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Abstract Background Kidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function (DGF), a common post-operative complication, may negatively impact the long-term outcomes of both the graft and the pediatric recipient. However, there is limited research on DGF in pediatric kidney transplant recipients. This study aims to develop a predictive model for the risk of DGF occurrence after pediatric kidney transplantation by integrating donor and recipient characteristics and utilizing machine learning algorithms, ultimately providing guidance for clinical decision-making. Methods This single-center retrospective cohort study includes all recipients under 18 years of age who underwent single-donor kidney transplantation at our hospital between 2016 and 2023, along with their corresponding donors. Demographic, clinical, and laboratory examination data were collected from both donors and recipients. Univariate logistic regression models and differential analysis were employed to identify features associated with DGF. Subsequently, a risk score for predicting DGF occurrence (DGF-RS) was constructed based on machine learning combinations. Model performance was evaluated using the receiver operating characteristic curves, decision curve analysis (DCA), and other methods. Results The study included a total of 140 pediatric kidney transplant recipients, among whom 37 (26.4%) developed DGF. Univariate analysis revealed that high-density lipoprotein cholesterol (HDLC), donor after circulatory death (DCD), warm ischemia time (WIT), cold ischemia time (CIT), gender match, and donor creatinine were significantly associated with DGF (P

Abstract Posttransplantation complications pose a major challenge to the long‐term survival and quality of life of organ transplant recipients. These complications encompass immune‐mediated complications, infectious complications, metabolic complications, and malignancies, with each type influenced by various risk factors and pathological mechanisms. The molecular mechanisms underlying posttransplantation complications involve a complex interplay of immunological, metabolic, and oncogenic processes, including innate and adaptive immune activation, immunosuppressant side effects, and viral reactivation. Here, we provide a comprehensive overview of the clinical features, risk factors, and molecular mechanisms of major posttransplantation complications. We systematically summarize the current understanding of the immunological basis of allograft rejection and graft‐versus‐host disease, the metabolic dysregulation associated with immunosuppressive agents, and the role of oncogenic viruses in posttransplantation malignancies. Furthermore, we discuss potential prevention and intervention strategies based on these mechanistic insights, highlighting the importance of optimizing immunosuppressive regimens, enhancing infection prophylaxis, and implementing targeted therapies. We also emphasize the need for future research to develop individualized complication control strategies under the guidance of precision medicine, ultimately improving the prognosis and quality of life of transplant recipients.

ABSTRACTThe vertebrate central nervous system (CNS) is the most complex system of the body. The CNS, especially the brain, is generally regarded as immune-privileged. However, the specialized immune strategies in the brain and how immune cells, specifically macrophages in the brain, respond to virus invasion remain poorly understood. Therefore, this study aimed to examine the potential immune response of macrophages in the brain of orange-spotted groupers (Epinephelus coioides) following red-spotted grouper nervous necrosis virus (RGNNV) infection. We observed that RGNNV induced macrophages to produce an inflammatory response in the brain of orange-spotted grouper, and the macrophages exhibited M1-type polarization after RGNNV infection. In addition, we found RGNNV-induced macrophage M1 polarization via the CXCR3.2- CXCL11 pathway. Furthermore, we observed that RGNNV triggered M1 polarization in macrophages, resulting in substantial proinflammatory cytokine production and subsequent damage to brain tissue. These findings reveal a unique mechanism for brain macrophage polarization, emphasizing their role in contributing to nervous tissue damage following viral infection in the CNS.

Purpose: This study aimed to investigated the key chemical components and the effect of the aqueous extract of Schisandra sphenanthera (SSAE) on alcoholic liver disease (ALD) and the related molecular mechanism. Methods: This study employed UPLC-Q-TOF-MS/MS to identify the chemical compositions in SSAE. ALD rat model was established through oral administration of white spirit. Transcriptome sequencing, weighted gene co-expression network construction analysis (WGCNA), and network pharmacology were used to predict key compositions and pathways targeted by SSAE for the treatment of ALD. Enzyme-linked immunosorbent assay (ELISA), biochemical kits, hematoxylin-eosin (HE) staining, Western blotting (WB) analysis, and immunohistochemical analysis were used to validate the mechanism of action of SSAE in treating ALD. Results: Active ingredients such as schisandrin A, schisandrol A, and schisandrol B were found to regulate the PI3K/AKT/IKK signaling pathway. Compared to the model group, the SSAE group demonstrated significant improvements in cellular solidification and tissue inflammation in the liver tissues of ALD model rats. Additionally, SSAE regulated the levels of a spartate aminotransferase (AST), alanine aminotransferase (ALT), alcohol dehydrogenase (ADH), and aldehyde Dehydrogenase (ALDH) in serum (P

ABSTRACT Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by social communication deficiencies and stereotypic behaviors influenced by hereditary and/or environmental risk factors. There are currently no approved medications for treating the core symptoms of ASD. Human fecal microbiota transplantation (FMT) has emerged as a potential intervention to improve autistic symptoms, but the underlying mechanisms are not fully understood. In this study, we evaluated the effects of human-derived FMT on behavioral and multi-omics profiles of the BTBR mice, an established model for ASD. FMT effectively alleviated the social deficits in the BTBR mice and normalized their distinct plasma metabolic profile, notably reducing the elevated long-chain acylcarnitines. Integrative analysis linked these phenotypic changes to specific Bacteroides species and vitamin B6 metabolism. Indeed, vitamin B6 supplementation improved the social behaviors in BTBR mice. Collectively, these findings shed new light on the interplay between FMT and vitamin B6 metabolism and revealed a potential mechanism underlying the therapeutic role of FMT in ASD.IMPORTANCEAccumulating evidence supports the beneficial effects of human fecal microbiota transplantation (FMT) on symptoms associated with autism spectrum disorder (ASD). However, the precise mechanism by which FMT induces a shift in the microbiota and leads to symptom improvement remains incompletely understood. This study integrated data from colon-content metagenomics, colon-content metabolomics, and plasma metabolomics to investigate the effects of FMT treatment on the BTBR mouse model for ASD. The analysis linked the amelioration of social deficits following FMT treatment to the restoration of mitochondrial function and the modulation of vitamin B6 metabolism. Bacterial species and compounds with beneficial roles in vitamin B6 metabolism and mitochondrial function may further contribute to improving FMT products and designing novel therapies for ASD treatment.

A three-dimensional (3D) thermal lattice Boltzmann model of crystal growth is presented to study the magneto-hydrodynamics (MHD) mixed convection in the melt of Czochralski silicon single crystal growth in this paper. In order to solve the problem of lattice Boltzmann method’s difficulty in solving the flow of 3D cylindrical cavity, the 3D physical model of crystal growth is transformed from the Cartesian coordinate system to the Curvilinear coordinate system. And the irregular grids in the Cartesian coordinate system are transformed into regular grids in the Curvilinear coordinate system to facilitate the establishment of the Lattice Boltzmann model. Under the above operations, two coupled distribution functions are established to solve the governing equations. Two D3Q19 models are adopted to solve the melt velocity and temperature. And the 3D lattice Boltzmann modeling of crystal growth with cylinder boundary is realized by interpolation in Curvilinear coordinate system. The comparison with the experiment and other references proves that the proposed model and boundary treatment scheme is correct and it proposes a new way for studying the 3D crystal growth or other similar 3D cylindrical cavity simulations. Furthermore, the unstable melt flow for two cases are also simulated in this paper and the critical Reynolds number are also obtained. Results show that the introduction of the transverse magnetic field enhances the stability of the crystal growth system and increases the adjustable range of crystal rotation parameter. The obtained conclusion has an important reference for adjusting the process parameters during the CZ crystal growth.

Deadlock/livelock problems can cause catastrophic results in flexible manufacturing systems (FMS) by forcing them to stop production processes at an unpredictable stage of production. Therefore, they must be handled properly in order to obtain flawless production in FMS. Petri net (PN) based deadlock/livelock studies for FMS have been widely carried out to obtain live PN models. When a PN model is live, this ensures that the modelled FMS is deadlock-free. Firstly, an uncontrolled (plant) PN model is constructed for the FMS suffering from deadlocks/livelocks. Then reachability graph (RG) analysis of this PN model is carried out to provide all possible reachable states of the modelled system. An RG of a PN model of an FMS suffering from deadlock/livelock problems can be split into the live zone (LZ) and the deadlock zone (DZ). The former is the set of system states considered to be legal (good), while the latter is the set of system states regarded to be illegal (bad). The LZ represents the optimal (maximally permissive) system behavior that must be provided by an optimal control policy. Generally, in PN based liveness studies, a liveness enforcing supervisor (LES) consisting of a set of control places (CP) is computed. Then the controlled (closed-loop) PN model is obtained by merging the plant PN model and the LES. If the number of reachable live states of the controlled PN model is the same as the ones that exist in the LZ, then the controlled model is optimal. Otherwise, it is suboptimal. The greater the number of live reachable states of the RG, the more products to be produced within the FMS. Therefore, the number of reachable live states of the controlled PN model is considered as a quality measure. It defines the behavioral permissiveness of the controlled model. In the literature, to verify the behavioral permissiveness of the PN models, the PN tool called INA (Integrated Net Analyzer) has been widely used. Recently, by means of INA, a method was proposed to compute the number of states within the LZ and the DZ of a given PN model suffering from deadlocks/livelocks. Unfortunately, INA cannot compute RGs of large PNs having a few millions of states. Therefore, in this paper by means of TINA (TIme petri Net Analyzer), an alternative faster and much more efficient method is proposed for the computation of the number of states within the LZ and the DZ for PN models with large RGs. In addition, in order to enable the development of new PN based deadlock prevention methods, further computations are also proposed. These include the computation of the set of states in the DZ, LZ and first met bad markings (FBMs) for a given PN model suffering from deadlocks/livelocks. The applicability and the effectiveness of the proposed methods are demonstrated by considering several well-known examples of FMS from the relevant literature.INDEX TERMS Flexible manufacturing systems (FMS), deadlock, deadlock prevention, Petri Nets (PN), liveness enforcing supervisor (LES), PN computation tools.

The Czochralski method is the primary technique for single-crystal silicon production. However, anomalous states such as crystal loss, twisting, swinging, and squareness frequently occur during crystal growth, adversely affecting product quality and production efficiency. To address this challenge, we propose an enhanced multimodal fusion classification model for detecting and categorizing these four anomalous states. Our model initially transforms one-dimensional signals (diameter, temperature, and pulling speed) into time–frequency domain images via continuous wavelet transform. These images are then processed using a Dense-ECA-SwinTransformer network for feature extraction. Concurrently, meniscus images and inter-frame difference images are obtained from the growth system’s meniscus video feed. These visual inputs are fused at the channel level and subsequently processed through a ConvNeXt network for feature extraction. Finally, the time–frequency domain features are combined with the meniscus image features and fed into fully connected layers for multi-class classification. The experimental results show that the method can effectively detect various abnormal states, help the staff to make a more accurate judgment, and formulate a personalized treatment plan for the abnormal state, which can improve the production efficiency, save production resources, and protect the extraction equipment.

Abstract Background Paroxysmal kinesigenic dyskinesia (PKD) is the most prevalent kind type of paroxysmal Dyskinesia, characterized by recurrent and transient episodes of involuntary movements. Most PKD cases were attributed to the proline‐rich transmembrane protein 2 (PRRT2) gene, in which the c.649 region is a hotspot for known mutations. Even though some patients with PKD have been genetically diagnosed using whole‐exome sequencing (WES) and Sanger sequencing, there are still cases of missed diagnoses due to the limitations of sequencing technology and analytic methods on throughput. Methods Patients meeting the diagnosis criteria of PKD with negative results of PRRT2‐Sanger sequencing and WES were included in this study. Mutation screening and targeted high‐throughput sequencing were performed to analyze and verify the sequencing results of the potential mutations. Results Six patients with PKD with high mutation ratios of c.649dupC were screened using our targeted high‐throughput sequencing from 26 PKD patients with negative results of PRRT2‐Sanger sequencing and WES (frequency = 23.1%), which compensated for the comparatively shallow sequencing depth and statistical flaws in this region. Compared with the local normal population and other patients with PKD, the mutation ratios of c.649dupC of these six patients with PKD were much higher and also had truncated protein structures and differentially altered mRNA expression. Conclusion Based on the above studies, we emphasize the routine targeted high‐throughput sequencing of the c.649 site in the PRRT2 gene in so‐called genetic‐testing‐negative patients with PKD, and manually calculate the deletion and duplication mutations depth and ratios to lower the rate of clinical misdiagnosis.

Rosemary (Rosmarinus officinalis L) is a medicinal and edible plant. Rosemary essential oil (REO) is the principal constituent of rosemary and possesses anti-inflammatory and antioxidant properties. In this study, we evaluated the cell viability and anti-inflammatory activity of REO through the MTT assay and LPS-induced NO production in mouse RAW264.7 cells, respectively. The optimal formulation for the preparation of REO- microemulsion (REO-ME) was determined employing reversed-phase emulsification in conjunction with pseudo-triphasic diagrams. Transcriptomics, combined with network pharmacology, was employed to identify key signaling pathways, and molecular docking analysis was performed to investigate the relationship between the active ingredients of REO-ME and the key targets. The results showed that REO-ME is an O/W type ME. In addition, further pharmacodynamic experiments showed that REO-ME inhibits the expression of inflammatory factors such as IL-6, IL-1β, and TNF-α by regulating the key targets in the IL-17 signaling pathway, thereby to alleviating UC.

Summary: Kidney transplantation is essential for patients with end-stage renal disease; however, ischemia-reperfusion injury (IRI) during transplantation can lead to acute kidney damage and compromise survival. Recent studies have reported that antiferroptotic agents may be a potential therapeutic strategy, by reducing production of reactive oxygen species (ROS). Therefore, we constructed rutin-loaded polydopamine nanoparticles (PEG-PDA@rutin NPs, referred to as PPR NPs) to eliminate ROS resulting from IRI. Physicochemical characterization showed that the PPR NPs were ∼100 nm spherical particles with good ROS scavenging ability. Notably, PPR NPs could effectively enter lipopolysaccharide (LPS)-treated renal tubular cells, then polydopamine (PDA) released rutin to eliminate ROS, repair mitochondria, and suppress ferroptosis. Furthermore, in vivo imaging revealed that PPR NPs efficiently accumulated in the kidneys after IRI and effectively protected against IRI damage. In conclusion, PPR NPs demonstrated an excellent ability to eliminate ROS, suppress ferroptosis, and protect kidneys from IRI.

The coronavirus disease 2019 (COVID-19) pandemic caused by frequently mutating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a worldwide impact. However, detailed data on the potential aerosol transmission of SARS-CoV-2 in real-world and controlled laboratory settings remain sparse. During the COVID-19 pandemic in Shanghai, China in 2022, samples were collected in a Fangcang shelter hospital, a large-scale temporary hospital rapidly built by converting the existing National Exhibition and Convention Center (Shanghai) into a health care facility. Aerosol samples at different sites and intervals around patients and in public areas, surface samples, and pharyngeal swab samples from corresponding patients were included. Samples were tested for SARS-CoV-2 using real-time quantitative polymerase chain reaction (RT-qPCR) assays, followed by sequencing if the cycle threshold (Ct) value was < 30. The positivity rate for SARS-CoV-2 in aerosol samples was high in contaminated zones (37.5%, 104/277), especially around the bed (41.2%, 68/165) and near ventilation inlets (45.2%, 14/31). The prevalence of SARS-CoV-2 around the bed, public areas, and air inlets of exhaust vents fluctuated and was closely related to the positivity rate among patients at corresponding sampling sites. Some surface samples of different personal protective equipment from medical staff had high positivity rates. Sixty sequences of joined ORF1ab and spike genes obtained from sixty samples represented two main clusters of Omicron SARS-CoV-2. There was consistency in virus sequences from the same patient and their environment, and the detected virus sequences matched those of virus strains in circulation during the collection periods, which indicated a high likelihood of cross-contamination in the Fangcang shelter hospital. In summary, the results provide a quantitative and real landscape of the aerosol transmission of SARS-CoV-2 and a patient-centered view of contamination in large and enclosed spaces and offer a useful guide for taking targeted measures to avoid nosocomial infections during the management of SARS-CoV-2 or other respiratory virus diseases in a Fangcang shelter hospital.

This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We first formulate the process of online fault diagnosis under attack. Then, from the attack viewpoint, we define a sensor network attacker as successful if it can degrade the fault diagnosis in the case of maintaining itself as undiscovered by the operator. To verify such an attacker, an information structure called a joint diagnoser (JD) is proposed, which describes all possible attacks in a given attack scenario. Based on the refined JD, i.e., stealthy joint diagnoser (SJD), we present an algorithmic procedure for synthesizing a successful attacker if it exists.

Abstract Objective This study aimed to investigate the safety of coronavirus disease 2019 (COVID‐19) vaccination in patients with epilepsy (PWE) and their willingness to undergo vaccination. Methods This study was a survey the survey questionnaire, which included general, epilepsy‐specific, and COVID‐specific questions, was completed by patients of the outpatient clinic and hospital ward at The Third Xiangya Hospital in 2021. Results A total of 120 valid questionnaires were returned. Eighty‐nine of 120 responders (74.2%) were not vaccinated, and 31 (25.8%) were vaccinated against COVID‐19. Of the 31 vaccinated PWE, one (3.2%) had worsening of seizures and four (12.9%) had adverse reactions that were characteristic of the COVID‐19 vaccine. The other 26 patients (83.9%) reported no adverse reactions, Moreover, there was no significant difference between the 18 PWE with well‐controlled seizures and the 13 PWE with poorly‐controlled seizures. Of the 89 unvaccinated PWE, 69.7% (62/89) were willing to receive the COVID‐19 vaccine, 28.1% (25/89) were unsure, and 2.2% (2/89) declined to be vaccinated. Significance Among PWE, few adverse reactions occurred following the COVID‐19 vaccination. Most PWE were willing to receive the COVID‐19 vaccine. COVID‐19 vaccination is safe for PWE.

ABSTRACT Metagenomic next-generation sequencing (mNGS) can be used to detect pathogens, but there are limited data on its role in detecting viral central nervous system (CNS) infections. This was a multi-center retrospective study of patients clinically diagnosed with suspected viral CNS infection in Hunan between January 2018 and July 2021. mNGS detection was performed on cerebrospinal fluid (CSF) cell-free DNA (cfDNA) and whole-cell DNA (wcDNA) for diagnostic comparison. A total of 195 patients with suspected viral CNS infection underwent mNGS of CSF samples, and 175 of them received a final clinical diagnosis of CNS viral infection. A total of 100 (57.1%) cases were found to be virus-positive by mNGS, including 49 VZV, 32 HSV-1, 13 EBV, 5 HSV-2, and 1 porcine HSV. Compared with wcDNA, cfDNA mNGS showed a significantly lower proportion of human DNA (n = 26 patients; 0.91 ± 0.1 vs 0.96 ± 0.08, P < 0.01). Of these 26 patients, 24 were finally diagnosed with viral infections. The sensitivity of mNGS for detecting viruses using cfDNA and wcDNA was 66.7% (16/24) and 33.3% (8/24) (P < 0.01), respectively. Herpesviruses dominated the spectrum of DNA viruses in patients with viral CNS infections in Hunan, China, with VZV being the most common. mNGS, especially using cfDNA, is a promising complementary diagnostic method in detecting viral CNS infections. IMPORTANCE This study provides significant new data on the application of metagenomic next-generation sequencing (mNGS) to clinical diagnostics of central nervous system (CNS) viral infections, which can have high mortality rates and severe sequelae. Conventional diagnostic procedures for identifying viruses can be inefficient and rely on preconceived assumptions about the pathogen, making mNGS an appealing alternative. However, the effectiveness of mNGS is affected by the presence of human DNA contamination, which can be minimized by using cell-free DNA (cfDNA) instead of whole-cell DNA (wcDNA). This multi-center retrospective study of patients with suspected viral CNS infection found that mNGS using cfDNA had a significantly lower proportion of human DNA and higher sensitivity for detecting viruses than mNGS using wcDNA. Herpesviruses, particularly VZV, were found to be the most common DNA viruses in these patients. Overall, mNGS using cfDNA is a promising complementary diagnostic method for detecting CNS viral infections.

Traditional magnetic levitation planar micromotors suffer from poor controllability, short travel range, low interference resistance, and low precision. To address these issues, a distributed coil magnetically levitated planar micromotor with a gated recurrent unit (GRU)-extended state observer (ESO) control strategy is proposed in this paper. First, the structural design of the distributed coil magnetically levitated planar micromotor employs a separation of levitation and displacement, reducing system coupling and increasing controllability and displacement range. Then, theoretical analysis and model establishment of the system are conducted based on the designed distributed coil magnetically levitated planar micromotor and its working principles, followed by simulation verification. Finally, based on the established system model, a GRU-ESO controller is designed. An ESO feedback control term is introduced to enhance the system’s anti-interference capability, and the GRU feedforward compensation control term is used to improve the system’s tracking control accuracy. The experimental results demonstrate the reliability of the designed distributed coil magnetic levitation planar micromotor and the effectiveness of the controller.

Pueraria thomsonii Radix. as an important edible and medicinal plant in China, its effects and mechanisms on alcoholic liver disease (ALD) remain unclear. Herein, we investigated the effects and mechanisms of the water extracts of Pueraria thomsonii (WEPT) in the ALD rat model induced by Chinese Baijiu. The results revealed that, a total of 23 compounds were obtained from the WEPT by UPLC-Q-TOF-MS. Compared with the model group, WEPT group showed a significant decrease in liver index and serum levels of alanine transaminase (ALT), aspartate aminotransferase (AST) and Cytochrome P450 2E1 (CYP2E1) levels in the livers, with a significant increase in ethanol dehydrogenase (ADH) and acetaldehyde dehydrogenase (ALDH) levels in the livers, significantly reduced the expression of HSP90, p-AKT, p-IKK and NOX4 in the liver tissues. Our study demonstrates that Pueraria thomsonii Radix may be beneficial food for alcoholic liver disease, by inhibited the PI3K/AKT and NOX4/ROS signaling pathways.

In the process of silicon single-crystal preparation, the timely identification and adjustment of abnormal conditions are crucial. Failure to promptly detect and resolve issues may result in a substandard silicon crystal product quality or even crystal pulling failure. Therefore, the early identification of abnormal furnace conditions is essential for ensuring the preparation of perfect silicon single crystals. Additionally, since the thermal field is the fundamental driving force for stable crystal growth and the primary assurance of crystal quality, this paper proposes a silicon single-crystal growth temperature gradient trend classification algorithm based on multi-level feature fusion. The aim is to accurately identify temperature gradient changes during silicon crystal growth, in order to promptly react to early growth failures and ensure the stable growth of high-quality silicon single crystals to meet industrial production requirements. The algorithm first divides the temperature gradient trend into reasonable categories based on expert knowledge and qualitative analysis methods. Then, it fuses the original features of actual production data, shallow features extracted based on statistical information, and deep features extracted through deep learning. During the fusion process, the algorithm considers the impact of different features on the target variable and calculates mutual information based on the difference between information entropy and conditional entropy, ultimately using mutual information for feature weighting. Subsequently, the fused multi-level feature vectors and their corresponding trend labels are input into a Deep Belief Network (DBN) model to capture process dynamics and classify trend changes. Finally, the experimental results demonstrate that the proposed algorithm can effectively predict the changing trend of thermal field temperature gradients. The introduction of this algorithm will help improve the accuracy of fault trend prediction in silicon single-crystal preparation, thereby minimizing product quality issues and production interruptions caused by abnormal conditions.

The effects of selenium supplementation in Tachysurus fulvidraco (T. fulvidraco) on low-temperature stress are not known. In this study, 280 healthy T. fulvidraco were divided into two groups, the G0 group (a control group) and the T0 group (a selenium treatment group on a 0.22 mg/kg diet), for a 6-week feeding time. Then, low-temperature stress (water temperature dropped from 26 to 13 °C, with a rate of 1 °C/h) was administered after that. The feeding results showed that selenium increased the percent weight gain (PWG), specific growth rate (SGR), and survival rate (SR) of T. fulvidraco and decreased the feed conversion rate (FCR), but these differences were not significant (p > 0.05). Under low temperatures, selenium still has no significant effects on antioxidant indexes such as glutathione peroxidase (GSH-Px) activity, superoxide dismutase (SOD) activity, and malondialdehyde (MDA) content in serum (p > 0.05). However, metabolomic analysis revealed that selenium caused changes in lipids and lipid-like molles, organic acids and their derivatives, and fatty acyls. Choline, linoleic acid, and glycerophospholipid metabolism pathways; d-arginine and d-ornithine metabolism; valine, leucine, and isoleucine degradation; and biosynthesis pathways, as well as pyrimidine metabolism pathways, were activated to produce these metabolites to combat against this stress. In addition, selenium increased the diversity of intestinal microbes in T. fulvidraco and decreased the relative abundance of Plesiomonas. However, the combined analysis showed the intestinal microbe changes did not affect metabolite production. In summary, selenium activated lipid, carbohydrate, and amino acid metabolism for energy substance provision, reduced the oxidation and production of other harmful substances, and increased the intestinal microbe diversity of T. fulvidraco to improve resistance to low-temperature stress.

BackgroundFrontal lobe epilepsy (FLE) is the second most common type of focal epilepsy, however, imaging studies of FLE have been far less than Temporal lobe epilepsy (TLE) and the structural findings were not consistent in previous literature.ObjectInvestigate the changes in cortical thickness in patients with FLE and the alteration of the structural covariance networks (SCNs) of cortical thickness with graph-theory.MethodThirty patients with FLE (18 males/12 females; 28.33 ± 11.81 years) and 27 demographically matched controls (15 males/12 females; 29.22 ± 9.73 years) were included in this study with high-resolution structural brain MRI scans. The cortical thickness was calculated, and structural covariance network (SCN) of cortical thickness were reconstructed using 68 × 68 matrix and analyzed with graph-theory approach.ResultCortical thickness was not significantly different between two groups, but path length and node betweenness were significantly increased in patients with FLE, and the regional network alterations were significantly changed in right precentral gyrus and right temporal pole (FDR corrected, p

Cyclin-dependent kinases (CDKs) are a large family of proteins exerting different regulatory functions in eukaryotic cells, including control over the cell cycle and gene transcription. CDK1 controls the cell cycle and promotes cell proliferation by regulating the initiation and transition of the G2/M phase. Similar to mammals, CDK1 has also been characterized in arthropods; however, its functions and physiological importance in crustaceans remain unknown. Our pervious transcriptomic data indicated that CDK1 was a candidate cold-stress response gene in the Pacific white shrimp (Litopenaeus vannamei). Therefore, in the present study, the full-length sequence of CDK1 (LvCDK1) was obtained from L. vannamei containing a conserved PSTAIRE sequence and ATP-binding region. The expression of LvCDK1 is tissue-specific, with relatively higher in the hepatopancreas. The in situ hybridization (ISH) signals for LvCDK1 were present in the distal zone of hepatopancreas and mainly located in the cytoplasm and nucleus. The expression of LvCDK1 in hepatopancreatic was sensitive to environmental temperature. Furthermore, under cold stress, knockdown of LvCDK1 led to increased mortality in shrimp. Our study provides the first evidence of LvCDK1 responding to cold stress in shrimps. This mechanism for modifying the transcriptional roles of LvCDK1 may show new light on the molecular regulation of crustaceans in response to cold stress.

The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios.

This article aims to explore the inner relationship between targeted poverty alleviation and financing constraints of listed companies, and further reveal the underlying impact mechanism. Based on the data of listed companies in Shanghai and Shenzhen A-share main board participating in targeted poverty alleviation from 2017 to 2020, graded response models and mediation models are conducted. From the perspective of resource motivation, the study examined the relationship between the investment in targeted poverty alleviation and financing constraints of listed companies and introduces policy resources as mediator variable to reveal the underlying mechanism. The study found that the participation of listed companies in targeted poverty alleviation can significantly ease financing constraints, and policy resources can play an intermediary effect in the process. For enterprises with different equity nature, in state-owned enterprises, policy resources play a small intermediary role between poverty alleviation inputs and financing constraints. Meanwhile, in non-state-owned enterprises, government subsidies play a significant intermediary role, and tax incentives play a small intermediary role. In response to this, policy suggestions such as improving the disclosure of poverty alleviation information, expanding policy preferences, and increasing poverty alleviation subsidies have been suggested.

Abstract Model predictive direct power control (MPDPC) has strong robustness and fast dynamic response, so it is widely applied in the control system of grid‐connected converter. However, the traditional FCS‐MPC bears with variable switching state, which will reduce the current tracking accuracy, accidentally produce current ripple and electromagnetic noise. Here, a double closed‐loop control strategy with constant switching frequency based on optimal switching sequence synthesis of model predictive direct power control (MPDPC‐CF) in the inner loop and SMC control in the outer loop is proposed for the non‐linear system of Vienna rectifier. Firstly, the Vienna rectifier is modelled to obtain the predicted values of input power. Then, the given values of active power and reactive power are obtained through the outer loop. Three adjacent voltage vectors with the lowest cost function in the sector are selected to synthesize the optimal voltage vector, and the pulse time of the corresponding vector is calculated according to the cost function of the voltage vector. To verify the correctness of the theoretical analysis, the Vienna rectifier is taken as the research object, and the comparison with the traditional MPDPC shows that the proposed constant frequency model predictive control has good steadystate and dynamic performance.

To solve seeding failures due to the misjudgment caused by manual observation in the traditional melting process of Czochralski (CZ) monocrystalline silicon, a method for predicting the melting progress of CZ monocrystalline silicon based on Convolutional Neural Network (CNN) deep learning was proposed. The deep learning method and image classification of the melting process were combined. By taking CNN as the research object, the AlexNet network-based melting classification model was constructed. Meanwhile, the comparative analysis was performed by adjusting the number of AlexNet network convolution layers and the size of the convolution kernel. After several experiments, a CNN-based melting stage classification model was finally determined. Simulation results showed that the model could achieve higher accuracy when predicting the melting process. This paper focuses on the key technical issues such as polycrystalline silicon melting and temperature predication in the growth process of the monocrystalline silicon, and predicts the melting process of silicon materials, which lays the foundation for the quality improvement of monocrystalline silicon growth process in the semiconductor field.

BackgroundRenal transplantation is a very effective treatment for renal failure patients following kidney transplant. However, the clinical benefit is restricted by the high incidence of organ rejection. Therefore, there exists a wealth of literature regarding the mechanism of renal transplant rejection, including a large library of expression data. In recent years, research has shown the immune microenvironment to play an important role in renal transplant rejection. Nephrology web analysis tools currently exist to address chronic nephropathy, renal tumors and children’s kidneys, but no such tool exists that analyses the impact of immune microenvironment in renal transplantation rejection.MethodsTo fill this gap, we have developed a web page analysis tool called Comprehensive Analysis of Renal Allograft Rerejction in Immune Microenvironment (CARARIME).ResultsCARARIME analyzes the gene expression and immune microenvironment of published renal transplant rejection cohorts, including differential analysis (gene expression and immune cells), prognosis analysis (logistics regression, Univariable Cox Regression and Kaplan Meier), correlation analysis, enrichment analysis (GSEA and ssGSEA), and ROC analysis.ConclusionsUsing this tool, researchers can easily analyze the immune microenvironment in the context of renal transplant rejection by clicking on the available options, helping to further the development of approaches to renal transplant rejection in the immune microenvironment field. CARARIME can be found in http://www.cararime.com.

BackgroundThe coronavirus omicron variant outbroke in early 2022 in Shanghai. Although previous studies indicated that long working hours in a square cabin hospital might increase the risk of mental health among frontline healthcare providers, few studies have investigated whether the mental health risk could be reduced among well-trained professionals following the new guidelines.ObjectiveThis study aimed to investigate the health situation of frontline healthcare providers in Shanghai square cabin during the omicron variant circulation.MethodsAn online survey was used to evaluate those healthcare providers working in the square cabin hospitals from March 1, 2022, to May 31, 2022. The first online survey was conducted and emailed to the health providers on April 1. The second survey was conducted and sent to the nonrespondents on May 31. Overall, 142 frontline healthcare providers completed the online survey. Their mental health was assessed by the Insomnia Severity Index Scale, the Generalized Anxiety Disorder Scale, the Patient Health Questionnaire-9, and the Psychological Resilience Scale. We estimated multiple clinical systems and identified factors associated with those symptoms among participants. Multivariable logistic regression models were used to assess the risk factors of these symptoms.ResultsOverall, 66.20%, 45.07%, and 27.46% of frontline healthcare providers in Shanghai City reported symptoms of insomnia, depression, and anxiety, respectively. In addition, the most common symptoms included dry eyes (57.75%), lumbar muscle strain (47.18%), dry mouth (35.92%), itching (31.69%), headache (29.58%), and sore throat (28.87%) among the frontline healthcare providers. There was no statistical difference in symptoms by gender, age, personnel category, or job position (p > 0.05).ConclusionIn the case of an unexpected pandemic, the mental health of healthcare providers is not optimistic. This situation still exists more than 2 years after the global outbreak of the COVID-19 pandemic. Therefore, the physical and mental health of long-term healthcare providers working in a square cabin hospital still needs monitoring.

ObjectivesThe study aimed to determine whether SARS-CoV-2 Omicron variant could be detected in the vaginal fluid and anal swabs of reproductive-aged and postmenopausal women infected with SARS-CoV-2 Omicron variant.MethodsIncluded in this study were 63 women who were laboratory confirmed as having SARS-CoV-2 Omicron variant infection and admitted to the responsible ward of Daping Hospital of at the National Exhibition and Convention Center(Shanghai) Makeshift Hospital from May 1–24, 2022.From them, vaginal and anal swabs were obtained with informed consent. The demographic and baseline clinical characteristics and the swab test results were analyzed.ResultsThe 63 included patients ranged in age from 18 to 72 years with a median of 47.71 ± 15.21 years. Of them, 38 women (60.3%) were in their reproductive years. Most of the participants (77.8%) were healthy without significant underlying diseases. Fourteen patients (22.2%) had asymptomatic infection and the remaining 49 (77.8%) had mild infection. The upper respiratory tract symptoms including cough (40/63.5%) and sore throat (18/28.6%)were the most common clinical manifestations of these mildly infected patients. Only 5 patients (7.8%) had gastrointestinal (GI) symptoms, including simple diarrhea in 4 patients, and diarrhea with vomiting in one patient. Pharyngeal,vaginal and anal swabs were collected simultaneously from all 63 patients 8–16 (mean 11.25 ± 2.23) days after SARS-Cov-2 Omicron variant infection. The vaginal swabs were negative for SARS-CoV-2 in all 63 patients, and the anal swabs were positive in 4 patients (6.5%). The overall median hospitalization duration was 16.73 ± 3.16 days.ConclusionThe results of the present study suggest that there is a low possibility of SARS-Cov-2 Omicron variant transmission via the digestive tract and vaginal fluid. The correlation between the GI symptoms and the presence of viral RNA in anal swabs is uncertain.

Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (SAA1, FUT6, SPATA18, SHROOM3, AJAP1, and NPEPL1) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.

Background: Autosomal dominant polycystic kidney disease (ADPKD) is mainly caused by PKD1 and PKD2 mutations. However, only a few studies have investigated the genotype and phenotype characteristics of Asian patients with ADPKD. This study aimed to investigate the relationship between the natural course of ADPKD genotype and phenotype.Methods: Genetic studies of PKD1/2 genes of Chinese patients with ADPKD in a single center were performed using targeted exome sequencing and next-generation sequencing on peripheral blood DNA.Results: Among the 140 patients analyzed, 80.00% (n = 112) harbored PKD1 mutations, 11.43% (n = 16) harbored PKD2 mutations, and 8.57% (n = 12) harbored neither PKD1 nor PKD2 mutations. The average age at dialysis was 52.60 ± 11.36, 60.67 ± 5.64, and 52.11 ± 14.63 years, respectively. The renal survival rate of ADPKD patients with PKD1 mutations (77/112) was significantly lower than that of those with PKD2 mutations (9/16), leading to an earlier onset of end-stage renal disease (ESRD). Renal prognosis was poor for those with nonsense mutations, and they required earlier renal replacement therapy.Conclusions: The genotype and phenotype characteristics of ADPKD patients potentially vary across ethnic groups. Our findings supplement the genetic profiles of Chinese ADPKD patients, could serve as a guide for therapy monitoring and prognosis assessment of ADPKD, and may improve the clinical diagnosis.

Monocrystalline silicon is an important raw material in the semiconductor and photovoltaic industries. In the Czochralski (CZ) method of growing monocrystalline silicon, various factors may cause node loss and lead to the failure of crystal growth. Currently, there is no efficient method to detect the node loss of monocrystalline silicon at industrial sites. Therefore, this paper proposed a monocrystalline silicon node-loss detection method based on multimodal data fusion. The aim was to explore a new data-driven approach for the study of monocrystalline silicon growth. This article first collected the diameter, temperature, and pulling speed signals as well as two-dimensional images of the meniscus. Later, the continuous wavelet transform was used to preprocess the one-dimensional signals. Finally, convolutional neural networks and attention mechanisms were used to analyze and recognize the features of multimodal data. In the article, a convolutional neural network based on an improved channel attention mechanism (ICAM-CNN) for one-dimensional signal fusion as well as a multimodal fusion network (MMFN) for multimodal data fusion was proposed, which could automatically detect node loss in the CZ silicon single-crystal growth process. The experimental results showed that the proposed methods effectively detected node-loss defects in the growth process of monocrystalline silicon with high accuracy, robustness, and real-time performance. The methods could provide effective technical support to improve efficiency and quality control in the CZ silicon single-crystal growth process.

Abstract O-GalNAc glycans (or mucin O-glycans) play pivotal roles in diverse biological and pathological processes, including tumor growth and progression. Structurally defined O-GalNAc glycans are essential for functional studies but synthetic challenges and their inherent structural diversity and complexity have limited access to these compounds. Herein, we report an efficient and robust chemoenzymatic modular assembly (CEMA) strategy to construct structurally diverse O-GalNAc glycans. The key to this strategy is the convergent assembly of O-GalNAc cores 1–4 and 6 from three chemical building blocks, followed by enzymatic diversification of the cores by 13 well-tailored enzyme modules. A total of 83 O-GalNAc glycans presenting various natural glycan epitopes are obtained and used to generate a unique synthetic mucin O-glycan microarray. Binding specificities of glycan-binding proteins (GBPs) including plant lectins and selected anti-glycan antibodies towards these O-GalNAc glycans are revealed by this microarray, promoting their applicability in functional O-glycomics. Serum samples from colorectal cancer patients and healthy controls are assayed using the array reveal higher bindings towards less common cores 3, 4, and 6 than abundant cores 1 and 2, providing insights into O-GalNAc glycan structure-activity relationships.

Ethnopharmacological relevance: Agrimonia pilosa Ledeb. is the dried above-ground part of dragon's tooth grass, a plant of the Rosaceae family, which is widely distributed in China, Korea, and Japan. Agrimonia pilosa Ledeb. is a herbal medicine with great scope for development and use. It is astringent and hemostatic, and it is used for treating malaria, preventing dysentery, detoxification, and as a tonic for deficiency. Aim of the review: We summarize the traditional uses, botanical and chemical composition, extraction methods, and pharmacological and toxicological progress of Agrimonia pilosa Ledeb. and discuss the future research trends and development prospects of this plant. Materials and methods: Information on Agrimonia pilosa Ledeb. was gathered via the Internet (China National Knowledge Infrastructure, Google Scholar, PubMed, Web of Science, SpringerLink, Wiley, Wanfang Data, and Baidu Academic). Additional information was obtained from books (Ben Cao Tu Jing, A Textual Research on the Name and Reality of Plants, Modern Practical Chinese Medicine, Zhen Nan Ben Cao) and PhD and MS dissertations. Results: Phytochemical studies have identified more than 252 compounds from Agrimonia pilosa Ledeb., including flavonoids, volatile oils, tannins, phenols, m-benzotrienols, pentacyclic triterpenoids, isocoumarins, lignans, organic acids, and other chemical constituents. The compounds and extracts isolated from Agrimonia pilosa Ledeb. show various pharmacological activities, including anti-inflammatory, anticancer, antitumor effects, antioxidant, analgesic effects, and other pharmacological effects. Conclusion: This review highlights the botany, phytochemistry, pharmacology, toxicology, and traditional uses of Agrimonia pilosa Ledeb., providing a basis for future research and clinical applications. Agrimonia pilosa Ledeb. has shown remarkable effectiveness in the treatment of various diseases, especially enteritis, gastric ulcers, and gastrointestinal bleeding. Most prescriptions for Agrimonia pilosa Ledeb. are empirical and lack rigorous clinical observation. For these reasons, the toxicology, standardized clinical studies, nature of active ingredients, pharmacokinetics, mechanism, and metabolism of Agrimonia pilosa Ledeb. should be deepened, especially through clinical trials, to ensure the clinical safety of its use for further research.

AbstractThe mechanical properties of rock mass are mostly controlled by the structural plane. Rock bridge has an important effect on the failure and stability of rock mass with structural plane. In this paper, the uniaxial compression test of hard rock containing rock bridge is carried out, accompanied by acoustic emission (AE) and high-speed video monitoring. Within the scope of this study, the results show that the uniaxial compressive strength (UCS) of sample with rock bridge increases with the rock bridge length. Under the same stress condition before peak, the strain of the sample decreases with the increase of the rock bridge length (RBL). Under the influence of stress concentration, the cracking of the sample starts from the prefabricated crack tip. The peak AE count of the sample decreases with the increase of RBL. The AE cumulative energy of the sample increases with the increase of RBL. The number of cracks in the sample before the peak is small, and the rapid increase of cracks is mainly concentrated in the main fracturing stage after the peak, while after reaching the residual stage, the increase rate of various types of microcracks in the sample is relatively slow. The numerical simulation results show that the number of all kinds of cracks in the sample before the peak is small, and the number of tensile cracks in the sample is very close to the total number of cracks under different RBLs. With the increase of RBL, the tensile/shear crack ratio decreases. The internal cracks of the sample are mainly concentrated at the peak stress and postpeak stage. The research results of this paper are of great significance to the engineering excavation and safe construction of jointed rock mass engineering.

For the unsteady multi-boundary inverse heat conduction problem, a real-time solution method for boundary heat flux based on dynamic matrix control is proposed in the paper. The method solves the heat flux at the boundary in real-time by measuring the temperature information at the measurement points of the heat transfer system. A two-dimensional direct heat conduction model of the heat transfer system is established in the paper, and is solved by the finite difference method to obtain the temperature information of the measurement points under any heat flux boundary. Then, the correspondence between the heat flux of boundary and the temperature information is presented by means of a step-response model. The regularization parameters are introduced into the method to improve the stability of the inversion process, and the effect of real-time inversion on the heat flux of the boundary is achieved through rolling optimization. The experimental results show that the proposed method can achieve real-time inversion of the heat fluxes of the two-dimensional boundary with good accuracy.

Silicon single crystal (SSC) quality monitoring and control has been a hot research topic in the field of the Czochralski crystal growth process. Considering that the traditional SSC control method ignores the crystal quality factor, this paper proposes a hierarchical predictive control strategy based on a soft sensor model for online control of SSC diameter and crystal quality. First, the proposed control strategy considers the V/G variable (V is the crystal pulling rate, and G is the axial temperature gradient at the solid–liquid interface), a factor related to crystal quality. Aiming at the problem that the V/G variable is difficult to measure directly, a soft sensor model based on SAE-RF is established to realize the online monitoring of the V/G variable and then complete hierarchical prediction control of SSC quality. Second, in the hierarchical control process, PID control of the inner layer is used to quickly stabilize the system. Model predictive control (MPC) of the outer layer is used to handle system constraints and enhance the control performance of the inner layer. In addition, the SAE-RF-based soft sensor model is used to monitor the crystal quality V/G variable online, thereby ensuring that the output of the controlled system meets the desired crystal diameter and V/G requirements. Finally, based on the industrial data of the actual Czochralski SSC growth process, the effectiveness of the proposed crystal quality hierarchical predictive control method is verified.

Objectives: We investigated whether early worsening of cerebrospinal fluid (CSF) predicts the later paradoxical tuberculomas and is a potential predictive biomarker. Methods: Patients of HIV-negative tuberculous meningitis fulfilling the inclusion criteria(n = 98) underwent clinical and CSF evaluation, together with repeated neuroimaging. We compared the baseline clinical data and continuous CSF of patients who did (n = 36) and did not (n = 62) develop paradoxical tuberculomas, and reported the changes associated with symptomatic tuberculomas. A logistic regression analysis was developed to reveal predictors for paradoxical tuberculomas. Results: The proportion of worsening CSF parameters (WBC count and percent neutrophils) in the paradoxical tuberculomas group (27/36, 75.0%) was significantly higher than the non-paradoxical tuberculomas group (15/62, 24.2%). The logistic regression analysis revealed that worsening CSF parameters was the highest risk predictor for paradoxical tuberculomas. Most worsening CSF parameters (81.0%) occurred within two weeks after treatment (2–24 days, median 7 days), and paradoxical tuberculomas commonly happened two weeks later (12 days to 13 months, median 22 days). The period between worsening CSF parameters and paradoxical tuberculomas ranged from 6 to 383 days (median 21days). There were no significant differences in mortality and prognosis between the two groups. Conclusions: Early worsening of CSF parameters predicts subsequent development or progression of tuberculomas.

Background: Ferroptosis is a potential target for cancer therapy, and lncRNAs can also affect ferroptosis by regulating related genes. The pathogenesis of clear cell renal cell carcinoma (ccRCC) regarding the regulation of ferroptosis by lncRNAs is still unknown.Methods: We constructed a risk model based on data in ccRCC patients obtained from the TCGA database and validated the diagnostic and prognostic value of the model. In addition, immune function and immune checkpoint variability analysis validated the association of ferroptosis with ccRCC tumor immunity.Results: The characteristics of ferroptosis-related lncRNAs (FRLs) were significantly correlated with the prognosis of ccRCC patients. The prognostic characteristics of FRLs were independent prognostic factors in ccRCC patients. Gene function in the high-risk group was associated with oxygen metabolic processes and immune pathways. Immune checkpoint variability analysis showed that HAVCR2, NRP1, and HHLA2 were upregulated in the low-risk group, while CD44, TNFRSF18, TNFSF14, TNFRSF8, CD276, and TNFRSF25 were upregulated in the high-risk group.Conclusions: The prognostic characteristics of FRLs can effectively predict the prognosis of ccRCC patients and provide a new direction for clinical treatment.

Abstract Background Mesenchymal stem cells (MSCs) represent a promising treatment option for acute kidney injury (AKI). The main drawbacks of MSCs therapy, including the lack of specific homing after systemic infusion and early cell death in the inflammatory microenvironment, directly affect the therapeutic efficacy of MSCs. Erythropoietin (EPO)-preconditioning of MSCs promotes their therapeutic effect, however, the underlying mechanism remains unknown. In this study, we sought to investigate the efficacy and mechanism of EPO in bone marrow derived mesenchymal stem cells (BMSCs) for AKI treatment. Results We found that incubation of BMSCs with ischemia/reperfusion(I/R)-induced AKI kidney homogenate supernatant (KHS) caused apoptosis in BMSCs, which was decreased by EPO pretreatment, indicating that EPO protected the cells from apoptosis. Further, we showed that EPO up-regulated silent information regulator 1 (SIRT1) and Bcl-2 expression and down-regulated p53 expression. This effect was partially reversed by SIRT1 siRNA intervention. The anti-apoptotic effect of EPO in pretreated BMSCs may be mediated through the SIRT1 pathway. In a rat AKI model, 24 h after intravenous infusion, GFP-BMSCs were predominantly located in the lungs. However, EPO pretreatment reduced the lung entrapment of BMSCs and increased their distribution in the target organs. AKI rats infused with EPO-BMSCs had significantly lower levels of serum IL-1β and TNF-α, and a significantly higher level of IL-10 as compared to rats infused with untreated BMSCs. The administration of EPO-BMSCs after reperfusion reduced serum creatinine, blood urea nitrogen, and pathological scores in I/R-AKI rats more effectively than BMSCs treatment did. Conclusions Our data suggest that EPO pretreatment enhances the efficacy of BMSCs to improve the renal function and pathological presentation of I/R-AKI rats.

Abstract Background Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States with no effective treatment. The current diagnostic method, spirometry, does not accurately reflect the severity of COPD disease status. Therefore, there is a pressing unmet medical need to develop noninvasive methods and reliable biomarkers to detect early stages of COPD. Lipids are the fundamental components of cell membranes, and dysregulation of lipids was proven to be associated with COPD. Lipidomics is a comprehensive approach to all the pathways and networks of cellular lipids in biological systems. It is widely used for disease diagnosis, biomarker identification, and pathology disorders detection relating to lipid metabolism. Methods In the current study, a total of 25 serum samples were collected from 5 normal control subjects and 20 patients with different stages of COPD according to the global initiative for chronic obstructive lung disease (GOLD) (GOLD stages I ~ IV, 5 patients per group). After metabolite extraction, lipidomic analysis was performed using electrospray ionization mass spectrometry (ESI-MS) to detect the serum lipid species. Later, the comparisons of individual lipids were performed between controls and patients with COPD. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) and receiver operating characteristic (ROC) analysis were utilized to test the potential biomarkers. Finally, correlations between the validated lipidomic biomarkers and disease stages, age, FEV1% pack years and BMI were evaluated. Results Our results indicate that a panel of 50 lipid metabolites including phospholipids, sphingolipids, glycerolipids, and cholesterol esters can be used to differentiate the presence of COPD. Among them, 10 individual lipid species showed significance (p

Abstract Background A novel coronavirus pneumonia outbreak began in Wuhan, Hubei Province, in December 2019; the outbreak was caused by a novel coronavirus previously never observed in humans. China has imposed the strictest quarantine and closed management measures in history to control the spread of the disease. However, a high level of evidence to support the surgical management of potential trauma patients during the novel coronavirus outbreak is still lacking. To regulate the emergency treatment of trauma patients during the outbreak, we drafted this paper from a trauma surgeon perspective according to practical experience in Wuhan. Main body The article illustrates the general principles for the triage and evaluation of trauma patients during the outbreak of COVID-19, indications for emergency surgery, and infection prevention and control for medical personnel, providing a practical algorithm for trauma care providers during the outbreak period. Conclusions The measures of emergency trauma care that we have provided can protect the medical personnel involved in emergency care and ensure the timeliness of effective interventions during the outbreak of COVID-19.

In the vision-based shape control system of silicon single crystal (SSC) growth, shape parameters are used as the control variables to make the grown SSC approximate to a perfect cylinder. A highlight halo at the junction of the solid crystal and the liquid crysta is a distinctive feature that reflects the current shape of SSC, and the edge of the captured halo is an elliptic arc that consistently changes with the shape of the halo. To estimate the shape parameters through the elliptic arc, we propose a two-side (TS) constraint method to fit the elliptic arc. Firstly, the error correction parameter is introduced into the second-order polynomial model to make the model more robust in the case of noise disturbing, and the resultant non-convex TS constraint model is constructed. Then, two weighted functions, which give the data out of TS constraint a small weight coefficient to resist the large level noise disturbing, are brought into the TS constraint optimization problem tactfully. Simulation and real SSC data examples show that the proposed method can obtain the elliptic arc parameters effectively.

The weakly cemented rock layer is easily damaged under the combined effect of seepage and mining disturbance and creates massive engineering disasters. This paper uses the self-designed weakly cemented remodeling mechanism to prepare weakly cemented rock samples with clay minerals accounting for 20%, 30%, 40%, 50%, and 60% of the total. The MTS816 system, in conjunction with an acoustic emission system and a high-speed camera, completed mechanical and seepage tests on the sample. The results show that when the quantity of clay minerals increases, the uniaxial compressive strength, elastic modulus, and rapid crack propagation stress of the weakly cemented rock samples decrease. Initial stress of crack development increases and then decreases, as well as the sample’s failure mode changes from shear to tensile. The sample’s permeability increases with increasing osmotic and axial pressure differential but decreases with increasing confining pressure under the same amount of clay minerals. The sensitivity of permeability to changes in osmotic pressure differential, axial pressure, and confining pressure increases as the amount of clay minerals increases. The mechanical and seepage characteristics can show significant changes in clay minerals in 20% to 30%.

Determination of the representative elementary volume (REV) of fractured rock masses based on equivalent permeability (K) is significantly dependent on the geometric characteristics of fractures. In this work, a series of numerical simulations were performed to analyze the relationship between geometric characteristics of fractures and the REV size, in which fracture length follows a power-law distribution. A method to evaluate the K of a three-dimensional (3D) discrete fracture network (DFN) by extracting the equivalent pipe network (EPN) model from the DFN model was utilized and verified. The results show that K of the 3D DFN model has an exponential relationship with the power exponent (a) of fracture length distribution and the evaluation of K agrees well with that reported in previous studies, confirming the reliability of the EPN model for calculating seepage properties of complex 3D DFN models. When the side length of submodels (Ln) is small, the K varies significantly due to the influence of random number seeds used to generate fracture length, location, and orientation. The K holds a constant value after Ln exceeds some specific value. The critical model scale is determined as the REV size, and the corresponding volume of the 3D DFN model is represented by VREV. The VREV varies within a narrow range when a≤4.0. When a=4.5, the VREV rapidly increases to more than 3.4 times than that when a=4.0. The fluid flow becomes more inhomogeneous due to the small nonpersistent fractures that dominate the preferential flow paths when a exceeds a certain value (i.e., 4.5). The K at the REV size decreases exponentially with the increment of a. This tendency can be explained by the decrease of the average intersection length (Li) with the increment of a, which is a geometric parameter for reflecting the connectivity of the fracture network.

This study adopts the revised gravity model to construct the spatial association network of trade in RECP countries and reveals its characteristics through a social network analysis method. The results are as follows: the spatial correlation of trade among RECP countries presents a complex, multithreaded network structure; the spatial correlation network of trade among RECP countries appears to fluctuate, indicating that their correlations, although influenced by the national environment, are still moving in the direction of regional integration; the degree centrality of China, Australia, and Korea is higher in terms of intermediary centrality and proximity centrality. This indicates that these countries are not only at the core of the network and have many associated relationships with other countries but also all are located at the center of the trade spatial association network; and the analysis results of the block model show that the trade spatial association network of RECP countries can be divided into four sections: net spillover, net benefit, broker, and two-way spillover. The spillover effect between the two sections has obvious gradient transmission characteristics.

The rapid development of electric vehicle (EV) technology and the consequent charging demand have brought challenges to the stable operation of distribution networks (DNs). The problem of the collaborative optimization of the charging scheduling of EVs and voltage control of the DN is intractable because the uncertainties of both EVs and the DN need to be considered. In this paper, we propose a deep reinforcement learning (DRL) approach to coordinate EV charging scheduling and distribution network voltage control. The DRL-based strategy contains two layers, the upper layer aims to reduce the operating costs of power generation of distributed generators and power consumption of EVs, and the lower layer controls the Volt/Var devices to maintain the voltage stability of the distribution network. We model the coordinate EV charging scheduling and voltage control problem in the distribution network as a Markov decision process (MDP). The model considers uncertainties of charging process caused by the charging behavior of EV users, as well as the uncertainty of uncontrollable load, system dynamic electricity price and renewable energy generation. Since the model has a dynamic state space and mixed action outputs, a framework of deep deterministic policy gradient (DDPG) is adopted to train the two-layer agent and the policy network is designed to output discrete and continuous control actions. Simulation and numerical results on the IEEE-33 bus test system demonstrate the effectiveness of the proposed method in collaborative EV charging scheduling and distribution network voltage stabilization.

Abstract The growing share of renewable energy has an increasingly significant impact on the electricity market. Locational marginal price (LMP), as a sensitive price signal, has a potential but considerable correlation with renewable energy generation. In this context, this paper focuses on the joint fluctuation laws between LMP and renewables consumed to generate electricity. At first, LMP and renewables consumption data from Independent System Operator New England (ISO‐NE) are collected and preprocessed. Then, through highly comparative time‐series analysis (hctsa), it is found that discrete symbolization features have effective description ability. Therefore, a coarse‐grained method is conducted to convert the real matrix data into a symbol vector based on numerical distribution analysis. Next, joint fluctuation complex networks are constructed according to the symbol vector. From a global perspective, the joint fluctuation network follows an exponential distribution and exhibits hierarchical modularity. From a local perspective, the joint fluctuation network follows a power‐law distribution. The research results of this paper provide a new perspective to understand the evolution of the electricity market with large‐scale renewable energy and have reference value for identifying the valuable joint fluctuation patterns and improving the LMP forecast results.

Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately by video electroencephalogram. In this study, we analyzed microstate epileptic electroencephalogram (EEG) to aid in the diagnosis and identification of epilepsy. We recruited patients with focal epilepsy and healthy participants from the Third Xiangya Hospital and recorded their resting EEG data. In this study, the EEG data were analyzed by microstate analysis, and the support vector machine (SVM) classifier was used for automatic epileptic EEG classification based on features of the EEG microstate series, including microstate parameters (duration, occurrence, and coverage), linear features (median, second quartile, mean, kurtosis, and skewness) and non-linear features (Petrosian fractal dimension, approximate entropy, sample entropy, fuzzy entropy, and Lempel–Ziv complexity). In the gamma sub-band, the microstate parameters as a model were the best for interictal epilepsy recognition, with an accuracy of 87.18%, recall of 70.59%, and an area under the curve of 94.52%. There was a recognition effect of interictal epilepsy through the features extracted from the EEG microstate, which varied within the 4~45 Hz band with an accuracy of 79.55%. Based on the SVM classifier, microstate parameters and EEG features can be effectively used to classify epileptic EEG, and microstate parameters can better classify epileptic EEG compared with EEG features.

The diameter prediction of silicon ingots in the Czochralski process is a complex problem because the process is highly nonlinear, time-varying, and time-delay. To address this problem, this paper presents a novel hybrid deep learning model, which combines the deep belief network (DBN), support vector regression (SVR), and the ant lion optimizer (ALO). Continuous restricted Boltzmann machines (CRBMs) are used in DBN for working with continuous industrial data. The feature aggregates the outputs from various DBNs through an SVR model. Additionally, the ALO algorithm is used for the parameter’s optimization of SVR. The newly developed model is verified with the actual production data and compared with the back propagation neural network (BPNN) and the SVR model. The simulation results demonstrate the availability and accuracy of the CRBM-DBN-ALO-SVR hybrid deep learning model.