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Abstract Background G-protein coupled receptors (GPCRs) are recognized as attractive targets for drug therapy. However, it remains poorly understood how GPCRs, except for a few chemokine receptors, regulate the progression of liver fibrosis. Here, we aimed to reveal the role of GPR65, a proton-sensing receptor, in liver fibrosis and to elucidate the underlying mechanism. Methods The expression level of GPR65 was evaluated in both human and mouse fibrotic livers. Furthermore, Gpr65-deficient mice were treated with either bile duct ligation (BDL) for 21 d or carbon tetrachloride (CCl4) for 8 weeks to investigate the role of GPR65 in liver fibrosis. A combination of experimental approaches, including Western blotting, quantitative real-time reverse transcription‑polymerase chain reaction (qRT-PCR), and enzyme-linked immunosorbent assay (ELISA), confocal microscopy and rescue studies, were used to explore the underlying mechanisms of GPR65’s action in liver fibrosis. Additionally, the therapeutic potential of GPR65 inhibitor in the development of liver fibrosis was investigated. Results We found that hepatic macrophages (HMs)-enriched GPR65 was upregulated in both human and mouse fibrotic livers. Moreover, knockout of Gpr65 significantly alleviated BDL- and CCl4-induced liver inflammation, injury and fibrosis in vivo, and mouse bone marrow transplantation (BMT) experiments further demonstrated that the protective effect of Gpr65 knockout is primarily mediated by bone marrow-derived macrophages (BMMs). Additionally, in vitro data demonstrated that Gpr65 silencing and GPR65 antagonist inhibited, while GPR65 overexpression and application of GPR65 endogenous and exogenous agonists enhanced the expression and release of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and transforming growth factor-β (TGF-β), all of which subsequently promoted the activation of hepatic stellate cells (HSCs) and the damage of hepatocytes (HCs). Mechanistically, GPR65 overexpression, the acidic pH and GPR65 exogenous agonist induced up-regulation of TNF-α and IL-6 via the Gαq-Ca2+-JNK/NF-κB pathways, while promoted the expression of TGF-β through the Gαq-Ca2+-MLK3-MKK7-JNK pathway. Notably, pharmacological GPR65 inhibition retarded the development of inflammation, HCs injury and fibrosis in vivo. Conclusions GPR65 is a major regulator that modulates the progression of liver fibrosis. Thus, targeting GPR65 could be an effective therapeutic strategy for the prevention of liver fibrosis.

Path planning is a key problem in the autonomous navigation of mobile robots and a research hotspot in the field of robotics. Harris Hawk Optimization (HHO) faces challenges such as low solution accuracy and a slow convergence speed, and it easy falls into local optimization in path planning applications. For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm’s possibility of falling into local optimal solutions during path planning. The experimental results of the comparison of the test functions show that the MIHHO algorithm is ranked first in terms of performance, with significant improvements in optimization seeking ability, convergence speed, and stability. Finally, MIHHO is applied to robot path planning, and the test results show that in four environments with different complexities and scales, the average path lengths of MIHHO are improved by 1.99%, 14.45%, 4.52%, and 9.19% compared to HHO, respectively. These results indicate that MIHHO has significant performance advantages in path planning tasks and helps to improve the path planning efficiency and accuracy of mobile robots.

In view of the current problems of slow crystallization rate, varying grain sizes, complex process conditions, and low safety in the preparation of CL−20/TNT cocrystal explosives in the laboratory, an opposite spray crystallization method is provided to quickly prepare ultrafine explosive cocrystal particles. CL−20/TNT cocrystal explosive was prepared using this method, and the obtained cocrystal samples were characterized by electron microscopy morphology, differential thermal analysis, infrared spectroscopy, and X-ray diffraction analysis. The effects of spray temperature, feed ratio, and preparation method on the formation of explosive cocrystal were studied, and the process conditions of the pneumatic atomization spray crystallization method were optimized. The crystal plane binding energy and molecular interaction forces between CL−20 and TNT were obtained through molecular dynamic simulation, and the optimal binding crystal plane and cocrystal mechanism were analyzed. The theoretical calculation temperature of the binding energy was preliminarily explored in relation to the preparation process temperature of cocrystal explosives. The mechanical sensitivity of ultrafine CL−20/TNT cocrystal samples was tested. The results showed that choosing acetone as the cosolvent, a spraying temperature of 30 °C, and a feeding ratio of 1:1 was beneficial for the formation and growth of cocrystal. The prepared CL−20/TNT cocrystal has a particle size of approximately 10 μm. The grain size is small, and the crystallization rate is fast. The impact and friction sensitivity of ultrafine CL−20/TNT cocrystal samples were significantly reduced. The experimental process conditions are simple and easy to control, and the safety of the preparation process is high, providing certain technical support for the preparation of high-quality cocrystal explosives.

3,4-bis(3-nitrofurazan-4-yl) furoxan (DNTF) is an explosive with excellent performance, and the use of DNTF as a high-energy component is of great significance for improving the comprehensive performance of weapons. To explore the effect of DNTF on low-melting-point molten carrier explosives, the compatibility between DNTF and other low-melting-point explosives was analyzed by differential scanning calorimetry, and mechanical sensitivity was tested. The compatibility and cohesive energy density between DNTF and other low-melting-point explosives were calculated by Materials Studio. The results showed that DNTF has good compatibility with most low-melting-point explosives, and the peak temperature change of the mixed system formed by melt-casting is not obvious. Among them, DNTF has the best compatibility with MTNP, TNT, and DNAN; moderate compatibility with DFTNAN and DNP; and the worst compatibility with DNMT. The sensitivity test results indicate that the combination of DNTF and TNT has the most significant reduction in mechanical sensitivity. DFTNAN and MTNP have better stability than DNTF and can generate strong interaction forces with DNTF. Other low-melting-point explosives mixed with DNTF have lower intermolecular forces than DNTF. The DNTF/MTNP system requires the most energy to phase change when heated compared to other mixed systems and is the least sensitive to heat. The DNTF/DNMT system has the lowest cohesive energy density and is the most sensitive to heat.

Immunosenescence refers to the immune system changes observed in individuals over 50 years old, characterized by diminished immune response and chronic inflammation. Recent investigations have highlighted similar immune alterations in patients with reduced kidney function. The immune system and kidney function have been found to be closely interconnected. Studies have shown that as kidney function declines, both innate and adaptive immunity are affected. Chronic kidney disease (CKD) patients exhibit decreased levels of naive and regular T cells, as well as naive and memory B cells, while memory T cell counts increase. Furthermore, research suggests that CKD and end-stage kidney disease (ESKD) patients experience early thymic dysfunction and heightened homeostatic proliferation of naive T cells. In addition to reduced thymic T cell production, CKD patients display shorter telomeres in both CD4+ and CD8+ T cells.Declining kidney function induces uremic conditions, which alter the intestinal metabolic environment and promote pathogen overgrowth while reducing diversity. This dysbiosis-driven imbalance in the gut microbiota can result in elevated production of uremic toxins, which, in turn, enter the systemic circulation due to compromised gut barrier function under uremic conditions. The accumulation of gut-derived uremic toxins exacerbates local and systemic kidney inflammation. Immune-mediated kidney damage occurs due to the activation of immune cells in the intestine as a consequence of dysbiosis, leading to the production of cytokines and soluble urokinase-type plasminogen activator receptor (suPAR), thereby contributing to kidney inflammation.In this review, we delve into the fundamental mechanisms of immunosenescence in CKD, encompassing alterations in adaptive immunity, gut dysbiosis, and an overview of the clinical findings pertaining to immunosenescence.

Background: Since 2019, Perampanel (PER) has been endorsed in China as an adjunctive treatment for focal seizures, both with and without impaired awareness, and for the transition from focal to bilateral tonic-clonic seizures. Limited research exists regarding the efficacy of PER in treating post-stroke epilepsy (PSE) in China. Empirical studies are essential to guide treatment protocols. We conducted a retrospective study to assess the efficacy and tolerability of PER in 58 PSE patients treated between October 2019 and July 2023. Method: This study encompassed 58 patients with PSE, treated with PER either as monotherapy or as part of adjunctive therapy, and underwent follow-up for a minimum duration of 6 months. The study assessed changes in seizure frequency, adverse events (AEs), drug retention rate, maintenance dose, and adverse reactions following PER treatment. Results: The study included 58 PSE patients, with 60.3% males and 39.7% females, ranging in age from 18 to 89, mostly within the 61–70 age group. Ischemic strokes constituted 58.6% of cases, while hemorrhagic strokes accounted for 41.4%. Focal seizures, either with or without impaired awareness, were noted in 62.1% of patients, and a transition from focal to bilateral tonic-clonic seizures was seen in 32.8%. The retention rates for PER at 3 and 6 months stood at 94.8% and 84.5% respectively, and the most commonly administered maintenance dose was 4 mg/day (41.28%). In the adjunctive therapy group, efficacy rates were 66.7% at 3 months and 78.6% at 6 months, compared to 80.0% at 3 months and 85.7% at 6 months in the monotherapy group. In the efficacy analysis, with a criterion of ≥50% reduction in seizure frequency, the overall efficacy rates at 3 and 6 months were 69.1% and 79.6%, respectively. Adverse reactions occurred in 46.6% of patients, primarily involving irritability and somnolence (both 27.6%), with no marked difference in incidence between the adjunctive and monotherapy groups (P > 0.05). Conclusion: PER exhibits favorable efficacy and tolerability in Chinese PSE patients, possibly at lower doses.

We point out a new mechanism giving rise to anomalous tau neutrino appearance at the near detectors of beam-focused neutrino experiments, without extending the neutrino sector. The charged mesons (π±,K±) produced and focused in the target-horn system can decay to a (neutrino-philic) light mediator via the helicity-unsuppressed three-body decays. If such a mediator carries non-vanishing hadronic couplings, it can also be produced via the bremsstrahlung of the incident proton beam. The subsequent decay of the mediator to a tau neutrino pair results in tau neutrino detection at the near detectors, which is unexpected under the standard three-flavor neutrino oscillation paradigm. We argue that the signal flux from the charged meson decays can be significant enough to discover the light mediator signal at the on-axis liquid-argon near detector of the DUNE experiment, due to the focusing of charged mesons. In addition, we show that ICARUS-NuMI, an off-axis near detector of the NuMI beam, as well as DUNE, can observe a handful of tau neutrino events induced by beam-proton bremsstrahlung.

Abstract Background Hepatic fibrosis is a common consequence of chronic liver diseases without approved antifibrotic therapies. Long noncoding RNAs (lncRNAs) play an important role in various pathophysiological processes. However, the functions of certain lncRNAs involved in mediating the antifibrotic role remain largely unclear. Methods The RNA level of lnc-High Expressed in Liver Fibrosis (Helf) was detected in both mouse and human fibrotic livers. Furthermore, lnc-Helf-silenced mice were treated with carbon tetrachloride (CCl4) or bile duct ligation (BDL) to investigate the function of lnc-Helf in liver fibrosis. Results We found that lnc-Helf has significantly higher expression in human and mouse fibrotic livers as well as M1 polarized hepatic macrophages (HMs) and activated hepatic stellate cells (HSCs). In vivo studies showed that silencing lnc-Helf by AAV8 vector alleviates CCl4- and BDL-induced hepatic inflammation and fibrosis. Furthermore, in vitro experiments revealed that lnc-Helf promotes HSCs activation and proliferation, as well as HMs M1 polarization and proliferation in the absence or presence of cytokine stimulation. Mechanistically, our data illustrated that lnc-Helf interacts with RNA binding protein PTBP1 to promote its interaction with PIK3R5 mRNA, resulting in increased stability and activating the AKT pathway, thus promoting HSCs and HMs activation and proliferation, which augments hepatic inflammation and fibrosis. Conclusion Our results unveil a lnc-Helf/PTBP1/PIK3R5/AKT feedforward, amplifying signaling that exacerbates the process of hepatic inflammation and fibrosis, thus providing a possible therapeutic strategy for hepatic fibrosis.

Abstract Dihydroquercetin (DHQ) is a natural occurring dihydroflavonol that has strong antioxidant and antibacterial activities. However, its application is limited due to its poor solubility. This study aims to improve the aqueous solubility of DHQ by complexing DHQ with β-cyclodextrin (β-CD) to boost its biological activity. DHQ was encapsulated with β-CD by freeze drying at a 1:1-M ratio. The structure of DHQ/β-CD complex prepared was elucidated by using Fourier transform infrared spectroscopy, differential scanning calorimetry, X-ray diffraction, scanning electron microscopy, and 1H nuclear magnetic resonance (1H NMR). In addition, molecular docking further revealed two energetically favorable conformations of the DHQ/β-CD complex, in which DHQ interacted with β-CD via hydrogen bonds. Experimental results showed that the solubility of the DHQ increased 22.63-fold by encapsulating with β-CD. Also the dissolution rate, antioxidant activity and antibacterial activity of the DHQ were significantly improved by encapsulating. The encapsulating with β-CD solves the problem of the poor aqueous solubility of DHQ, and broadens the path for a more optimal use of the health promoting effect of DHQ in pharmaceutical and food products.

This paper proposes an improved bubble entropy algorithm called Improved Hierarchical Refined Composite Multiscale Multichannel Bubble Entropy (IHRCMMCBE) to characterize the fault characteristics of rotating machinery. By introducing the refined composite multiscale analysis algorithm, the improved hierarchical decomposition algorithm, and the multi-channel data analysis method, the bubble entropy algorithm can more fully characterize the fault characteristics. Then, this method is combined with the machine learning algorithm to realize automatic fault diagnosis of rotating machinery. Multi-Cluster Feature Selection (MCFS) is used for feature selection to screen out useless features and improve feature extraction efficiency and feature quality. The MCFS method has excellent performance and does not need parameters, so it is suitable for adaptive fault diagnosis. Meanwhile, the EigenClass classifier using the concept of generalized eigenvalue is used to realize fault recognition. Besides, the recently proposed Gorilla Troops Optimizer (GTO) algorithm is used to improve EigenClass, and a GTO-optimized EigenClass (GTO-EigenClass) classifier is proposed to adaptively select parameters and achieve adaptive fault identification. The proposed fault diagnosis method of rotating machinery based on the improved bubble entropy is independent of data and application scenarios and is hardly affected by subjective factors. The ablation experiment proves the effectiveness of each improvement of bubble entropy. Also, the performance verification experiments and the comparative experiments prove that the method can effectively characterize different types of rotating machinery, different fault types, fault degrees, and composite faults, and it can achieve higher calculation efficiency and classification accuracy. When the sample length is 1024, the average accuracy of bearing and gearbox fault identification can reach 97.85 % and 97.57 % respectively, and when the sample length is 2048, the identification accuracy can reach 100 %.

Abstract A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work.

Abstract Background There are few studies on acute-on-chronic liver failure (ACLF) in patients with recompensated cirrhosis. This study was aimed to investigate the clinical features of ACLF patients with recompensated cirrhosis. Methods A total of 461 ACLF patients were enrolled and divided into three groups: compensated, recompensated, and decompensated cirrhosis with ACLF. The baseline clinical data and 1-year survival rates were compared among the three groups. Results Compared with the decompensated group, in the recompensated group, the levels of hemoglobin, albumin, and serum sodium were significantly higher and the white blood cell count, international normalized ratio, and incidence of respiratory failure were significantly lower; there were no evident differences in other organ failures. The proportion of patients with ACLF grade 3 and 1-year survival rates significantly differed between the two groups. Conversely, compared with the compensated group, in the recompensated group, the platelet and total bilirubin levels were significantly lower and the proportion of patients with ACLF grade 1 was significantly higher. However, other clinical indicators or 1-year survival rates did not significantly differ between the two groups. Conclusions Compared with patients who developed ACLF with decompensated cirrhosis, those who developed ACLF with recompensated cirrhosis had a less severe condition, lower incidence of respiratory failure, and better 1-year prognosis. However, the baseline clinical features and prognosis were similar between ACLF patients with recompensated and compensated cirrhosis. Trial registration Chinese clinical trials registry: ChiCTR1900021539.

Abstract Smart sensors with excellent performance are accelerating the development of biomedicine and the Internet of Energy. Nanodielectrics exhibit unique electrical and mechanical properties. As the predominant materials in optical magnetic field sensor (MFS), they can not only exert the anti‐interference of optical sensing, but improve the measuring characteristics of optical sensors. For instance, the optical fibre quantum probe for the magnetic field can obtain a higher sensitivity of 0.57 nT/Hz1/2, while the measurement range of the sensor that uses Co‐doped ZnO nanorods as cladding is 17–180 mT. Here, these exciting recent achievements in the realm of optical sensing methods for magnetic field detection are reviewed, with a focus on nanodielectrics, which provide an emerging opportunity to achieve higher sensitivity and a wider measurement range of MFS.

Abstract Hepatic leukemia factor (HLF) is aberrantly expressed in human malignancies. However, the role of HLF in the regulation of ovarian cancer (OC) remains unknown. Herein, we reported that HLF expression was upregulated in OC tissues and ovarian cancer stem cells (CSCs). Functional studies have revealed that HLF regulates OC cell stemness, proliferation, and metastasis. Mechanistically, HLF transcriptionally activated Yes-associated protein 1 (YAP1) expression and subsequently modulated the Hippo signaling pathway. Moreover, we found that miR-520e directly targeted HLF 3′-UTR in OC cells. miR-520e expression was negatively correlated with HLF and YAP1 expression in OC tissues. The combined immunohistochemical (IHC) panels exhibited a better prognostic value for OC patients than any of these components alone. Importantly, the HLF/YAP1 axis determines the response of OC cells to carboplatin treatment and HLF depletion or the YAP1 inhibitor verteporfin abrogated carboplatin resistance. Analysis of patient-derived xenografts (PDXs) further suggested that HLF might predict carboplatin benefits in OC patients. In conclusion, these findings suggest a crucial role of the miR-520e/HLF/YAP1 axis in OC progression and chemoresistance, suggesting potential therapeutic targets for OC.

A correction to this paper has been published: https://doi.org/10.1007/JHEP04(2023)013

Because LiFePO4 batteries are widely used in the energy storage system, their safety has received a great deal of research. If an internal short circuit (ISC) in a Li-ion battery energy storage system leads to thermal runaway, it will pose an uncontrollable hazard. Therefore, there is a proposed method in this study that is a one-dimensional voltage-correlated convolutional neural network (1DVCNN) to detect and localize ISC faults in time, which is a combination of feature extraction, correlation analysis, and convolutional neural networks. First, the voltage signal of the LiFePO4 battery collected in the energy storage system is then converted into a Pearson Correlation Coefficient (PCC) by correlation analysis of the data. Specific PCCs are then selected as features of a one-dimensional convolutional neural network (1DCNN) to diagnose faults. Finally, the fault dataset is used to train the model and the results are then validated against the untrained fault data. In this paper, the ISC faults are modeled by both building an actual training platform for lithium batteries and MATLAB simulations to obtain the dataset used to train the proposed model. By comparing the accuracy of the training results of the models using different features, it is found that 1DVCNN is able to detect the ISC fault in the energy storage system in a more timely and effective manner than in other studies.

Abstract We present a comprehensive study on how to distinguish the properties of heavy dijet resonances at hadron colliders. A variety of spins, chiral couplings, charges, and QCD color representations are considered. Distinguishing the different color representations is particularly difficult at hadron colliders. To determine the QCD color structure, we consider a third jet radiated in a resonant dijet event. We show that the relative rates of three-jet versus two-jet processes are sensitive to the color representation of the resonance. We also show analytically that the antennae radiation pattern of soft radiation depends on the color structure of dijet events and develops an observable that is sensitive to the antennae patterns. Finally, we exploit a Convolutional Neural Network with Machine Learning techniques to differentiate the radiation patterns from different colored resonances and find encouraging results to discriminate them. We demonstrate our results numerically at a 14 TeV LHC, and the methodology presented here should be applicable to other future hadron colliders.

As global climate change intensifies, nations around the world are implementing policies aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market-based tool. China has launched carbon-trading markets in several cities, achieving significant trading volumes. Carbon-trading mechanisms encompass cap-and-trade markets and voluntary markets, influenced by various factors, including policy changes, economic conditions, energy prices, and climate fluctuations. The complexity of these factors, coupled with the nonlinear and non-stationary nature of carbon prices, makes forecasting a substantial challenge. This paper proposes a dynamic weight hybrid forecasting model based on a dual sliding window approach, effectively integrating multiple forecasting models such as LSTM, Random Forests, and LASSO. This model facilitates a thorough analysis of the influences of policy, market dynamics, technological advancements, and climatic conditions on carbon pricing. It serves as a potent tool for predicting carbon market price fluctuations and offers valuable decision support to stakeholders in the carbon market, ultimately aiding in the global efforts towards emission reduction and achieving sustainable development goals.