Global Trends and Hotspots in Pediatric Anesthetic Neurotoxicity Research: A Bibliometric Analysis From 2000 to 2023.

The impact of general anesthetics on brain function development is one of the top frontier issues of public concern. However, little bibliometric analysis has investigated this territory systematically. Our study aimed to visualize the publications between 2000 and 2023 to inspire the trends and hotspots in anesthetic neurodevelopmental toxicity research. Publications from 2000 to 2023 were collected from the Web of Science Core Collection. CiteSpace was utilized to plot and analyze the network maps of countries, institutions, authors, journals, and keywords associated with these publications. A total of 864 publications, consisting of 786 original articles and 78 reviews, were extracted from 2000 to 2023. The annual publications have increased constantly over the past two decades. The USA and the People's Republic of China were the leading driving forces in this field. Harvard University was the most productive institution. Zhang Y published the most related articles, and Jevtovic-Todorovic V was mostly cited in this field. The most prolific journal was Pediatric Anesthesia, and the most frequently co-cited journal was Anesthesiology. Keywords were divided into nine clusters: "apoptosis", "propofol", "developing brain", "cognitive dysfunction", "neuronal cell degeneration", "brain", "neuroinflammation", "local anesthesia", and "oxygen therapy". The strongest citation bursts in earlier years were "learning disability", "cell death", and "cognitive function". The emerging trends in the coming years were "awake regional anesthesia", "behavioral outcome", and "infancy general anesthesia compared to spinal anesthesia". We conclude that anesthetic-induced neurotoxicity has received growing attention in the past two decades. Our findings evaluated the present status and research trends in this area, which may provide help for exploring further potential prospects on hot topics and frontiers.

Neural Radiance Fields-Based 3D Reconstruction of Power Transmission Lines Using Progressive Motion Sequence Images.

To address the fuzzy reconstruction effect on distant objects in unbounded scenes and the difficulty in feature matching caused by the thin structure of power lines in images, this paper proposes a novel image-based method for the reconstruction of power transmission lines (PTLs). The dataset used in this paper comprises PTL progressive motion sequence datasets, constructed by a visual acquisition system carried by a developed Flying-walking Power Line Inspection Robot (FPLIR). This system captures close-distance and continuous images of power lines. The study introduces PL-NeRF, that is, an enhanced method based on the Neural Radiance Fields (NeRF) method for reconstructing PTLs. The highlights of PL-NeRF include (1) compressing the unbounded scene of PTLs by exploiting the spatial compression of normal L∞; (2) encoding the direction and position of the sample points through Integrated Position Encoding (IPE) and Hash Encoding (HE), respectively. Compared to existing methods, the proposed method demonstrates good performance in 3D reconstruction, with fidelity indicators of PSNR = 29, SSIM = 0.871, and LPIPS = 0.087. Experimental results highlight that the combination of PL-NeRF with progressive motion sequence images ensures the integrity and continuity of PTLs, improving the efficiency and accuracy of image-based reconstructions. In the future, this method could be widely applied for efficient and accurate 3D reconstruction and inspection of PTLs, providing a strong foundation for automated monitoring of transmission corridors and digital power engineering.

Reconstitution of phytochrome A-mediated light modulation of the ABA signaling pathways in yeast.

Abscisic acid (ABA), a classical plant hormone, plays an essential role in plant adaptation to environmental stresses. The ABA signaling mechanisms have been extensively investigated, and it was shown that the PYR1 (PYRABACTIN RESISTANCE1)/PYL (PYR1-LIKE)/RCAR (REGULATORY COMPONENT OF ABA RECEPTOR) ABA receptors, the PP2C coreceptors, and the SnRK2 protein kinases constitute the core ABA signaling module responsible for ABA perception and initiation of downstream responses. We recently showed that ABA signaling is modulated by light signals, but the underlying molecular mechanisms remain largely obscure. In this study, we established a system in yeast cells that was not only successful in reconstituting a complete ABA signaling pathway, from hormone perception to ABA-responsive gene expression, but also suitable for functionally characterizing the regulatory roles of additional factors of ABA signaling. Using this system, we analyzed the roles of several light signaling components, including the red and far-red light photoreceptors phytochrome A (phyA) and phyB, and the photomorphogenic central repressor COP1, in the regulation of ABA signaling. Our results showed that both phyA and phyB negatively regulated ABA signaling, whereas COP1 positively regulated ABA signaling in yeast cells. Further analyses showed that photoactivated phyA interacted with the ABA coreceptors ABI1 and ABI2 to decrease their interactions with the ABA receptor PYR1. Together, data from our reconstituted yeast ABA signaling system provide evidence that photoactivated photoreceptors attenuate ABA signaling by directly interacting with the key components of the core ABA signaling module, thus conferring enhanced ABA tolerance to light-grown plants.

Vehicle Trajectory Prediction via Urban Network Modeling.

Taxis are an important component of the transportation system, and empty taxis represent a significant waste of transportation resources. To alleviate the imbalance between supply and demand and relieve traffic congestion, real-time prediction of taxi trajectories is necessary. Most existing trajectory prediction studies focus on extracting time-series information but do not capture spatial information sufficiently. In this paper, we focus on the construction of an urban network and propose an urban topology-encoding spatiotemporal attention network (UTA) to address destination prediction problems. Firstly, this model discretizes the production and attraction units of transportation, combining them with key nodes in the road network to form an urban topological network. Secondly, GPS records are matched to the urban topological map to construct a topological trajectory, which significantly improves trajectory consistency and endpoint certainty, helping to model destination prediction problems. Thirdly, semantic information concerning surrounding space is attached to effectively mine the spatial dependencies of trajectories. Finally, after the topological encoding of city space and trajectories, this algorithm proposes a topological graph neural network to model the attention calculation with the trajectory context, comprehensively considering the spatiotemporal characteristics of the trajectories and improving prediction accuracy. We solve the prediction problems with the UTA model and also compare it with some other classical models, such as the HMM, RNN, LSTM, and transformer. The results suggest that all the models work well in combination with the proposed urban model (with a rough increase of 2%), while the UTA model is less affected by data sparsity.

A Novel Auto-Synthesis Dataset Approach for Fitting Recognition Using Prior Series Data.

To address power transmission line (PTL) traversing complex environments leading to data collection being difficult and costly, we propose a novel auto-synthesis dataset approach for fitting recognition using prior series data. The approach mainly includes three steps: (1) formulates synthesis rules by the prior series data; (2) renders 2D images based on the synthesis rules utilizing advanced virtual 3D techniques; (3) generates the synthetic dataset with images and annotations obtained by processing images using the OpenCV. The trained model using the synthetic dataset was tested by the real dataset (including images and annotations) with a mean average precision (mAP) of 0.98, verifying the feasibility and effectiveness of the proposed approach. The recognition accuracy by the test is comparable with training by real samples and the cost is greatly reduced to generate synthetic datasets. The proposed approach improves the efficiency of establishing a dataset, providing a training data basis for deep learning (DL) of fitting recognition.

Goodness-of-fit test for exponentiality based on spacings for general progressive Type-II censored data.

There have been numerous tests proposed to determine whether or not the exponential model is suitable for a given data set. In this article, we propose a new test statistic based on spacings to test whether the general progressive Type-II censored samples are from exponential distribution. The null distribution of the test statistic is discussed and it could be approximated by the standard normal distribution. Meanwhile, we propose an approximate method for calculating the expectation and variance of samples under null hypothesis and corresponding power function is also given. Then, a simulation study is conducted. We calculate the approximation of the power based on normality and compare the results with those obtained by Monte Carlo simulation under different alternatives with distinct types of hazard function. Results of simulation study disclose that the power properties of this statistic by using Monte Carlo simulation are better for the alternatives with monotone increasing hazard function, and otherwise, normal approximation simulation results are relatively better. Finally, two illustrative examples are presented.

Transmission of SARS-CoV-2 during air travel: a descriptive and modelling study.

OBJECTIVES: To explore the potential of SARS-CoV-2 spread during air travel and the risk of in-flight transmission. METHODS: We enrolled all passengers and crew suspected of being infected with SARS-CoV-2, who bounded for Beijing on international flights. We specified the characteristics of all confirmed cases of COVID-19 infection and utilised Wells-Riley equation to estimate the infectivity of COVID-19 during air travel. RESULTS: We screened 4492 passengers and crew with suspected COVID-19 infection, verified 161 confirmed cases (mean age 28.6 years), and traced two confirmed cases who may have been infected in the aircraft. The estimated infectivity was 375 quanta/h (range 274-476), while the effective infectivity was only 4 quanta/h (range 2-5). The risk of per-person infection during a 13 h air travel in economy class was 0.56‰ (95% CI 0.41‰-0.72‰). CONCLUSION: We found that the universal use of face masks on the flight, together with the plane's ventilation system, significantly decreased the infectivity of COVID-19.KEY MESSAGESThe COVID-19 pandemic is changing the lifestyle in the world, especially air travel which has the potential to spread SARS-CoV-2.The universal use of face masks on the flight, together with the plane's ventilation system, significantly decreased the infectivity of COVID-19 on an aircraft.Our findings suggest that the risk of infection in aircraft was negligible.

Epidemiological and clinical characteristics of imported cases of COVID-19: a multicenter study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to expand. Herein, we report the epidemiological and clinical features of 478 patients with confirmed COVID-19 from a multicenter study conducted in four cities in China excluding Wuhan. METHODS: A total of 478 patients transferred by emergency medical services to designated hospitals in four major cities in China (Beijing, Chongqing, Jinan, and Nanning) were enrolled. We compared the characteristics of imported and indigenous cases and calculated the frequencies of fatal, severe, mild, and asymptomatic disease. The results were used to generate a pyramid of COVID-19 severity. RESULTS: The mean age of patients with COVID-19 was 46.9 years and 49.8% were male. The most common symptoms at onset were fever (69.7%), cough (47.5%), fatigue (24.5%), dyspnea (8.4%), and headache (7.9%). Most cases (313, 65.5%) were indigenous, while 165 (34.5%) were imported. Imported cases dominated during the early stages of the pandemic, but decreased from 1 February 2020 as indigenous cases rose sharply. Compared with indigenous cases, imported cases differed significantly in terms of sex (P = 0.002), severity of disease (P = 0.006), occurrence of fever (P < 0.001), family clustering (P < 0.001), history of contact (P < 0.001), and primary outcome (P < 0.001). CONCLUSIONS: Within the population studied, imported cases had distinct characteristics from those of indigenous cases, with lower fatality rates and higher discharge rates. New infections shifted from imported cases to local infection gradually, and overall infections have declined to a low level. We suggest that preventing import of cases and controlling spread within local areas can help prevent SARS-CoV-2 infection spread.

A LexA-based yeast two-hybrid system for studying light-switchable interactions of phytochromes with their interacting partners.

Phytochromes are a family of photoreceptors in plants that perceive the red (R) and far-red (FR) components of their light environment. Phytochromes exist in vivo in two forms, the inactive Pr form and the active Pfr form, that are interconvertible by treatments with R or FR light. It is believed that phytochromes transduce light signals by interacting with their signaling partners. A GAL4-based light-switchable yeast two-hybrid (Y2H) system was developed two decades ago and has been successfully employed in many studies to determine phytochrome interactions with their signaling components. However, several pairs of interactions between phytochromes and their interactors, such as the phyA-COP1 and phyA-TZP interactions, were demonstrated by other assay systems but were not detected by this GAL4 Y2H system. Here, we report a modified LexA Y2H system, in which the LexA DNA-binding domain is fused to the C-terminus of a phytochrome protein. The conformational changes of phytochromes in response to R and FR light are achieved in yeast cells by exogenously supplying phycocyanobilin (PCB) extracted from Spirulina. The well-defined interaction pairs, including phyA-FHY1 and phyB-PIFs, are well reproducible in this system. Moreover, we show that our system is successful in detecting the phyA-COP1 and phyA-TZP interactions. Together, our study provides an alternative Y2H system that is highly sensitive and reproducible for detecting light-switchable interactions of phytochromes with their interacting partners. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-021-00034-5.

MYB30 Is a Key Negative Regulator of Arabidopsis Photomorphogenic Development That Promotes PIF4 and PIF5 Protein Accumulation in the Light.

Phytochromes are red (R) and far-red (FR) light photoreceptors in plants, and PHYTOCHROME-INTERACTING FACTORS (PIFs) are a group of basic helix-loop-helix family transcription factors that play central roles in repressing photomorphogenesis. Here, we report that MYB30, an R2R3-MYB family transcription factor, acts as a negative regulator of photomorphogenesis in Arabidopsis (Arabidopsis thaliana). We show that MYB30 preferentially interacts with the Pfr (active) forms of the phytochrome A (phyA) and phytochrome B (phyB) holoproteins and that MYB30 levels are induced by phyA and phyB in the light. It was previously shown that phytochromes induce rapid phosphorylation and degradation of PIFs upon R light exposure. Our current data indicate that MYB30 promotes PIF4 and PIF5 protein reaccumulation under prolonged R light irradiation by directly binding to their promoters to induce their expression and by inhibiting the interaction of PIF4 and PIF5 with the Pfr form of phyB. In addition, our data indicate that MYB30 interacts with PIFs and that they act additively to repress photomorphogenesis. In summary, our study demonstrates that MYB30 negatively regulates Arabidopsis photomorphogenic development by acting to promote PIF4 and PIF5 protein accumulation under prolonged R light irradiation, thus providing new insights into the complicated but delicate control of PIFs in the responses of plants to their dynamic light environment.

Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data.

Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasingly being used to solve these difficulties. Based on precise cable inspection robot (CIR) LiDAR data and the distinctive position and orientation system (POS) data, we propose a novel methodology to detect inspection objects surrounding power lines. The proposed method mainly includes four steps: firstly, the original point cloud is divided into single-span data as a processing unit; secondly, the optimal elevation threshold is constructed to remove ground points without the existing filtering algorithm, improving data processing efficiency and extraction accuracy; thirdly, a single power line and its surrounding data can be respectively extracted by a structured partition based on a POS data (SPPD) algorithm from "layer" to "block" according to power line distribution; finally, a partition recognition method is proposed based on the distribution characteristics of inspection objects, highlighting the feature information and improving the recognition effect. The local neighborhood statistics and the 3D region growing method are used to recognize different inspection objects surrounding power lines in a partition. Three datasets were collected by two CIR LIDAR systems in our study. The experimental results demonstrate that an average 90.6% accuracy and average 98.2% precision at the point cloud level can be achieved. The successful extraction indicates that the proposed method is feasible and promising. Our study can be used to obtain precise dimensions of fittings for modeling, as well as automatic detection and location of security risks, so as to improve the intelligence level of power line inspection.

A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data.

With the growth of the national economy, there is increasing demand for electricity, which forces transmission line corridors to become structurally complicated and extend to complex environments (e.g., mountains, forests). It is a great challenge to inspect transmission line in these regions. To address these difficulties, a novel method of autonomous inspection for transmission line is proposed based on cable inspection robot (CIR) LiDAR data, which mainly includes two steps: preliminary inspection and autonomous inspection. In preliminary inspection, the position and orientation system (POS) data is used for original point cloud dividing, ground point filtering, and structured partition. A hierarchical classification strategy is established to identify the classes and positions of the abnormal points. In autonomous inspection, CIR can autonomously reach the specified points through inspection planning. These inspection targets are imaged with PTZ (pan, tilt, zoom) cameras by coordinate transformation. The feasibility and effectiveness of the proposed method are verified by test site experiments and actual line experiments, respectively. The proposed method greatly reduces manpower and improves inspection accuracy, providing a theoretical basis for intelligent inspection of transmission lines in the future.

[Comparative study on Chinese medicine and western medicine for treatment of osteoarthritis of the knee in Caucasian patients].

OBJECTIVE: To compare the efficacy, safety and tolerability of different therapies in Caucasian patients with osteoarthritis (QA) of the knee. METHODS: Seventy-five cases (90 knee joints) of osteoarthritis were randomly divided into 3 groups, western medicine group, traditional Chinese medicine (TCM) group, integrated Chinese and western medicine group. The western medicine group were treated with oral administration of Glucosamine Sulfate, oral administration and external application of non-steroid anti-inflammatory agent, ultrasound physiotherapy, etc. The TCM group were treated with oral administration of J uanbi Decoction, acupuncture and moxibustion, cupping, massage of acupoint and ear acupuncture. The integrated Chinese and western medicine group were treated with oral administration of Glucosamine Sulfate, oral administration and external application of non-steroid anti-inflammatory agent, acupuncture and moxibustion, cupping, massage of acupoint and ear acupuncture. The intensity of knee joint pain on walking, resting and standing, the nocturnal pain, stiffness, the maximum walking distance and the daily living ability were monitored after 30 days, 60 days and 90 days of treatment. RESULTS: After 90 days of treatment, the integrated Chinese and western medicine group was better than other two groups in improvement of percentages in self pain assessment with visual analog scale (VAS), pain and stiffness measured by WOMAC scale, pain and maximum walking distance measured by Lequesne scale (P < 0.05 or P < 0.01). There were no significant differences in the therapeutic effects between the TCM group and the western medicine group. All of these three treatments were well tolerated, and no severe adverse events were found. CONCLUSION: Combined TCM and western medicine treatment has rapid and definite therapeutic effect in reducing pain and improving mobility of knee joints and daily living ability in Caucasian patients of knee osteoarthritis.

[Comparative study on Chinese medicine and western medicine for treatment of prolapse of lumbar intervertebral disc].

OBJECTIVE: To compare therapeutic effects, safety and tolerance of TCM, western medicine and integrated Chinese and western medicine for treatment of acute lumbosacral pain induced by prolapse of lumbar intervertebral disc. METHODS: Ninety cases were randomly divided into 3 groups, 30 cases in each group. They were treated respectively with western medicine, TCM and combined TCM and western medicine, and the pain intensity, activity, muscular tension, and other indexes were monitored after 7 days and 30 days of treatment. RESULTS: After treatment of 7 days, the combined treatment group in improvement of VAS scores of lumbosacral pain and radiating pain of the lower limbs was superior to the TCM group with no significant difference between the two groups, and in improvement of VAS scores of lumbosacral pain and radiating pain of the lower limbs, Lasegue's sign, activity of spinal column (Schober test and distance from finger tip to floor), etc. were superior to the western medicine group (P < 0.05). After treatment of 30 days, there was no significant differences in the therapeutic effect among the 3 groups. The patients in the 3 groups had good tolerance with no severe adverse reaction. CONCLUSION: Combined TCM and western medicine treatment has rapid effects and definite therapeutic effect in alleviating pain, improving activity for acute lumbosacral pain induced by prolapse of lumbar intervertebral disc.