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Lunar grabens are the largest tensional linear structures on the Moon. In this paper, 17 grabens were selected to investigate the dips and displacement–length ratios (γ) of graben-bounding faults. Several topographic profiles were generated from selected grabens to measure their rim elevation, width and depth through SLDEM2015 (+LOLA) data. The differences in rim elevation (∆h) and width (∆W) between two topographic profiles on each graben were calculated, yielding 146 sets of data. We plotted ∆h vs. ∆W for each and calculated the dip angle (α) of graben-bounding faults. A dip of 39.9° was obtained using the standard linear regression method. In order to improve accuracy, large error data were removed based on error analysis. The results, 49.4° and 52.5°, were derived by the standard linear regression and average methods, respectively. Based on the depth and length of grabens, the γ value of the graben-bounding normal fault is also studied in this paper. The γ value is 3.6 × 10−3 for lunar normal faults according to the study of grabens and the Rupes Recta normal fault. After obtaining the values of α and γ, the increase in lunar radius indicated by the formation of grabens was estimated. We suggest that the lunar radius has increased by approximately 130 m after the formation of grabens. This study could aid in the understanding of normal fault growth and provide important constraints on the thermal evolution of the Moon.

Tectonic features on the Moon can reflect the state of stress during the formation of the structure, and sinuous rilles can provide further insight into the tectonic-thermal evolution of the Moon. Manual visual interpretation is the primary method for extracting these linear structures due to their complex morphology. However, extracting these features from the vast amount of lunar remote sensing data requires significant time and effort from researchers, especially for small-scale tectonic features, such as wrinkle ridges, lobate scarps, and high-relief ridges. In order to enhance the efficiency of linear structure detection, this paper conducts research on the automatic detection method of linear structures using sinuous rilles as an example case. In this paper, a multimodal semantic segmentation method, “Sinuous Rille Network (SR-Net)”, for detecting sinuous rilles is proposed based on DeepLabv3+. This method combines advanced techniques such as ECA-ResNet and dynamic feature fusion. Compared to other networks, such as PSPNet, ResUNet, and DeepLabv3+, SR-Net demonstrates superior precision (95.20%) and recall (92.18%) on the multimodal sinuous rille test set. The trained SR-Net was applied in detecting lunar sinuous rilles within the range of 60°S to 60°N latitude. A new catalogue of sinuous rilles was generated based on the results of the detection process. The methodology proposed in this paper is not confined to the detection of sinuous rilles; with further improvements, it can be extended to the detection of other linear structures.

Reiner Gamma is a potential target for low-orbiting spacecraft or even surface-landed missions in the near future. Unfortunately, thus far, no comprehensive low-altitude (below 20 km) or surface measurements of the magnetic field, magnetic source and plasma environment have been made post-Apollo to complement and complete our understanding of the solar wind interaction with lunar magnetic anomalies and swirl formation. Acquiring the detailed geological knowledge of the Reiner Gamma region is significant for the above scientific targets. In this study, the following research work in the lunar Reiner Gamma magnetic anomaly region was carried out for the regional geological investigation: (1) topographic and geomorphologic analysis; (2) element, mineral, and sequence analysis; and (3) a 1:10,000 regional geological map analysis. Our work helps define measurement requirements for possible future low-orbiting or surface-landed missions to the Reiner Gamma area or similarly magnetized regions of the lunar surface.

The capabilities of cloud-resolving numerical models, observational instruments and cloud seeding have improved greatly over recent years in China. The subject of this review focuses on the main progresses made in China in the areas of cloud modeling, field observations, aerosol–cloud interactions, the effects of urbanization on cloud and precipitation, and weather modification.Well-equipped aircraft and ground-based advanced Doppler and polarized radars have been rapidly applied in cloudseeding operations. The combined use of modern techniques such as the Global Positioning System, remote sensing, and Geographical Information Systems has greatly decreased the blindness and uncertainties in weather-modification activities.Weather-modification models based on state-of-the-art cloud-resolving models are operationally run at the National Weather Modification Centre in China for guiding weather-modification programs.Despite important progress having been made, many critical issues or challenges remain to be solved, or require stronger scientific evidence and support, such as the chain of physical events involved in the effects induced by cloud seeding. Current important progresses in measurements and seeding techniques provide the opportunity and possibility to reduce these deficiencies. Long-term scientific projects aimed at reducing these key uncertainties are extremely urgent and important for weather-modification activities in China.

Background The machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP. Methods A total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models. Results The deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet ( p  = 0.009). Conclusion Based on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.

This study examined the role of vaginolysin (VLY), a virulence factor of the bacterium Gardnerella vaginalis (GV), in bacterial vaginosis (BV). In a group of 112 women with BV (diagnosis on the Nugent scale ≥7 points) and 122 control cases with normal microbiota, VLY levels, the state of the vaginal microecology (colposcopy, laboratory markers, pH), GV genotypes (clades 1–4), and clinical symptoms were assessed. It was found that GV also occurs in healthy women, but VLY levels are significantly higher in BV and correlate with inflammatory markers (e.g., leukocyte esterase) and symptom severity. However, the relationship is nonlinear: low and moderate VLY levels have little effect on symptoms, while high levels cause a sharp increase in symptoms. Thus, VLY is potentially important for the pathophysiology and clinical assessment of BV.

Thyroid dysfunction (TD) induced by programmed death-1 (PD-1) or programmed cell death-ligand 1 (PD-L1) immune checkpoint inhibitors (ICIs) has been widely reported. However, the effects of ICI-induced TD on the survival of patients with esophageal squamous cell carcinoma (ESCC) have not been described. Herein, a retrospective study was conducted, which 82 patients with advanced metastatic or recurrent ESCC treated with camrelizumab were enrolled. Twenty patients (24.4%) experienced TD during camrelizumab treatment with or without chemotherapy. The median onset time of TD was 1.7 months. The incidence of TD was 35.6% in patients who previously received thoracic radiotherapy versus 10.8% in patients who did not (P =0.009). Patients with TD had significantly longer median progression-free survival (5.5 months vs 3.5 months, P =0.035) and overall survival (26.7 months vs 11.5 months, P <0.001). TD is frequently observed in ESCC patients treated with camrelizumab and especially in patients who received radiotherapy previously. ESCC patients with TD during ICIs treatment often have better prognosis.

Coronavirus disease 2019 (COVID-19) is caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 1 – 3 and individuals with COVID-19 have symptoms that can be asymptomatic, mild, moderate or severe 4 , 5 . In the early phase of infection, T- and B-cell counts are substantially decreased 6 , 7 ; however, IgM 8 – 11 and IgG 12 – 14 are detectable within 14 d after symptom onset. In COVID-19-convalescent individuals, spike-specific neutralizing antibodies are variable 3 , 15 , 16 . No specific drug or vaccine is available for COVID-19 at the time of writing; however, patients benefit from treatment with serum from COVID-19-convalescent individuals 17 , 18 . Nevertheless, antibody responses and cross-reactivity with other coronaviruses in COVID-19-convalescent individuals are largely unknown. Here, we show that the majority of COVID-19-convalescent individuals maintained SARS-CoV-2 spike S1- and S2-specific antibodies with neutralizing activity against the SARS-CoV-2 pseudotyped virus, and that some of the antibodies cross-neutralized SARS-CoV, Middle East respiratory syndrome coronavirus or both pseudotyped viruses. Convalescent individuals who experienced severe COVID-19 showed higher neutralizing antibody titres, a faster increase in lymphocyte counts and a higher frequency of CXCR3 + T follicular help (T FH ) cells compared with COVID-19-convalescent individuals who experienced non-severe disease. Circulating T FH cells were spike specific and functional, and the frequencies of CXCR3 + T FH cells were positively associated with neutralizing antibody titres in COVID-19-convalescent individuals. No individuals had detectable autoantibodies. These findings provide insights into neutralizing antibody responses in COVID-19-convalescent individuals and facilitate the treatment and vaccine development for SARS-CoV-2 infection. COVID-19-convalescent individuals maintain a strong neutralizing antibody response to SARS-CoV-2 that has cross-reactivity to SARS-CoV and MERS-CoV. Neutralizing antibody titres depend on the severity of the disease and are positively correlated with the frequency of CXCR3 + T follicular helper cells and lymphocyte counts.

Organic-pillared vermiculite was applied as the support to fabricate Ru catalyst via adsorption-precipitation method. The catalytic performances of the catalyst were investigated for the selective hydrogenation of levulinic acid (LA) or methyl levulinate (ML) to γ-valerolactone (GVL). The 100% selectivity of GVL and 100% conversion of LA were obtained at 403 K, 4.0 MPa using water as solvent. Moreover, a good recyclability was observed even after 14 reaction cycles as a slight conversion decrease of the LA and no change of the selectivity of GVL. The Characterization of the fresh catalysts and reused catalysts were performed using various techniques including XRD, FT-IR, N 2 adsorption-desorption, XPS and TEM. The catalytic tests and characterization data confirmed that the sample of Ru/Modified-Vermiculite can be taken as a robust and effective catalyst for the conversion of the LA to GVL. Graphical Abstracts The modified vermiculite supported-Ru catalyst has super activity and stability in the hydrogenation of levulinic acid to γ-valerolactone. Ru active components dispersed uniformly on the surface of organic modified vermiculite, and there was no obvious aggregation of the components. Even after fourteen cycles, levulinic acid conversion and γ-valerolactone selectivity remained 95% and 100%, respectively. The present work provides an efficient route for the preparing the Ru-based catalyst of excellent stability in the process of hydrogenation of levulinic acid.

Background Perioperative neurocognitive dysfunction (PND), a prevalent complication affecting elderly surgical patients, poses substantial challenges to postoperative rehabilitation and long-term functional independence. Despite growing awareness of its clinical significance, current evidence regarding effective neuroprotective anesthetic strategies remains inconclusive. Where emerging evidence suggests opioid-free anesthesia (OFA) strategies could maintain analgesic efficacy while potentially attenuating opioid-associated neuroinflammatory mechanisms implicated in PND pathogenesis. This multicenter trial investigates the efficacy of OFA combined with ultrasound-guided iliofascial nerve block in mitigating PND among geriatric patients undergoing hip fracture surgery. Methods This multicenter, randomized controlled trial will enroll 348 patients, who will be randomly assigned to receive either OFA combined with iliofascial nerve block or opioid-based anesthesia (OBA) combined with iliofascial nerve block. All patients will undergo hip fracture surgery under general anesthesia with tracheal intubation. The primary outcome will be the change in composite neurocognitive scores, assessed through a battery of neuropsychological tests from baseline to 3 months postoperatively. Secondary outcomes include alterations in serum protein and inflammatory markers, extubation time, postoperative pain incidence, intraoperative hemodynamic stability, and postoperative recovery parameters. The safety profile of OFA in hip surgery will also be assessed. Discussion Effective prevention of PND is crucial for optimizing postoperative recovery and long-term functional outcomes in elderly patients. This trial aims to refine and optimize anesthesia management strategies to reduce the incidence of PND, improve postoperative quality of life, and ultimately enhance perioperative neurocognitive outcomes. Trial registration This trial protocol was registered with the China Clinical Trial Registry on December 14, 2023, under the registration number: ChiCTR2300078647.