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Achieving carbon neutrality in wastewater treatment plants relies heavily on mainstream anaerobic ammonia oxidation. However, the stability of this process is often compromised, largely due to the significant influence of microbial morphology. This study analyzed 208 microbial samples using bioinformatics and machine learning (ML) across four different morphologies: Suspended Sludge (SS), Biofilm, Granular Sludge (GS) and the Integrated Fixed-film Activated Sludge process (IFAS). The results revealed IFAS’s notably complex and stable community structure, along with the identification of endemic genera and common genera among the four microbial morphologies. Through co-occurrence network analysis, the interaction between microorganisms of various genera was displayed. Utilizing the Extreme Gradient Boosting (XGBoost) model, a ML modeling framework based on microbiome data was developed. The ML-based feature importance analysis identified LD-RB-34 as a key organism in SS and BSV26 was an important bacterium in IFAS. Additionally, functional bacteria KF-JG30-C25 occupied a higher proportion in GS, and Unclassified Brocadiaceae occupied a higher proportion in Biofilm. Furthermore, dissolved oxygen, temperature and pH were identified as the primary factors determining microbial communities and influencing anammox activity. Overall, this study deepens our understanding of bacterial communities to enhance the mainstream anammox nitrogen removal.

Abstract Malus hybrid ‘SH6’ (M. honanensis × M. domestica)is a commonly used apple interstock in China, known for its excellent dwarfing characteristics and cold tolerance. In this study, a combined strategy utilizing PacBio HiFi, Hi-C and parental resequencing data were employed to assemble two haploid genomes for ‘SH6’. After chromosome anchoring, the final hapH genome size was 596.63 Mb, with a contig N50 of 34.38 Mb. The hapR genome was 649.37 Mb, with a contig N50 of 36.84 Mb. Further analysis predicted that repeated sequences made up 59.69% and 62.52% of the entire genome, respectively. Gene annotations revealed 45,435 genes for hapH and 48,261 genes for hapR. Combined with genomic synteny we suggest that the hapR genome originates from its maternal parent M. domestica cv. Ralls Janet, while the hapH genome comes from its paternal parent, M. honanensis. The assembled genome significantly contributes to the discovery of genes associated with apple dwarfing and the molecular mechanisms governing them.

To improve the performance of PcBN composites prepared under high temperature and high pressure, mixed cBN particle sizes ranging from 0 to 0.5 μm and 0.5 to 1.0 μm were used as the raw material, Al-Ti-Al2O3 was used as the binder and the carbon nanotubes with different contents were added. The PcBN composites were prepared by sintering under high temperature and high pressure conditions. The effect of carbon nanotube content on the structure and properties of PcBN composites was investigated. The results show that there is no chemical reaction between PcBN and carbon nanotubes after the addition of carbon nanotubes, and the carbon nanotubes exist in the form of reinforcement inside the composite. The composite material is relatively dense, and the relative density of PcBN increases first and then decreases with the addition of carbon nanotubes. When the mass fraction of carbon nanotubes added is 1.5%, the relative density of PcBN reaches its maximum value of 97.9%, while PcBN has the maximum microhardness and fracture toughness of 3 892 HV and 6.82 MPa·m1/2, respectively. When the mass fraction of carbon nanotubes added is 1.0%, PcBN has the maximum bending strength and wear ratio, which are 584 MPa and 6 873 MPa, respectively. The pull-out and bridging effects of carbon nanotubes improve the mechanical properties of PcBN composites.

The monolithic PcBN composite tool materials were prepared at 5.5 GPa and 1450 ℃ with different contents of Ti3AlC2 as the binder phase. The effects of different mass fractions of Ti3AlC2 on the phase composition, microstructure, and mechanical properties of PcBN tool materials were studied. The results show that Ti3AlC2 can decompose completely to form TiC and Al-Ti alloys under high temperature and high pressure, and reacts with cBN to form AlN, TiB2, and TiC0.7N0.3 phases. TiC, AlN, TiB2, and TiC0.7N0.3 are uniformly distributed around the cBN and tightly bonded to the cBN, thereby improving the mechanical properties of PcBN. When the mass fraction of Ti3AlC2 is 25 %, the relative density, bending strength, fracture toughness, and wear ratio of PcBN reach the maximum values, which are 98.9%, 592 MPa, 6.87 MPa·m1/2and 7 350, respectively. When the mass fraction of Ti3AlC2 is 20%, the microhardness of PcBN reaches the maximum value of 4 786.7 HV.

Significant improvement of bending properties (approximately 64.7%) for WE43 magnesium alloy was realized by weakening the basal texture and raising the deformation temperature. The mechanisms for such improvement were investigated in detail by combining the experiment and finite element model. The results concluded that the coupling effects of weak basal texture and high deformation temperature was the key reason producing the above improvement of bending properties. Specially, the TD sample with weak basal texture was more conducive to activating basal slip than the RD sample, resulting in its higher strain hardenability and consequently more significant bending properties. Based on weak basal texture, raising the deformation temperature could further improve the bending properties. This improvement mechanism was attributed to the abundant activations of non-basal slip instead of basal slip, which could increase the independent slip systems and efficiently delay the development of the strong basal texture. Moreover, the occurrence of partial DRV and DRX relieved the stress concentrations by consuming the dislocations, further improving the bending properties. The activations of these deformation mechanisms matching the corresponding bending stress states caused the crystal rotation and the texture variety in the bending samples. Basal slip activation aligned the (0002) basal planes parallel to tensile stress in the outer region or perpendicular to compressive stress in the inner region. Additionally, prismatic slip activation induced crystal rotation around the c-axis, developing //LD texture in the outer region but ⊥LD texture in the inner region.

Abstract Background Ubiquitin carboxyl-terminal hydrolase L1 (UCHL1) is a deubiquitinating enzyme that regulates ERα expression in triple-negative cancer (TNBC). This study aimed to explore the deubiquitination substrates of UCHL1 related to endocrine therapeutic responses and the mechanisms of UCHL1 dysregulation in TNBC. Methods Bioinformatics analysis was conducted using online open databases. TNBC representative MDA-MB-468 and SUM149 cells were used for in vitro and in-vivo studies. Co-immunoprecipitation was used to explore the interaction between UCHL1 and KLF5 and UCHL1-mediated KIF5 deubiquitination. CCK-8, colony formation and animal studies were performed to assess endocrine therapy responses. The regulatory effect of TET1/3 on UCHL1 promoter methylation and transcription was performed by Bisulfite sequencing PCR and ChIP-qPCR. Results UCHL1 interacts with KLF5 and stabilizes KLF5 by reducing its polyubiquitination and proteasomal degradation. The UCHL1-KLF5 axis collaboratively upregulates EGFR expression while downregulating ESR1 expression at both mRNA and protein levels in TNBC. UCHL1 knockdown slows the proliferation of TNBC cells and sensitizes the tumor cells to Tamoxifen and Fulvestrant. KLF5 overexpression partially reverses these trends. Both TET1 and TET3 can bind to the UCHL1 promoter region, reducing methylation of associated CpG sites and enhancing UCHL1 transcription in TNBC cell lines. Additionally, TET1 and TET3 elevates KLF5 protein level in a UCHL1-dependent manner. Conclusion UCHL1 plays a pivotal role in TNBC by deubiquitinating and stabilizing KLF5, contributing to endocrine therapy resistance. TET1 and TET3 promote UCHL1 transcription through promoter demethylation and maintain KLF5 protein level in a UCHL1-dependent manner, implying their potential as therapeutic targets in TNBC.

Overgrazing and climate change are the main causes of grassland degradation, and grazing exclusion is one of the most common measures for restoring degraded grasslands worldwide. Soil fungi can respond rapidly to environmental stresses, but the response of different grassland types to grazing control has not been uniformly determined. Three grassland types (temperate desert, temperate steppe grassland, and mountain meadow) that were closed for grazing exclusion for 9 years were used to study the effects of grazing exclusion on soil nutrients as well as fungal community structure in the three grassland types. The results showed that (1) in the 0–5 cm soil layer, grazing exclusion significantly affected the soil water content of the three grassland types (P < 0.05), and the pH, total phosphorous (TP), and nitrogen-to-phosphorous ratio (N/P) changed significantly in all three grassland types (P < 0.05). Significant changes in soil nutrients in the 5–10 cm soil layer after grazing exclusion occurred in the mountain meadow grasslands (P < 0.05), but not in the temperate desert and temperate steppe grasslands. (2) For the different grassland types, Archaeorhizomycetes was most abundant in the montane meadows, and Dothideomycetes was most abundant in the temperate desert grasslands and was significantly more abundant than in the remaining two grassland types (P < 0.05). Grazing exclusion led to insignificant changes in the dominant soil fungal phyla and α diversity, but significant changes in the β diversity of soil fungi (P < 0.05). (3) Grazing exclusion areas have higher mean clustering coefficients and modularity classes than grazing areas. In particular, the highest modularity class is found in temperate steppe grassland grazing exclusion areas. (4) We also found that pH is the main driving factor affecting soil fungal community structure, that plant coverage is a key environmental factor affecting soil community composition, and that grazing exclusion indirectly affects soil fungal communities by affecting soil nutrients. The above results suggest that grazing exclusion may regulate microbial ecological processes by changing the soil fungal β diversity in the three grassland types. Grazing exclusion is not conducive to the recovery of soil nutrients in areas with mountain grassland but improves the stability of soil fungi in temperate steppe grassland. Therefore, the type of degraded grassland should be considered when formulating suitable restoration programmes when grazing exclusion measures are implemented. The results of this study provide new insights into the response of soil fungal communities to grazing exclusion, providing a theoretical basis for the management of degraded grassland restoration.

Fatty infiltration denotes the anomalous accrual of adipocytes in non-adipose tissue, thereby generating toxic substances with the capacity to impede the ordinary physiological functions of various organs. With aging, the musculoskeletal system undergoes pronounced degenerative alterations, prompting heightened scrutiny regarding the contributory role of fatty infiltration in its pathophysiology. Several studies have demonstrated that fatty infiltration affects the normal metabolism of the musculoskeletal system, leading to substantial tissue damage. Nevertheless, a definitive and universally accepted generalization concerning the comprehensive effects of fatty infiltration on the musculoskeletal system remains elusive. As a result, this review summarizes the characteristics of different types of adipose tissue, the pathological mechanisms associated with fatty infiltration in bone, muscle, and the entirety of the musculoskeletal system, examines relevant clinical diseases, and explores potential therapeutic modalities. This review is intended to give researchers a better understanding of fatty infiltration and to contribute new ideas to the prevention and treatment of clinical musculoskeletal diseases.

Abstract Malus hybrid ‘Flame’ and Malus hybrid ‘Royalty’ are representative ornamental crabapples, rich in flavonoids and serving as the preferred materials for studying the coloration mechanism. We generated two sets of high-quality chromosome-level and haplotype-resolved genome of ‘Flame’ with sizes of 688.2 Mb and 675.7 Mb, and those of ‘Royalty’ with sizes of 674.1 Mb and 663.6 Mb, all anchored to 17 chromosomes and with a high BUSCO completeness score nearly 99.0%. A total of 47,833 and 47,307 protein-coding genes were annotated in the two haplotype genomes of ‘Flame’, and the numbers of ‘Royalty’ were 46,305 and 46,920 individually. The assembled high-quality genomes offer new resources for studying the origin and adaptive evolution of crabapples and the molecular basis of the accumulation of flavonoids and anthocyanins, facilitating molecular breeding of Malus plants.

This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer. Firstly, the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the barrel. Subsequently, the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test platform. Furthermore, the probability density evolution method (PDEM) is developed to high-dimensional uncertainty quantification of projectile motion. The engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation (MCS). Finally, the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is analyzed. The results show that the disturbance of the pitch angular, pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle, and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle.

Objective To explore the model of "One Core, Multiple Elements" emergency blood donation volunteer team (referred to as the Model) . Methods The Nanjing City Voluntary Blood Donation Joint Meeting serves as the core, with diverse entities including party committees, government departments, district governments, social organizations, enterprises, blood donors, etc. Following the principles of "emergency response in emergencies, wartime readiness, combining regular and wartime efforts," and adhering to the framework of the Model, the emergency blood donation volunteer team system in Nanjing was constructed. Results 1) After the construction of the Model (2018—2022), the total number of emergency blood donation volunteers in Nanjing City increased by 191% compared to the pre-construction period (2013—2017), with an average annual blood donation of 20 929, showing significant differences (P

Abstract The lower tropospheric subtropical circulation (SC) is characterized by monsoons and subtropical highs, playing an important role in global teleconnections and climate variability. The SC changes in a warmer climate are influenced by complex and region-specific mechanisms, resulting in uneven projections worldwide. Here, we present a method to quantify the overall intensity change in global SC, revealing a robust weakening across CMIP6 models. The weakening is primarily caused by global-mean surface warming, and partly counteracted by the direct CO2 effect. The direct CO2 effect is apparent in the transient response but is eventually dominated by the surface warming effect in a slow response. The distinct response timescales to global-mean warming and direct CO2 radiative forcing can well explain the time-varying SC changes in other CO2 emission scenarios. The declined SC implies a contracted monsoon range and drying at its boundary with arid regions under CO2-induced global warming.

Abstract Background Both genetic factors and air pollution are risk factors for coronary artery disease (CAD), but their combined effects on CAD are uncertain. The study aimed to comprehensively investigate their separate, combined and interaction effects on the onset of CAD. Methods We utilized data from the UK Biobank with a recruitment of 487,507 participants who were free of CAD at baseline from 2006 to 2010. We explored the separate, combined effect or interaction association among genetic factors, air pollution and CAD with the polygenic risk score (PRS) and Cox proportional hazard models. Results The hazard ratios (HRs) [95% confidence interval (CI)] of CAD for 10-µg/m3 increases in PM2.5, NO2 and NOx concentrations were 1.25 (1.09, 1.44), 1.03 (1.01, 1.05) and 1.01 (1.00, 1.02), respectively. Participants with high PRS and air pollution exposure had a higher risk of CAD than those with the low genetic risk and low air pollution exposure, and the HRs (95% CI) of CAD in the PM2.5, PM10, NO2 and NOx high joint exposure groups were 1.56 (1.48, 1.64), 1.55(1.48, 1.63), 1.57 (1.49, 1.65), and 1.57 (1.49, 1.65), respectively. Air pollution and genetic factors exerted significant additive effects on the development of CAD (relative excess risk due to the interaction [RERI]: 0.12 (0.05, 0.19) for PM2.5, 0.17 (0.10, 0.24) for PM10, 0.14 (0.07, 0.21) for NO2, and 0.17 (0.10, 0.24) for NOx; attributable proportion due to the interaction [AP]: 0.09 (0.04, 0.14) for PM2.5, 0.12 (0.07, 0.18) for PM10, 0.11 (0.06, 0.16) for NO2, and 0.13 (0.08, 0.18) for NOx). Conclusion Exposure to air pollution was significantly related to an increased CAD risk, which could be further strengthened by CAD gene susceptibility. Additionally, there were positive additive interactions between genetic factors and air pollution on the onset of CAD. This can provide a more comprehensive, precise and individualized scientific basis for the risk assessment, prevention and control of CAD.

Cardiac conduction velocity (CV) is a critical electrophysiological characteristic of the myocardium, representing the speed at which electrical pulses propagate through cardiac tissue. It can be delineated into longitudinal, transverse, and normal components in the myocardium. The CV and its anisotropy ratio are crucial to both normal electrical conduction and myocardial contraction, as well as pathological conditions where it increases the risk of conduction block and reentry. This comprehensive review synthesizes longitudinal and transverse CV values from clinical and experimental studies of human infarct hearts, including findings from the isthmus and outer loop, alongside data derived from animal models. Additionally, we explore the anisotropic ratio of conductivities assessed through both animal and computational models. The review culminates with a synthesis of scientific evidence that guides the selection of CV and its corresponding conductivity in cardiac modeling, particularly emphasizing its application in patient-specific cardiac arrhythmia modeling.

In recent years, unmanned aerial vehicles (UAVs) vision systems based on deep neural networks (DNNs) have made remarkable advancements, demonstrating impressive performance. However, due to the inherent characteristics of DNNs, these systems have become increasingly vulnerable to adversarial attacks. Traditional black-box attack methods typically require a large number of queries to generate adversarial samples successfully. In this paper, we propose a novel adversarial attack technique designed to achieve efficient black-box attacks with a minimal number of queries. We define a perturbation generator that first decomposes the image into four frequency bands using wavelet decomposition and then searches for adversarial perturbations across these bands by minimizing a weighted loss function on a set of fixed surrogate models. For the target victim model, the perturbation images generated by the perturbation generator are used to query and update the weights in the loss function, as well as the weights for different frequency bands. Experimental results show that, compared to state-of-the-art methods on various image classifiers trained on ImageNet (such as VGG-19, DenseNet-121, and ResNext-50), our method achieves a success rate over 98% for targeted attacks and nearly a 100% success rate for non-targeted attacks with only 1–2 queries per image.

Abstract Soil fungi are involved in the decomposition of organic matter, and they alter soil structure and physicochemical properties and drive the material cycle and energy flow in terrestrial ecosystems. Fungal community assembly processes were dissimilar in different soil layers and significantly affected soil microbial community function and plant growth. Grazing exclusion is one of the most common measures used to restore degraded grasslands worldwide. However, changes in soil fungal community characteristics during grazing exclusion in different types of grasslands are unknown. Here, we investigated the effects of a 9‐year grazing exclusion on soil properties, fungal community composition, and diversity in three grassland types (temperate desert, temperate steppe, and mountain meadow). The results showed that (1) in the 0–5 cm soil layer, grazing exclusion significantly increased the differences in SWC, SOC, KN, and N:P among the three grassland types, while the final pH, BD, TP, C:N, and C:P values were consistent with the results before exclusion. In the 5–10 cm soil layer, grazing exclusion significantly increased total phosphorus (TP) in temperate deserts by 34.1%, while significantly decreasing bulk density (BD) by 9.8% and the nitrogen: phosphorus ratio (N:P) by 47.1%. (2) The soil fungal community composition differed among the grassland types, For example, significant differences were found among the three grassland types for the Glomeromycota and Mucoromycota. (3) Under the influence of both grazing exclusion and grassland type, there was no significant change in soil fungal alpha diversity, but there were significant differences in fungal beta diversity. (4) Grassland type was the most important factor influencing changes in fungal community diversity, and vegetation cover and soil kjeldahl nitrogen were the main factors influencing fungal diversity. Our research provides a long‐term perspective for better understanding and managing different grasslands, as well as a better scientific basis for future research on grass–soil–microbe interactions.

Red-bed mudstone (RBM) has the characteristics of being easily softened and disintegrated under dry and wet cycles. To investigate the weakening and disintegration characteristics of the RBM, typical mudstone from Guangxi highway slope was taken as the research object in this study. Disintegration tests, water chemistry tests, and microscopic tests were performed to investigate the disintegration mechanism of this RBM. The results indicate that the RBM exhibits strong disintegration, releasing soluble salts such as potassium chloride and sulfates during the disintegration process. The hydrolysis of clay minerals is the main cause of changes in ion concentration in the solution. Throughout the dry and wet cycles, the proportion of macro-pores and meso-pores significantly increases, while the percentage of small and micro-pores gradually decreases. Under the influence of dry and wet cycles, the contact area between particles gradually decreases, transitioning from face-to-face contact to face-edge contact and finally to edge-to-edge contact. These research findings provide a basis for the stability analysis and prevention of landslide hazards in the red-bed soft rock areas.

Radiation therapy is a primary treatment for hepatocellular carcinoma (HCC), but its effectiveness can be diminished by various factors. The over-expression of PD-L1 has been identified as a critical reason for radiotherapy resistance. Previous studies have demonstrated that nifuroxazide exerts antitumor activity by damaging the Stat3 pathway, but its efficacy against PD-L1 has remained unclear. In this study, we investigated whether nifuroxazide could enhance the efficacy of radiotherapy in HCC by reducing PD-L1 expression. Our results showed that nifuroxazide significantly increased the sensitivity of tumor cells to radiation therapy by inhibiting cell proliferation and migration while increasing apoptosis in vitro. Additionally, nifuroxazide attenuated the up-regulation of PD-L1 expression induced by irradiation, which may be associated with increased degradation of PD-L1 through the ubiquitination-proteasome pathway. Furthermore, nifuroxazide greatly enhanced the efficacy of radiation therapy in H22-bearing mice by inhibiting tumor growth, improving survival, boosting the activation of T lymphocytes, and decelerating the ratios of Treg cells in spleens. Importantly, nifuroxazide limited the increased expression of PD-L1 in tumor tissues induced by radiation therapy. This study confirms, for the first time, that nifuroxazide can augment PD-L1 degradation to improve the efficacy of radiation therapy in HCC-bearing mice.

Ginsenoside Rg5 is a rare ginsenoside showing promising tumor-suppressive effects. This study aimed to explore its radio-sensitizing effects and the underlying mechanisms. Human lung adenocarcinoma cell lines A549 and Calu-3 were used for in vitro and in vivo analysis. Bioinformatic molecular docking prediction and following validation by surface plasmon resonance (SPR) technology, cellular thermal shift assay (CETSA), and isothermal titration calorimetry (ITC) were conducted to explore the binding between ginsenoside Rg5 and 90 kD heat shock protein alpha (HSP90α). The effects of ginsenoside Rg5 on HSP90-cell division cycle 37 (CDC37) interaction, the client protein stability, and the downstream regulations were further explored. Results showed that ginsenoside Rg5 could induce cell-cycle arrest at the G1 phase and enhance irradiation-induced cell apoptosis. It could bind to HSP90α with a high affinity, but the affinity was drastically decreased by HSP90α Y61A mutation. Co-immunoprecipitation (Co-IP) and ITC assays confirmed that ginsenoside Rg5 disrupts the HSP90-CDC37 interaction in a dose-dependent manner. It reduced irradiation-induced upregulation of the HSP90-CDC37 client proteins, including SRC, CDK4, RAF1, and ULK1 in A549 cell-derived xenograft (CDX) tumors. Ginsenoside Rg5 or MRT67307 (an IKKε/TBK1 inhibitor) pretreatment suppressed irradiation-induced elevation of the LC3-II/β ratio and restored irradiation-induced downregulation of p62 expression. In A549 CDX tumors, ginsenoside Rg5 treatment suppressed LC3 expression and enhanced irradiation-induced DNA damage. In conclusion, ginsenoside Rg5 may be a potential radiosensitizer for lung adenocarcinoma. It interacts with HSP90α and reduces the binding between HSP90 and CDC37, thereby increasing the ubiquitin-mediated proteasomal degradation of the HSP90-CDC37 client proteins.

Background: Glioblastoma multiforme is the most common primary intracranial tumor, with a high degree of malignancy, poor therapeutic effect, and poor prognosis. According to previous studies, CHI3L1 and EMP3 are two independent tumor predictors that are of great significance for the prognostic prediction of other tumors, and their expression levels may be related to the prognosis of glioma patients. Methods: using Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), the Chinese Glioma Genome Atlas (CGGA), cBioPortal, LinkedOmics, and other databases, 693 glioma patients were screened to analyze the relationship between EMP3 and CHI3L1 expression and prognosis in glioma patients. Results: low-grade glioma patients with a low expression of EMP3/CHI3L1 had a better prognosis, and the combination of EMP3/CHI3L1 is a new predictor for glioma patients. Conclusion: We used the TCGA and CGGA databases to analyze the effect of EMP3 and CHI3L1 expression on the prognosis of glioma patients and their correlation with gene expression using bioinformation analysis. The results showed that low-grade glioma patients with a low expression of EMP3 and CHI3L1 had a better prognosis, and EMP3 and CHI3L1 co-expression genes were correlated. The combination of these two factors could be a new prognostic index for glioma patients.

Abstract Seasonally frozen soils are exposed to freeze‒thaw cycles every year, leading to mechanical property deterioration. To reasonably describe the deterioration of soil under different conditions, machine learning (ML) technology is used to establish a prediction model for soil static strength. Six key influencing factors (moisture content, compaction degree, confining pressure, freezing temperature, number of freeze‒thaw cycles and thawing duration) are included in the modelling database. The accuracy of three typical ML algorithms (support vector machine (SVM), random forest (RF) and artificial neural network (ANN)) is compared. The results show that the ANN outperforms the SVM and RF. Principal component analysis (PCA) is combined with the ANN, and the PCA–ANN algorithm is proposed, which further improves the prediction accuracy. The deterioration of soil static strength is systematically researched using the PCA–ANN algorithm. The results show that the soil static strength decreased considerably after the first several freeze‒thaw cycles before the strength plateau occurred, and the strength reduction increased significantly with increasing moisture content and compaction degree. The PCA–ANN model can generate a reasonable prediction for the static strength or other soil properties of seasonally frozen soil, which will provide a scientific reference for practical engineering.

Cerussite is an essential lead oxide mineral with increasing economic importance as lead sulfide resources deplete. This study utilizes density functional theory (DFT) to investigate the structural and electronic properties of the sulfurized cerussite (110) surface. The results show that when the cerussite crystal cleaves along the (110) plane, only the surface layer atoms undergo relaxation to reconstruct the surface, while the atoms located deeper have almost no impact on the reconstructed surface structure. The Pb atoms on the cerussite (110) surface react with the sulfurizing agent to form a PbS deposition layer with a structure similar to galena. This PbS deposition layer is tightly adsorbed onto the lead oxide layer through Pb-S bonds formed by S and subsurface lead oxide structure Pb atoms. The chemical reactivity of Pb atoms in the PbS layer on the sulfurized cerussite (110) surface is more potent than that of Pb atoms on the galena surface; additionally, the Pb atoms closer to the lead oxide layer exhibit slightly higher chemical reactivity than those farther away. This study provides insight into sulfurized cerussite surfaces’ structure and properties at an atomic level and assists in explaining the floating behavior of cerussite.

Abstract Hepatocellular carcinoma (HCC), the most prevalent type of aggressive liver cancer, accounts for the majority of liver cancer diagnoses and fatalities. Despite recent advancements in HCC treatment, it remains one of the deadliest cancers. Radiation therapy (RT) is among the locoregional therapy modalities employed to treat unresectable or medically inoperable HCC. However, radioresistance poses a significant challenge. It has been demonstrated that RT induced the upregulation of programmed death ligand 1 (PD-L1) on tumor cells, which may affect response to PD-1-based immunotherapy, providing a rationale for combining PD-1/PD-L1 inhibitors with radiation. Here, we utilized attenuated Salmonella as a carrier to explore whether attenuated Salmonella carrying siRNA-PD-L1 could effectively enhance the antitumor effect of radiotherapy on HCC-bearing mice. Our results showed that a combination of siRNA-PD-L1 and radiotherapy had a synergistic antitumor effect by inhibiting the expression of PD-L1 induced by radiation therapy. Mechanistic insights indicated that the combination treatment significantly suppressed tumor cell proliferation, promoted cell apoptosis, and stimulated immune cell infiltration and activation in tumor tissues. Additionally, the combination treatment increased the ratios of CD4+ T, CD8+ T, and NK cells from the spleen in tumor-bearing mice. This study presents a novel therapeutic strategy for HCC treatment, especially for patients with RT resistance.

Abstract Background Chronic musculoskeletal pain (CMP) is a complex condition that is mainly treated with analgesic drugs. However, antidepressant intervention is also an important factor in the treatment of CMP. Duloxetine is an effective treatment option for patients with CMP as its antidepressant effect. The purpose of this article is to evaluate the efficacy and safety of duloxetine in treating CMP. Databases and data treatment We searched PubMed, Web of Science, Embase, Cochrane Library from inception to May, 2022. Randomized controlled trials (RCTs) evaluating the efficacy and safety of duloxetine versus placebo in patients with CMP were included. We identified 13 articles and studied a population of 4201 participants in 4 countries. Results This meta-analysis showed that the duloxetine has statistically significant compared with the placebo control, benefits on 24-hour average pain, living quality, physical function, and global impressions and there was no difference in the incidence of serious adverse event. In general, duloxetine can improve mood and pain level at the same time. Conclusions This review shows a significant contribution of duloxetine to CMP symptom relief. This meta-analysis improved that duloxetine can significantly reduce the pain level of patients, improve depressive symptoms and global impression, and has no obvious serious adverse reactions. However, additional studies are required to confirm the relationship between psychological diseases and chronic pain and explore their internal links.

Abstract Real-world industrial systems contain a large number of interconnected sensors that generate a significant amount of time series data during system operation. Performing anomaly detection on these multivariate time series data can timely find faults, prevent malicious attacks, and ensure these systems safe and reliable operation. However, the rarity of abnormal instances leads to a lack of labeled data, so the supervised machine learning methods are not applicable. Furthermore, most current techniques do not take full advantage of the spatial and temporal dependencies implied among multiple variables to detect anomalies. Hence, we propose STADN, a novel Anomaly Detection Network Using Spatial and Temporal Information. STADN models the relationship graph between variables for a graph attention network to capture the spatial dependency between variables and utilizes a long short-term memory network to mine the temporal dependency of time series to fully use the spatial and temporal information of multivariate time series. STADN predicts the future behavior of each sensor by combining the historical behavior of the sensor and its neighbors, then detects and locates anomalies according to the prediction error. Furthermore, we improve the proposed model’s ability to discriminate anomaly and regularity and expand the prediction error gap between normal and abnormal instances by reconstructing the prediction errors. We conduct experiments on two real-world datasets, and the experimental results suggested that STADN achieves state-of-the-art outperformance.

The synergistic effect among flotation agents is why combined flotation agents exhibit superior performance compared to single flotation agents. This research investigates the influence of three surfactants with different charges of polar groups, sodium dodecyl sulfate (SDS), cetyltrimethylammonium bromide (CTAB), and octanol, combined with dodecylamine (DDA), on quartz flotation. Through the implementation of flotation tests, bubble–particle adhesion induction time testing, gas–liquid two-phase foam properties testing, and surface tension testing, it is revealed that substituting part of the DDA with these surfactants can either enhance or at least maintain the quartz recovery, affect the adhesion induction time, reduce the surface tension of the flotation system, and change the foaming performance and foam stability, depending on their mole ratio in the combined collector. Compared to DDA alone, combining CTAB or OCT with DDA can significantly increase quartz recovery, while SDS with DDA only yields an approximate recovery. Combining SDS or OCT with DDA can reduce the foam stability, while CTAB with DDA enhances the foam stability. The effect of the combination of surfactants and DDA on the adhesion induction time of quartz grains of different sizes with bubbles is the same; furthermore, there is a negative correlation between the adhesion induction time and the recovery, while the foaming properties and stability of foam are positively correlated with the recovery.

Summary: The spliceosomal snRNP cores, each comprised of a snRNA and a seven-membered Sm ring (D1/D2/F/E/G/D3/B), are assembled by twelve chaperoning proteins in human. However, only six assembly-assisting proteins, ICln and the SMN complex (SMN/Gemin2/Gemin6-8), have been found in Schizosaccharomyces pombe (Sp). Here, we used recombinant proteins to reconstitute the chaperone machinery and investigated the roles of these proteins systematically. We found that, like the human system, the assembly in S. pombe requires ICln and the SMN complex sequentially. However, there are several significant differences. For instance, h_F/E/G forms heterohexamers and heterotrimers, while Sp_F/E/G only forms heterohexamers; h_Gemin2 alone can bind D1/D2/F/E/G, but Sp_Gemin2 cannot. Moreover, we found that Sp_Gemin2 is essential using genetic approaches. These mechanistic studies reveal that these six proteins are necessary and sufficient for Sm core assembly at the molecular level, and enrich our understanding of the chaperone systems in species variation and evolution.

In computer vision, physics plays an important role in several applications. In this work, we teach a machine to detect the mechanical laws of motion of physical objects using video, and show how the results can be useful for computer vision tasks. We assume no prior knowledge of physics, beyond a temporal stream of bounding boxes. The problem is very difficult because a machine must learn not only a governing equation (e.g. projectile motion) but also the existence of governing parameters (e.g. velocities). We evaluate our ability to represent the physical laws of motion in video, such as the movement of a projectile or circular motion, in both real and constructed videos. These elementary tasks have textbook governing equations and enable ground truth verification of our approach. To establish the importance of the proposed method, we show a real-world use case in the domain of object tracking in confounding scenes, where existing state-of-the-art algorithms fail. Incorporating physics into computer vision not only serves the purpose of curiosity-driven research, but also provides an inductive bias for computer vision applications like object tracking.

The foreseeable large-scale deployment of intermittent renewable energy systems in the future and fluctuations in energy markets can severely affect the operation and stability of smart grids, creating significant uncertainty and instability in the electricity supply and demand. It is believed that the mismatch between energy demand of a building and instantaneous renewable energy generation can be reduced by controlling energy use through the energy flexibility of the respective building. Enhancing the energy flexibility of a zero-emission building can satisfy the demands of the energy network around the building and contribute to the resilience of the energy system. Meanwhile, in an electricity tariff model with a distinction between peak and off-peak periods, enhancing energy flexibility in buildings can reduce the operating costs of electricity consumption, with benefits to the economic performance of the building itself. In this study, a simulated hypothetical zero-emission office building near the coast with a floating photovoltaic system and tidal stream generator system in Hong Kong from previous study were used in a case study to investigate the impact of energy flexibility control with stationary batteries as the source of energy flexibility. Two flexibility control strategies were designed to demonstrate their impact on the economic performance of the system. After incorporating the “Peak Demand Management” (PDM) programme in Hong Kong, variations in the economic performance of the system were demonstrated through simulations. Considering the incentive provided by the PDM programme, the possibility of achieving a neutral economic performance at different percentages of renewable energy generation over a 20-year life cycle without the feed-in tariff was investigated. Furthermore, two possible modifications for business models and the PDM programme are proposed. The simulation results indicate that both suggestions can significantly improve the economic performance of the system.

Abstract Background Copy number variation sequencing (CNV‐seq) could detect most chromosomal abnormalities except polyploidy, and quantitative fluorescence polymerase chain reaction (QF‐PCR) is a supplementary method to CNV‐seq in triploid detection. This study aimed to evaluate the feasibility of sequential application of CNV‐seq and QF‐PCR in genetic analysis of miscarriage and stillbirth. Methods A total of 261 fetal specimens were analyzed by CNV‐seq, and QF‐PCR was only further performed for samples with normal female karyotype identified by CNV‐seq. Cost and turnaround time (TAT) was analyzed for sequential detection strategy. Subgroup analysis and logistic regression were carried out to evaluate the relationship between clinical characteristics (maternal age, gestational age, and number of pregnancy losses) and the occurrence of chromosomal abnormalities. Results Abnormal results were obtained in 120 of 261 (45.98%) cases. Aneuploidy was the most common abnormality (37.55%), followed by triploidy (4.98%) and pathogenic copy number variations (pCNVs) (3.45%). CNV‐seq could detect the triploidy with male karyotype, and QF‐PCR could further identify the remaining triploidy with female karyotype. In this study, we found more male triploidies than female triploidies. With the same ability in chromosomal abnormalities detection, the cost of sequential strategy decreased by 17.35% compared with combined strategy. In subgroup analysis, significant difference was found in the frequency of total chromosomal abnormalities between early abortion group and late abortion group. Results of logistic regression showed a trend that pregnant women with advanced age, first‐time abortion, and abortion earlier than 12 weeks were more likely to detect chromosomal aberrations in their products of conception. Conclusion Sequential application of CNV‐seq and QF‐PCR is an economic and practical strategy to identify chromosomal abnormalities in fetal tissue.

Background: We previously developed a non-invasive approach to localize the site of early left ventricular activation origin in real time using 12-lead ECG, and to project the predicted site onto a generic LV endocardial surface using the smallest angle between two vectors algorithm (SA).Objectives: To improve the localization accuracy of the non-invasive approach by utilizing the K-nearest neighbors algorithm (KNN) to reduce projection errors.Methods: Two datasets were used. Dataset #1 had 1012 LV endocardial pacing sites with known coordinates on the generic LV surface and corresponding ECGs, while dataset #2 included 25 clinically-identified VT exit sites and corresponding ECGs. The non-invasive approach used “population” regression coefficients to predict the target coordinates of a pacing site or VT exit site from the initial 120-m QRS integrals of the pacing site/VT ECG. The predicted site coordinates were then projected onto the generic LV surface using either the KNN or SA projection algorithm.Results: The non-invasive approach using the KNN had a significantly lower mean localization error than the SA in both dataset #1 (9.4 vs. 12.5 mm, p < 0.05) and dataset #2 (7.2 vs. 9.5 mm, p < 0.05). The bootstrap method with 1,000 trials confirmed that using KNN had significantly higher predictive accuracy than using the SA in the bootstrap assessment with the left-out sample (p < 0.05).Conclusion: The KNN significantly reduces the projection error and improves the localization accuracy of the non-invasive approach, which shows promise as a tool to identify the site of origin of ventricular arrhythmia in non-invasive clinical modalities.

ObjectiveDespite the promising efficacy and tolerability of alectinib in treating advanced anaplastic lymphoma kinase (ALK) positive non-small cell lung cancer (NSCLC), the role of alectinib in neoadjuvant setting remains understudied in ALK-rearranged resectable lung cancer.MethodsOur report concerns two cases of early-stage NSCLC with complete pathologic responses to off-label use of long-course neoadjuvant alectinib. PubMed, Web of Science, and Cochrane Library were searched comprehensively for ALK-positive resectable cases with neoadjuvant alectinib. The papers were chosen following PRISMA recommendations. Seven cases from the literature and two present cases were evaluated.ResultsTwo cases with stage IIB (cT3N0M0) EML4-ALK lung adenocarcinoma received long-course (more than 30 weeks) of neoadjuvant alectinib followed by R0 lobectomy with the complete pathological response. In our systematic review, 74 studies were included in the original search. Application of the screening criteria resulted in 18 articles deemed eligible for full-text reading. Following the application of the exclusion criteria, out of six papers, seven cases were selected for inclusion in the final analysis and were included in the systematic review. None of the studies were included in the quantitative analysis.ConclusionWe report two cases of lung adenocarcinoma with resectable ALK-positive that achieved pCR with long-course neoadjuvant alectinib. Our cases and a systematic review of the literature support the feasibility of neoadjuvant alectinib treatment for NSCLC. However, large clinical trials must be conducted in the future to determine the treatment course and efficacy of the neoadjuvant alectinib modality.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42022376804.

Currently, space-based information networks, represented by satellite Internet, are rapidly developing. UAVs can serve as airborne mobile terminals, representing a novel node in satellite IoT, offering more accurate and robust data streaming for connecting global satellite–UAV collaborative IoT systems. It is characterized by high-speed dynamics, with node distances and visibility constantly changing over time. Therefore, there is a need for faster and higher-quality topology optimization research. A reliable, secure, and adaptable network topology optimization algorithm has been proposed to handle various complex scenarios. Additionally, considering the dynamic and time-varying nature of these types of networks, the concept of time slices has been introduced to accelerate the iterative efficiency of problem-solving. Experimental results demonstrate that the proposed algorithm is expected to exhibit better convergence and performance in subsequent iterations compared with traditional solutions. Besides being a solution for topology optimization, the proposed algorithm offers a new way of thinking, enabling the handling of larger satellite–UAV collaborative IoT systems.

As an excellent chicken breed found in a high-altitude zone of northern China, Lindian chickens are characterized by good egg and meat production, strong adaptability, cold tolerance, rough feeding resistance, excellent egg quality, and delicious meat quality. To facilitate the exploitation of the unique qualities of the Lindian chicken, the varying patterns and correlations of various body size and carcass traits of 3–22-week-old Lindian chickens were analyzed in this study. The optimal growth model of these traits was determined by growth curve fitting analysis. The results showed that most traits of Lindian chickens increased steadily with increasing age, and most of them increased rapidly before 10 weeks of age. In addition, the inflection point age of each trait was predicted to be between 4 and 10 weeks. Furthermore, this study revealed that body size traits were closely related to carcass traits in Lindian chickens. In summary, Lindian chickens are in a rapid growth stage before the age of 10 weeks, and better slaughter performance can be achieved through good feeding management during this stage. The reproductive traits and muscles are the main developmental focus after the age of 19 weeks, so it is important to adequately meet their energy requirements for subsequent good breeding performance.

Artificial Intelligence has been widely applied in intelligent transportation systems. In this work, Swin-APT, a deep learning-based approach for semantic segmentation and object detection in intelligent transportation systems is presented. Swin-APT includes a lightweight network and a multiscale adapter network designed for image semantic segmentation and object detection tasks. An inter-frame consistency module is proposed to extract more accurate road information from images. Experimental results on four datasets: BDD100K, CamVid, SYNTHIA, and CeyMo, demonstrate that Swin-APT outperforms the baseline by 13.1%. Furthermore, experiments on the road marking detection benchmark show an improvement of 1.85% of mAcc.

With the rapid advancement of unmanned aerial vehicles (UAVs) and the Internet of Things (IoTs), UAV-assisted IoTs has become integral in areas such as wildlife monitoring, disaster surveillance, and search and rescue operations. However, recent studies have shown that these systems are vulnerable to adversarial example attacks during data collection and transmission. These attacks subtly alter input data to trick UAV-based deep learning vision systems, significantly compromising the reliability and security of IoTs systems. Consequently, various methods have been developed to identify adversarial examples within model inputs, but they often lack accuracy against complex attacks like C&W and others. Drawing inspiration from model visualization technology, we observed that adversarial perturbations markedly alter the attribution maps of clean examples. This paper introduces a new, effective detection method for UAV vision systems that uses attribution maps created by model visualization techniques. The method differentiates between genuine and adversarial examples by extracting their unique attribution maps and then training a classifier on these maps. Validation experiments on the ImageNet dataset showed that our method achieves an average detection accuracy of 99.58%, surpassing the state-of-the-art methods.

Abstract Fibrosis is characterized by the excessive extracellular matrix deposition due to dysregulated wound and connective tissue repair response. Multiple organs can develop fibrosis, including the liver, kidney, heart, and lung. Fibrosis such as liver cirrhosis, idiopathic pulmonary fibrosis, and cystic fibrosis caused substantial disease burden. Persistent abnormal activation of myofibroblasts mediated by various signals, such as transforming growth factor, platelet-derived growth factor, and fibroblast growh factor, has been recongized as a major event in the occurrence and progression of fibrosis. Although the mechanisms driving organ-specific fibrosis have not been fully elucidated, drugs targeting these identified aberrant signals have achieved potent anti-fibrotic efficacy in clinical trials. In this review, we briefly introduce the aetiology and epidemiology of several fibrosis diseases, including liver fibrosis, kidney fibrosis, cardiac fibrosis, and pulmonary fibrosis. Then, we summarise the abnormal cells (epithelial cells, endothelial cells, immune cells, and fibroblasts) and their interactions in fibrosis. In addition, we also focus on the aberrant signaling pathways and therapeutic targets that regulate myofibroblast activation, extracellular matrix cross-linking, metabolism, and inflammation in fibrosis. Finally, we discuss the anti-fibrotic drugs based on their targets and clinical trials. This review provides reference for further research on fibrosis mechanism, drug development, and clinical trials.

Forests are generally extracted from remotely sensed images based on the spectral features, ignoring other important auxiliary information, and the techniques of precise extraction need to be further improved. By using the Sentinel–2 image and auxiliary factors (AFs) including site conditions (SCs) and vegetation indices (VIs), the random forest model with AFs (RF–AFs) was adopted for the extraction of the economic forests in Lancang County, which is a mountainous area with rich biodiversity and is witnessing rapid development of economic forests in Yunnan province of China. The results obtained using the RF–AFs model were compared with those obtained using the random forest model without AFs (RF). The results were as follows: (1) The kappa coefficient for extracting the first–level land use obtained using the RF model was 0.9531. Lancang County is dominated by forests, accounting for 73.76% of the total area. (2) After parameter optimization, the RF–AFs model yielded the highest accuracy in the extraction of the second–level forests, with a kappa coefficient value of 0.9493, which was 14.69% higher than that of the RF model. Thus, the RF–AFs model is more suitable for the precise extraction of economic forests. (3) The evaluation results of the factors’ importance of the RF–AFs model showed that the cumulative importance values of SCs such as temperature (TEM), elevation (EL), precipitation (PRE) and VIs such as plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), transformed soil–adjusted vegetation index (TSAVI) was 76.09%, indicating that they were the main factors for the extraction of economic forests. (4) Economic forests are dominated by Simao pines in Lancang County, which are mainly distributed in the central, southwestern and northern regions, accounting for 31.37% of forests area. The proportion of tea plantations, eucalyptus, and rubber trees is 9.05%, 6.71%, and 3.05% of forests area, respectively. The RF–AFs model is conducive for precisely extracting the economic forests and is thus of great significance in studying the ecological and environmental effects of economic forests, performing forestry management, and maintaining regional ecological security.

Spatial information network is a kind of satellite network with high speed node movement and fast dynamic topology change. With the increasing number of low-orbit satellites, the research on the subnets topology and dynamic optimization of space information networks has become an important direction to study the destructibility of spatial information network. In this paper, two common objective functions in inter-satellite link assignment, network observation position and network communication factor are studied, and a multi-objective optimization model is constructed. Depth first search, simulated annealing, NSGA-II and adaptive optimization simulated annealing were used to analyze and solve the model. By comparing the solving efficiency of the model through simulation experiments, the difference of the results caused by the four algorithms is verified.

Visual simultaneous localization and mapping is a widely used technology for mobile robots to carry out precise positioning in the environment of GNSS technology failure. However, as the robot moves around indoors, its position accuracy will gradually decrease over time due to common and unavoidable environmental factors. In this paper, we propose an improved method called RTABMAP-VIWO, which is based on RTABMAP. The basic idea is to use an Extended Kalman Filter (EKF) framework for fusion attitude estimates from the wheel odometry and IMU, and provide new prediction values. This helps to reduce the local cumulative error of RTABMAP and make it more accurate. We compare and evaluate three kinds of SLAM methods using both public datasets and real indoor scenes. In the dataset experiments, our proposed method reduces the Root-Mean-Square Error (RMSE) coefficient by 48.1% compared to the RTABMAP, and the coefficient is also reduced by at least 29.4% in the real environment experiments. The results demonstrate that the improved method is feasible. By incorporating the IMU into the RTABMAP method, the trajectory and posture errors of the mobile robot are significantly improved.

Significant progress has been made in object tracking tasks thanks to the application of deep learning. However, current deep neural network-based object tracking methods often rely on stacking sub-modules and introducing complex structures to improve tracking accuracy. Unfortunately, these approaches are inefficient and limit the feasibility of deploying efficient trackers on drone AI devices. To address these challenges, this paper introduces ConcatTrk, a high-speed object tracking method designed specifically for drone AI devices. ConcatTrk utilizes a lightweight network architecture, enabling real-time tracking on edge devices. Specifically, the proposed method primarily uses the concatenation operation to construct its core tracking steps, including multi-scale feature fusion, intra-frame feature matching, and dynamic template updating, which aim to reduce the computational overhead of the tracker. To ensure tracking performance in UAV tracking scenarios, ConcatTrk implements a learnable feature matching operator along with a simple and efficient template constraint branch, which enables accurate tracking by discriminatively matching features and incorporating periodic template updates. Results of comprehensive experiments on popular benchmarks, including UAV123, OTB100, and LaSOT, show that ConcatTrk has achieved promising accuracy and attained a tracking speed of 41 FPS on an edge AI device, Nvidia AGX Xavier. ConcatTrk runs 8× faster than the SOTA tracker TransT while using 4.9× fewer FLOPs. Real-world tests on the drone platform have strongly validated its practicability, including real-time tracking speed, reliable accuracy, and low power consumption.

BackgroundThere were new points of interest in performing subsegmentectomy and segmentectomy for patients with early stage non-small cell lung cancer (NSCLC). However, whether patients who underwent subsegmentectomy could obtain satisfactory clinical outcomes remains unclear. The present study aimed to compare the clinical outcomes and security of surgical procedures between subsegmentectomy and segmentectomy.MethodsA systematic review and meta-analysis was performed through five online databases to identify the included literatures which presented intact clinical outcome data among different surgical procedures. The included studies were evaluated based on precise and predefined inclusion criteria.ResultsThere were 4 published studies identified in this meta-analysis. A total of 325 patients who underwent subsegmentectomy and 904 patients who underwent segmentectomy were involved in this analysis. The duration of drainage [MD −0.19; 95%CI (−0.36, −0.02), p = 0.03] and postoperative hospital stay [MD −0.30; 95%CI (−0.58, −0.02), p = 0.009] of subsegmentectomy were significantly less than that of segmentectomy. There was no statistically significant difference among recurrence rate [OR 0.85; 95%CI (0.21, 3.42), p = 0.82], operation time, blood loss, incidence of complications [OR 0.83; 95%CI (0.58, 1.20), p = 0.33] between subsegmentectomy and segmentectomy in patients with stage IA NSCLC.ConclusionThe meta-analysis was firstly performed to compare perioperative outcomes among surgical procedures. The perioperative outcomes were comparable between subsegmentectomy and segmentectomy. Subsegmentectomy might be an alternative treatment for the deep tumor with size less than 1.5 cm and mainly composed of Ground Glass Opacity (GGO).

Background: The underlying mechanisms of the potential tumor-suppressive effects of ginsenoside Rh2 are complex. N6-methyladenosine (m6A) RNA methylation is usually dysregulated in cancer. This study explored the regulatory effect of ginsenoside Rh2 on m6A RNA methylation in cancer.Methods: m6A RNA quantification and gene-specific m6A RIP-qPCR assays were applied to assess total and gene-specific m6A RNA levels. Co-immunoprecipitation, fractionation western blotting, and immunofluorescence staining were performed to detect protein interactions and distribution. QRT-PCR, dual-luciferase, and ChIP-qPCR assays were conducted to check the transcriptional regulation. Results: Ginsenoside Rh2 reduces m6A RNA methylation and KIF26B expression in a dose-dependent manner in some cancers. KIF26B interacts with ZC3H13 and CBLL1 in the cytoplasm of cancer cells and enhances their nuclear distribution. KIF26B inhibition reduces m6A RNA methylation level in cancer cells. SRF bound to the KIF26B promoter and activated its transcription. SRF mRNA m6A abundance significantly decreased upon KIF26B silencing. SRF knockdown suppressed cancer cell proliferation and growth both in vitro and in vivo, the effect of which was partly rescued by KIF26B overexpression.Conclusion: ginsenoside Rh2 reduces m6A RNA methylation via downregulating KIF26B expression in some cancer cells. KIF26B elevates m6A RNA methylation via enhancing ZC3H13/CBLL1 nuclear localization. KIF26B-SRF forms a positive feedback loop facilitating tumor growth.

Abstract Space information networks is network systems that can receive, transmit, and process spatial information lively. It uses satellites, stratosphere airships, Unmanned Aerial Vehicles, and other platforms as the carrier. It supports high-dynamic, real-time broadband transmission of earth observations and ultra-long-distance, long-delay reliable transmission of deep space exploration. The deeper the network integration, the higher the system’s security concerns and the more likely SINs will be controlled and destroyed in terms of cybersecurity. How to integrate new IT technologies such as artificial intelligence, digital twins, and blockchain to diverse application scenarios of SINs while maintaining SIN cybersecurity will be a long-term critical technical issue. This study is a review of the security issues for space information networks. First, this paper examines space information networks’ security issues and figures out the relationship between the main security threats, services, and mechanisms. Then, this article selects secure routing and anomaly detection from many security technologies to conduct a detailed overview from two perspectives of traditional methods and artificial intelligence. Subsequently, this paper investigates anomaly detection schemes for spatial information networks and proposes a deep learning-based anomaly detection scheme. Finally, we suggest the potential research directions and opening problems of space information network security. Overall, this paper aims to give readers an overview of the newly emerging technologies in space information networks’ security issues and provide inspiration for future exploration.

Background and objective Preliminary researches conformed that neoadjuvant immunotherapy combined with chemotherapy had a significant short-term effect in resectable non-small cell lung cancer (NSCLC), but there were few clinical trials about neoadjuvant chemoimmunotherapy in China. We aimed to assess retrospectively the antitumour activity and safety of neoadjuvant chemoimmunotherapy for resectable stage Ib-IIIb NSCLC. Methods Twenty patients who had been diagnosed as stage Ib-IIIb NSCLC and received chemoimmunotherapy as neoadjuvant treatment between November 2019 and December 2020, in Beijing Chest Hospital, Capital Medical University were recruited. These patients received neoadjuvant treatment for 21 days as a cycle and antitumour activity and safety were evaluated every two cycles. Results Of 20 patients received neoadjuvant chemoimmunotherapy, 17 patients underwent surgical resection. 16 patients had R0 resection (no residual tumor resection) and 1 patient had R1 resection (microscopic residual tumor resection). Radiographic objective response rate (ORR) was 85.0% (4 complete response, 13 partial response). 5.0% (1/20) of patients had stable disease, and 10.0% (2/20) of patients had progression disease. The major pathologic response (MPR) was 47.1% (8/17), and complete pathologic response (CPR) was 29.4% (5/17). 1 case developed grade IV immune-related pneumonia (IRP) and 9 (45.0%) cases had grade III hematologic toxicity. Conclusion Immunotherapy combined with chemotherapy as neoadjuvant therapy has a better efficiency and tolerable adverse effects for patients with resectable NSCLC in stage Ib-IIIb.

Purpose The function of BZRAP1-AS1 is unknown in lung cancer. We evaluated the clinicopathologic significance of BZRAP1-AS1, and its role in non-small-cell lung cancer (NSCLC) progression. Patient and methods Sixty-three NSCLC patients from Beijing Chest Hospital were included. The expression of BZRAP1-AS1 was detected by real-time quantitative polymerase chain reaction (RT-qPCR) in tumor tissues and adjacent normal tissues. Then, the clinicopathological significance and prognostic value of BZRAP1-AS1 were analyzed by using our cohort and TCGA cohort. Finally, the effect of BZRAP1-AS1 on proliferation and motility of NSCLC cell lines were evaluated by cell growth assay, colony formation assay, xenograft tumorigenesis experiment in nude mice and transwell assays respectively. Results Compared with adjacent normal tissues, BZRAP1-AS1 showed lower expression in NSCLC tumor tissues. As for the relationship between BZRAP1-AS1 and clinical characteristics, our results were consistent with those of TCGA data. BZRAP1-AS1 was lower in T1 than T2–T4 patients, N1-N3 than N0 patients. Low level BZRAP1-AS1 was related to shorter overall survival time (OS) in lung adenocarcinoma (LUAD), and poor first progression time (FP) in LUAD and lung squamous cell carcinoma (LUSC) patients. BZRAP1-AS1 was significantly associated with the prognosis of NSCLC patients. Overexpression of BZRAP1-AS1 inhibited proliferation and migration of H1299 and HCC827 cells. Conclusion BZRAP1-AS1 expression decreases in tumor tissues with the increase of malignancy grades in NSCLC. BZRAP1-AS1 plays an anticancer role by inhibiting cell proliferation, invasion, and metastasis, and has a potential prognostic value in NSCLC. BZRAP1-AS1 may serve as a diagnostic marker and therapeutic target for NSCLC.

Abstract Seasonal forecasts at lead times of 1–12 months for sea surface temperature (SST) anomalies (SSTAs) in the offshore area of China are a considerable challenge for climate prediction in China. Previous research suggests that a model-based analog forecasting (MAF) method based on the simulations of coupled global climate models provide skillful climate forecasts of tropical Indo-Pacific SSTAs. This MAF method selects the model-simulated cases close to the observed initial state as a model-analog ensemble, and then uses the subsequent evolution of the SSTA to generate the forecasts. In this study, the MAF method is applied to the offshore area of China (0°–45°N, 105°–135°E) based on the simulations of 23 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the period 1981–2010. By optimizing the key factors in the MAF method, we suggest that the optimal initial field for the analog criteria should be concentrated in the western North Pacific. The multi-model ensemble of the optimized MAF prediction using these 23 CMIP6 models shows anomaly correlation coefficients exceeding 0.6 at the 3-month lead time, which is much improved relative to previous SST-initialized hindcasts and appears practical for operational forecasting.

Mobile payment services have been widely applied in our daily life, where users can conduct transactions in a convenient way. However, critical privacy concerns have arisen. Specifically, a risk of participating in a transaction is the disclosure of personal privacy. This might occur if, for example, the user pays for some special medicine, such as AIDS medicine or contraceptives. In this paper, we propose a mobile payment protocol that is suitable for mobile devices only with limited computing resources. In particular, the user in a transaction can confirm the identity of others in the same transaction while the user cannot show convincing evidence to prove that others also take part in the same transactions. We implement the proposed protocol and test its computation overhead. The experiment results corroborate that the proposed protocol is suitable for mobile devices with limited computing resources.

Meaningful representation of large-scale non-Euclidean structured data, especially in complex domains like network security and IoT system, is one of the critical problems of contemporary machine learning and deep learning. Many successful cases of graph-based models and algorithms deal with non-Euclidean structured data. However, It is often undesirable to derive node representations by walking through the complete topology of a system or network (graph) when it has a very big or complicated structure. An important issue is using neighborhood knowledge to deduce the symmetric network’s topology or graph. The traditional approach to solving the graph representation learning issue is surveyed from machine learning and deep learning perspectives. Second, include local neighborhood data encoded to the attention mechanism to define node solidarity and enhance node capture and interactions. The performance of the proposed model is then assessed for transduction and induction tasks that include downstream node categorization. The attention model taking clustering into account has successfully equaled or reached the state-of-the-art performance of several well-established node classification benchmarks and does not depend on previous knowledge of the complete network structure, according to experiments. Following a summary of the research, we discuss problems and difficulties that must be addressed for developing future graph signal processing algorithms and graph deep learning models, such as graph embeddings’ interpretability and adversarial resilience. At the same time, it has a very positive impact on network security and artificial intelligence security.

Recently, security and privacy issues in mobile communication systems became an enormous challenge due to the limited resources of mobile networks regarding communication overhead, computational cost, and battery power. 5G systems provide high performance and flexibility that can connect billion of objects through Heterogeneous Wireless Networks (HWN) concerning the Internet of Things (IoT). However, some of these security challenges can be addressed by using outsourced assistants such as Server-Aided Verification (SAV) to contribute partially with the authentication process among 5G network entities. In this paper, we propose an efficient and secure identity-based signature scheme with Server-Aided Verification for 5G mobile systems (IBS-SAV). We provide a performance evaluation based on security proof of the proposed IBS-SAV scheme under existential unforgeable in the random oracle model as well as the security against collusion and adaptive chosen message attack (EUF-CMA). The performance evaluation and security analysis demonstrate that our IBS-SAV scheme is not only secure but also can reduce the communication and computation complexity for mobile systems efficiently.