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Abstract The aims of the present stud were to optimize fermentation parameters of seedless wampee wine using response surface methodology (RSM) and evaluate the changes in flavor metabolites during fermentation. Seedless wampee wine of optimal sensory quality was produced using an inoculum concentration of 0.6%, initial sugar levels of 200 g/L, a fermentation temperature of 22 °C, and a fermentation period of 9 days. Then the flavor compound profiles (amino acids, organic acids and volatile aroma compounds) of seedless wampee wine during the fermentation under optimal conditions were analyzed using high performance liquid chromatography (HPLC) and gas chromatography–mass spectrometr (GC-MS). The main fermented phase of fermentation resulted in fluctuations in both total amino acids and organic acids, with stabilization occurring later on. A total of 54 volatile components, including esters, alcohols, terpenes, and acids, were putatively identified. Terpenes were the primary drivers of the flavor characteristics of seedless wampee. The rise of esters and decline of terpenes have the potential to significantly alter the flavor of wine during fermentation. These results would contribute to the further development of seedless wampee wine. Graphical Abstract

Background Bone fractures are a common medical issue worldwide, causing a serious economic burden on society. In recent years, the application of artificial intelligence (AI) in the field of fracture has developed rapidly, especially in fracture diagnosis, where AI has shown significant capabilities comparable to those of professional orthopedic surgeons. This study aimed to review the development process and applications of AI in the field of fracture using bibliometric analysis, while analyzing the research hotspots and future trends in the field. Materials and methods Studies on AI and fracture were retrieved from the Web of Science Core Collections since 1990, a retrospective bibliometric and visualized study of the filtered data was conducted through CiteSpace and Bibliometrix R package. Results A total of 1063 publications were included in the analysis, with the annual publication rapidly growing since 2017. China had the most publications, and the United States had the most citations. Technical University of Munich, Germany, had the most publications. Doornberg JN was the most productive author. Most research in this field was published in Scientific Reports . Doi K's 2007 review in Computerized Medical Imaging and Graphics was the most influential paper. Conclusion AI application in fracture has achieved outstanding results and will continue to progress. In this study, we used a bibliometric analysis to assist researchers in understanding the basic knowledge structure, research hotspots, and future trends in this field, to further promote the development of AI applications in fracture.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignant tumor with a high metastatic potential. Perineural invasion (PNI) occurs in the early stages of PDAC with a high incidence rate and is directly associated with a poor prognosis. It involves close interaction among PDAC cells, nerves and the tumor microenvironment. In this review, we detailed discuss PNI-related pain, six specific steps of PNI, and treatment of PDAC with PNI and emphasize the importance of novel technologies for further investigation.

Abstract The neural circuits underlying sleep-wakefulness and general anesthesia have not been fully investigated. The GABAergic neurons in the bed nucleus of the stria terminalis (BNST) play a critical role in stress and fear that relied on heightened arousal. Nevertheless, it remains unclear whether BNST GABAergic neurons are involved in the regulation of sleep-wakefulness and anesthesia. Here, using in vivo fiber photometry combined with electroencephalography, electromyography, and video recordings, we found that BNST GABAergic neurons exhibited arousal-state-dependent alterations, with high activities in both wakefulness and rapid-eye movement sleep, but suppressed during anesthesia. Optogenetic activation of these neurons could initiate and maintain wakefulness, and even induce arousal from anesthesia. However, chronic lesion of BNST GABAergic neurons altered spontaneous sleep-wakefulness architecture during the dark phase, but not induction and emergence from anesthesia. Furthermore, we also discovered that the BNST-ventral tegmental area pathway might participate in promoting wakefulness and reanimation from steady-state anesthesia. Collectively, our study explores new elements in neural circuit mechanisms underlying sleep-wakefulness and anesthesia, which may contribute to a more comprehensive understanding of consciousness and the development of innovative anesthetics.

College students are inevitably online and at risk of becoming addicted. Life history theory provides an explanatory framework for individual differences in Internet addiction, and childhood harshness and unpredictability may be important antecedents. However, it is unclear whether and how childhood harshness and/or unpredictability affect Internet addiction during college. In this study, we recruited 483 Chinese college students and assessed their childhood harshness, unpredictability, self-control, and Internet addiction. The results of path analysis showed that childhood unpredictability was positively associated with Internet addiction among college students and was partially mediated by self-control. The effect of harshness on Internet addiction showed a suppression effect, i.e., the direct effect of harshness on Internet addiction was negative and the indirect effect through self-control was positive. This suggests that the high risk of Internet addiction stems from harshness and unpredictability in childhood, but that the effects of these factors are independent and distinct. Self-control plays an important role in this process, but many internal mechanisms remain to be tested in future research.

Abstract The hypothalamic supramammillary nucleus (SuM) plays a crucial role in controlling wakefulness, but the downstream target regions participating in this control process remain unknown. Here, using circuit-specific fiber photometry and single-neuron electrophysiology together with electroencephalogram, electromyogram and behavioral recordings, we find that approximately half of SuM neurons that project to the medial septum (MS) are wake-active. Optogenetic stimulation of axonal terminals of SuM-MS projection induces a rapid and reliable transition to wakefulness from non-rapid-eye movement or rapid-eye movement sleep, and chemogenetic activation of SuMMS projecting neurons significantly increases wakefulness time and prolongs latency to sleep. Consistently, chemogenetically inhibiting these neurons significantly reduces wakefulness time and latency to sleep. Therefore, these results identify the MS as a functional downstream target of SuM and provide evidence for the modulation of wakefulness by this hypothalamic-septal projection.

Abstract The sensory neocortex has been suggested to be a substrate for long-term memory storage, yet which exact single cells could be specific candidates underlying such long-term memory storage remained neither known nor visible for over a century. Here, using a combination of day-by-day two-photon Ca2+ imaging and targeted single-cell loose-patch recording in an auditory associative learning paradigm with composite sounds in male mice, we reveal sparsely distributed neurons in layer 2/3 of auditory cortex emerged step-wise from quiescence into bursting mode, which then invariably expressed holistic information of the learned composite sounds, referred to as holistic bursting (HB) cells. Notably, it was not shuffled populations but the same sparse HB cells that embodied the behavioral relevance of the learned composite sounds, pinpointing HB cells as physiologically-defined single-cell candidates of an engram underlying long-term memory storage in auditory cortex.

Using rape bee pollen as raw material, the Candida apicola LGL-2 selected from honey was used to investigate the effect of fermentation factors including fermentation time, fermentation temperature, inoculum amount and substrate content on the total phenolic content of rape bee pollen jiaosu. The fermentation process of rape bee pollen jiaosu was optimized by response surface methodology (RSM) using total phenolic content as the response variable. Based on these conditions, the phenolic compounds, volatile aroma components and antioxidant activities of rape bee pollen and jiaosu were further compared. The results showed that the optimal conditions for rape bee pollen jiaosu by Candida apicola LGL-2 were fermentation time 48 h, fermentation temperature 30 ℃, inoculum amount 20% and substrate content 30%, and the total phenolic content of rape bee pollen jiaosu was 11.48 mg GAE/g after fermentation. Phenolic compounds including gallic acid, p-coumaric acid, rutin, ellagic acid and quercetin were detected in rape bee pollen and jiaosu by high performance liquid chromatography (HPLC), and the contents of all the phenolic compounds above were increased after the fermentation by Candida apicola LGL-2. The volatile aroma compounds of rape bee pollen and jiaosu were analyzed by headspace solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC-MS) combined with the relative odor activity value (ROAV). Twenty-six volatile aroma compounds were detected, 7 of which were identified as key aroma components of rape bee pollen jiaosu, and the contents of some alcohols and esters were enhanced to bring excellent flavor to jiaosu. Compared with rape bee pollen, rape bee pollen jiaosu exhibited stronger antioxidant activities as determined by DPPH·, ABTS+· and FRAP assay. These results indicated that fermentation by Candida apicola LGL-2 could effectively improve the phenolic content, flavor and antioxidant power of rape bee pollen.

Summary: The integrated energy station of new energy vehicle hydrogenation/charging/power exchange is proposed, which also includes hydrogen production, hydrogen storage, electricity sales to users and the grid (WPIES). To address the efficiency of renewable energy use, this paper proposes a future value competition strategy for wind and photovoltaic (PV) allocation based on goal optimization (FVCS). In order to better realize the distribution of wind power/PV in the integrated energy station and improve the energy utilization efficiency of the integrated energy station, a two-layer optimization model of FVCS-WPIES is proposed, in which the upper layer model aims to maximize the expected income. The goals of the lower-level model are to maximize total profit, minimize battery losses, and minimize pollutant emissions. The model also considers the hydrogen power constraint and the upper-level model penalty. The comparison results show that the Pareto solution set is superior to the traditional model.

The construction of the Electric Vehicle Integrated Energy Station (EV-IES) is a prerequisite for the rapid development of the EV industry. However, how to optimize the operation of the EV-IES is a problem worthy of study. Therefore, this paper designs an EV-IES model with PV and Energy Storage System (ESS). Fully consider the peak-valley time-of-use electricity price, user traffic flow, PV output, and other factors. On this basis, the three objectives of the maximum daily revenue of the EV-IES, the minimum exchanged energy between the EV-IES and the Regional Power System (RPS), and the minimum pollutant emission are optimized at the same time. Secondly, this paper proposes a Many-objective Stochastic Competition Optimization (MOSCO) algorithm, which is utilized to assess the DTLZ1-7 benchmark functions and the optimization scheduling problem of EV-IES. By comparing its simulation results with those of five other optimization algorithms, it is evident that the MOSCO algorithm outperforms the other five in terms of IGD, GD, HV, and Spread values. This indicates the effectiveness of the MOSCO algorithm in addressing many-objective optimization problems. Finally, in order to illustrate the feasibility of designing the EV-IES model, three comparative cases were designed. The Pareto solutions of these cases were obtained using the MOSCO algorithm, and the Entropy-Technique for Order Preference by Similarity to Ideal Solution (ETOPSIS) method was applied to determine the optimal solution for each case. Compared to the traditional charging station (case 1), the daily revenue of the EV-IES increased by 27.97%. Pollutant emissions were reduced by 25.29%.

The quality of optical remote sensing images is largely affected by clouds and haze. In addition, the mosaicking image of multiple remote sensing images, due to objective factors such as acquiring time or climate conditions, will lead to large spectral differences in the area around the seamline. The aforementioned scenarios will seriously affect the accuracy of change detection models based on deep learning. However, there is still a lack of methods to address such issues. To solve these problems, from the perspective of training samples, this article proposed a simple but effective data augmentation method to improve the generalization ability of the deep change detection model in the region of haze cover and the seamline. First, from the characteristics of the optical remote sensing image itself, two image simulation methods are designed to conduct data augmentation, named mosaic simulation and haze image simulation. Then, the newly augmented training samples are mixed with the original training samples and then input into a deep learning model for model training. Finally, the change detection results indicate that the proposed data augmentation method can effectively improve the generalization ability of the change detection model in the region of haze cover and seamline, which has high practical value for improving the deep learning model's performance in real-world scenarios and also provides a simple but effective algorithm reference for other intelligent interpretation tasks from the perspective of training data.

BackgroundChronic pain poses a significant social burden. Spinal cord stimulation (SCS) is considered to be the most promising treatment for refractory pain. The aim of this study was to summarize the current research hotspots on SCS for pain treatment during the past two decades and to predict the future research trends by bibliometric analysis.MethodsThe literature over the last two decades (2002–2022) which was related to SCS in pain treatment was obtained from the Web of Science Core Collection. Bibliometric analyses were conducted based on the following aspects: (1) Annual publication and citation trends; (2) Annual publication changes of different publication types; (3) Publications and citations/co-citations of different country/institution/journal/author; (4) Citations/co-citation and citation burst analysis of different literature; and (5) Co-occurrence, cluster, thematic map, trend topics, and citation burst analysis of different keywords. (6) Comparison between the United States and Europe. All analyses were performed on CiteSpace, VOSviewer, and R bibliometrix package.ResultsA total of 1,392 articles were included in this study, with an increasing number of publications and citations year by year. The most highly published type of literature was clinical trial. United States was the country with the most publications and citations; Johns Hopkins University was the institution with the most publications; NEUROMODULATION published the most papers; the most published author was Linderoth B; and the most cited paper was published in the PAIN by Kumar K in 2007. The most frequently occurring keywords were “spinal cord stimulation,” “neuropathic pain,” and “chronic pain,” etc.ConclusionThe positive effect of SCS on pain treatment has continued to arouse the enthusiasm of researchers in this field. Future research should focus on the development of new technologies, innovative applications, and clinical trials for SCS. This study might facilitate researchers to comprehensively understand the overall perspective, research hotspots, and future development trends in this field, as well as seek collaboration with other researchers.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer. It has a poor response to conventional therapy and has an extremely poor 5-year survival rate. PDAC is driven by multiple oncogene mutations, with the highest mutation frequency being observed in KRAS. The KRAS protein, which binds to GTP, has phosphokinase activity, which further activates downstream effectors. KRAS mutation contributes to cancer cell proliferation, metabolic reprogramming, immune escape, and therapy resistance in PDAC, acting as a critical driver of the disease. Thus, KRAS mutation is positively associated with poorer prognosis in pancreatic cancer patients. This review focus on the KRAS mutation patterns in PDAC, and further emphases its role in signal transduction, metabolic reprogramming, therapy resistance and prognosis, hoping to provide KRAS target therapy strategies for PDAC.

The development of two-photon microscopy and Ca2+ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca2+ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca2+ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca2+ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca2+ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process.

Background: The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients. Methods: A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision–recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not. Results: The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis. Conclusions: ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.

Quantitative and mechanistic understanding of learning and long-term memory at the level of single neurons in living brains require highly demanding techniques. A specific need is to precisely label one cell whose firing output property is pinpointed amidst a functionally characterized large population of neurons through the learning process and then investigate the distribution and properties of dendritic inputs. Here, we disseminate an integrated method of daily two-photon neuronal population Ca2+ imaging through an auditory associative learning course, followed by targeted single-cell loose-patch recording and electroporation of plasmid for enhanced chronic Ca2+ imaging of dendritic spines in the targeted cell. Our method provides a unique solution to the demand, opening a solid path toward the hard-cores of how learning and long-term memory are physiologically carried out at the level of single neurons and synapses.

ObjectivesThe study aimed to conduct a bibliometric analysis of publications concerning lumbar spondylolisthesis, as well as summarize its research topics and hotspot trends with machine-learning based text mining.MethodsThe data were extracted from the Web of Science Core Collection (WoSCC) database and then analyzed in Rstudio1.3.1 and CiteSpace5.8. Annual publication production and the top-20 productive authors over time were obtained. Additionally, top-20 productive journals and top-20 influential journals were compared by spine-subspecialty or not. Similarly, top-20 productive countries/regions and top-20 influential countries/regions were compared by they were developed countries/regions or not. The collaborative relationship among countries and institutions were presented. The main topics of lumbar spondylolisthesis were classified by Latent Dirichlet allocation (LDA) analysis, and the hotspot trends were indicated by keywords with strongest citation bursts.ResultsUp to 2021, a total number of 4,245 articles concerning lumbar spondylolisthesis were finally included for bibliometric analysis. Spine-subspecialty journals were found to be dominant in the productivity and the impact of the field, and SPINE, EUROPEAN SPINE JOURNAL and JOURNAL OF NEUROSURGERY-SPINE were the top-3 productive and the top-3 influential journals in this field. USA, Japan and China have contributed to over half of the publication productivity, but European countries seemed to publish more influential articles. It seemed that developed countries/regions tended to produce more articles and more influential articles, and international collaborations mainly occurred among USA, Europe and eastern Asia. Publications concerning surgical management was the major topic, followed by radiographic assessment and epidemiology for this field. Surgical management especially minimally invasive technique for lumbar spondylolisthesis were the recent hotspots over the past 5 years.ConclusionsThe study successfully summarized the productivity and impact of different entities, which should benefit the journal selection and pursuit of international collaboration for researcher who were interested in the field of lumbar spondylolisthesis. Additionally, the current study may encourage more researchers joining in the field and somewhat inform their research direction in the future.

Brain-wide axonal projections of single neurons have been extensively reconstructed without any functional characterization. The authors present a method that allows for developing a precise one-to-one map of both projection patterns and functional features of single neurons in mice.

Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions.

Abstract Background Tumor phototherapy especially photodynamic therapy (PDT) or photothermal therapy (PTT), has been considered as an attractive strategy to elicit significant immunogenic cell death (ICD) at an optimal tumor retention of PDT/PTT agents. Heptamethine cyanine dye (IR-780), a promising PDT/PTT agent, which can be used for near-infrared (NIR) fluorescence/photoacoustic (PA) imaging guided tumor phototherapy, however, the strong hydrophobicity, short circulation time, and potential toxicity in vivo hinder its biomedical applications. To address this challenge, we developed mesoporous polydopamine nanoparticles (MPDA) with excellent biocompatibility, PTT efficacy, and PA imaging ability, facilitating an efficient loading and protection of hydrophobic IR-780. Results The IR-780 loaded MPDA (IR-780@MPDA) exhibited high loading capacity of IR-780 (49.7 wt%), good physiological solubility and stability, and reduced toxicity. In vivo NIR fluorescence and PA imaging revealed high tumor accumulation of IR-780@MPDA. Furthermore, the combined PDT/PTT of IR-780@MPDA could induce ICD, triggered immunotherapeutic response to breast tumor by the activation of cytotoxic T cells, resulting in significant suppression of tumor growth in vivo. Conclusion This study demonstrated that the as-developed compact and biocompatible platform could induce combined PDT/PTT and accelerate immune activation via excellent tumor accumulation ability, offering multimodal tumor theranostics with negligible systemic toxicity. Graphical Abstract

BackgroundThis study aimed to conduct a bibliometric analysis of publications on connectomes and illustrate its trends and hotspots using a machine-learning-based text mining algorithm.MethodsDocuments were retrieved from the Web of Science Core Collection (WoSCC) and Scopus databases and analyzed in Rstudio 1.3.1. Through quantitative and qualitative methods, the most productive and impactful academic journals in the field of connectomes were compared in terms of the total number of publications and h-index over time. Meanwhile, the countries/regions and institutions involved in connectome research were compared, as well as their scientific collaboration. The study analyzed topics and research trends by R package “bibliometrix.” The major topics of connectomes were classified by Latent Dirichlet allocation (LDA).ResultsA total of 14,140 publications were included in the study. NEUROIMAGE ranked first in terms of publication volume (1,427 articles) and impact factor (h-index:122) among all the relevant journals. The majority of articles were published by developed countries, with the United States having the most. Harvard Medical School and the University of Pennsylvania were the two most productive institutions. Neuroimaging analysis technology and brain functions and diseases were the two major topics of connectome research. The application of machine learning, deep learning, and graph theory analysis in connectome research has become the current trend, while an increasing number of studies were concentrating on dynamic functional connectivity. Meanwhile, researchers have begun investigating alcohol use disorders and migraine in terms of brain connectivity in the past 2 years.ConclusionThis study illustrates a comprehensive overview of connectome research and provides researchers with critical information for understanding the recent trends and hotspots of connectomes.

Astrocytic Ca2+ transients are essential for astrocyte integration into neural circuits. These Ca2+ transients are primarily sequestered in subcellular domains, including primary branches, branchlets and leaflets, and endfeet. In previous studies, it suggests that aging causes functional defects in astrocytes. Until now, it was unclear whether and how aging affects astrocytic Ca2+ transients at subcellular domains. In this study, we combined a genetically encoded Ca2+ sensor (GCaMP6f) and in vivo two-photon Ca2+ imaging to determine changes in Ca2+ transients within astrocytic subcellular domains during brain aging. We showed that aging increased Ca2+ transients in astrocytic primary branches, higher-order branchlets, and terminal leaflets. However, Ca2+ transients decreased within astrocytic endfeet during brain aging, which could be caused by the decreased expressions of Aquaporin-4 (AQP4). In addition, aging-induced changes of Ca2+ transient types were heterogeneous within astrocytic subcellular domains. These results demonstrate that the astrocytic Ca2+ transients within subcellular domains are affected by aging differently. This finding contributes to a better understanding of the physiological role of astrocytes in aging-induced neural circuit degeneration.

IntroductionThree-dimensional (3D) reconstruction of fracture fragments on hip Computed tomography (CT) may benefit the injury detail evaluation and preoperative planning of the intertrochanteric femoral fracture (IFF). Manually segmentation of bony structures was tedious and time-consuming. The purpose of this study was to propose an artificial intelligence (AI) segmentation tool to achieve semantic segmentation and precise reconstruction of fracture fragments of IFF on hip CTs.Materials and MethodsA total of 50 labeled CT cases were manually segmented with Slicer 4.11.0. The ratio of training, validation and testing of the 50 labeled dataset was 33:10:7. A simplified V-Net architecture was adopted to build the AI tool named as IFFCT for automatic segmentation of fracture fragments. The Dice score, precision and sensitivity were computed to assess the segmentation performance of IFFCT. The 2D masks of 80 unlabeled CTs segmented by AI tool and human was further assessed to validate the segmentation accuracy. The femoral head diameter (FHD) was measured on 3D models to validate the reliability of 3D reconstruction.ResultsThe average Dice score of IFFCT in the local test dataset for “proximal femur”, “fragment” and “distal femur” were 91.62%, 80.42% and 87.05%, respectively. IFFCT showed similar segmentation performance in cross-dataset, and was comparable to that of human expert in human-computer competition with significantly reduced segmentation time (p 0.05). All ICCs were greater than 0.8.ConclusionThe proposed AI segmentation tool could effectively segment the bony structures from IFF CTs with comparable performance of human experts. The 2D masks and 3D models generated from automatic segmentation were effective and reliable, which could benefit the injury detail evaluation and preoperative planning of IFFs.

Neurostimulant drugs or magnetic/electrical stimulation techniques have shown limited effects on learning capabilities of healthy subjects. The authors show that, without introducing an exogeneous gene, mid-infrared light can modulate firing activity of neurons in vivo and accelerate learning in mice.

Two-photon Ca2+ imaging is a widely used technique for investigating brain functions across multiple spatial scales. However, the recording of neuronal activities is affected by movement of the brain during tasks in which the animal is behaving normally. Although post-hoc image registration is the commonly used approach, the recent developments of online neuroscience experiments require real-time image processing with efficient motion correction performance, posing new challenges in neuroinformatics. We propose a fast and accurate image density feature-based motion correction method to address the problem of imaging animal during behaviors. This method is implemented by first robustly estimating and clustering the density features from two-photon images. Then, it takes advantage of the temporal correlation in imaging data to update features of consecutive imaging frames with efficient calculations. Thus, motion artifacts can be quickly and accurately corrected by matching the features and obtaining the transformation parameters for the raw images. Based on this efficient motion correction strategy, our algorithm yields promising computational efficiency on imaging datasets with scales ranging from dendritic spines to neuronal populations. Furthermore, we show that the proposed motion correction method outperforms other methods by evaluating not only computational speed but also the quality of the correction performance. Specifically, we provide a powerful tool to perform motion correction for two-photon Ca2+ imaging data, which may facilitate online imaging experiments in the future.

BackgroundThis study aimed to investigate risk factors and prognostic factors in patients with clear cell renal cell carcinoma (ccRCC) with bone metastasis (BM) and establish nomograms to provide a quantitative prediction of the risk of BM and survival probability.MethodsThe clinicopathological characteristics of patients with ccRCC between January 2010 and December 2015 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Independent factors for BM in ccRCC patients were identified using univariate and multivariate logistic regression analyses. Prognostic factors for predicting cancer-specific death were evaluated using univariate and multivariate analyses based on a competing risk regression model. We then constructed a diagnostic nomogram and a prognostic nomogram. The two nomograms were evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis.ResultsOur study included 34,659 patients diagnosed with ccRCC in the SEER database, with 1,415 patients who presented with bone metastasis. Risk factors for BM in patients with ccRCC included age, stage T, stage N, brain metastasis, liver metastasis, lung metastasis, tumor size, and laterality. Independent prognostic factors for patients with ccRCC patients with BM were Fuhrman grade, tumor size, T stage, N stage, brain metastases, lung metastasis, and surgery. For the diagnostic nomogram, the area under the curve values in the training and testing cohorts were 0.863 (95% CI, 0.851–0.875) and 0.859 (95% CI, 0.839–0.878), respectively. In the prognostic cohort, the area under the curve values for 1-, 2-, and 3-year cancer-specific survival rates in the training cohort were 0.747, 0.774, and 0.780, respectively, and 0.671, 0.706, and 0.696, respectively, in the testing cohort. Through calibration curves and decision curve analyses, the nomograms displayed excellent performance.ConclusionsSeveral factors related to the development and prognosis of BM in patients with ccRCC were identified. The nomograms constructed in this study are expected to become effective and precise tools for clinicians to improve cancer management.

The accuracy of localization and mapping of automated guided vehicles (AGVs) using visual simultaneous localization and mapping (SLAM) is significantly reduced in a dynamic environment compared to a static environment due to incorrect data association caused by dynamic objects. To solve this problem, a robust stereo SLAM algorithm based on dynamic region rejection is proposed. The algorithm first detects dynamic feature points from the fundamental matrix of consecutive frames and then divides the current frame into superpixels and labels its boundaries with disparity. Finally, dynamic regions are obtained from dynamic feature points and superpixel boundaries types; only the static area is used to estimate the pose to improve the localization accuracy and robustness of the algorithm. Experiments show that the proposed algorithm outperforms ORB-SLAM2 in the KITTI dataset, and the absolute trajectory error in the actual dynamic environment can be reduced by 84% compared with the conventional ORB-SLAM2, which can effectively improve the localization and mapping accuracy of AGVs in dynamic environments.

Green plants provide food, energy and oxygen sources for human beings and animals on Earth through photosynthesis, which is essential to maintain regional ecological balance. However, few studies have focused on the natural oxygen supply capacity of urban green spaces. As a companion to photosynthesis in leaves, solar-induced chlorophyll fluorescence (SIF) contains abundant photosynthetic information. Currently, satellite-based SIF observations are considered to be a rapid and nondestructive ‘indicator’ of plant photosynthesis, which provides an alternative way to quantitatively assess the spatio-temporal dynamics of oxygen supply capacity in urban green spaces. This study examined the spatial patterns, long-term trends, and environmental control factors of SIF in the nine central cities in China from 2001 to 2020 based on the time-series of the global reconstructed GOSIF-v2 SIF dataset. The results were as follows: (1) There was a contrasting spatial difference between southern and northern cities in China, and multi-year mean SIF values of the southern cities were generally higher than those of the northern cities; (2) The interannual dynamics of SIF in each city generally showed an upward trend, with fluctuations, and the intraannual seasonal differences were more significant in northern cities than those in the southern cities; (3) The spatial trend analysis showed that Beijing, Guangzhou, and Chongqing have had the most significant improvements, followed by Xi’an, Wuhan, Chengdu, and Zhengzhou, while Tianjin and Shanghai have had the least improvements; and (4) The expansion of construction land has exerted significant impacts on the dynamics of the SIF trend in several cities, but it is not the only factor. All analyses indicated that the improvement of vegetation structure and function in the area can offset its negative effect.

Forests play a critical role in global carbon cycling, and continuous forest destruction together with other factors has accelerated global warming. Furthermore, continued decline of forest areas will critically hinder the accomplishment of carbon neutrality goals. Although the geographical location of deforestation can now be rapidly and accurately detected with remote sensing technology, current forest change products are still not fine-grained, especially from the perspective of carbon trading. Here, we used a deep learning method to detect deforestation in large regions based on 2 m high-resolution optical remote sensing images. Firstly, we proposed a new deforestation detection dataset, which was generated from 11 provincial regions in the Yangtze River Economic Zone of China, containing a total number of 8330 samples (the size of each sample being 512 × 512 pixels). Then, a new deforestation detection model, SiamHRnet-OCR, was designed, based on this dataset. Compared with other deep learning models, SiamHRnet-OCR achieves better results in terms of precision, F1-score, and OA indicator: 0.6482, 0.6892, and 0.9898, respectively. Finally, two large-scale scenarios of deforestation experiments in Southern China were further tested; the deforestation detection results demonstrate that SiamHRnet-OCR can not only detect deforestation effectively but also capture the accurate boundary of the changing area.

Background: Our study aimed to explore the prognostic factors of bladder cancer with bone metastasis (BCBM) and develop prediction models to predict the overall survival (OS) and cancer-specific survival (CSS) of BCBM patients. Methods: A total of 1438 patients with BCBM were obtained from the SEER database. Patients from 2010 to 2016 were randomly divided into training and validation datasets (7:3), while patients from 2017 were divided for external testing. Nomograms were established using prognostic factors identified through Cox regression analyses and validated internally and externally. The concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the discrimination and calibration of nomogram models, while decision curve analyses (DCA) and Kaplan-Meier (KM) curves were used to estimate the clinical applicability. Results: Marital status, tumor metastasis (brain, liver, and lung), primary site surgery, and chemotherapy were indicated as independent prognostic factors for OS and CSS. Calibration plots and the overall C-index showed a novel agreement between the observed and predicted outcomes. Nomograms revealed significant advantages in OS and CSS predictions. AUCs for internal and external validation were listed as follows: for OS, 3-month AUCs were 0.853 and 0.849; 6-month AUCs were 0.873 and 0.832; 12-month AUCs were 0.825 and 0.805; for CSS, 3-month AUCs were 0.849 and 0.847; 6-month AUCs were 0.870 and 0.824; 12-month AUCs were 0.815 and 0.797, respectively. DCA curves demonstrated good clinical benefit, and KM curves showed distinct stratification performance. Conclusion: The nomograms as web-based tools were proved to be accurate, efficient, and clinically beneficial, which might help in patient management and clinical decision-making for BCBM patients.

BackgroundThe study aimed to investigate the prognostic factors of spinal cord astrocytoma (SCA) and establish a nomogram prognostic model for the management of patients with SCA.MethodsPatients diagnosed with SCA between 1975 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and testing datasets (7:3). The primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS). Cox hazard proportional regression model was used to identify the prognostic factors of patients with SCA in the training dataset and feature importance was obtained. Based on the independent prognostic factors, nomograms were established for prognostic prediction. Calibration curves, concordance index (C-index), and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the calibration and discrimination of the nomogram model, while Kaplan-Meier (KM) survival curves and decision curve analyses (DCA) were used to evaluate the clinical utility. Web-based online calculators were further developed to achieve clinical practicability.ResultsA total of 818 patients with SCA were included in this study, with an average age of 30.84 ± 21.97 years and an average follow-up time of 117.57 ± 113.51 months. Cox regression indicated that primary site surgery, age, insurance, histologic type, tumor extension, WHO grade, chemotherapy, and post-operation radiotherapy (PRT) were independent prognostic factors for OS. While primary site surgery, insurance, tumor extension, PRT, histologic type, WHO grade, and chemotherapy were independent prognostic factors for CSS. For OS prediction, the calibration curves in the training and testing dataset illustrated good calibration, with C-indexes of 0.783 and 0.769. The area under the curves (AUCs) of 5-year survival prediction were 0.82 and 0.843, while 10-year survival predictions were 0.849 and 0.881, for training and testing datasets, respectively. Moreover, the DCA demonstrated good clinical net benefit. The prediction performances of nomograms were verified to be superior to that of single indicators, and the prediction performance of nomograms for CSS is also excellent.ConclusionsNomograms for patients with SCA prognosis prediction demonstrated good calibration, discrimination, and clinical utility. This result might benefit clinical decision-making and patient management for SCA. Before further use, more extensive external validation is required for the established web-based online calculators.

Using a combination of two-photon imaging and single-cell electrophysiology, the authors discover that associative learning induces the emergence of a unique subset of neurons in the auditory cortex, exhibiting high-rate bursting responses to the learned complex sounds but not to any of the constituents.

Osteoarthritis is a common type of degenerative joint disease. Inflammation-related chondrocyte senescence plays a major role in the pathogenesis of osteoarthritis. Omentin-1 is a newly identified anti-inflammatory adipokine involved in lipid metabolism. In this study, we examined the biological function of omentin-1 in cultured chondrocytes. The presence of omentin-1 potently suppresses IL-1β-induced cellular senescence as revealed by staining with senescence-associated beta-galactosidase (SA-β-Gal). At the cellular level, omentin-1 attenuates IL-1β-induced G1 phase cell-cycle arrest. Mechanistically, we demonstrate that omentin-1 reduced IL-1β-induced expression of senescent factors including caveolin-1, p21, and PAI-1 as well as p53 acetylation through ameliorating SIRT1 reduction. Notably, silencing of SIRT1 abolishes IL-1β-induced senescence along with the induction of p21 and PAI-1, suggesting that the action of omentin-1 in chondrocytes is dependent on SIRT1. Collectively, our results revealed the molecular mechanism through which the adipokine omentin-1 exerts a beneficial effect, thereby protecting chondrocytes from senescence. Thus, omentin-1 could have clinical implication in the treatment of osteoarthritis.

Nanozymes, a new generation of enzyme mimics, have recently attracted great attention. Nanozymes could catalyze chemical reactions as biological enzymes under physiologically mild conditions with higher-efficiency catalytic activities. Moreover, nanozymes could overcome the shortcomings of natural enzymes, such as easy inactivation, high cost, and low yield. With the development of more and more smart and multi-functional nanosystems, nanozymes display great achievement in tumor biology. In this review, we outline the recent advances of nanozymes in tumor and tumor microenvironment diagnosis, therapy, and theranostics.

Intracerebral hemorrhage (ICH) accounts for ~15% of all strokes and is associated with high mortality and disability rates. The systemic inflammation response index (SIRI) is a novel systemic inflammatory marker based on peripheral neutrophil, monocyte, and lymphocyte counts. This study aimed to evaluate the prognostic significance of admission SIRI in patients with spontaneous ICH and compare its predictive ability with that of the neutrophil-to-lymphocyte ratio (NLR). This retrospective study was conducted based on a prospectively collected database of patients with ICH between June 2016 and January 2019. Propensity score matching (PSM) was conducted to adjust for potential imbalances in the clinical parameters. A total of 403 patients were included in the original cohort. The optimal SIRI cut-off value was 2.76. After 1:1 PSM based on potential confounding variables, a new cohort containing 262 patients was established for further analysis. In the original cohort, SIRI served as an independent predictor of 3-month functional outcome [odds ratio (OR), 1.302; 95% CI, 1.120–1.512; p = 0.001] and 1-month mortality (OR, 1.072; 95% CI, 1.020–1.126; p = 0.006), while NLR was independently associated with only 3-month functional outcomes (OR, 1.051; 95% CI, 1.004–1.100; p = 0.031) and not 1-month mortality. The same applied to the PSM cohort. Receiver operating characteristic analyses and predictive models indicated that in most instances, SIRI was superior to NLR and their components in predicting the outcomes of patients with ICH. Our study found that SIRI is determined to be an independent predictive indicator for ICH patients in 3-month functional outcomes and 1-month mortality. The prognostic predictive ability of SIRI was stronger than that of NLR.

Two-photon Ca2+ imaging is a leading technique for recording neuronal activities in vivo with cellular or subcellular resolution. However, during experiments, the images often suffer from corruption due to complex noises. Therefore, the analysis of Ca2+ imaging data requires preprocessing steps, such as denoising, to extract biologically relevant information. We present an approach that facilitates imaging data restoration through image denoising performed by a neural network combining spatiotemporal filtering and model blind learning. Tests with synthetic and real two-photon Ca2+ imaging datasets demonstrate that the proposed approach enables efficient restoration of imaging data. In addition, we demonstrate that the proposed approach outperforms the current state-of-the-art methods by evaluating the qualities of the denoising performance of the models quantitatively. Therefore, our method provides an invaluable tool for denoising two-photon Ca2+ imaging data by model blind spatiotemporal processing.

Due to the inflexibility of cogeneration power plants and the uncertainty of wind power production, the excess power of the distribution network brings challenges to the power grid operation. This paper introduced an improved moth-flame optimization algorithm to meet the challenge of energy complementary dispatching. The proposed algorithm adopts three effective strategies, namely inertia weight, unified initialization, and the spiral position update strategy, which maintains a strong global search ability and a potent compromise between global and local search. The effectiveness of the proposed method was evaluated by benchmark functions. Furthermore, the proposed method was applied to combine heat and power system operation problems and economic dispatch in light load and wind power unpredictability. In order to verify the robustness of the algorithm and solve the complex constraints of power systems under extreme conditions, three different cases had been discussed. The experimental findings indicate that the proposed algorithm shows better performances in terms of convergence speed, ability to escape from a local optimum solution, and population diversity maintenance under different complexity conditions of engineering problems.

Monitoring astrocytic Ca2+ activity is essential to understand the physiological and pathological roles of astrocytes in the brain. However, previous commonly used methods for studying astrocytic Ca2+ activities can be applied in only anesthetized or head-fixed animals, which significantly affects in vivo astrocytic Ca2+ dynamics. In the current study, we combined optic fiber recordings with genetically encoded Ca2+ indicators (GECIs) to monitor astrocytic activity in freely behaving mice. This approach enabled selective and reliable measurement of astrocytic Ca2+ activity, which was verified by the astrocyte-specific labeling of GECIs and few movement artifacts. Additionally, astrocytic Ca2+ activities induced by locomotion or footshock were stably recorded in the cortices and hippocampi of freely behaving mice. Furthermore, this method allowed for the longitudinal study of astrocytic activities over several weeks. This work provides a powerful approach to record astrocytic activity selectively, stably, and chronically in freely behaving mice.

Oxytocin (OT), a neuropeptide involved in the regulation of complex social and sexual behavior in mammals, has been proposed as a treatment for a number of psychiatric disorders including pain. It has been well documented that central administration of OT elicits strong scratching and grooming behaviors in rodents. However, these behaviors were only described as symptoms, few studies have investigated their underlying neural mechanisms. Thus, we readdressed this question and undertook an analysis of spinal circuits underlying OT-induced scratching behavior in the present study. We demonstrated that intrathecal OT induced robust but transient hindpaw scratching behaviors by activating spinal OT receptors (OTRs). Combining the pre-clinical and clinical evidence, we speculated that OT-induced scratching may be an itch symptom. Further RNAscope studies revealed that near 80% spinal GRP neurons expressed OTRs. OT activated the expression of c-fos mRNA in spinal GRP neurons. Chemical ablation of GRPR neurons significantly reduced intrathecal OT-induced scratching behaviors. Given GRP/GRPR pathway plays an important role in spinal itch transmission, we proposed that OT binds to the OTRs expressed on the GRP neurons, and activates GRP/GRPR pathway to trigger itch-scratching behaviors in mice. These findings provide novel evidence relevant for advancing understanding of OT-induced behavioral changes, which will be important for the development of OT-based drugs to treat a variety of psychiatric disorders.

Abstract Background Interleukin-33 (IL-33) is increasingly being recognized as a key immunomodulatory cytokine in many neurological diseases. Methods In the present study, wild-type (WT) and IL-33−/− mice received intracerebroventricular (i.c.v.) injection of lipopolysaccharide (LPS) to induce neuroinflammation. Intravital microscopy was employed to examine leukocyte–endothelial interactions in the brain vasculature. The degree of neutrophil infiltration was determined by myeloperoxidase (MPO) staining. Real-time PCR and western blotting were used to detect endothelial activation. Enzyme-linked immunosorbent assay and quantitative PCR were conducted to detect pro-inflammatory cytokine levels in the brain. Results In IL-33−/− mice, neutrophil infiltration in the brain cortex and leukocyte–endothelial cell interactions in the cerebral microvessels were significantly decreased as compared to WT mice after LPS injection. In addition, IL-33−/− mice showed reduced activation of microglia and cerebral endothelial cells. In vitro results indicated that IL-33 directly activated cerebral endothelial cells and promoted pro-inflammatory cytokine production in LPS-stimulated microglia. Conclusions Our study indicated that IL-33/ST2 signaling plays an important role in the activation of microglia and cerebral endothelial cells and, therefore, is essential in leukocyte recruitment in brain inflammation. Graphical abstract The role of IL-33/ST2 in LPS induced neuroinflammation

Phenotypic switch of vascular smooth muscle cells (VSMCs) is characterized by increased expressions of VSMC synthetic markers and decreased levels of VSMC contractile markers, which is an important step for VSMC proliferation and migration during the development and progression of cardiovascular diseases including atherosclerosis. Chicoric acid (CA) is identified to exert powerful cardiovascular protective effects. However, little is known about the effects of CA on VSMC biology. Herein, in cultured VSMCs, we showed that pretreatment with CA dose-dependently suppressed platelet-derived growth factor type BB (PDGF-BB)-induced VSMC phenotypic alteration, proliferation and migration. Mechanistically, PDGF-BB-treated VSMCs exhibited higher mammalian target of rapamycin (mTOR) and P70S6K phosphorylation, which was attenuated by CA pretreatment, diphenyleneiodonium chloride (DPI), reactive oxygen species (ROS) scavenger N-acetyl-l-cysteine (NAC) and nuclear factor-κB (NFκB) inhibitor Bay117082. PDGF-BB-triggered ROS production and p65-NFκB activation were inhibited by CA. In addition, both NAC and DPI abolished PDGF-BB-evoked p65-NFκB nuclear translocation, phosphorylation and degradation of Inhibitor κBα (IκBα). Of note, blockade of ROS/NFκB/mTOR/P70S6K signaling cascade prevented PDGF-BB-evoked VSMC phenotypic transformation, proliferation and migration. CA treatment prevented intimal hyperplasia and vascular remodeling in rat models of carotid artery ligation in vivo. These results suggest that CA impedes PDGF-BB-induced VSMC phenotypic switching, proliferation, migration and neointima formation via inhibition of ROS/NFκB/mTOR/P70S6K signaling cascade. Keywords: VSMCs, Chicoric acid, PDGF-BB, Proliferation, Migration

Abstract Nonhuman primates provide an important model not only for understanding human brain but also for translational research in neurological and psychiatric disorders. However, many high-resolution techniques for recording neural activity in vivo that were initially established for rodents have not been yet applied to the nonhuman primate brain. Here, we introduce a combination of two-photon targeted patching and dendritic Ca2+ imaging to the neocortex of adult common marmoset, an invaluable primate model for neuroscience research. Using targeted patching, we show both spontaneous and sensory-evoked intracellular dynamics of visually identified neurons in the marmoset cortex. Using two-photon Ca2+ imaging and intracellular pharmacological manipulation, we report both action-potential-associated global and synaptically-evoked NMDA (N-methyl-D-aspartate) receptor-mediated local Ca2+ signals in dendrites and spines of the superficial-layer cortical neurons. Therefore, we demonstrate the presence of synaptic Ca2+ signals in neuronal dendrites in living nonhuman primates. This work represents a proof-of-principle for exploring the primate brain functions in vivo by monitoring neural activity and morphology at a subcellular resolution.

Understanding the complex neural mechanisms controlling urinary bladder activity is an extremely important topic in both neuroscience and urology. Simultaneously recording of the bladder activity and neural activity in related brain regions will largely advance this field. However, such recording approach has long been restricted to anesthetized animals, whose bladder function and urodynamic properties are largely affected by anesthetics. In our recent report, we found that it is feasible to record bladder pressure (cystometry) and the related cortical neuron activity simultaneously in freely moving mice. Here, we aimed to demonstrate the use of this combined method in freely moving mice for recording the activity of the pontine micturition center (PMC), a more difficultly approachable small region deeply located in the brainstem and a more popularly studied hub for controlling bladder function. Interestingly, we found that the duration of urination events linearly correlated to the time course of neuronal activity in the PMC. We observed that the activities of PMC neurons highly correlated with spike-like increases in bladder pressure, reflecting bladder contractions. We also found that anesthesia evoked prominent changes in the dynamics of the Ca2+ signals in the PMC during the bladder contraction and even induced the dripping overflow incontinence due to suppression of the neural activity in the PMC. In addition, we described in details both the system for cystometry in freely moving mice and the protocols for how to perform this combined method. Therefore, this work provides a powerful approach that enables the simultaneous measurement of neuronal activity of the PMC or any other brain sites and bladder function in freely behaving mice. This approach offers a promising possibility to examine the neural mechanisms underlying neurogenic bladder dysfunction.

The traditional simultaneous localization and mapping (SLAM) system uses static points of the environment as features for real-time localization and mapping. When there are few available point features, the system is difficult to implement. A feasible solution is to introduce line features. In complex scenarios containing rich line segments, the description of line segments is not strongly differentiated, which can lead to incorrect association of line segment data, thus introducing errors into the system and aggravating the cumulative error of the system. To address this problem, a point-line stereo visual SLAM system incorporating semantic invariants is proposed in this paper. This system improves the accuracy of line feature matching by fusing line features with image semantic invariant information. When defining the error function, the semantic invariant is fused with the reprojection error function, and the semantic constraint is applied to reduce the cumulative error of the poses in the long-term tracking process. Experiments on the Office sequence of the TartanAir dataset and the KITTI dataset show that this system improves the matching accuracy of line features and suppresses the cumulative error of the SLAM system to some extent, and the mean relative pose error (RPE) is 1.38 and 0.0593 m, respectively.

In order to analyze the year-end water level of multi-year regulating reservoir of the cascade hydropower system, this paper studied the joint operation optimization model of cascade reservoirs and its solving method based on multi-dimensional dynamic programming, and analyzed the power generation impact factors of cascade system that contains multi-year regulating reservoir. In particular, taking the seven reservoirs in the middle and lower reaches of Yalong River as an example, the optimal year-end water levels of multi-year regulating reservoir under the multi-year average situation and different inflow frequencies situation were studied. Based on the optimal calculation results of multi-dimensional dynamic programming, the inflow frequency difference considered operation rule of year-end water level of Lianghekou reservoir was extracted using the least square principle. The simulation results showed that, compared with the fixed year-end water level in multi-year, the extracted rule can improve the cascade power generation by more than 400 million kWh in an average year, representing an increase of 0.4%. This result means that the extracted rule can give full play to the regulation performance of multi-year regulating reservoir and improve the conversion efficiency of hydropower resources in cascade system. This is of great significance to the practical operation of cascade reservoirs system that contains multi-year regulating reservoir.

In vivo two-photon Ca2+ imaging is a powerful tool for recording neuronal activities during perceptual tasks and has been increasingly applied to behaving animals for acute or chronic experiments. However, the auditory cortex is not easily accessible to imaging because of the abundant temporal muscles, arteries around the ears and their lateral locations. Here, we report a protocol for two-photon Ca2+ imaging in the auditory cortex of head-fixed behaving mice. By using a custom-made head fixation apparatus and a head-rotated fixation procedure, we achieved two-photon imaging and in combination with targeted cell-attached recordings of auditory cortical neurons in behaving mice. Using synthetic Ca2+ indicators, we recorded the Ca2+ transients at multiple scales, including neuronal populations, single neurons, dendrites and single spines, in auditory cortex during behavior. Furthermore, using genetically encoded Ca2+ indicators (GECIs), we monitored the neuronal dynamics over days throughout the process of associative learning. Therefore, we achieved two-photon functional imaging at multiple scales in auditory cortex of behaving mice, which extends the tool box for investigating the neural basis of audition-related behaviors.