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Abstract. Background:. Posttraumatic stress disorder (PTSD) and depression are highly comorbid. Psilocybin exerts substantial therapeutic effects on depression by promoting neuroplasticity. Fear extinction is a key process in the mechanism of first-line exposure-based therapies for PTSD. We hypothesized that psilocybin would facilitate fear extinction by promoting hippocampal neuroplasticity. Methods:. First, we assessed the effects of psilocybin on percentage of freezing time in an auditory cued fear conditioning (FC) and fear extinction paradigm in mice. Psilocybin was administered 30 min before extinction training. Fear extinction testing was performed on the first day; fear extinction retrieval and fear renewal were tested on the sixth and seventh days, respectively. Furthermore, we verified the effect of psilocybin on hippocampal neuroplasticity using Golgi staining for the dendritic complexity and spine density, Western blotting for the protein levels of brain derived neurotrophic factor (BDNF) and mechanistic target of rapamycin (mTOR), and immunofluorescence staining for the numbers of doublecortin (DCX)- and bromodeoxyuridine (BrdU)-positive cells. Results:. A single dose of psilocybin (2.5 mg/kg, i.p.) reduced the increase in the percentage of freezing time induced by FC at 24 h, 6th day and 7th day after administration. In terms of structural neuroplasticity, psilocybin rescued the decrease in hippocampal dendritic complexity and spine density induced by FC; in terms of neuroplasticity related proteins, psilocybin rescued the decrease in the protein levels of hippocampal BDNF and mTOR induced by FC; in terms of neurogenesis, psilocybin rescued the decrease in the numbers of DCX- and BrdU-positive cells in the hippocampal dentate gyrus induced by FC. Conclusions:. A single dose of psilocybin facilitated rapid and sustained fear extinction; this effect might be partially mediated by the promotion of hippocampal neuroplasticity. This study indicates that psilocybin may be a useful adjunct to exposure-based therapies for PTSD and other mental disorders characterized by failure of fear extinction.

AbstractAgriculture plays an indispensable role in the economic system of all countries, and the issue of agricultural carbon emissions has become a challenge to the sustainable development of the global economy. Against this backdrop, based on the data of 30 provinces, municipalities directly under the central government and autonomous regions (excluding Hong Kong, Macau and Tibet) in China from 2005 to 2019, this current study comprehensively uses the two-way fixed effect panel model and panel vector autoregressive (PVAR) model to explore the static and dynamic relationships between agricultural mechanization, large-scale operation and agricultural carbon emissions. Analysis show that agricultural mechanization and large-scale operation promote the agricultural carbon emissions, and increasing the level of urbanization will help curb the increase of agricultural carbon emissions. However, in a dynamic perspective, there is a two-way granger causality relationship between agricultural mechanization and agricultural carbon emissions. Agricultural mechanization is an important cause of agricultural carbon emissions and its promoting effect is sustainable, while the short-term effect of large-scale operation on agricultural carbon emissions is positive, and in the long run it helps reduce agricultural carbon emissions. Accordingly, the study recommends that the government should make the following efforts: firstly, speed up the research and development of agricultural machinery driven by clean energy; secondly, guide the effective transfer of land and encourage farmers to reduce the amount of agricultural chemical materials input while improving the use efficiency of pesticide chemical materials; thirdly, uphold the concept of ecological civilization in the process of promoting urbanization.

Abstract. Background:. Acute kidney injury (AKI) is a common complication in patients, especially elderly patients, who undergo cardiac surgery with cardiopulmonary bypass. Studies have indicated a protective role of autophagy in AKI. However, the mechanisms underlying the regulatory effect of autophagy in AKI among patients undergoing cardiac surgeries are poorly understood. In this study, we aimed to test the hypothesis that exosomal microRNAs (miRNAs) regulate autophagy in tubular epithelial cells after AKI. Methods:. Plasma exosomal RNA was extracted from young and elderly AKI patients undergoing cardiac surgery, and the miRNAs expression during the perioperative period were analyzed using next-generation sequencing. The screened miRNAs and their target genes were subjected to gene oncology function and Kyoto Encyclopedia of Genes and Genome enrichment analyses. Renal tubular epithelial cell line (HK-2 cells) was cultured and hypoxia/reoxygenation (H/R) model was established, which is an in vitro renal ischemia/reperfusion (I/R) model. We used Western blot analysis, cell viability assay, transfection, luciferase assay to investigate the mechanisms underlying the observed increases in the levels of renal I/R injury-mediated exosomal miRNAs and their roles in regulating HK-2 cells autophagy. Results:. miR-590-3p was highly enriched in the plasma exosomes of young AKI patients after cardiac surgery. Increased levels of miR-590-3p led to the increases in the expression of autophagy marker proteins, including Beclin-1 and microtubule associated protein 1 light chain 3 beta (LC3II), and prolonged the autophagic response in HK-2 cells after H/R treatment. These effects were achieved mainly via increases in the exosomal miR-590-3p levels, and the tumor necrosis factor receptor-associated factor 6 protein was shown to play a key role in I/R injury-mediated autophagy induction. Conclusion:. Exosomes released from HK-2 cells after renal I/R injury regulate autophagy by transferring miR-590-3p in a paracrine manner, which suggests that increasing the miR-590-3p levels in HK-2 cell-derived exosomes may increase autophagy and protect against kidney injury after renal I/R injury.

Prompt detection of landslides is crucial for reducing the disaster risk and preventing landslides. However, landslide detection in practical applications still faces many challenges, such as the complexity of environmental backgrounds, the diversity of target scales, and the enormity of model weights. To address these issues, this paper proposes a lightweight LBE-YOLO model for real-time landslide detection. Firstly, a lightweight model is designed by integrating the GhostConv lightweight network with the YOLOv8n model. Inspired by GhostConv, this study innovatively designed the GhostC2f structure, which leverages linear thinking to further reduce the model parameters and computational burden. Additionally, the newly designed EGC2f structure, incorporating an attention mechanism, not only maintains the model’s lightweight characteristics but also enhances the network’s capability to extract valid information. Subsequently, the Path Aggregation Network (PAN) was optimized by introducing a bidirectional feature propagation mechanism to improve the model’s feature fusion ability. Additionally, the Bijie landslide dataset was expanded through data augmentation strategies, thereby further improving the model’s generalization capability. The experimental results indicate that, compared to the YOLOv8n model, the proposed model increased accuracy by 4.2%, while the model’s weight and computational load were reduced by 32.0% and 35.5%, respectively. This verifies the superiority of the LBE-YOLO model in landslide target detection, which will help mitigate the impacts of natural disasters.

The rapid detection of distracted driving behaviors is crucial for enhancing road safety and preventing traffic accidents. Compared with the traditional methods of distracted-driving-behavior detection, the YOLOv8 model has been proven to possess powerful capabilities, enabling it to perceive global information more swiftly. Currently, the successful application of GhostConv in edge computing and embedded systems further validates the advantages of lightweight design in real-time detection using large models. Effectively integrating lightweight strategies into YOLOv8 models and reducing their impact on model performance has become a focal point in the field of real-time distracted driving detection based on deep learning. Inspired by GhostConv, this paper presents an innovative GhostC2f design, aiming to integrate the idea of linear transformation to generate more feature maps without additional computation into YOLOv8 for real-time distracted-driving-detection tasks. The goal is to reduce model parameters and computational load. Additionally, enhancements have been made to the path aggregation network (PAN) to amplify multi-level feature fusion and contextual information propagation. Furthermore, simple attention mechanisms (SimAMs) are introduced to perform self-normalization on each feature map, emphasizing feature maps with valuable information and suppressing redundant information interference in complex backgrounds. Lastly, the nine distinct distracted driving types in the publicly available SFDDD dataset were expanded to 14 categories, and nighttime scenarios were introduced. The results indicate a 5.1% improvement in model accuracy, with model weight size and computational load reduced by 36.7% and 34.6%, respectively. During 30 real vehicle tests, the distracted-driving-detection accuracy reached 91.9% during daylight and 90.3% at night, affirming the exceptional performance of the proposed model in assisting distracted driving detection when driving and contributing to accident-risk reduction.

Heritage crimes can result in the significant loss of cultural relics and predicting them is crucial. To address the issues of inconsistent textual information format and the challenge of preventing and combating heritage crimes, this paper develops a system that extracts crime elements and predict heritage crime occurrences. The system comprises two deep-learning models. The first model, Bi-LSTM + CRF, is constructed to automatically extract crime elements and perform spatio-temporal analysis of crimes based on them. By integrating routine activity theory, social disorder theory, and practical field experience, the research reveals that holidays and other special days (SD) perform a critical role as influential factors in heritage crimes. Building upon these findings, the second model, LSTM + SD, is constructed to predict excavation-type heritage crimes. The results demonstrate that the model with the introduction of the holiday factor improves the RMSE and MAE by 6.4% and 47.8%, respectively, when compared to the original LSTM model. This paper presents research aimed at extracting crime elements and predicting excavation-type heritage crimes. With the ongoing expansion of data volume, the practical significance of the proposed system is poised to escalate. The results of this study are expected to provide decision-making support for heritage protection departments and public security authorities in preventing and combating crimes.

Crime is always one of the most important social problems, and it poses a great threat to public security and people. Accurate crime prediction can help the government, police, and citizens to carry out effective crime prevention measures. In this paper, the research on crime prediction is systematically reviewed from a variety of temporal and spatial perspectives. We describe the current state of crime prediction research from four perspectives (prediction content, crime types, methods, and evaluation) and focus on the prediction methods. According to various temporal and spatial scales, temporal crime prediction is divided into short-term prediction, medium-term prediction, and long-term prediction, and spatial crime prediction is divided into micro-, meso-, and macro-level prediction. Spatio-temporal crime prediction classification can be a permutation of temporal and spatial crime prediction classifications. A variety of crime prediction methods and evaluation metrics are also summarized, and different prediction methods and models are compared and evaluated. After sorting out the literature, it was found that there are still many limitations in the current research: (i) data sparsity is difficult to deal with effectively; (ii) the practicality, interpretability, and transparency of predictive models are insufficient; (iii) the evaluation system is relatively simple; and (iv) the research on decision-making application is lacking. In this regard, the following suggestions are proposed to solve the above problems: (i) the use of transformer learning technology to deal with sparse data; (ii) the introduction of model interpretation methods, such as Shapley additive explanations (SHAPs), to improve the interpretability of the models; (iii) the establishment of a set of standard evaluation systems for crime prediction at different scales to standardize data use and evaluation metrics; and (iv) the integration of reinforcement learning to achieve more accurate prediction while promoting the transformation of the application results.

ABSTRACTThis study examines the association between the globalised board of directors and corporate environmental performance, and then investigates the moderating effect of regional green development on the above association. Using a sample from the Chinese stock market during the period of 2008–2016, the findings reveal that the globalised board of directors is significantly positively associated with corporate environmental performance, suggesting that the globalised board of directors can play an important role in strengthening the moral and strategic motivations of corporate environmental responsibility, and enhance corporate environmental performance. Further, regional green development attenuates the positive relation between the globalised board of directors and corporate environmental performance. Above findings are robust to a variety of sensitivity tests and our main conclusions are still valid after controlling for the potential endogeneity between the globalised board of directors and corporate environmental performance. Interestingly, additional tests reveal that the positive effect of foreign directors on corporate environmental performance is more pronounced for those from the countries (regions) with shorter time zone difference, stronger investor protection and better performance in environmental responsibilities.

Abstract Background There is very little published literature and none that discussed care in a neonate regarding anesthetic risk and management of neonate with congenital bronchobiliary fistula during thoracoscopy and thoracotomy. This article analyzes related risk factors and literature review from perioperative ventilation, circulation and other aspects of management. Case presentation A neonate diagnosed as congenital bronchobiliary fistula combined with severe chemical pneumonia, consolidation of the lungs, and infection was facing the risk of anaesthesia under thoracoscopy exploration surgery, who experiened more than 20 days diagnostic period before operation. Many risk factors have led to conversion from minimally invasive surgery to thoracotomy, including persistent hypoxemia, hypercapnia, difficult surgical exposure and extremly difficulty of intraoperative ventilation management. Anesthesia maintenance after conversion to open access remained problematic. Fortunately the patient showed no sign of any adverse CNS effects after 4 months of follow-up. Conclusions The most prominent anesthesia challenges are hypoxemia, increased airway resistance, impaired ventilation, and the risk of metabolic acidosis. Close cooperation among the entire neonatal medical team is the key factors in successful management of this rare case.

Traditional methods of cow estrus monitoring technology are not suitable for the current needs of large-scale, intensive and welfare-based farming. There is a need to improve the detection rate of cow estrus and to reduce the emergency response caused by wearing contact devices. Furthermore, it is necessary to verify the practical effectiveness of the LOGISITC and SV (support vector machine) models for modeling cow estrus. In this paper, we have proposed a non-contact cow estrus monitoring method based on the thermal infrared images of cows and have proposed a lab-color-space-based feature extraction method for the thermal infrared images of cow eyes and vulvas. The test subjects were 10 Holstein cows, monitored on a fixed basis, to determine the best segmentation contour. The LOGISTIC and SVM (support vector machine) models were used to establish the cow estrus model using the thermal infrared temperature variation in cows in estrus and cows not in estrus. The experimental results showed that the heat detection rate of the LOGISTIC-based model was 82.37% and the heat detection rate of the SVM-based model was 81.42% under the optimal segmentation profile. The highest temperature in the eye and vulva of cows was the input, and the recall rate was above 86%. The heat monitoring method based on thermal infrared images does not cause stress to cows and meets the needs of modern dairy farming for welfare breeding.

Since ancient times, the Chinese have had a special understanding of the “Three”. Chinese philosophy originates from the I Ching, and the philosophical concept of “Three” is the core of the I Ching. The philosophical thinking about “Three” entails a complete dialectical thinking method that is consistent with the Western philosophical concept of “One Dividing into Three”. In this paper, we explain the philosophical concept of “Three” and suggest its application to medical education, including the learning and application of new technology, shared decision making between doctors and patients, and integration of medical humanities and medical science.

Background: The objective of this study was to compare the incidence of severe systemic inflammatory response syndrome (sSIRS) after total aortic arch replacement between patients who underwent moderate hypothermic circulatory arrest (MHCA) and those who underwent deep hypothermic circulatory arrest (DHCA).Methods: At Fuwai Hospital, 600 patients who underwent total aortic arch replacement with MHCA or DHCA from January 2013 to December 2016 were consecutively enrolled and divided into DHCA (14.1–20.0°C) and MHCA (20.1–28.0°C) groups. Preliminary statistical analysis revealed that some baseline indicators differed between the two groups; therefore, propensity score matching (PSM) was used to balance the covariates. Post-operative sSIRS as the primary outcome was compared between the groups both before and after PSM.Results: A total of 275 (45.8%) patients underwent MHCA, and 325 (54.2%) patients underwent DHCA. After PSM analysis, a total of 191 matched pairs were obtained. The overall incidence of sSIRS was 27.3%. There was no significant difference in post-operative sSIRS between the MHCA group and the DHCA group in either the overall cohort or the PSM cohort (no-PSM: P = 0.188; PSM: P = 0.416); however, post-operative sSIRS was increased by ~4% in the DHCA group compared with the MHCA group in both the no-PSM and PSM cohorts (no-PSM: 29.5 vs. 24.7%; PSM: 29.3 vs. 25.1%). Both before and after PSM, the rates of gastrointestinal hemorrhage and pulmonary infection and post-operative length of stay were significantly increased in the DHCA group compared with the MHCA group (P < 0.05), and the remaining secondary outcomes were not significantly different between the groups.Conclusions: MHCA and DHCA are associated with comparable incidences of sSIRS in patients following total aortic arch replacement for type A aortic dissection. However, the MHCA group had a shorter cardiopulmonary bypass time, a shorter post-operative length of stay and lower pulmonary infection and gastrointestinal hemorrhage rates than the DHCA group. We cautiously recommend the use of MHCA for most total arch replacements in patients with type A aortic dissection.

Video object segmentation has been extensively investigated in computer vision recently because of its wide range of applications. The key factor of the segmentation is the construction of the spatiotemporal coherence. Inaccurate motion approximation as a measurement of the coherence usually leads to an inaccurate segmentation result. To obtain an accurate segmentation result, a low‐rank sparse (LRS)‐based approach is proposed. Regarding each superpixel as an element, this algorithm has a good segmentation accuracy compared with other pixel‐level algorithms. Each element can be represented by the sparse linear combinations of dictionary templates, and this algorithm capitalises on the inherent low‐rank structure of representations that are learnt jointly. The represented coefficients construct an affinity matrix which measures the elements’ similarity between the current frame and the templates in the dictionary. For video object segmentation, a principled spatiotemporal objective function that uses LRS saliency term to propagate information between frames. Furthermore, an online parameter updating scheme is proposed to enhance the system's robustness. The online model propagates information forward without the need to access future frames. Evaluations on many challenging sequences demonstrate that the authors' approach outperforms the state‐of‐the‐art methods in terms of object segmentation accuracy.

Graphcut-based algorithm is adopted in many video object segmentation systems because different terms can be probabilistically fused together in a framework. Constructing spatio-temporal coherences is an important stage in segmentation systems. However, many steps are involved when computing a key term with good discriminative power. If the cascade steps are adopted, the inaccurate output of the previous step will definitely affect the next step, leading to inaccurate segmentation. In this paper, a key term that is computed by a single framework referred to as boundary-constrained low-rank sparse representation (BCLRSR) is proposed to achieve the accurate segmentation. By treating the elements as linear combinations of dictionary templates, low-rank sparse optimization is adopted to achieve the spatio-temporal saliency. For adding the spatial information to the low-rank sparse model, a boundary constraint is adopted in the framework as a Laplacian regularization. A BCLRSR saliency is then obtained by the represented coefficients, which measure the similarity between the elements in the current frame and the ones in the dictionary. At last, the object is segmented by minimizing the energy function, which is formalized by the spatio-temporal coherences. The experiments on some public datasets show that our proposed algorithm outperforms the state-of-the-art methods.

The novel hollowed Ni-Co layered double hydroxide polyhedron (H-(Ni, Co)-LDHP) is synthesized via a template-sacrificing approach using ZIF-67 as template. The morphology, crystallinity, porous texture, and chemical state of H-(Ni, Co)-LDHP are examined. It demonstrates that the H-(Ni, Co)-LDHP not only provides rich redox sites but also promotes the kinetics due to presence of numerous rational channels. As a result, the H-(Ni, Co)-LDHP manifests the desirable lithium ions storage performance when employed as anode. This study paves a new way for preparing hollowed nanostructure toward advanced electrochemical applications.

The α7nicotinic receptor (nAChR) is a major subtype of the nAChRs in the central nervous system, and the receptor plays an important role in brain function. In the dbSNP database, there are 55 single nucleotide polymorphisms (SNPs) that cause missense mutations of the human α7nAChR in the coding region. In this study, we tested the impact of 14 SNPs that cause missense mutations in the agonist binding site or the coupling region between binding site and channel gate on the receptor function. The wild type or mutant receptors were expressed or co-expressed in Xenopus oocytes, and the agonist-induced currents were tested using two-electrode voltage clamp. Our results demonstrated that 6 mutants were nonfunctional, 4 mutants had reduced current expression, and 1 mutants altered ACh and nicotine efficacy in the opposite direction, and one additional mutant had slightly reduced agonist sensitivity. Interestingly, the function of most of these nonfunctional mutants could be rescued by α7nAChR positive allosteric modulator PNU-120596 and agonist-PAM 4BP-TQS. Finally, when coexpressed with the wild type, the nonfunctional mutants could also influence the receptor function. These changes of the receptor properties by the mutations could potentially have an impact on the physiological function of the α7nAChR-mediated cholinergic synaptic transmission and anti-inflammatory effects in the human SNP carriers. Rescuing the nonfunctional mutants could provide a novel way to treat the related disorders.

To solve the problem of antenna miniaturization and mutual interference between the communication band of the UWB system and other wireless communication system bands, this paper proposes a UWB monopole antenna which has frequency notch characteristics. By applying two pairs of Split Ring Resonator (SRR) structures on a CPW transmission line, a coupling resonance is generated in a specific frequency band, and the antenna has a dual frequency notch and a wide band notch function. The measured results show that the antenna has good band-notch characteristics in the frequency ranges of 3.3 GHz to 4 GHz and 5.1 GHz to 6.2 GHz, suppressing ultra-wideband interference between WiMAX (3.3 GHz ~ 3.8 GHz) and WLAN (5.15 GHz ~ 5.35 GHz and 5.725 GHz ~ 5.825 GHz) in a wireless communication system. The volume of the antenna is 40 mm × 36 mm × 1 mm, and the measured results are compared with the simulated model results. Besides, the measured and simulated results have a good consistency. [ABSTRACT FROM AUTHOR]

In nearly all cases of underwater adhesion, water molecules typically act as a destroyer. Thus, removing interfacial water from the substrate surfaces is essential for forming super-strong underwater adhesion. However, current methods mainly rely on physical means to dislodge interfacial water, such as absorption, hydrophobic repulsion, or extrusion, which are inefficient in removing obstinate hydrated water at contact interface, resulting in poor adhesion. Herein, we present a unique means of reversing the role of water to assist in realizing a self-strengthening liquid underwater adhesive (SLU-adhesive) that can effectively remove water at contact interface. This is achieved through multiscale physical-chemical coupling methods across millimeter to molecular levels and self-adaptive strengthening of the cohesion during underwater operations. As a result, strong adhesion over 1,600 kPa (compared to ~100 to 1,000 kPa in current state of the art) can be achieved on various materials, including inorganic metal and organic plastic materials, without preloading in different environments such as pure water, a wide range of pH solutions (pH = 3 to 11), and seawater. Intriguingly, SLU-adhesive/photothermal nanoparticles (carbon nanotubes) hybrid materials can significantly reduce the time required for complete curing from 24 h to 40 min using near-infrared laser radiation due to unique thermal-response of the chemical reaction rate. The excellent adhesion property and self-adaptive adhesion procedure allow SLU-adhesive materials to demonstrate great potential for broad applications in underwater sand stabilization, underwater repair, and even adhesion failure detection as a self-reporting adhesive. This concept of "water helper" has potential to advance underwater adhesion and manufacturing strategies. [ABSTRACT FROM AUTHOR]