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ObjectivesThe study aimed to translate and culturally adapt the personal suicide stigma questionnaire (PSSQ) into simplified Chinese and evaluate its psychometric properties among adolescents who have attempted suicide in mainland China.MethodsFollowing Brislin’s translation model and using purposive sampling, we surveyed 440 adolescents who had attempted suicide at Hangzhou Seventh People’s Hospital in Zhejiang Province, China. Content validity was determined by a panel of experts, and the construct validity of the scale was assessed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), convergent validity, and discriminant validity. Reliability analysis was evaluated using Cronbach’s α coefficient, test–retest reliability, and half-split reliability.ResultsThe Chinese version of the PSSQ consists of three dimensions and 14 items. After two rounds of expert consultation, the item-content validity index for all items exceeded 0.70, and the scale-content validity index exceeded 0.90. EFA extracted three factors and retained all 14 items. The CFA indicators demonstrated a good fit. The Cronbach’s α coefficient of the scale was 0.880, the half-split reliability was 0.681, and the test–retest reliability was 0.862. It is evident that the PSSQ and its subscales demonstrate stable structural validity and good internal consistency in measuring self-stigma among individuals with suicidal tendencies, indicating that the PSSQ is a reliable tool for assessing the degree of personal stigma in Chinese adolescents who have attempted suicide.ConclusionThis study ensured the linguistic and cultural appropriateness of the Chinese version of the PSSQ through cross-cultural adaptation and validation of its reliability and validity, thereby enhancing the accuracy and reliability of assessing personal stigma among Chinese adolescents who have attempted suicide. The validation of the Chinese version of the scale not only enriches the research tools available for studying personal stigma related to suicide in mainland China, but also provides a reliable quantitative tool for future research on the psychological states of individuals who have attempted suicide, the impact of stigma, and the effectiveness of interventions.

Abstract Background Among the neurological complications of influenza in children, the most severe is acute necrotizing encephalopathy (ANE), with a high mortality rate and neurological sequelae. ANE is characterized by rapid progression to death within 1–2 days from onset. However, the knowledge about the early diagnosis of ANE is limited, which is often misdiagnosed as simple seizures/convulsions or mild acute influenza-associated encephalopathy (IAE). Objective To develop and validate an early prediction model to discriminate the ANE from two common neurological complications, seizures/convulsions and mild IAE in children with influenza. Methods This retrospective case-control study included patients with ANE (median age 3.8 (2.3,5.4) years), seizures/convulsions alone (median age 2.6 (1.7,4.3) years), or mild IAE (median age 2.8 (1.5,6.1) years) at a tertiary pediatric medical center in China between November 2012 to January 2020. The random forest algorithm was used to screen the characteristics and construct a prediction model. Results Of the 433 patients, 278 (64.2%) had seizures/convulsions alone, 106 (24.5%) had mild IAE, and 49 (11.3%) had ANE. The discrimination performance of the model was satisfactory, with an accuracy above 0.80 from both model development (84.2%) and internal validation (88.2%). Seizures/convulsions were less likely to be wrongly classified (3.7%, 2/54), but mild IAE (22.7%, 5/22) was prone to be misdiagnosed as seizures/convulsions, and a small proportion (4.5%, 1/22) of them was prone to be misdiagnosed as ANE. Of the children with ANE, 22.2% (2/9) were misdiagnosed as mild IAE, and none were misdiagnosed as seizures/convulsions. Conclusion This model can distinguish the ANE from seizures/convulsions with high accuracy and from mild IAE close to 80% accuracy, providing valuable information for the early management of children with influenza.

To solve the problems of limited computing power resources, low accuracy of small target detection, high miss rate, and poor real-time detection of mobile vehicle platforms in the automatic driving environment, The present study introduced a one-stage target detection algorithm TTD-YOLO (Traffic Target Detection YOLO) that improved YOLOV5-S, which is enhanced in four aspects: Enhanced the network’s multi-scale feature extraction performance through the utilization of the improved M-ELAN architecture; added 3D attention mechanism SimAM to the network structure to enable the network to learn important feature information and enhance the efficiency of detecting accuracy; the parameter ratio of backbone and neck is adjusted to close to 1:1 by adjusting the number of output channels and stacking times of CSPLayer modules in the backbone and neck, while maintaining the model complexity, experiments show that improving the neck’s parameter ratio helps enhance the efficiency of detecting accuracy without changing the network structure; used EIoU loss instead of the bounding box loss function accelerates network convergence and improved detection accuracy. Under the condition of avoiding significantly changing the network structure, our TTD-YOLO outperforms the baseline model and other mainstream object detection algorithms such as Faster RCNN, SSD, and YOLOX-S on the autopilot dataset SODA10M with fewer parameters, higher detection accuracy, and faster inference speed. Compared to the baseline model, the model parameters decreased by 8.6%, average precision(mAP@.5:.95)increased by 2.5%, and the inference speed under the same experimental platform increased by 4.8%.

To address the shortcomings of the classical RRT (Rapidly exploring Range Tree) path planning algorithm, such as long planning time and path curvature in some narrow and complex environments, an improved APG-RRT (Adaptive Path Guide RRT) algorithm is proposed. First, a guiding path is introduced in the sampling stage, and a node on the preset guiding path is selected first to expand the node tree so as to guide the algorithm to plan the exploration process. Secondly, the selection weight of the loading guidance path is dynamically adjusted according to the probability of collision between obstacles during the exploration process, and a safe and feasible path point is generated in the path expansion stage by combining the expansion information of the obstacles. Finally, in the path post-processing stage, combined with the vehicle kinematic constraints, the triangular inequality method is used to remove redundant path points, making the path more smooth, so as to fulfill the specific operational needs of the vehicle. The results of the simulation experiment demonstrate that the suggested method exhibits superior planning efficiency compared to the previous algorithm, resulting in a higher quality final path. At the same time, the verification of the algorithm’s feasibility and effectiveness is conducted through real car testing.

Abstract Objective Explore the clinical characteristics and prognosis of children with norovirus (NoV)‐associated benign convulsions with mild gastroenteritis (CwG). Methods We retrospectively analyzed the Clinical and laboratory data of children with NoV‐associated CwG admitted to the emergency department of Guangzhou Children's Hospital between January 2019 and January 2020. And patients were followed up for 23–36 months. Results There are 49 cases met the CwG criteria. Vomiting was the first symptom in 31 (63.3%) patients, and vomiting could be the main or the only gastrointestinal symptom. The mean frequency of seizures was 3.8 ± 2.4 episodes. Most patients (95.9%) experienced seizures that lasted for less than 5 min. Of the 43 (87.8%) cases followed up from 23 to 36 months, only one experienced recurrent convulsions (after rotavirus infection). Significance NoV‐associated CwG patients were prone to experiencing more convulsions. However, because most NoV‐associated CwG patients had good prognosis, long‐term use of anticonvulsants are unnecessary.

Motion planning between dynamic obstacles is an essential capability to achieve real-world navigation. In this study, we investigated the problem of avoiding dynamic obstacles in complex environments for a car-like mobile robot with an incompletely constrained Ackerman front wheel steering. To address the problems of weak dynamic obstacle avoidance and poor path smoothing in motion planning with the traditional Timed Elastic Band (TEB) algorithm, We proposed a hybrid motion planning algorithm (TEB-CA,Timed Elastic Band-Collision Avoidance) that combines an improved traditional TEB algorithm and Optimal Reciprocal Collision Avoidance (ORCA) model to improve the ability of the robot to predict dynamic obstacles in advance and avoid collisions safely. Moreover, We also add new constraints to the traditional TEB algorithm, including: jerk constraints, smoothness constraints, and curvature constraints. The algorithm is implemented in $C++$ and evaluated experimentally in the Gazebo and Rviz simulation environments of the Robot Operating System (ROS), as well as in actual experimental tests on our car-like autonomous mobile robot “Little Ant” which proves the effectiveness of the method, and that the motion planning scheme is more effective in avoiding dynamic obstacles than the traditional TEB and DWA algorithms.

Purpose – This paper aimed a fractional-order sliding mode-based lateral lane-change control method that was proposed to improve the path-tracking accuracy of vehicle lateral motion. Design/methodology/approach – In this paper the vehicle presighting and kinematic models were established, and a new sliding mode control isokinetic convergence law was devised based on the fractional order calculus to make the front wheel turning angle approach the desired value quickly. On this basis, a fractional gradient descent algorithm was proposed to adjust the radial basis function (RBF) neuron parameter update rules to improve the compensation speed of the neural network. Findings – The simulation results revealed that, compared to the traditional sliding mode control strategy, the designed controller eliminated the jitter of the sliding mode control, sped up the response of the controller, reduced the overshoot of the system parameters and facilitated accurate and fast tracking of the desired path when the vehicle changed lanes at low speeds. Originality/value – This paper combines the idea of fractional order calculus with gradient descent algorithm, proposed a fractional-order gradient descent method applied to RBF neural network and fast adjustment the position and width of neurons.

To mitigate potential safety hazards during expressway operation, safety evaluation plays a crucial role. This study develops a fuzzy comprehensive evaluation model to assess the safety of expressway operations. First, an expressway operation safety risk assessment index system consisting of four criteria-layer indicators and 10 sub-criteria layer indicators is established. Then, the comprehensive weight of each evaluation index is calculated based on the combined AHP and entropy methods. Finally, the expressway safety level is obtained by converting qualitative indicators into fuzzy sets and defining corresponding membership functions to describe their degree of membership. The evaluation model is applied to the Jinliwen Expressway in a mountainous area in Zhejiang Province, China. The results show that the method could quantitatively and comprehensively evaluate the safety status of expressways and identify the weak links of operational safety management, which can effectively improve the safety level of expressway operations. The research findings will assist decision-makers in gaining a better understanding of the safety issues of expressways and, thus, using corresponding measures to enhance the safety level of the expressway.

Background and aim: The effect of structural viral protein 1 (VP1) on neurological damage caused by enterovirus 71 (EV71) infection is unclear. This study aimed to explore the transcriptome changes in EV infected patients and the role of VP1 on the cell secretion pathway of neuron cells. Methods: In our cohort, EV infected patients were enrolled, and RNA-seq analysis was used to evaluate the distinct transcript patterns of cerebrospinal fluid (CSF). The EV71 VP1-overexpressing vector (pEGFP-c3-VP1) was generated and transfected into neuron cells. The relationship between Glutamate Rich 3 (ERICH3) and methyltransferase Zinc Finger CCCH-Type Containing 13 (ZC3H13) and their effect on the serotonin (5-HT) release of neuron cells were explored using small interfering RNA. The expression of ERICH3 and ZC3H13 and concentration of 5-HT were determined using real-time PCR, Western blot, and ELISA, respectively. Result: The expression of ERICH3 and ZC3H13 were significantly upregulated in EV infected patients with neurological symptoms compared to those without (P

Abstract This study was focused on Hangzhou in China that are undergoing large‐scale subway construction, and an improved momentum back‐propagation (BP) neural network model was trained. The model can analyze the complex traffic data, evaluate the service quality of bus line, and improve the estimation accuracy and convergence speed. For the same training data set, the convergence time of the BP algorithm with momentum term is reduced by 0.043 secs, the iterative convergence speed is improved by 0.66%, and the estimation accuracy is improved by 26.7% compared with the standard BP algorithm. Under similar conditions, the convergence time is 1.562 secs less than that of the standard BP algorithm, and the convergence speed was 24.1% higher than that of the standard BP algorithm, and the absolute value of the estimated error was less than 1%. Finally, a representative bus line in Hangzhou was used as an example to evaluate the model. The results showed that the improved momentum BP neural network model had a faster convergence speed and higher prediction accuracy of the comprehensive weight of bus line service quality. The prediction results of the model are consistent with the actual survey results, which indicates that the model constructed is reasonable.

ObjectiveAcute necrotizing encephalopathy (ANE) is a rare but severe encephalopathy and is associated with a high morbidity and mortality. We aimed to analyze and compare the clinical features and predictive indicators of pediatric ANE.Materials and methodsThis retrospective study included children with ANE diagnosed at Guangzhou Women and Children’s Medical Center between November 2018 and January 2020. Pediatric patients’ information, including clinical characteristics, laboratory tests, neuroelectrophysiology and brain magnetic resonance imaging (MRI) findings, MRI score, brainstem auditory evoked potential (BAEP) grades, ANE severity scores (ANE-SS), and modified Rankin scale (mRS), were collected.ResultsTwelve ANE patients were included. Among them, one patient (8.3%) died from brainstem dysfunction, one (8.3%) recovered and 10 (83.3%) experienced neurological sequelae. All patients had an initial viral infection and neurological symptoms such as acute disturbance of consciousness (ADOC) or seizure, and the interval from onset of the disease to neurological manifestations was 3 (1.25–3) days. MRI score-I ranged from 1 to 3 (1.8 ± 0.7), MRI score-II ranged from 1 to 4 (2.5 ± 1.1). ANE-SS varied from 1 to 6 (3.9 ± 1.3). The scores of mRS were from 0 to 6 (2.9 ± 1.7). Higher MRI score were associated with worse outcomes, while the BAEP grade and ANE-SS score were not significantly associated with mRS.ConclusionANE is a severe encephalopathy syndrome with rapid progression, resulting in serious neurological sequelae. Compared with BAEP grade and ANE-SS, brain MRI shows more comprehensive advantages in predicting the prognosis of ANE patients. More in-depth research and better indicators are still needed to support the evaluation and treatment of ANE.

High mud content in the sand has a negative impact on cement mortar but there is little research on Alkali-activated slag (AAS) mortar. In order to explore the impacts of mud content in the sand on the performance of AAS mortar, this paper used sand that contains silt, clay, and a mixture of silt and clay; tested the setting time of AAS with different mud contents of 0%, 2%, 4%, 6%, 8%, and 10%; and measured the unconfined compressive strength and beam flexural strength of 3 d, 7 d, and 28 d AAS mortar specimens. The microstructure of AAS mortar with different kinds of mud was observed by scanning electron microscope (SEM), the elemental composition of the hydration product was tested by energy dispersive spectroscopy (EDS), and the AAS interaction mechanism with different kinds of mud was analyzed. The main conclusions are: the higher the mud content in the sand, the shorter the initial setting time and the longer the final setting time of AAS, mainly because the mud in the sand affects the hydration process; mud content above 4% causes a rapid decrease in the compressive and flexural strengths of AAS mortar, mainly because the mud affects the hydration process and hinders the bonding of the hydration product with the sand. When there is no mud in the sand, the main hydration product of AAS is dense calcium-alumina-silicate-hydrate (C-A-S-H) gel. When the sand contains silt, the hydration product of AAS is loose C-A-S-H gel. When the sand contains clay, the hydration products of AAS contain C-A-S-H gel and a small amount of sodium-aluminum-silicate-hydrate (N-A-S-H), and needle-like crystals. Loose gel and crystals have a negative effect on the AAS mortar strength.

The setting time of alkali-activated slag (AAS) binders is extremely short, while traditional retarders of Portland cement may be invalid for AAS. To find an effective retarder with a less negative impact on strength, borax (B), sucrose (S), and citric acid (CA) were selected as potential retarders. The setting time of AAS with different admixtures dosages of 0%, 2%, 4%, 6%, and 8%, and the unconfined compressive strength and beam flexural strength of 3 d, 7 d, and 28 d AAS mortar specimens were tested. The microstructure of AAS with different additives was observed by scanning using an electron microscope (SEM), and the hydration products were analyzed by energy dispersive spectroscopy (EDS), X-ray diffraction analysis (XRD), and thermogravimetric analysis (DT-TGA) to explain the retarding mechanism of AAS with different additives. The results showed that the incorporation of borax and citric acid could effectively prolong the setting time of AAS more than that of sucrose, and the retarding effect is more and more obvious with the increase in borax and citric acid dosages. However, sucrose and citric acid negatively influence AAS’s unconfined compressive strength and flexural stress. The negative effect becomes more evident with the increase in sucrose and citric acid dosages. Borax is the most suitable retarder for AAS among the three selected additives. SEM-EDS analysis showed that the incorporation of borax does three things: produces gels, covers the surface of the slag, and slows down the hydration reaction rate.

Purpose – The operation state of lithium-ion battery for vehicle is unknown and the remaining life is uncertain. In order to improve the performance of battery state prediction, in this paper, a joint estimation method of state of charge (SOC) and state of health (SOH) for lithium-ion batteries based on multi-scale theory is designed. Design/methodology/approach – In this paper, a joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is designed. The venin equivalent circuit model and fast static calibration method are used to fit the relationship between open-circuit voltage and SOC, and the resistance and capacitance parameters in the model are identified based on exponential fitting method. A battery capacity model for SOH estimation is established. A multi-time scale EKF filtering algorithm is used to estimate the SOC and SOH of lithium-ion batteries. Findings – The SOC and SOH changes in dynamic operation of lithium-ion batteries are accurately predicted so that batteries can be recycled more effectively in the whole vehicle process. Originality/value – A joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is accurately predicted and can be recycled more effectively in the whole vehicle process.

Abstract Circular RNAs (circRNAs) have confirmed to participate in diverse biological functions in cancer. However, the expression patterns of circRNAs on hepatocellular carcinoma (HCC) remains unclear. In the present study, we clarified that hsa_circRNA_104348 was dramatically upregulated in HCC tissues and cells. Patients with HCC displaying high hsa_circRNA_104348 level possessed poor prognosis. Has_circ_104348 facilitated proliferation, migration, and invasion, meanwhile suppressed apoptosis of HCC cell. Furthermore, hsa_circRNA_104348 directly targeted miR-187–3p, could regulate miR-187-3p to affect proliferation, migration, invasion, and apoptosis of HCC cells, and may have effect on Wnt/β-catenin signaling pathway. Moreover, RTKN2 could be a direct target of miR-187-3p. In addition, knockdown of hsa_circRNA_104348 attenuated HCC tumorigenesis and lung metastasis in vivo. Taken together, these findings indicated that circular RNA hsa_circRNA_104348 might function as a competing endogenous RNA (ceRNA) to promotes HCC progression by targeting miR-187–3p/RTKN2 axis and activating Wnt/β-catenin pathway.

Mediating the divergent interest of vehicle stability and strengthened path tracking performance when aiming at the design of a path tracking controller for autonomous vehicles is a challenging issue. Accordingly, this paper proposes an improved-LQR (linear quadratic regulator) control applied using an improved path planning algorithm. A feedforward and feedback LQR control is constructed by applying the path optimization solution method, which is a different traditional polynomial trajectory fitting method, and then solving the path planning information and the control input parameter in real time to make the tracking error as convergent as possible. To verify the superiority of the improved-LQR, this study compares the proposed controller and model predictive control by the traditional path solving method on a closed-loop test road using Carsim/Simulink. The comparative results show the efficiency, accurate tracking, vehicle stability, and reliability of the proposed controller.

Little is known about the particular changes of N6-methyladenosine (m6A) RNA methylation in enterovirus (EV) infection among children with neurologic symptoms. Here, we determined the characterization of EV associated m6A RNA methylation in this population. A prospective cohort study was conducted from 2018/2 to 2019/12 at the Guangzhou Women and Children’s Medical Center. We included EV infected children with and without neurological symptoms. High-throughput m(6)A-RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-seq analysis were used to evaluate the m6A RNA methylation and transcript expression of cerebrospinal fluid samples. The functional annotation and pathways of differentially methylated m6A genes with synchronously differential expression were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Seven patients were enrolled in the control group, and 13 cases were in the neurological symptoms (NS) group. A total of 3472 differentially expressed genes and 957 m6A modified genes were identified. A conjoint analysis of MeRIP-seq and RNA-seq data found 1064 genes with significant changes in both the m6A modifications and mRNA levels. The different m6A RNA methylation was increased in the transcriptome’s CDS regions but decreased in both the 3′UTRs and stop codon among the NS group. Functional annotation like the “oxidative phosphorylation” gene pathway, “Parkinson’s disease” and GO terms like “respiratory electron transport chain,” “cellular metabolic process,” and “oxidation-reduction process” was enriched in symptomatic patients. Our study elucidated the changes of RNA m6A methylation patterns and related cellular functions and signaling pathways in EV patients with neurologic symptoms.

Background: This study aimed to identify potential risk factors for severe hand-foot-mouth disease (HFMD).Methods: The PubMed, Embase, the Cochrane Library, Sinomed, WanFang, CNKI, and VIP databases were searched (up to August 2021).Results: Twenty-nine studies (9,241 and 927,355 patients with severe HFMD and controls, respectively; all from China) were included. EV71 was associated with higher odds of severe HFMD compared with other agents (OR = 4.44, 95%CI: 3.12–6.33, p < 0.001). Being home-raised (OR = 1.99, 95%CI: 1.59–2.50, p < 0.001), higher number of children in the family (OR = 2.09, 95%CI: 1.93–2.27, p < 0.001), poor hand hygiene (OR = 2.74, 95%CI: 1.78–4.23, p < 0.001), and no breastfeeding (OR = 2.01, 95%CI: 1.45–2.79, p < 0.001) were risk factors for severe HFMD. First consulting to a district-level or above hospital (OR = 0.34, 95%CI: 0.25–0.45, p < 0.001) and diagnosis of HFMD at baseline (OR = 0.17, 95%CI: 0.13–0.24, p < 0.001) were protective factors against severe HFMD. Fever, long fever duration, vomiting, lethargy, leukocytosis, tic, and convulsions were each associated with severe HFMD (all p < 0.05), while rash was not.Conclusions: EV71, lifestyle habits, frequent hospital visits, and symptoms are risk factors for severe HFMD in children in China, while early diagnosis and admission to higher-level hospitals are protective factors.

Research on optimization of control strategy for hybrid energy storage system (HESS) of the electric vehicle (EV), a new adaptive control strategy based on particle swarm optimization (PSO) algorithm is proposed in this paper. The steady-state power of the filtered power is used as the ideal output power of the battery. For the steady-state current output of the battery, the output power of the ultracapacitor is dynamically adjusted by the proportional-integral-derivative (PID) controller to construct a power difference control structure. The parameters of PID controller are optimized by PSO algorithm, and the target test is compared and analyzed based on MATLAB/Advisor. The research results show that the proposed PSO-PID control strategy can quickly eliminate the power deviation and achieve the approximate global optimization of the EV energy management strategy. Compared with the pre-optimized PID control strategy, the output current and power of the battery pack are smoother and the total power consumption is reduced by 3.8360% and 0.5125%, respectively. Then, the energy consumption parameters of PSO-PID are compared with the theoretical minimum energy consumption calculated by dynamic programming (DP) algorithm, and the deviation is less than 1% under both conditions.

This paper proposes an evaluation method based on a T-S fuzzy neural network for evaluating the speed grade of public-transport lines in the context of large-scale rail-transit planning and construction in Hangzhou. The six-dimensional data of morning peak/evening peak average speed, average speed at peak, average station distance, proportion of dedicated lanes, and nonlinear coefficients were selected as input data for the neural network to output the operating speed grade of bus lines. Improving and optimizing the membership function of the Takagi–Sugeno (T-S) model improves its predicted result accuracy compared to a traditional T-S model. The line data of 28 typical trunk lines or expressways in Hangzhou were used as an example; the results demonstrate that the speed grade evaluation method based on an improved T-S fuzzy neural network can effectively and quickly evaluate the speed grade of Hangzhou public-transportation lines. This paper presents a novel analysis and method for large-scale rail-transit planning and evaluation of urban public-transport lines. The aim is to provide practical instruction for the subsequent optimization of public-transportation lines in Hangzhou.

Fresh fruits and vegetables, perishable by nature, are subject to additional deterioration and bruising in the distribution process due to vibration and shock caused by road irregularities. A nonlinear mathematical model was developed that considered not only the vehicle routing problem with time windows but also the effect of road irregularities on the bruising of fresh fruits and vegetables. The main objective of this work was to obtain the optimal distribution routes for fresh fruits and vegetables considering different road classes with the least amount of logistics costs. An improved genetic algorithm was used to solve the problem. A fruit delivery route among the 13 cities in Jiangsu Province was used as a real analysis case. The simulation results showed that the vehicle routing problem with time windows, considering road irregularities and different classes of toll roads, can significantly influence total delivery costs compared with traditional VRP models. The comparison between four models to predict the total cost and actual total cost in distribution showed that the improved genetic algorithm is superior to the Group-based pattern, CW pattern, and O-X type cross pattern.

Background: During Enterovirus type 71 (EV71) infection, the structural viral protein 1 (VP1) activates endoplasmic reticulum (ER) stress associated with peripheral myelin protein 22 (PMP22) accumulation and induces autophagy. However, the specific mechanism behind this process remains elusive. Methods: In this research, we used the VP1-overexpressing mouse Schwann cells (SCs) models co-transfected with a PMP22 silencing or Autocrine motility factor receptor (AMFR/gp78) overexpressing vector to explore the regulation of gp78 on PMP22 and its relationship with autophagy and apoptosis. Results: The activity of gp78 could be influenced by EV71-VP1, leading to a decrease in the ubiquitination and degradation of PMP22, resulting in PMP22 accumulation in ER. In VP1-overexpressing mouse SCs, all three ER stress sensors, including pancreatic endoplasmic reticulum kinase (PERK), activating transcription factor 6 (ATF6) and inositol-requiring enzyme 1 (IRE1) and the related downstream signals (C/EBP-homologous protein (CHOP) and Caspase 12) were activated, as well as the ER-resident chaperone Glucoseregulated protein 78 (GRP78). In addition, VP1 upregulated the autophagy marker Microtubule-associated protein 1 light chain 3 beta (LC3B), while PMP22 silencing or gp78 overexpression reversed the phenomenon. Meanwhile, PMP22 silencing or gp78 overexpression increased proliferation of EV71-VP1-transfected mouse SCs. Conclusion: Gp78 could regulate PMP22 accumulation through ubiquitination degradation and cause ER stress and autophagy in EV71-VP1-overexpressing mouse SCs. Therefore, the gp78/PMP22/ER stress axis might emerge as a promising therapeutic target for myelin and neuronal damage induced by EV71 infection. [ABSTRACT FROM AUTHOR]

Abstract Small-cell lung cancer (SCLC) is the most aggressive and lethal type of lung cancer, characterized by limited treatment options, early and frequent metastasis. However, the determinants of metastasis in SCLC are poorly defined. Here, we show that estrogen-related receptor gamma (ERRγ) is overexpressed in metastatic SCLC tumors, and is positively associated with SCLC progression. ERRγ functions as an essential activator of extracellular matrix (ECM) remodeling and cell adhesion, two critical steps in metastasis, by directly regulating the expression of major genes involved in these processes. Genetic and pharmacological inhibition of ERRγ markedly reduces collagen production, cell-matrix adhesion, microfilament production, and eventually blocks SCLC cell invasion and tumor metastasis. Notably, ERRγ antagonists significantly suppressed tumor growth and metastasis and restored SCLC vulnerability to chemotherapy in multiple cell-derived and patient-derived xenograft models. Taken together, these findings establish ERRγ as an attractive target for metastatic SCLC and provide a potential pharmacological strategy for treating this lethal disease.

Sedimentary facies identification is critical for carbonate oil and gas reservoir development. The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long time. Identifying carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers' subjective influence. Although many references reported the application of machine learning to identify lithofacies, but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic movement. This paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models, and the optimal machine learning with the highest prediction accuracy is recommended. First, the carbonate sedimentary facies are classified into the lagoon, shallow sea, shoal, fore-shoal, and inter-shoal five tags based on the well loggings. Then, five well log curves including spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), true formation resistivity (RT), shallow lateral resistivity (RS) are used as the input, and the manual identified carbonate sedimentary facies are used as the output of the machine learning model. The performance of four different machine learning algorithms, including support vector machine (SVM), deep neural network (DNN), long short-term memory (LSTM) network, and random forest (RF) are compared. The other two wells are used for model validation. The research results show that the RF method has the highest accuracy of sedimentary facies prediction, and the average prediction accuracy is 78.81%; the average accuracy of sedimentary facies prediction using SVM is 77.93%. The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM, and the average accuracy is 69.94% and 73.05%, respectively. The predicted carbonate sedimentary facies by LSTM are more continuous compared with other machine learning models. This study is helpful for identifying compelx sedimentary facies of carbonate reservoirs from well logs.

Many research findings have proven that the system of porous medium reservoirs exhibits different heterogeneous structures at various scales, demonstrating some form of self-similarity with fractal characteristics. In this paper, fractal theory is incorporated into the reservoir to investigate coupled flow between reservoir and horizontal well. By examining the pore structure of highly heterogeneous reservoirs, the fractal dimension can be determined. Analytical methods are utilized to solve the Green function of a point source in a reservoir with fractal characteristics. Employing Green's function and the principle of spatial superposition, a finite flow model for a horizontal well coupled with a fractal reservoir is developed to calculate the flow rate and flow profile of the horizontal well. The model also accounts for the impact of wellbore friction and is solved numerically. A specific example is used for calculation to analyze the influence of fractal parameters on the production and flow rate of the horizontal well. When considering the fractal characteristics of oil reservoirs, the flow rate of the horizontal well is lower than that in Euclidean space. As the fractal dimension increases, the connectivity of pores in the reservoir improves, making it easier to drive the fluid into the wellbore, and the flow distribution along the wellbore becomes more uniform. Conversely, as the anomalous diffusion index increases, the connectivity between pores deteriorates, thus the distribution of flow rate along the wellbore becomes more uneven.