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Heteroatom doping is considered an effective method to substantially improve the electrochemical performance of Ti3C2Tx MXene for supercapacitors. Herein, a facile and controllable strategy, which combines heat treatment with phosphorous (P) doping by using sodium phosphinate (NaH2PO2) as a phosphorus source, is used to modify Ti3C2Tx. The intercalated ions from NaH2PO2 act as “pillars” to expand the interlayer space of MXene, which is conducive to electrolyte ion diffusion. On the other hand, P doping tailors the surface electronic state of MXene, optimizing electronic conductivity and reducing the free energy of H+ diffusion on the MXene surface. Meanwhile, P sites with lower electronegativity owning good electron donor characteristics are easy to share electrons with H+, which is beneficial to charge storage. Moreover, the adopted heat treatment replaces –F terminations with O-containing groups, which enhances the hydrophilicity and provides sufficient active sites. The change in surface functional groups increases the content of high valence-stated Ti with a high electrochemical activity that can accommodate more electrons during discharge. Synergistic modification of interlayer structure and chemical state improves the possibility of Ti3C2Tx for accommodating more H+ ions. Consequently, the modified electrode delivers a specific capacitance of 510 F g−1 at 2 mV s−1, and a capacitance retention of 90.2% at 20 A g−1 after 10,000 cycles. The work provides a coordinated strategy for the rational design of high-capacitance Ti3C2Tx MXene electrodes.

Abstract Glioma, the most common primary malignant tumor of the central nervous system, lacks effective targeted therapies. This study investigates the role of SOAT1, a key gene involved in cholesterol esterification, in glioma prognosis and its association with ferroptosis. Although the impact of SOAT1 on glioma prognosis has been recognized, its precise mechanism remains unclear. In this study, we demonstrate that inhibiting SOAT1 increases the sensitivity of glioma cells to ferroptosis, both in vitro and in vivo. Mechanistically, SOAT1 positively modulates the expression of SLC40A1, an iron transporter, resulting in enhanced intracellular iron outflow, reduced intracellular iron levels, and subsequent disruption of ferroptosis. Importantly, we find that SOAT1 regulates ferroptosis independently of SREBPs, which are known to be involved in ferroptosis regulation. Furthermore, we identify the involvement of the PI3K-AKT-mTOR signaling pathway in mediating the regulatory effects of SOAT1 on SLC40A1 expression and ferroptosis sensitivity. These findings highlight the contribution of intracellular signaling cascades in the modulation of ferroptosis by SOAT1. We show that inhibiting SOAT1 enhances the efficacy of radiotherapy in gliomas, both in vitro and in vivo, by promoting sensitivity to ferroptosis. This suggests that targeting SOAT1 could potentially improve therapeutic outcomes for glioma patients. In summary, this study uncovers the pivotal role of SOAT1 as a link between cholesterol esterification and ferroptosis in glioma. Our findings underscore the potential of SOAT1 as a promising clinical therapeutic target, providing new avenues for the development of effective treatments for glioma. Further research is warranted to unravel the complete regulatory mechanisms of SOAT1 and explore its clinical applications.

Phosphoric acid is used as a chemical activator to prepare coconut shell carbon (PCSC), and for investigating rhodamine B (RhB) adsorption performance. The optimal conditions for the preparation of PCSC (calcined temperature, phosphoric acid concentration), and the influence of adsorption conditions (concentration, pH, etc.) on RhB and the recovery performance of optimal carbon are investigated. Experimental results show that when the amount of PCSC (600 °C, 2 h) is 0.2 g, the initial RhB concentration is 10 mg/L, pH = 6, and the adsorption time is 30 min, it can have 95.84% RhB adsorption efficiency. Liquid ultraviolet spectroscopy also supports this adsorption performance. Characterization data showed that hydroxyl and ester groups, aromatic structures, and PO43− existed on the surface of PCSC, and the amount decreased with increasing calcined temperature. PCSC has a BET (N2) surface area of 408.59 m2/g and has a micropore distribution, EDS-detected P content is 3.91%. SEM showed that the PCSC formed micropores which could better adsorb RhB. The kinetic and thermodynamic analysis of the adsorption of RhB by PCSC showed that the adsorption process was in accord with quasi-secondary kinetic equations and ΔGθ was between −1.65 and −18.75 kJ/mol. The adsorption was a physical adsorption and a spontaneous endothermic reaction, and the obtained PCSC sorption isotherms were classified as Langmuir-type. The RhB adsorption mechanism on PCSC includes pore diffusion, hydrogen bonding, and π−π conjugation. The PCSC prepared by H3PO4 modification has superior adsorption and recycling performance for RhB, providing a reference for the preparation of other biomass carbon materials for the treatment of dye wastewater.

Abstract Coalescing supermassive black hole binaries (SMBHBs) are the primary source candidates for low frequency gravitational wave (GW) detections, which could bring us deep insights into galaxy evolutions over cosmic time and violent processes of spacetime dynamics. Promising candidates had been found based on optical and X-ray observations, which claims for new and ready-to-use GW detection approaches before the operations of space-borne antennas. We show that, satellite laser ranging (SLR) missions could serve as probes of coalescing SMBHBs through the GW-induced resonant effects. Lasting and characteristic imprints caused by such resonances in the residual distances or accelerations from SLR measurements are studied, and the detection SNR is analyzed with both the current and future improved ranging precisions. Within redshift $$z \sim 1$$ z ∼ 1 , the threshold SNR = 5 requires 1–2 years of accumulated data for the current precision and months of data for improved precision, which are workable for the data processing of SLR missions. Meanwhile, joint detections with multiple SLR missions could further improve the total SNR and the confidence level. Such a detection scheme could fulfill the requirement of a tentative SMBHB probe during the preparing stage of LISA and Taiji, and it requires no further investment to any new and advanced facilities. It is also worthwhile to look back and re-process the archived data from the past decades, in where resonant signals from SMBHBs might be hidden.

Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire–Landrace crossbred sows. We selected 200 Yorkshire–Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs’ backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire–Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement.

Purpose: The aim of present study was to investigate the efficiency of 18F-FDG uptake in predicting major pathological response (MPR) in resectable non-small cell lung cancer (NSCLC) patients with neoadjuvant immunotherapy. Methods: A total of 104 patients with stage I-IIIB NSCLC were retrospectively derived from National Cancer Center of China, of which 36 cases received immune checkpoint inhibitors (ICIs) monotherapy (I-M) and 68 cases with ICI combination therapy (I-C). 18F-FDG PET-CT scans were performed at baseline and after neoadjuvant therapy (NAT). Receiver‐operating characteristic (ROC) curve analyses were conducted and area under ROC curve (AUC) was calculated for biomarkers including maximum standardized uptake value (SUVmax), inflammatory biomarkers, tumor mutation burden (TMB), PD-L1 tumor proportion score (TPS) and iRECIST. Results: Fifty-four resected NSCLC tumors achieved MPR (51.9%, 54/104). In both neoadjuvant I-M and I-C cohorts, post-NAT SUVmax and the percentage changes of SUVmax (ΔSUVmax%) were significantly lower in the patients with MPR versus non-MPR (p < 0.01), and were also negatively correlated with the degree of pathological regression (p < 0.01). The AUC of ΔSUVmax% for predicting MPR was respectively 1.00 (95% CI: 1.00–1.00) in neoadjuvant I-M cohort and 0.94 (95% CI: 0.86–1.00) in I-C cohort. Baseline SUVmax had a statistical prediction value for MPR only in I-M cohort, with an AUC up to 0.76 at the threshold of 17.0. ΔSUVmax% showed an obvious advantage in MPR prediction over inflammatory biomarkers, TMB, PD-L1 TPS and iRECIST. Conclusion: 18F-FDG uptake can predict MPR in NSCLC patients with neoadjuvant immunotherapy.

In the optimization scheduling of power systems containing renewable energy, to ensure that the scheduling results have higher security and better economy, this study proposes a multi-objective forecasting, scenario generation, and decision scheduling integrated stochastic optimal scheduling method. Firstly, to improve the accuracy and stability of wind-photovoltaic power forecasting, a novel multi-objective wind-photovoltaic forecasting model is proposed based on the Laguerre polynomial, pseudo-inverse learning, and hybrid multi-objective Runge-Kutta algorithm (HMORUN). Secondly, to deal with wind-photovoltaic uncertainty, scenarios with representative wind-photovoltaic uncertainty characteristics are generated by scenario generation and reduction techniques using wind-photovoltaic power forecast results. Finally, considering wind-photovoltaic output fluctuations, the degree of source-load matching, wind-photovoltaic utilization, and system economic efficiency factors, with the objective of maximizing the tracking of the load curve and minimizing system economic costs, a stochastic optimized scheduling model for power systems is established. This study uses HMORUN as a solution tool for the multi-objective stochastic optimization scheduling problem (MOSSP). This study uses constraint repair techniques to deal with the complex constraints of the MOSSP model to avoid system load shedding and minimize wind and photovoltaic generation curtailment. To verify the effectiveness of the proposed model, a 10-generator power system, including a wind farm and a photovoltaic plant, is used as a test case for simulation experiments and compared with other multi-objective scheduling models. The experimental results show that the stability of the proposed scheduling model has been improved by a maximum of 0.4%, and the economy has been improved by a maximum of 7.5%.

Abstract Background Traditional Chinese medicine (TCM) posits that Chinese medicinal materials can only be clinically used after being processed and prepared into decoction pieces. Schisandra Chinensis Fructus (derived from the dried and mature fruits of Schisandra chinensis (Turcz.) Baill.) has been used as a traditional antiasthmatic, kidney strengthening, and hepatoprotective agent for 2000 years. The results of previous research show that decoction pieces of wine-steamed Schisandra chinensis (WSC) are more effective than raw decoction pieces of Schisandra chinensis (RSC) for treating cough and asthma. Steaming with wine was demonstrated to promote the dissolution of ingredients. However, the relationship between the changes in the components of the decoction pieces of WSC and the therapeutic effect remains unclear. Methods The efficacies of decoctions of RSC and WSC were compared using allergic asthma rats. The potential bioactive components in the serum of the WSC treatment group and the changes in the chemical composition of the RSC decoction pieces before and after wine steaming were determined by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS) and ultra-high-performance liquid chromatography tandem mass spectrometry (UPLC H-CLASS XEVO TQD) to speculate quality markers (Q-markers) related to the efficacy of WSC, which were subsequently verified based on a zebrafish inflammation model. Results Steaming RSC decoction pieces with wine was found to promote improvement of allergic asthma. Reverse tracing of 22 components detected in the serum of the high dose group of WSC (WSC-H) resulted in 12 ingredients being finally designated as potential effective components. Among these ingredients, 5 components, Schisandrin, Schisandrol B, Schisandrin A, Schisandrin B, and Gomisin D, had higher dissolution rates than RSC after steaming with wine. Validation by an inflammatory zebrafish model showed that these 5 ingredients had a dose-dependent effect and were therefore Q-markers for WSC in the treatment of allergic asthma. Conclusion In this study, changes in the components of decoction pieces of RSC and WSC and Q-markers related to WSC efficacy were identified, providing valuable information for expanding the application of WSC and establishing a specific quality standard for WSC.

This paper provides a comprehensive overview of affective computing systems for facial expression recognition (FER) research in naturalistic contexts. The first section presents an updated account of user-friendly FER toolboxes incorporating state-of-the-art deep learning models and elaborates on their neural architectures, datasets, and performances across domains. These sophisticated FER toolboxes can robustly address a variety of challenges encountered in the wild such as variations in illumination and head pose, which may otherwise impact recognition accuracy. The second section of this paper discusses multimodal large language models (MLLMs) and their potential applications in affective science. MLLMs exhibit human-level capabilities for FER and enable the quantification of various contextual variables to provide context-aware emotion inferences. These advancements have the potential to revolutionize current methodological approaches for studying the contextual influences on emotions, leading to the development of contextualized emotion models.

To solve the serious problem of residual swirl of flue gas and gas temperature deviation between two sides at furnace outlet in a 330MW tangentially fired boiler, a numerical simulation on combustion under multiple conditions was proposed. The effect of SOFA nozzle tilt angle on the residual swirl and gas temperature deviation of furnace exit was carefully studied. In addition, a monitoring system for cross-section temperature fields at furnace exit was designed. Four flame detectors were installed on a section of furnace exit. Through processing flame radiative image, the temperature fields of the section were detected. Combined with field experimental conditions, the SOFA nozzle vertical angle experiment was carried out with detection system. The results show that as SOFA nozzle swings downward, it is beneficial to weaken the residual swirl of flue gas and reduce the gas temperature deviation of boiler.

Over the past few decades, drug-induced liver damage (DILI) has become a serious public health problem due to drug abuse. Among multifarious reactive oxygen species, mounting evidence attests that ClO− has been used as a potential biomarker in DILI. In this work, a new “turn-on” fluorescent probe 1 was designed and synthesized by modifying 4′-hydroxybiphenyl-4-carbonitrile (dye 2) with N, N-dimethylthiocarbamate as a response site for detecting ClO−. Probe 1 displayed a low detection limit (72 nM), fast response time (30 s), wide pH operating range (6–8), great tissue penetration, large Stokes shift (125 nm) and 291-fold fluorescence enhancement at 475 nm in the mapping of ClO−. Probe 1 could trace amounts of exogenous and endogenous ClO− with high sensitivity in MCF-7 cells and HeLa cells. Expectantly, the fluoxetine-induced liver injury model is successfully established, and probe 1 has been used for detecting the fluctuation of ClO− levels in the mouse model of fluoxetine-induced liver injury. All in all, probe 1 with its high specificity, good biological compatibility and liver tissue penetration ability is expected to assist with the early diagnosis of DILI and the clinical screening of various new drugs. We expect that probe 1 could be efficiently used as a powerful molecular tool to predict clinical DILI and explore molecular mechanisms between molecules and disease.

Abstract Bushings are served as an important component of the power transformers; it's of great significance to keep the bushings in good insulation condition. The infrared images of the bushing are proposed to diagnose the fault with the combination of image segmentation and deep learning, including object detection, fault region extraction, and fault diagnosis. By building an object detection system with the frame of Mask Region convolutional neural network (CNN), the bushing frame can be exactly extracted. To distinguish the fault region of bushings and the background, a simple linear iterative clustering‐based pulse coupled neural network is proposed to improve the fault region segmentation performance. Then, two infrared image feature parameters, the relative position and area, are explored to classify fault type effectively based on the K‐means cluster technique. With the proposed joint algorithm on bushing infrared images, the accuracy reaches 98%, compared with 44% by the conventional CNN classification method. The integrated algorithm provides a feasible and advantageous solution for the field application of bushing image‐based diagnosis.

The increasing household loads make series arc faults more complex, which are difficult to be detected by traditional circuit breakers and lead to the frequent occurrence of residential fire accidents. In this paper, a comprehensive approach of complex load recognition and series arc detection is proposed on the basis of principal component analysis and support vector machine (PCA-SVM) combination model. Several typical loads were selected and analyzed, especially nonlinear and complex loads like power electronics load and multi-state load. Three time-domain parameters, maximum slip difference (MSD), zero current period (ZCP) and maximum Euclidean distance (MED), and nine frequency-domain harmonics information are collected to complex waveforms. To decrease the computation cost and further to enhance the response velocity, all the time-domain and frequency-domain information were blended and dimensionally reduced to three parameters by principal component analysis (PCA). Prior to the series arc detection, load recognition is trained and completed with the artificial intelligence (AI) algorithm. At last, the comprehensive model of load recognition and series arc detection is achieved based on a support vector machine (SVM). The accuracy of load recognition and series arc detection reaches 99.1% and 99.3%, respectively, demonstrating the excellent performances of the intelligent approach to diagnose the series arcing activities in modern household applications.

Risk stratification using molecular features could potentially help distinguish indolent from aggressive prostate cancer (PCa). Mutations in isocitrate dehydrogenase (IDH) acquire an abnormal enzymatic activity, resulting in the production of 2-hydroxyglutarate and alterations in cellular metabolism, histone modification, and DNA methylation. Mutant IDH1 has been identified in various human malignancies, and IDH1R132H constituted the vast majority of mutational events of IDH1. Most recent studies suggested that IDH1 mutations define a methylator subtype in PCa. However, the function of IDH1R132H in PCa development and progression is largely unknown. In this study, we showed that the prevalence of IDH1R132H in Chinese PCa patients is 0.6% (2/336). Of note, IDH1R132H-mutant PCa patients lacked other canonical genomic lesions (e.g., ERG rearrangement, PTEN deletion) that are common in most other PCa patients. The in vitro experiment suggested that IDH1R132H can promote proliferation of benign prostate epithelial cell RWPE-1 when under the situation of low cytokine. It could also promote migration capacity of RWPE-1 cells. Mechanistically, IDH1R132H was an important regulator of insulin-like growth factor 1receptor (IGF1R) by downregulating a set of microRNAs (miR-141-3p, miR-7-5p, miR-223-3p). These microRNAs were repressed by the alteration of epigenetic modification to decrease the enrichment of active marker H3K4me3 or to increase repressive marker H3K27me3 at their promoters. Collectively, we proposed a novel model for an IDH1R132H-microRNAs-IGF1R regulatory axis, which might provide insight into the function of IDH1R132H in PCa development.

Power transformer bushings withstand great electrical and mechanical stress during high voltage and high current working conditions. Sealing failure poses a great threat to the long-term and reliable operation of the bushing and power transformer; however, the criterion to evaluate the sealing status of a bushing caused by mechanical problems is still lacking. In this paper, a transformer bushing model is established to gain theoretical insight into the relationship between temperature and pressure of a compact multilayer bushing. To evaluate the bushing mechanical status, different sealing conditions are tested based on the temperature and pressure monitoring within the physical 110 kV bushing. The results show that mechanical sealing failure can be diagnosed when the fluctuation of the oil pressure value exceeds the theoretical curve in steady state by 3 kPa. With different reliability coefficients, gas leakage and oil leakage are available to be further determined. The primary and auxiliary criteria based on oil pressure and its gradient are proposed to evaluate comprehensively the actual sealing condition of the bushing, and a wireless oil pressure module is developed at the bottom valve, which is quite beneficial to field online application. It is promising to extend the online mechanical monitoring and diagnosis to oil-immersed power equipment.