phase carbon nitride catalysts (Cu-MOR/g-C3N4) for direct methanol synthesis from methane oxidation by photothermal catalysis. Four different preparation methods are employed: liquid phase ion exchange (Cu-MOR/g-C3N4-IE), isovolumetric impregnation (Cu-MOR/g-C3N4-IM), solid-state ion exchange (Cu-MOR/g-C3N4-GR), and hydrothermal synthesis (Cu-MOR/g-C3N4-HT). Cu-MOR/g-C3N4-IE shows the highest methanol yield (3.09 μmol h−1 gcat−1) due to strong interactions between the CuxOy species and g-C3N4, as well as smaller interfacial charge transfer forces. This study provides a new method for the design and synthesis of catalysts for the conversion of methane.

Abstract Background Neurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification. The morphology of a neuronal circuit is a fundamental component in neuron modeling. Recently, single-neuron morphologies of the whole brain have been used in many studies. The correctness and completeness of semimanually traced neuronal morphology are credible. However, there are some inaccuracies in semimanual tracing results. The distance between consecutive nodes marked by humans is very long, spanning multiple voxels. On the other hand, the nodes are marked around the centerline of the neuronal fiber, not on the centerline. Although these inaccuracies do not seriously affect the projection patterns that these studies focus on, they reduce the accuracy of the traced neuronal skeletons. These small inaccuracies will introduce deviations into subsequent studies that are based on neuronal morphology files. Results We propose a neuronal digital skeleton optimization method to evaluate and make fine adjustments to a digital skeleton after neuron tracing. Provided that the neuronal fiber shape is smooth and continuous, we describe its physical properties according to two shape restrictions. One restriction is designed based on the grayscale image, and the other is designed based on geometry. These two restrictions are designed to finely adjust the digital skeleton points to the neuronal fiber centerline. With this method, we design the three-dimensional shape restriction workflow of neuronal skeleton adjustment computation. The performance of the proposed method has been quantitatively evaluated using synthetic and real neuronal image data. The results show that our method can reduce the difference between the traced neuronal skeleton and the centerline of the neuronal fiber. Furthermore, morphology metrics such as the neuronal fiber length and radius become more precise. Conclusions This method can improve the accuracy of a neuronal digital skeleton based on traced results. The greater the accuracy of the digital skeletons that are acquired, the more precise the neuronal morphologies that are analyzed will be.

The lateral loading of a plate anchor is a complicated process that involves complex anchor-soil interactions. The deformation characteristics of the soil around an anchor have an important effect on its lateral bearing capacity. In this paper, the noncontact digital image correlation (DIC) technique is used to study the distribution and variation of the soil deformation field under a laterally loaded anchor in sandy soil. The results show that the sand density and embedment ratio significantly affect the distribution and influence range of the active and passive zones around the anchor. The active zone behind the rod gradually decreases with increasing sand density until ultimately disappearing, and the passive zone increases. The maximum influence height occurs in the passive zone behind the rod in dense sand, and the influence range of the passive zone in front of the rod expands with an increasing embedment ratio. Shear bands form during the lateral loading process, which are accompanied by dilatancy in the shear process. The motion path of the rotation center in loose and medium sand is initially rigid translational and then becomes rigid rotational, while the opposite trend occurs in compact dense sand. The results provide important guidance for the development of predictive models for anchor lateral loading and design.

Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of people, and analyze their features that are hidden in the trajectory data. Meanwhile, the algorithms for suspicious behavior detection are proposed based on the main features of the corresponding behavior, which employ spatiotemporal clustering, semantic annotation, outlier detection, and other methods. A practical trajectory dataset (i.e., TucityLife) containing more than 1000 suspicious behaviors was collected, and experiments were conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method for suspicious behavior detection has a recall of 93.5% and a precision of 87.6%, demonstrating its excellent performance in identifying the possible offenders and potential target places. The proposed methods are valuable for preventing city crime and supporting the appropriate allocation of police resources.

Prostatitis is a common disease in adult males, with characteristics of a poor treatment response and easy recurrence, which seriously affects the patient’s quality of life. The prostate is located deep in the pelvic cavity, and thus a traditional infusion or other treatment methods are unable to easily act directly on the prostate, leading to poor therapeutic effects. Therefore, the development of new diagnostic and treatment strategies has become a research hotspot in the field of prostatitis treatment. In recent years, nanomaterials have been widely used in the diagnosis and treatment of various infectious diseases. Nanotechnology is a promising tool for 1) the accurate diagnosis of diseases; 2) improving the targeting of drug delivery systems; 3) intelligent, controlled drug release; and 4) multimode collaborative treatment, which is expected to be applied in the diagnosis and treatment of prostatitis. Nanotechnology is attracting attention in the diagnosis, prevention and treatment of prostatitis. However, as a new research area, systematic reviews on the application of nanomaterials in the diagnosis and treatment of prostatitis are still lacking. In this mini-review, we will highlight the treatment approaches for and challenges associated with prostatitis and describe the advantages of functional nanoparticles in improving treatment effectiveness and overcoming side effects.

Abstract Corticofugal projection neurons are key components in connecting the neocortex and the subcortical regions. In the barrel field, these neurons have various projection targets and play crucial roles in the rodent whisker sensorimotor system. However, the projection features of corticofugal projection neurons at the single-axon level are far from comprehensive elucidation. Based on a brain-wide positioning system with high-resolution imaging for Thy1-GFP M-line mice brains, we reconstructed and analyzed more than one hundred corticofugal projection neurons in both layer V and VI of barrel cortex. The dual-color imaging made it possible to locate the neurons’ somata, trace their corresponding dendrites and axons and then distinguish the neurons as L5 type I/II or L6 type. The corticofugal projection pattern showed significant diversity across individual neurons. Usually, the L5 type I neurons have greater multi-region projection potential. The thalamus and the midbrain are the most frequent projection targets among the investigated multidirectional projection neurons, and the hypothalamus is particularly unique in that it only appears in multidirectional projection situations. Statistically, the average branch length of apical dendrites in multi-region projection groups is larger than that of single-region projection groups. This study demonstrated a single-axon-level analysis for barrel corticofugal projection neurons, which could provide a micro-anatomical basis for interpreting whisker sensorimotor circuit function.

The excitatory neurons in the visual cortex are of great significance for us in understanding brain functions. However, the diverse neuron types and their morphological properties have not been fully deciphered. In this paper, we applied the brain-wide positioning system (BPS) to image the entire brain of two Thy1-eYFP H-line male mice at 0.2 μm × 0.2 μm × 1 μm voxel resolution. A total of 103 neurons were reconstructed in layers 5 and 6 of the visual cortex with single-axon-level resolution. Based on the complete topology of neurons and the inherent positioning function of the imaging method, we classified the observed neurons into six types according to their apical dendrites and somata location: star pyramidal cells in layer 5 (L5-sp), slender-tufted pyramidal cells in layer 5 (L5-st), tufted pyramidal cells in layer 5 (L5-tt), spiny stellate-like cells in layer 6 (L6-ss), star pyramidal cells in layer 6 (L6-sp), and slender-tufted pyramidal cells in layer 6 (L6-st). By examining the axonal projection patterns of individual neurons, they can be categorized into three modes: ipsilateral circuit connection neurons, callosal projection neurons and corticofugal projection neurons. Correlating the two types of classifications, we have found that there are at least two projection modes comprised in the former defined neuron types except for L5-tt. On the other hand, each projection mode may consist of four dendritic types defined in this study. The axon projection mode only partially correlates with the apical dendrite feature. This work has demonstrated a paradigm for resolving the visual cortex through single-neuron-level quantification and has shown potential to be extended to reveal the connectome of other defined sensory and motor systems.

Resin embedding has been widely used for precise imaging of fluorescently labeled biological samples with optical and electron microscopy. The low preservation rate of fluorescence, especially for red fluorescent proteins, has limited the application of resin embedding in multifluorescent protein-labeled samples. Here, we optimized the embedding method to retain the intensity of multiple fluorescent proteins during resin embedding. By reducing the polymerization temperature from 50 to 35°C and adding a fluorescent protein protection reagent during the embedding process, we successfully increased the fluorescence preservation rate by nearly twofold for red fluorescent proteins, including tdTomato, mCherry, and DsRed. Meanwhile, the background fluorescence decreased significantly in the optimized embedding method. This method is suitable not only for red fluorescent protein-labeled samples but also for blue (BFP) and green fluorescent protein (GFP)-labeled samples. We embedded brains labeled with BFP, DsRed, and GFP via AAV and rabies virus and acquired the distribution of input neurons to different cortical areas. With GFP/tdTomato double-labeled samples in resin, we obtained the cholinergic projectome of the pedunculopontine tegmental nucleus (PPTg) and the distribution of cholinergic neurons at single-neuron resolution in the whole brain simultaneously. Input cholinergic terminals from the PPTg were found to innervate the cholinergic soma and fiber in the neocortex, basal forebrain and brainstem, indicating that local cholinergic neurons received long-range cholinergic modulation from the midbrain. Our optimized method is useful for embedding multicolor fluorescent protein-labeled samples to acquire multidimensional structural information on neural circuits at single-neuron resolution in the whole brain.

Resistance to intravenous immunoglobulin (IVIG) occurs in 10-20% of patients with Kawasaki disease (KD). The risk of resistance is about two-fold higher in patients with elevated gamma glutamyl transferase (GGT) levels. We sought to understand the biological mechanisms underlying IVIG resistance in patients with elevated GGT levels.We explored the association between elevated GGT levels and IVIG-resistance with a cohort of 686 KD patients (Cohort I). Gene expression data from 130 children with acute KD (Cohort II) were analyzed using the R square statistic and false discovery analysis to identify genes that were differentially represented in patients with elevated GGT levels with regard to IVIG responsiveness. Two additional KD cohorts (Cohort III and IV) were used to test the hypothesis that sialylation and GGT may be involved in IVIG resistance through neutrophil apoptosis.Thirty-six genes were identified that significantly explained the variations of both GGT levels and IVIG responsiveness in KD patients. After Bonferroni correction, significant associations with IVIG resistance persisted for 12 out of 36 genes among patients with elevated GGT levels and none among patients with normal GGT levels. With the discovery of ST6GALNAC3, a sialyltransferase, as the most differentially expressed gene, we hypothesized that sialylation and GGT are involved in IVIG resistance through neutrophil apoptosis. We then confirmed that in Cohort III and IV there was significantly less reduction in neutrophil count in IVIG non-responders.Gene expression analyses combining molecular and clinical datasets support the hypotheses that: (1) neutrophil apoptosis induced by IVIG may be a mechanism of action of IVIG in KD; (2) changes in sialylation and GGT level in KD patients may contribute synergistically to IVIG resistance through blocking IVIG-induced neutrophil apoptosis. These findings have implications for understanding the mechanism of action in IVIG resistance, and possibly for development of novel therapeutics.

Most differentiated cells convert glucose to pyruvate in the cytosol through glycolysis, followed by pyruvate oxidation in the mitochondria. These processes are linked by the mitochondrial pyruvate carrier (MPC), which is required for efficient mitochondrial pyruvate uptake. In contrast, proliferative cells, including many cancer and stem cells, perform glycolysis robustly but limit fractional mitochondrial pyruvate oxidation. We sought to understand the role this transition from glycolysis to pyruvate oxidation plays in stem cell maintenance and differentiation. Loss of the MPC in Lgr5-EGFP-positive stem cells, or treatment of intestinal organoids with an MPC inhibitor, increases proliferation and expands the stem cell compartment. Similarly, genetic deletion of the MPC in Drosophila intestinal stem cells also increases proliferation, whereas MPC overexpression suppresses stem cell proliferation. These data demonstrate that limiting mitochondrial pyruvate metabolism is necessary and sufficient to maintain the proliferation of intestinal stem cells.