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Abstract Superalloys are indispensable materials for the fabrication of high-temperature components in aircraft engines. The discovery of a novel class of γ/γ′ Co-Al-W alloys has ignited a surge of interest in Co-based superalloys, with the aspiration to transcend the inherent constraints of their Ni-based counterparts. However, the conventional methodologies utilized in the design and advancement of new γ/γ′ Co-based superalloys are frequently characterized by their laborious and resource-intensive nature. In this study, we employed a coupled Density Functional Theory (DFT) and machine learning (ML) approach to predict and analyze the stability of the crucial γ′ phase, which is instrumental in expediting the discovery of γ/γ′ Co-based alloys. A dataset comprised of thousands of reliable formation (H f) and decomposition (H d) energies was obtained through high-throughput DFT calculations. Through regression model selection and feature engineering, our trained Random Forest (RF) model achieved prediction accuracies of 98.07% for H f and 97.05% for H d. Utilizing the well-trained RF model, we predicted the energies of over 150,000 ternary and quaternary γ′ phases within the Co-Ni-Fe-Cr-Al-W-Ti-Ta-V-Mo-Nb system. The energy analyses revealed that the presence of Ni, Nb, Ta, Ti, and V significantly reduced the H f and the H d of γ′, while Mo and W deteriorate the stability by increasing both energy values. Interestingly, although Al reduces the H f, it increases H d, thereby adversely affecting the stability of γ′. Applying domain-specific screening based on our knowledge, we identified 1049 out of >150,000 compositions likely to form stable γ′ phases, predominantly distributed across 11 Al-containing systems and 25 Al-free systems. Combining the analysis of CALPHAD method, we experimentally synthesized two new Co-based alloys with γ/γ′ dual-phase microstructures, corroborating the reliability of our theoretical prediction model.

Abstract X-linked adrenoleukodystrophy (X-ALD) is a severe genetic disorder caused by ABCD1 mutations, resulting in the buildup of very-long-chain fatty acids, leading to significant neurological decline and adrenal insufficiency. Despite advancements in understanding the mechanisms of X-ALD, its pathophysiology remains incompletely understood, complicating the development of effective treatments. This review provides a comprehensive overview of X-ALD, with a focus on the genetic and biochemical roles of ABCD1 and the impacts of its mutations. Current therapeutic approaches are evaluated, discussing their limitations, and emphasizing the need to fully elucidate the pathogenesis of X-ALD. Additionally, this review highlights the importance of international collaboration to enhance systematic data collection and advance biomarker discovery, ultimately improving patient outcomes with X-ALD.

Abstract The Chinese Mars rover Zhurong successfully landed in southern Utopia Planitia on Mars in May 2021. Previous research suggested a Hesperian ocean may have existed in the northern lowland on Mars. Recent research observed water-related features at the Zhurong landing site from in situ data. In this study, we conducted a comprehensive geomorphological analysis of the landing area using remote sensing data, supplemented by in situ observations, and unveiled features consistent with the existence of a nearshore zone in southern Utopia. Different types of water-related geomorphological features were separated by specific topographic contours, suggesting different types of marine environments. The area was subdivided into a foreshore highland-lowland transition unit, a shallow marine unit and a deep marine unit. In situ observations including sedimentary deposit rocks, water-related lamination features, and subsurface sedimentary layers, also indicate past water activities. Results suggested an evolution scenario of the nearshore zone in southern Utopia: (1) Flooding of the Utopia Planitia in Late Noachian around 3.65–3.68 Ga reached the foreshore unit; (2) formation of the shallow and deep marine units after the flooding was completed by about 3.5 Ga and 3.42 Ga in Early Hesperian, respectively; (3) gradual loss of subsurface volatiles during the Amazonian epoch.

Abstract Purpose To investigate the role of Actin like 6 A (ACTL6A) in cancer and explore the potential mechanism of its function. Methods Differential expression of ACTL6A was analyzed using Oncomine and TIMER database. Then, we downloaded data sets from TCGA database. The correlation between ACTL6A expression and survival in pan-cancer were analyzed by “survival”, “survminer” R package and PrognoScan database. STRING (v 11.0) and stringAPP for Cytoscape v3.7.2 were used to predict ACTL6A associated genes. Copy number and methylation alterations of ACTL6A were analyzed using cBioPortal and GSCALite. Transcription factors were downloaded from The Human Transcription Factors Database and analyzed using “limma” R package, JASPAR and PROMO database. Correlations analysis between ACTL6A and immune cells were performed using TIMER and GEPIA database. Results In our studies, we found that ACTL6A was widely upregulated in cancers, which might be attributed to its gene amplifications. Moreover, ACTL6A might regulated by transcription factors (TFs), including E2F1, YY1, CDX2 and HOXD10. In addition, high ACTL6A expression was associated with poor prognosis in most cancers. Meanwhile, ACTL6A was associated with the infiltration of immune cells, especially in liver hepatocellular carcinoma and brain lower grade glioma. Conclusion Amplification of ACTL6A is correlated with poor prognosis and contribute to immune cells infiltration in LIHC and LGG, which may provide immune-related therapeutic targets to guide clinical strategies.

Abstract This study presents a global database of pitted cones on Mars. This database was created using an active deep-learning approach based on a global mosaic of Mars Context Camera (CTX) images, followed by extensive manual verification. The global database contains 250,547 pitted cones along with their central coordinates and diameters. 94.2% of the pitted cones have diameters ranging from 100 m to 1 km. Most of them are in the northern lowlands, while some are scattered in the southern highlands. Systematic evaluation of the extracted pitted cones is performed in Isidis Planitia, Acidalia Planitia, and Cerberus Palus on Mars. The results reveal that the precision ranges from 79.2% to 85.6%, and the recall rate ranges from 62.4% to 87.7%, indicating a varying among regions but satisfactory performance. Manual digitizing evaluation indicates that over 94.1% of manual labels by different annotators have a differentiation rate of 10%, showing promising precision and consistency. This global database can facilitate further research on the origin and evolution of pitted cones on Mars.

Abstract Background Understanding the release characteristics of soil colloids is a prerequisite for studying the co-transport of colloids and pollutants in subsurface environment. As a crucial agricultural management measure, fertilization not only alters the material composition of farmland soil, but also significantly regulates the properties and release patterns of soil colloids. This study systematically investigated the regulatory mechanism of short-term cattle manure fertilization on the macroscopic release and microscopic properties of soil colloids with different particle sizes, providing a theoretical foundation for subsequent research on the fate and transport of agricultural non-point source pollutants. Results The colloids in natural agricultural soil primarily consist of inorganic components. Graded extraction of the colloids has revealed that the combined proportion of colloids with particle sizes of 1–2 μm and 0.45–1 μm accounts for approximately 80.5%. Applying cattle manure inhibits the release of soil colloids, and the content of large particle size (1–2 μm) components increases. The content of organic colloids is increased due to the high total organic carbon (TOC) in cattle manure, particularly those with a particle size less than 1 μm. The characterization of organic colloid components revealed a significant increase in aromatic carbon and oxygen-containing functional groups, while the aliphatic content decreased. The response sequence regarding changes in functional groups within organic colloids induced by fertilization was as follows: –CH3, –CH2 > C–O > –OH > C=C. Fertilization promotes the release of 1:1-type inorganic mineral colloids, increasing the content of poorly crystalline minerals. The retention of aromatic carbon and oxygen-containing functional groups by poorly crystalline mineral colloids served as the primary mechanism leading to their increased content levels. Changes in environmental factors significantly impacted the release and properties of soil colloids. Conditions such as low cationic valence, high ionic strength, and high pH promoted the release of soil colloids. Conclusions The short-term fertilization resulted in a reduction in the release of soil colloids and brought about significant alterations in their particle size composition and properties. The findings of this study provide valuable insights into understanding the impact of fertilization-induced colloid release on the environmental behavior of agricultural non-point source pollutants.

In this research, a novel method for regulating components in RE2SiO5/RE2Si2O7 multiphase silicates was developed, combining the benefits of a suitable thermal expansion coefficient (CTE) and outstanding corrosion resistance against calcium–magnesium–alumino–silicate (CMAS). This approach enhanced the overall thermophysical properties. Additionally, the results from the CMAS corrosion resistance test indicated that (Lu1/3Yb1/3Tm1/3)2SiO5/(Lu1/3Yb1/3Tm1/3)2Si2O7 and (Lu1/4Yb1/4Tm1/4Er1/4)2SiO5/(Lu1/4Yb1/4Tm1/4Er1/4)2Si2O7 exhibited exceptional resistance to CMAS penetration, even at temperatures up to 1500 °C. To comprehend the corrosion mechanism of CMAS on these silicates, we introduced a reaction–diffusion model, which involved observing the changes in the interface between the corrosion product layer and the silicate block. This was achieved using electron backscatter diffraction (EBSD). These findings lay a theoretical basis for selecting rare earth elements in RE2SiO5/RE2Si2O7 multiphase silicates based on the radii of different rare earth cations.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and heterogeneous interstitial lung disease with a median survival of 2–5 years. Though the diagnosis has been improved due to newly published guidelines, the recognition of the prognosis of IPF remains a challenge. Recently, several studies attempted to build prognostic models by extracting predictive variates from pulmonary function data, basic information, or chest computed tomography (CT) and CT-derived parameters with clinical characteristics. Artificial intelligence (AI) algorithms, including principal component analysis, support vector machine, random survival forest, and convolutional neural network, could be applied to the procedure of IPF prognostic model, that is, region of interest extraction, image feature selection, clinical feature selection, and model construction. Compared to human visualization, AI algorithms show a higher efficiency in calculating and extracting deep features and a lower inter-observer variation. Thus, this review provides a comprehensive CT evaluation of IPF prognostic models and discusses the role of AI in constructing IPF prognostic models. The potential improvements of AI in CT assessments, including time-series CT analysis, optimization of AI algorithms, utilization of multi-modal data, and discovery of new biomarkers through unsupervised algorithms, could be introduced to make a more accurate and convenient assessment for the prognosis of IPF patients. This review describes the status quo and future direction of AI applications in CT analysis for prognostic models of IPF. Take home message The review summarizes the applications of CT and AI algorithms for prognostic models in IPF and procedures of model construction. It reveals the current limitations and prospects of AI-aid models, and helps clinicians to recognize the AI algorithms and apply them to more clinical work.

Abstract This research delves into the effects of 2D layers on the functionality of 3D perovskite using lock‐in amplifier‐based in situ surface photovoltage (SPV) and its phase spectroscopy, with an emphasis on elucidating the connection between the tuning of dipole moments and the photocurrent hysteresis. Conventionally, the SPV of a perovskite/hole transport layer is observed to diminish as positive bias escalates. However, this trend is reversed in the case of 3D perovskite samples, where an augmentation in SPV is noted under positive bias. Notably, 3D/2D perovskite structures initially show a decrease, then an increase in SPV as bias intensifies, a phenomenon more pronounced with larger dipole moments in 2D. However, there is no linear relationship between the dipole moment and the hysteresis factor. Furthermore, using in situ light‐chopping‐frequency‐modulated SPV and Kelvin Probe Force Microscopy, it is revealed that the dipole fields of 2D layers can hinder ion migration. This leads to efficient hole transfer and minimal photocurrent hysteresis in 3D/2D perovskites, providing strong evidence for the underlying cause of hysteresis. Additionally, these findings suggest intricate interplays among the external electric field, interface dipole moments, and surface photovoltaics, offering significant insights into perovskite optoelectronics.

Background: The gut microbiota thrives in a complex ecological environment and its dynamic balance is closely related to host health. Recent studies have shown that the occurrence of various diseases including prostate inflammation is related to the dysregulation of the gut microbiome. Objective: This review focus on the mechanisms by which the gut microbiota induces prostate inflammation and benign prostatic hyperplasia and its therapeutic implications. Materials and methods: Publications related to gut microbiota, prostate inflammation, and benign prostatic hyperplasia (BPH) until April 2023 were systematically reviewed. The research questions were formulated using the Problem, Intervention, Comparison/Control, and Outcome (PICO) frameworks. Results: Fifteen articles covering the relationship between the gut microbiota and prostate inflammation/BPH, the mechanisms by which the gut microbiota influences prostate inflammation and BPH, and potential therapeutic approaches targeting the gut microbiota for these conditions were included. Conclusion: Short-chain fatty acids (SCFAs), which are metabolites of the intestinal microbiota, protect the integrity of the intestinal barrier, regulate immunity, and inhibit inflammation. However, dysregulation of the gut microbiota significantly reduces the SCFA content in feces and impairs the integrity of the gut barrier, leading to the translocation of bacteria and bacterial components such as lipopolysaccharide, mediating the development of prostate inflammation through microbe-associated molecular patterns (MAMPs).

Abstract It is difficult to obtain a single-phase environmental barrier coating material that simultaneously offers the advantages of low thermal conductivity, a suitable coefficient of thermal expansion, and excellent corrosion resistance. Herein, to synthesize the advantages of single-phase materials, we have developed an effective approach for the design of high-entropy multiphase ceramics of rare earth oxides and silicates. Such a specific design approach is capable of making high-entropy RE2SiO5/RE2O3 and RE2SiO5/RE2Si2O7 (RE = Lu, Yb, Tm, Er, Ho, and Y) multiphase ceramics as two types of potential environmental barrier coating materials for Al2O3f/Al2O3 and SiCf/SiC ceramic matrix composites.

Abstract Purpose We aimed to determine the prognostic value of α-hydroxybutyrate dehydrogenase (α-HBDH) in upper tract urothelial carcinoma (UTUC) patients after radical nephroureterectomy (RNU). Materials and methods We retrospectively enrolled the data of 544 UTUC patients at West China Hospital from May 2003 to June 2019. Cancer-specific survival (CSS) was the endpoint of interest. The optimal cutoff value of α-HBDH was identified by X-Tile program. After propensity score matching (PSM), we utilized Kaplan‒Meier curves to estimate survival and Cox proportional hazard model for risk assessment. A nomogram was built based on the results of multivariate analysis, and calibration curve, time-dependent receiver operating characteristic (ROC) curves and decision curve analysis were also performed to evaluate the predictive accuracy. Results Overall, 394 and 150 patients were divided into the α-HBDH-low group and α-HBDH -high group at the cutoff value of 158 U/L, respectively. After PSM, the two groups were well matched for all confounding factors. High α-HBDH was associated with inferior CSS (P = 0.006), and preoperative α-HBDH was an independent predictor for CSS (HR: 1.36; 95% CI:1.08, 1.80), especially in localized UTUC patients (HR: 2.04; 95% CI:1.11, 3.74). Furthermore, the nomogram based on α-HBDH achieved great predictive ability for CSS with areas under the curves of 0.800 and 0.778 for 3-year and 5-year CSS, respectively. Conclusion Serum α-HBDH was a novel and reliable biomarker for predicting survival outcomes in UTUC patients after RNU but should be further explored.

Abstract Background Traditional process for clinically significant prostate cancer (csPCA) diagnosis relies on invasive biopsy and may bring pain and complications. Radiomic features of magnetic resonance imaging MRI and methylation of the PRKY promoter were found to be associated with prostate cancer. Methods Fifty-four Patients who underwent prostate biopsy or photoselective vaporization of the prostate (PVP) from 2022 to 2023 were selected for this study, and their clinical data, blood samples and MRI images were obtained before the operation. Methylation level of two PRKY promoter sites, cg05618150 and cg05163709, were tested through bisulfite sequencing PCR (BSP). The PI-RADS score of each patient was estimated and the region of interest (ROI) was delineated by 2 experienced radiologists. After being extracted by a plug-in of 3D-slicer, radiomic features were selected through LASSCO regression and t-test. Selected radiomic features, methylation levels and clinical data were used for model construction through the random forest (RF) algorithm, and the predictive efficiency was analyzed by the area under the receiver operation characteristic (ROC) curve (AUC). Results Methylation level of the site, cg05618150, was observed to be associated with prostate cancer, for which the AUC was 0.74. The AUC of T2WI in csPCA prediction was 0.84, which was higher than that of the apparent diffusion coefficient ADC (AUC = 0.81). The model combined with T2WI and clinical data reached an AUC of 0.94. The AUC of the T2WI-clinic-methylation-combined model was 0.97, which was greater than that of the model combined with the PI-RADS score, clinical data and PRKY promoter methylation levels (AUC = 0.86). Conclusions The model combining with radiomic features, clinical data and PRKY promoter methylation levels based on machine learning had high predictive efficiency in csPCA diagnosis.

Abstract Using deep learning technology to segment oral CBCT images for clinical diagnosis and treatment is one of the important research directions in the field of clinical dentistry. However, the blurred contour and the scale difference limit the segmentation accuracy of the crown edge and the root part of the current methods, making these regions become difficult-to-segment samples in the oral CBCT segmentation task. Aiming at the above problems, this work proposed a Difficult-to-Segment Focus Network (DSFNet) for segmenting oral CBCT images. The network utilizes a Feature Capturing Module (FCM) to efficiently capture local and long-range features, enhancing the feature extraction performance. Additionally, a Multi-Scale Feature Fusion Module (MFFM) is employed to merge multiscale feature information. To further improve the loss ratio for difficult-to-segment samples, a hybrid loss function is proposed, combining Focal Loss and Dice Loss. By utilizing the hybrid loss function, DSFNet achieves 91.85% Dice Similarity Coefficient (DSC) and 0.216 mm Average Symmetric Surface Distance (ASSD) performance in oral CBCT segmentation tasks. Experimental results show that the proposed method is superior to current dental CBCT image segmentation techniques and has real-world applicability.

Objective To investigate the practical value of the transrectal two-dimensional shear-wave elastography (SWE) in benign prostatic hyperplasia (BPH).Methods Consecutive male participants with and without BPH constituted the BPH and control group respectively were enrolled prospectively between March and December 2022. Transrectal conventional ultrasound and SWE examinations for the prostate were performed on these participants. Data of quantitative stiffness of the transitional zone (TZ) and peripheral zone (PZ) of prostate, volume of prostate (VP) and volume of TZ (VTZ) and prostate specific androgen (PSA), etc., were collected. Linear regression analyses were used to investigate the associations between quantitative stiffness data and other clinical parameters.Results There were 200 participants evaluated, including 100 healthy participants and 100 BPH patients. For every one-year increment in age, it was correlated with 0.50 kPa increasement of TZ stiffness. VP and VTZ were correlated with TZ stiffness. Higher TZ stiffness was associated with higher free prostate specific antigen (PSA) and total PSA.Conclusions The prostate is stiffer and larger in BPH group compared to control group. Quantitative stiffness of the TZ was related with age, VP, VTZ and PSA.

Abstract For decreasing the global cost of corrosion, it is essential to understand the intricate mechanisms of corrosion and enhance the corrosion resistance of materials. However, the ambiguity surrounding the dominant mechanism of calcium‐magnesium aluminosilicate (CMAS) molten salt corrosion in extreme environments hinders the mix‐and‐matching of the key rare earth elements for increasing the resistance of monosilicates against corrosion of CMAS. Herein, an approach based on correlated electron microscopy techniques combined with density functional theory calculations is presented to elucidate the complex interplay of competing mechanisms that control the corrosion of CMAS of monosilicates. These findings reveal a competition between thermodynamics and kinetics that relies on the temperatures and corrosion processes. Innovative medium‐entropy monosilicates with exceptional corrosion resistance even at 1500 °C are developed. This is achieved by precisely modulating the radii of rare earth ions in monosilicates to strike a delicate balance between the competition in thermodynamics and kinetics. After 50 and 100 h of corrosion, the thinnest reactive layers are measured to be only 28.8 and 35.4 µm, respectively.

Abstract cAMP responsive element binding protein 3 (CREB3), belonging to bZIP family, was reported to play multiple roles in various cancers, but its role in hepatocellular carcinoma (HCC) is still unclear. cAMP responsive element binding protein 3 like 3 (CREB3L3), another member of bZIP family, was thought to be transcription factor (TF) to regulate hepatic metabolism. Nevertheless, except for being TFs, other function of bZIP family were poorly understood. In this study, we found CREB3 inhibited growth and metastasis of HCC in vitro and in vivo. RNA sequencing indicated CREB3 regulated AKT signaling to influence HCC progression. Mass spectrometry analysis revealed CREB3 interacted with insulin receptor (INSR). Mechanistically, CREB3 suppressed AKT phosphorylation by inhibiting the interaction of INSR with insulin receptor substrate 1 (IRS1). In our study, CREB3 was firstly proved to affect activation of substrates by interacting with tyrosine kinase receptor. Besides, CREB3 could act as a TF to transactivate RNA‐binding motif protein 38 (RBM38) expression, leading to suppressed AKT phosphorylation. Rescue experiments further confirmed the independence between the two functional manners. In conclusion, CREB3 acted as a tumor suppressor in HCC, which inhibited AKT phosphorylation through independently interfering interaction of INSR with IRS1, and transcriptionally activating RBM38.

Cinnamomum cassia (L.) J.Presl, a tropical aromatic evergreen tree belonging to the Lauraceae family, is commonly used in traditional Chinese medicine. It is also a traditional spice used worldwide. However, little is currently known about the extent of the genetic variability and population structure of C. cassia. In this study, 71 individuals were collected from seven populations across two geographical provinces in China. Nine morphological features, three chemical components, and single nucleotide polymorphism (SNP) markers were used in an integrated study of C. cassia germplasm variations. Remarkable genetic variation exists in both phenotypic and chemical compositions, and certain traits, such as leaf length, leaf width, volatile oil content, and geographic distribution, are correlated with each other. One-year-old C. cassia seedling leaf length, leaf width, elevation, and volatile oil content were found to be the main contributors to diversity, according to principal component analysis (PCA). Three major groupings were identified by cluster analysis based on the phenotypic and volatile oil data. This was in line with the findings of related research using 1,387,213 SNP markers; crucially, they all demonstrated a substantial link with geographic origin. However, there was little similarity between the results of the two clusters. Analysis of molecular variance (AMOVA) revealed that the genetic diversity of C. Cassia populations was low, primarily among individuals within populations, accounting for 95.87% of the total. Shannon’s information index (I) varied from 0.418 to 0.513, with a mean of 0.478 (Na=1.860, Ne =1.584, Ho =0.481, He =0.325, and PPB =86.04%). Genetic differentiation across populations was not significant because natural adaptation or extensive exchange of seeds among farmers between environments, thus maintaining the relationship. Following a population structure analysis using the ADMIXTURE software, 71 accessions were found to be clustered into three groups, with 38% of them being of the pure type, a finding that was further supported by PCA. Future breeding strategies and our understanding of the evolutionary relationships within the C. cassia population would benefit greatly from a thorough investigation of phenotypic, chemical, and molecular markers.

Abstract It is of interest to develop wide-temperature domain damped hydrophobic materials. In this paper, we designed incorporating bio-based phenolic resin into the IIR matrix and introducing dibenzyl fork acetone (DBA) into the main chain structure with sodium hydroxide activation to construct three-dimensional network. In this paper, we designed incorporating bio-based phenolic resin into the IIR matrix and introducing dibenzyl fork acetone (DBA) into the main chain structure with sodium hydroxide activation to construct three-dimensional network. The added bio-based phenolic resin has reticulated structure blended with butyl rubber, combined with sodium hydride activation-modified IIR. The results show that sodium hydride activated modification of DBA is introduced into the main chain structure of IIR by infrared and 1H NMR analysis. The material hydrophobic is realized by the introduction of DBA with static water contact angle of 103.5°. The addition of 10phr lignin-based phenolic resin (LPF) is compatible with IIR, and the torque can reach 7.0 N-m. The tensile elongation of the modified butyl rubber composite can reach 2400% with tensile strength up to 11.43 MPa, while the damping factor can reach 0.37 even at 70 °C. The thermal stability of the composites is enhanced with mass retention rate of 28%. The bio-based PF/NaH activation-modified butyl rubber damping material has potential applications in damping hydrophobicity with wide temperature range.

Abstract Background In the management of urothelial carcinoma, patient selection for immunotherapy, particularly with immune checkpoint inhibitors such as PD-1 (programmed cell death protein 1), is important for treatment efficacy. Inflammatory markers are useful for predicting treatment outcomes and immune-related adverse events (irAEs). This study aims to retrospectively explore the associations between inflammatory markers and outcomes in patients with postoperative urothelial carcinoma undergoing tislelizumab (PD-1 inhibitor) adjuvant therapy. Methods A retrospective analysis was conducted on 133 patients with postoperative urothelial carcinoma who received tislelizumab adjuvant therapy at the Affiliated Hospital of Xuzhou Medical University from April 2020 to August 2023. The prognostic effects of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) on disease-free survival (DFS) and overall survival (OS) were assessed using Cox regression models. The correlation between inflammatory markers and the onset of irAEs was analyzed using logistic regression models. Results NLR 5 and MLR >0.31, respectively. Multivariate analysis revealed that an NLR 135. Patients who experienced irAEs had longer DFS and OS. Multivariate analysis demonstrated that irAEs were an independent prognostic risk factor for DFS and OS. There was no significant difference in the occurrence of irAEs among different NLR, PLR, and MLR groups. Conclusion In patients with postoperative urothelial carcinoma receiving tislelizumab adjuvant therapy, the assessment of NLR and MLR before treatment may serve as valuable predictive markers of clinical outcome.

Abstract Background A combination of immune checkpoint inhibitors (ICIs) and chemotherapy has demonstrated excellent clinical efficacy and safety in treating a variety of cancers, including urothelial carcinoma (UC). However, its efficacy and safety in patients with muscle-invasive upper tract urothelial carcinoma (UTUC) who are undergoing radical surgery remain uncertain. The purpose of this retrospective study was to examine the effectiveness and safety of tislelizumab combined with gemcitabine plus cisplatin (TGC) as a first-line postoperative adjuvant treatment in this population. Methods This single-center, real-world study retrospectively analyzed the data from 71 patients with muscle-invasive UTUC who had radical nephroureterectomy (RNU) at the Affiliated Hospital of Xuzhou Medical University between November 1, 2020, and November 1, 2023. Among the 71 patients, 30 received adjuvant therapy of TGC within 90 days after RNU and 41 underwent surveillance. No patients receive preoperative neoadjuvant therapy. The TGC therapy group received adjuvant therapy every 3 weeks postoperatively until the first recurrence, first metastasis, or death due to any reason, whichever occurred first. The patients were followed up telephonically and through outpatient visits to record and evaluate their disease-free survival (DFS) and treatment-related adverse events (TRAEs). Results This study assessed the DFS of 41 and 30 patients in the surveillance group and TGC therapy group, respectively. The median DFS of the surveillance group was 16.5 [95% confidence interval (CI), 14.7–18.3] months, while the median DFS of the TGC group has not yet reached [hazard ratio (HR) 0.367 (95% CI, 0.169–0.796); p = 0.008], with 21 patients still undergoing follow-up. Compared with the surveillance group, the TGC therapy group had dramatically improved DFS after RNU and reduced risk by 63.3%. Of the 30 patients receiving combination therapy, 28 experienced TRAEs; all TRAEs were consistent with the frequently reported events in the chemotherapy-alone regimens, and there were no treatment-related deaths. Conclusion This study demonstrates that TGC therapy exhibits excellent clinical efficacy in patients undergoing radical surgery, significantly improving DFS and displaying great safety.

This article tackles the visual localization of unmanned aerial vehicles (UAVs) in the presence of multisource and cross-view images are involved. We present a lightweight end-to-end scene graph encoding and matching network that finds the best matches for the airborne camera views from the reference image maps. The scene graph addresses the challenges of encoding the semantic scene by aggregating the image convolutional features into global and structured semiglobal descriptors. The principal contributions of this article are as follows: First, we develop a new network architecture that embeds a nonlocal block and a modified vector of locally aggregated descriptors network (NetVLAD) into a backbone convolutional neural network. The main component of the modified NetVLAD is a cluster similarity masking graph (CSMG) encoder, which is proposed to replace the feature-cluster residuals computing in NetVLAD with cluster consensus feature aggregation and structure-aware scene graph extraction. In addition, a global descriptor is extracted by a nonlocal block to label each image with a discriminative global feature descriptor. Second, we develop a new triplet loss for the network training procedure to learn the features at different semantic levels. The proposed global descriptor and CSMG encoder are trained together according to a weighted sum of cosine triplet losses. Third, the global descriptor from the nonlocal block and semiglobal descriptor from the CSMG encoder work hierarchically for coarse-to-fine image retrieval and can achieve real-time efficiency and favorable accuracy of image searching and matching from the reference image map. We train and test the model on two challenging benchmark datasets. We also test the pretrained model on a dataset collected by a fixed-wing UAV to further evaluate the model's generalizability. The benchmark evaluations and ablation experiments show that the developed method outperforms state-of-the-art methods and achieves superior performance in the real-time matching of UAV images and reference image maps for UAV visual localization. Open-source code is available on GitHub.

Abstract The study of walkability is of great significance to the construction of healthy cities. In this paper, taking 1283 articles of walkability, which were included in Web of Science, as the research object. This paper adopts to analysis the research progress by using the method of scientometrics and knowledge networks analysis. Objectively and systematically analyze the research progress of walkability abroad from the aspects of publication overview, knowledge foundation, research direction and hot spots, etc. It is found that foreign researches on walkability mainly include three core directions: walkability and physical activity, walkability evaluation, walkability and urban design. Among them, walkability and physical activity orientation have been studied from various perspectives, such as various groups, various environmental types, different behavioral patterns and various chronic diseases. With the increasingly prominent urbanization problems and the rapid development of new technologies, multiple data, new methods and interdisciplinary cooperation will actively promote the vigorous development of walking suitability research.

Several different designs of 1.2kV-rated 4H-SiC MOSFETs have been successfully fabricated under various ion implantation conditions. Implantation conditions consisted of different P+ profiles and implantation temperatures of both room temperature (25°C) and elevated temperatures (600°C) in order to monitor subsequent lattice damage. Through the use of X-Ray topography, SEM imaging, and electrical measurements, it was shown that room temperature implanted devices can mimic the static performances of high temperature implanted MOSFETs and reduce lattice damage suffered during the fabrication process, when the dose of high energy implants are suppressed.

Leakage issues have received increasing attention as the most common and significant source of complaints in residential construction quality problems. In this study, based on the classification of residential construction leakage problems, 1947 water spray tests and 2333 water storage tests were conducted on 18 construction projects. An empirical analysis of 432 leakage cases was conducted to determine the loss law for a single leakage point as well as the loss laws for different grades of leakage problems. Through empirical analysis, it can be concluded that more than 90% of the leakage problems are third-level. To better understand the quantitative law of the residential construction leakage problem, a total loss law model was developed. Finally, the law of leakage loss in residential construction is summarized, and measures to reduce leakage loss in residential construction are proposed. This research can provide a theoretical basis and model tools for inherent defect insurance and help insurance companies control insurance risks and drive the promotion of inherent defect insurance.

Multi-agent decision-making faces many challenges such as non-stationarity and sparse rewards, while the complexity and randomness of the real environment further complicate policy development. This paper addresses the high-dimensional policy optimization problems of unmanned aerial vehicle (UAV) swarms. By modeling the problem scenario as a Markov decision process, a real-time policy optimization algorithm based on evolution strategy (ES) pre-training is proposed. This approach combines decision-time planning with background planning to evaluate and integrate different sets of policy parameters in a temporal context. In the experimental phase, the policy network is trained using both ES and REINFORCE algorithms on a constructed simulation platform. Comparative experiments demonstrate the effectiveness of using ES for policy pre-training. Finally, the proposed real-time policy optimization algorithm further improves the performance of the swarm by approximately 10% in simulations, offering a feasible solution for adversarial games between swarms and extending the research scope of evolutionary algorithms.

Combining near-earth remote sensing spectral imaging technology with unmanned aerial vehicle (UAV) remote sensing sensing technology, we measured the Ningqi No. 10 goji variety under conditions of health, infestation by psyllids, and infestation by gall mites in Shizuishan City, Ningxia Hui Autonomous Region. The results indicate that the red and near-infrared spectral bands are particularly sensitive for detecting pest and disease conditions in goji. Using UAV-measured data, a remote sensing monitoring model for goji pest and disease was developed and validated using near-earth remote sensing hyperspectral data. A fully connected neural network achieved an accuracy of over 96.82% in classifying gall mite infestations, thereby enhancing the precision of pest and disease monitoring in goji. This demonstrates the reliability of UAV remote sensing. The pest and disease remote sensing monitoring model was used to visually present predictive results on hyperspectral images of goji, achieving data visualization.

With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 soil samples were collected from a typical area of the Pearl River Delta, and the Cu content in the soil was detected in the laboratory. Simultaneously, Sentinel-2 remote sensing image data were collected, and two-dimensional and three-dimensional spectral indices were established. Constructing independent decision trees based on pH values, using the Successive Projections Algorithm (SPA) combined with the Boruta algorithm to select the characteristic bands for soil Cu content, and this was combined with Optuna automatic hyperparameter optimization for ensemble learning models to establish a model for estimating Cu content in soil. The research results indicated that in the SPA combined with the Boruta feature selection algorithm, the characteristic spectral indices were mainly concentrated in the spectral transformation forms of TBI2 and TBI4. Full-sample modeling lacked predictive ability, but after classifying the samples based on soil pH value, the R2 of the RF and XGBoost models constructed with the samples with pH values between 5.85 and 7.75 was 0.54 and 0.76, respectively, with corresponding RMSE values of 22.48 and 16.12 and RPD values of 1.51 and 2.11. This study shows that the inversion of soil Cu content under different pH conditions exhibits significant differences, and determining the optimal pH range can effectively improve inversion accuracy. This research provides a reference for further achieving the efficient and accurate remote sensing of heavy metal pollution in agricultural soil.

Accurately predicting the state of health (SOH) of lithium-ion batteries is crucial for optimizing battery performance and achieving efficient energy management, especially in electric vehicle applications. However, the existing incremental capacity analysis methods, which are mostly based on curve multi-parameter analysis, still have limitations in terms of computation, prediction accuracy, and adaptability to actual operating conditions. This paper conducts an in-depth analysis of the incremental capacity (IC) curve and proposes a feature parameter based on the area under the IC curve. By incorporating charge and discharge data, a weighted health indicator sequence is constructed and three mathematical models are proposed to link health indicators with cycle number, capacity, and SOH. The feasibility of using impedance as an additional input is also explored, despite the challenges of measurement, revealing its potential applications. Validation of the models with different datasets shows that the proposed method achieves both average relative error and root mean square error within 5%, outperforming other methods in terms of minimizing error and ensuring stability. The results demonstrate that the area-weighted incremental capacity method significantly enhances battery health monitoring accuracy, contributing to the development of sustainable and efficient energy storage systems.

With over a million cases detected each year, skin disease is a global public health problem that diminishes the quality of life due to its difficulty to eradicate, propensity for recurrence, and potential for post-treatment scarring. Photodynamic therapy (PDT) is a treatment with minimal invasiveness or scarring and few side effects, making it well tolerated by patients. However, this treatment requires further research and development to improve its effective clinical use. Here, a piezoelectric-driven microneedle (PDMN) platform that achieves high efficiency, safety, and non-invasiveness for enhanced PDT is proposed. This platform induces deep tissue cavitation, increasing the level of protoporphyrin IX and significantly enhancing drug penetration. A clinical trial involving 25 patients with skin disease was conducted to investigate the timeliness and efficacy of PDMN-assisted PDT (PDMN-PDT). Our findings suggested that PDMN-PDT boosted treatment effectiveness and reduced the required incubation time and drug concentration by 25% and 50%, respectively, without any anesthesia compared to traditional PDT. These findings suggest that PDMN-PDT is a safe and minimally invasive approach for skin disease treatment, which may improve the therapeutic efficacy of topical medications and enable translation for future clinical applications.

Abstract Keloids are benign fibroproliferative tumors that severely diminish the quality of life due to discomfort, dysfunction, and disfigurement. Recently, ultrasound technology as a noninvasive adjuvant therapy is developed to optimize treatment protocols. However, the biophysical mechanisms have not yet been fully elucidated. Here, it is proposed that piezo‐type mechanosensitive ion channel component 1 (Piezo1) plays an important role in low‐frequency sonophoresis (LFS) induced mechanical transduction pathways that trigger downstream cellular signaling processes. It is demonstrated that patient‐derived primary keloid fibroblasts (PKF), NIH 3T3, and HFF‐1 cell migration are inhibited, and PKF apoptosis is significantly increased by LFS stimulation. And the effects of LFS is diminished by the application of GsMTx‐4, the selective inhibitor of Piezo1, and the knockdown of Piezo1. More importantly, the effects of LFS can be imitated by Yoda1, an agonist of Piezo1 channels. Establishing a patient‐derived xenograft keloid implantation mouse model further verified these results, as LFS significantly decreased the volume and weight of the keloids. Moreover, blocking the Piezo1 channel impaired the effectiveness of LFS treatment. These results suggest that LFS inhibits the malignant characteristics of keloids by activating the Piezo1 channel, thus providing a theoretical basis for improving the clinical treatment of keloids.

Abstract N6‐methyladenosine (m6A) modification orchestrates cancer formation and progression by affecting the tumor microenvironment (TME). For hepatocellular carcinoma (HCC), immune evasion and angiogenesis are characteristic features of its TME. The role of YTH N6‐methyladenosine RNA binding protein 2 (YTHDF2), as an m6A reader, in regulating HCC TME are not fully understood. Herein, it is discovered that trimethylated histone H3 lysine 4 and H3 lysine 27 acetylation modification in the promoter region of YTHDF2 enhanced its expression in HCC, and upregulated YTHDF2 in HCC predicted a worse prognosis. Animal experiments demonstrated that Ythdf2 depletion inhibited spontaneous HCC formation, while its overexpression promoted xenografted HCC progression. Mechanistically, YTHDF2 recognized the m6A modification in the 5′‐untranslational region of ETS variant transcription factor 5 (ETV5) mRNA and recruited eukaryotic translation initiation factor 3 subunit B to facilitate its translation. Elevated ETV5 expression induced the transcription of programmed death ligand‐1 and vascular endothelial growth factor A, thereby promoting HCC immune evasion and angiogenesis. Targeting YTHDF2 via small interference RNA‐containing aptamer/liposomes successfully both inhibited HCC immune evasion and angiogenesis. Together, this findings reveal the potential application of YTHDF2 in HCC prognosis and targeted treatment.

Abstract Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

Abstract Background Morinda officinalis How (MO) is a vine shrub distributed in tropical and subtropical regions, known as one of the “Four Southern Herbal Medicines” in China. The unclear responsive mechanism by which MO adapt to freezing stress limits progress in molecular breeding for MO freezing tolerance. Results In this study, morphological, physiological and microstructure changes in MO exposed to -2℃ for 0 h, 3 h, 8 h and 24 h were comprehensively characterized. The results showed that freezing stress caused seedling dehydration, palisade cell and spongy mesophyll destruction. A significant increase in the content of proline, soluble protein and soluble sugars, as well as the activity of superoxide dismutase and peroxidase was observed. Subsequently, we analyzed the transcriptomic changes of MO leaves at different times under freezing treatment by RNA-seq. A total of 24,498 unigenes were annotated and 3252 unigenes were identified as differentially expressed genes (DEGs). Most of these DEGs were annotated in starch and sucrose metabolism, plant hormone signal transduction and MAPK signaling pathways. Family Enrichment analysis showed that the glucosyl/glucuronosyl transferases, oxidoreductase, chlorophyll a/b binding protein and calcium binding protein families were significantly enriched. We also characterized 7 types of transcription factors responding to freezing stress, among which the most abundant family was the MYBs, followed by the AP2/ERFs and NACs. Furthermore, 10 DEGs were selected for qRT-PCR analysis, which validated the reliability and accuracy of RNA-seq data. Conclusions Our results provide an overall view of the dynamic changes in physiology and insight into the molecular regulation mechanisms of MO in response to freezing stress. This study will lay a foundation for freezing tolerance molecular breeding and improving the quality of MO.

Abstract Deep learning can be used to achieve single‐image superresolution (SR) reconstruction. To address problems encountered during this process, such as the number of network parameters, high training requirements on equipment performance, and inability to downsample certain SR images accurately, an image SR reconstruction algorithm based on deep residual network optimization is proposed. The model introduces wavelet transforms based on the original U‐Net, where the U‐Net is trained to obtain SR wavelet feature images at multiple scales simultaneously. This approach reduces the mapping space for the network to learn low‐ to high‐resolution image mapping, which in turn reduces the training difficulty of the model. In terms of network details, the inverse wavelet transform is used in image upsampling to enhance the sparsity of the reconstruction layer in the original network. The network structure of the U‐Net upsampling is adjusted slightly to enable the network to distinguish wavelet images from feature images, thereby improving the richness of the features extracted by the model. The experimental results show that the peak signal‐to‐noise ratio (PSNR) of the fourfold SR model is 32.35 and 28.68 on the Set5 and Set14 validation sets, respectively. Compared with networks that use wavelet prediction mechanisms, such as the deep wavelet prediction SR (DWSR) and deep wavelet prediction‐based residual SR (DWRSR) models, the PSNR for all the tested public datasets is improved by 0.5. The method yields superior results in terms of both visual effect and PSNR, demonstrating the feasibility of the wavelet prediction mechanism in SR reconstruction and thus offering application value and research significance.

In this study, ultrasound-assisted aqueous two-phase extraction (UA-ATPE) was adopted to extract flavonoids from Rosa roxburghii Tratt and investigated its inhibitory effect of this flavonoid on xanthine oxidase activity in vitro. In this experiment, flavonoids yield of Rosa roxburghii Tratt was taken as the index to verify the single factor and response surface optimization of flavonoids extraction process. The optimal extraction process was obtained as follows: Under the condition of 32% C2H5OH-20% (NH4)2SO4 two-phase aqueous extraction system, the yield of flavonoids was 140.57±1.78 mg/g when the solid-liquid ratio was 1:59 (g/g), the ultrasonic time was 30 min, the temperature was 50 ℃, and the power was 320 W. Under these conditions, the IC50 value of xanthine oxidase was 12.72 mg/mL in vitro, indicating a good inhibitory activity. The experiment showed that the optimized technology of UA-ATPE extraction of flavonoids from Rosa roxburghii Tratt effectively improved the yield of flavonoids from Rosa roxburghii Tratt. And the flavonoids extracted from Rosa roxburghii Tratt had a good effect of inhibiting the activity of xanthine oxidase and could be used as a natural xanthine oxidase inhibitor.

Amidation of lactobionic acid with N,N-dimethylaminopropyltriamine was conducted to obtain N-(3′-dimethylaminopropyl)-lactamido-3-aminopropane (DDLPD), which was quaternized with bromoalkanes of different carbon chain lengths to synthesize double-stranded lactosylamide quaternary ammonium salt N-[N′[3-(lactosylamide)]propyl-N′-alkyl] propyl-N,N-dimethyl-N-alkylammonium bromide (CnDDLPB, n = 8, 10, 12, 14, 16). The surface activity and the adsorption and aggregation behaviors of the surfactants were investigated via equilibrium surface tension, dynamic light scattering, and cryo-electron microscopy measurements in an aqueous solution. The application properties of the products in terms of wettability, emulsification, foam properties, antistatic, salt resistance, and bacteriostatic properties were tested. CnDDLPB exhibited a low equilibrium surface tension of 27.82 mN/m. With an increase in the carbon chain length, the critical micellar concentration of CnDDLPBD decreased. Cryo-electron microscopy revealed that all products except C8DDLPB formed stable monolayer, multi-layer, and multi-compartmental vesicle structures in an aqueous solution. C14DDLPB has the best emulsification performance on soybean oil, with a time of 16.6 min; C14DDLPB has good wetting and spreading properties on polytetrafluoroethylene (PTFE) when the length of carbon chain is from 8 to 14, and the contact angle can be lowered to 33°~40°; CnDDLPB has low foam, which is typical of low-foaming products; C8DDLPB and C10DDLPB both show good antistatic properties. C8DDLPB and C14DDLPB have good salt resistance, and C12DDLPB has the best antimicrobial property, with the inhibition rate of 99.29% and 95.28% for E. coli and Gluconococcus aureus, respectively, at a concentration of 350 ppm.

Abstract Background Radical prostatectomy remains the fundamental treatment for prostate cancer, and improving patients’ compliance with postoperative follow-ups is essential for improving patients’ quality of life. This study investigates the effect of education levels on patients’ recovery and follow-up after radical prostatectomy. Methods Data from 1,112 patients undergoing radical prostatectomy between 2011 and 2020 were collected using medical records, and “pc-follow” systems were used to collect patients’ baseline information, education level, pathological information, number of outpatient visits, the time interval between each visit, and PSA test data. Results Regarding postoperative outpatient data, there was no difference in the number of outpatient visits among the different education level groups in Shanghai (P = 0.063). A significant difference was found in the interval between outpatient visits among the groups (P

Abstract The problem that the texture details of low‐resolution (LR) digital images of ancient murals are ambiguous persists. To solve this problem, this study proposes a super‐resolution (SR) reconstruction method for fuzzy murals based on a generative adversarial network with self‐attention (SA). The network uses a blur kernel and realistic noise data to add blur and noise, respectively, to a high‐resolution (HR) image to obtain an original LR image. Then, a feature image with the same size as that of the input image is obtained through a SA module. Finally, the feature information extracted from the image is input into the high‐resolution image space by using a subpixel convolution layer to realize the image enlargement process from an LR to an HR. Experiments evaluate the proposed approach both objectively and subjectively. The objective evaluation results show that compared with other SR reconstruction algorithms, the proposed algorithm's peak signal‐to‐noise ratio (PSNR) is increased by 0.04 to 3.78 dB on average, and its structural similarity is increased by 0.002 to 0.191. A subjective perception evaluation shows that the developed algorithm can better reconstruct the texture details of murals, thus better meeting the visual perception needs of the public. The method proposed in this study can satisfactorily reconstruct the texture details of murals, which may provide technical guidance for the development of mural protection plans. Furthermore, it may be of certain practical significance for the SR reconstruction of ancient murals.

With the continuous improvement of people ‘s living standards, people have put forward higher requirements for the safety and comfort of housing. Therefore, Inherent Defect Insurance, a financial method to guarantee the quality of construction projects, has also emerged. At present, China ‘s Inherent Defect Insurance has been gradually promoted, but its claim mechanism has not been analyzed and studied. From the perspective of construction engineering, this paper first makes a bibliometric analysis of the influencing factors of insurance claims that may be caused by construction engineering quality through VOSViewers, and the evaluation index system of inherent defects is constructed. Then, according to the influencing factors, the PSO-LSSVR model is adopted to fit the performance function of the inherent defects. Finally, based on the reliability design principle of engineering structure, the reliability index and failure probability of Inherent Defect Insurance are derived from the performance function of inherent defects. This paper also analyzes its application in insurance practice and determines the relationship between the number of insurance underwriting policies and the initial reserve of insurance at a certain risk level. This paper studies the probability of Inherent Defect Insurance by constructing the reliability model of inherent defect risks in construction quality, and analyzes the anti-risk ability of insurance companies from the perspective of claim, which provides scientific analysis methods and theoretical basis for the scientific decision-making of insurance companies.

The gas-phase metallicity of galaxies is regulated by multiple astrophysical processes, which makes it a crucial diagnostic of galaxy formation and evolution. Beyond the fundamental mass–metallicity relation, a debate about the secondary galaxy property to predict the metallicity of galaxies arises. Motivated by this, we systematically examine the relationship between gas-phase metallicity and other galaxy properties, i.e., the star formation rate (SFR) and galaxy size, in addition to stellar mass in both observation and simulation. We utilize the data from the Mapping Nearby Galaxies at Apache Point Observatory survey and the TNG50 simulations. We find that the combination of ${M}_{* }/{R}_{{\rm{e}}}^{\beta }$ with β ∼ 0.6–1 is in much stronger correlation to the metallicity than stellar mass alone, regardless of whether the SFR is included or not, in both observation and simulation. This indicates that galaxy size plays a more important role in determining gas-phase metallicity of galaxies than SFR. In addition, The Next Generation simulation predicts that the SFR, although being a subdominant role, becomes increasingly important in the high- z universe. Finally, we speculate that the SFR modulates metallicity on the temporal dimension, synchronized with time-varying gas inflows, and galaxy size regulates metallicity on the spatial dimension by affecting the gravitational potential and the mass-loading factor.

Multipactor and low-pressure gas discharges have a negative impact on the performance and security of microwave components in space. In this manuscript, a 2-dimensional parallel flat plate structural model was designed to simulate the breakdown of multipactor to low-pressure gas discharges. The breakdown levels in the pressure range of 0.1 to 100 torr were verified under the conditions of the Ag material, 4-mm gap, 2.6-GHz frequency, and air as the background gas. Random secondary electron emission and gas molecule collision processes are incorporated, and the effect of their transition section is studied using trajectory tracking and Monte Carlo methods. A comparison of the model to the available experimental data demonstrates the model’s accuracy. On this basis, the variation of gas adsorption in relation to the surface secondary electron yields was considered, and the effect of the gas adsorption issues on breakdown level was calculated by means of numerical simulations based on some assumptions. The calculation reveals that in a finite volume of space, smaller desorption volumes cause negligible pressure changes, but the secondary electron emission capacity of the material decreases, which, in turn, leads to a flat or rising breakdown level. On the contrary, the larger the desorption volume, the more the gas is collisionally ionized and the breakdown level decreases.

Several clinical and preclinical studies have shown that nonsteroidal anti-inflammatory drugs (NSAIDs), particularly aspirin, reduce the incidence of various cancer types. However, there is still a lack of literature evaluating the overall association between multiple cancer morbidities and NSAIDs. Thus, we conducted an umbrella review to evaluate the quality of evidence, validity, and biases of the existing systematic reviews and meta-analyses on the relationships between NSAIDS and multiple tumor incidence outcomes. We found that NSAIDs might be associated with a decreased risk of several cancers, including the central nervous system, breast, esophageal, gastric, head and neck, hepatocellular, cholangiocarcinoma, colorectal, endometrial, lung, ovary, prostate, and pancreatic cancers, but regular intake of any dose of non-aspirin NSAIDs (NA-NSAIDs) could increase the incidence of kidney cancer. However, most of included studies are evaluated as low quality according to our evidence assessment. Furthermore, due to the potential side effects, such as hemorrhage, digestive symptoms and peptic ulcer, it is still not recommend to use NSAIDs regularly to prevent cancers.

IntroductionMorinda officinalis How (MO) is a Rubiaceae plant, and its medicinal part is dried root, which is one of the “Four Southern Medicines” in China. At present, the plant MO breed seedlings mainly by cutting methods. Long-term asexual propagation makes pathogenic fungi accumulate in MO, leading to stem-base rot, which is caused by Fusarium oxysporum (Fon).MethodsIn this study, we used Trichoderma harzianum and Pestalotiopsis sp. as biocontrol fungi to investigate their antagonistic ability to Fon through in vitro antagonism and pot experiments, and combined with transcriptome sequencing to explore the mechanism of biocontrol.ResultsThe results showed that both Trichoderma harzianum and Pestalotiopsis sp. could inhibit the growth of Fon. In addition, Trichoderma harzianum and Pestalotiopsis sp. could also enhance the basic immunity to Fon by increasing the activities of defensive enzymes such as POD and SOD, chlorophyll content, soluble sugar content, and oligosaccharide content of MO. The mechanism of biological control of stem-base rot of MO was discussed by transcriptome technology. MO was treated with two treatments, root irrigation with biocontrol fungi or inoculation with Fon after root irrigation with biocontrol fungi. Transcriptome sequencing revealed that nearly 11,188 differentially expressed genes (DEGs) were involved in the process of inducing MO systemic resistance to Fon by biocontrol fungi. Meanwhile, Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, as well as transcription factor (TFs) prediction showed that there were significant differences in the expression levels of MO roots under different treatments. Also, the genes of the “MAPK signaling pathway” and “plant hormone signaling pathway” were analyzed, in which the ERFs gene of the ethylene signal transduction pathway participated in the metabolism of glycosyl compounds. It is speculated that the ethylene signal may participate in the immune response of the sugar signal to the infection of Fon. After qRT-PCR verification of 10 DEGs related to the ethylene signal transduction pathway, the expression trend is consistent with the results of transcriptome sequencing, which proves the reliability of transcriptome sequencing.DiscussionIn conclusion, this study preliminarily identified the molecular mechanism of the biological control of MO stem-base rot and provided a scientific basis for further research on the prevention and control mechanism of MO stem-base rot.

Background Mohs micrographic surgery (MMS) is the preferable surgery for difficult -to-treat basal cell carcinoma (BCC) but is an expensive, labor-intensive, and time-consuming technique. The aim of this study is to compare the efficacy and safety of photodynamic therapy combined with surgery(S-PDT) versus Mohs micrographic surgery (MMS) for the treatment of difficult-to-treat BCC. Methods This was a retrospective, comparative study. A total of 32 patients, 16 patients with 48 lesions, were treated with S-PDT, and the other 16 patients with 17 lesions treated by MMS were enrolled in this study. Follow-up was at least 36 months posttreatment. Results The recurrence rate was no statistical difference between the S-PDT and MMS (p = 1.000, Fishers exact test). The median follow-up was 42.5 months (range 36–63 months). The mean healing time in the S-PDT [17.9 d (SD 9.8)] is longer than in MMS [7.5 d (SD 1.5)] during follow-up (p＜.001, Independent T-test). On the whole, the cosmetic outcome of patients in S-PDT was statistically no significant difference with that in MMS according to a 4-point scale (p = .719, chi-squared test). Conclusions S-PDT is a safe, effective, and novel cosmetic treatment, which holds the potential to be an alternative treatment to MMS for some cases.

High-entropy pyrosilicate element selection is relatively blind, and the thermal expansion coefficient (CTE) of traditional β-type pyrosilicate is not adjustable, making it difficult to meet the requirements of various types of ceramic matrix composites (CMCs). The following study aimed to develop a universal rule for high-entropy pyrosilicate element selection and to achieve directional control of the thermal expansion coefficient of high-entropy pyrosilicate. The current study investigates a high-entropy design method for obtaining pyrosilicates with stable β-phase and γ-phase by introducing various rare-earth (RE) cations. The solid-phase method was used to create 12 different types of high-entropy pyrosilicates with 4–6 components. The high-entropy pyrosilicates gradually transformed from β-phase to γ-phase with an increase in the average radius of RE3+ ions (r¯(RE3+)). The nine pyrosilicates with a small r¯(RE3+) preserve β-phase or γ-phase stability at room temperature to the maximum of 1400 ℃. The intrinsic relationship between the thermal expansion coefficient, phase structure, and RE–O bond length has also been found. This study provides the theoretical background for designing high-entropy pyrosilicates from the perspective of r¯(RE3+). The theoretical guidance makes it easier to synthesize high-entropy pyrosilicates with stable β-phase or γ-phase for the use in environmental barrier coatings (EBCs). The thermal expansion coefficient of γ-type high-entropy pyrosilicate can be altered through component design to match various types of CMCs.

Abstract Introduction The effectiveness and safety of immune checkpoint inhibitor (ICI) monotherapy in advanced upper tract urothelial carcinoma (UTUC) is less reported. Methods In total, 106 consecutive advanced UTUC patients receiving ICI monotherapy were collected from nine high volume centers. Clinical outcomes were analyzed according to multiple parameters (e.g., treatment line, metastatic sites). Objective response rate (ORR), overall survival (OS) and progression‐free survival (PFS) were captured after ICI initiation. Results With a median follow‐up of 12.0 months, 25 patients in the first‐line group and 15 patients in the second‐line group died of UTUC. We reported a median OS of 18.0 months, a median PFS of 5.0 months, and an ORR of 38.6% for patients in the first‐line group; a median OS of 10.0 months, a median OS of 4.0 months, and an ORR of 27.8% for patients in the second‐line group. Complete response was observed in two patients in the first‐line group and one patient in the second‐line group with a total complete response rate of 2.8%. In the univariate and multivariate analysis, visceral metastasis with a hazard ratio of 2.4 was associate with poor OS. The most common treatment‐related adverse events included fatigue (11.3%), pruritus (10.4%), and diarrhea (6.6%). Conclusions This real‐world study suggests that ICI monotherapy is active and has acceptable toxic effects for unresectable or metastatic UTUC as first‐line therapy in cisplatin‐ineligible patients or second‐line therapy in platinum‐refractory patients.

Centrifugal pumps are a crucial part of nuclear power plants, and their dependable and safe operation is crucial to the security of the entire facility. Cavitation will cause the centrifugal pump to violently vibration with the large number of vacuoles generated, which not only affect the hydraulic performance of the centrifugal pump but also cause structural damage to the impeller, seriously affecting the operational safety of nuclear power plants. A closed cavitation test bench of a centrifugal pump is constructed, and a method for precisely identifying the cavitation state is proposed based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Deep Residual Shrinkage Network (DRSN). First, we compared the cavitation sensitivity of pressure fluctuation, vibration, and liquid-borne noise and decomposed the liquid-borne noise by CEEMD to capture cavitation characteristics. The decomposition results are sent into a 12-layer deep residual shrinkage network (DRSN) for cavitation identification training. The results demonstrate that the liquid-borne noise signal is the most cavitation-sensitive signal, and the accuracy of CEEMD-DRSN to identify cavitation at different stages of centrifugal pumps arrives at 94.61%