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We investigated the challenge of unconstrained distributed optimization with a time-varying objective function, employing a prediction-correction approach. Our method introduced a backward Euler prediction step that used the differential information from consecutive moments to forecast the trajectory's future direction. This predicted value was then refined through an iterative correction process. Our analysis and experimental results demonstrated that this approach effectively addresses the optimization problem without requiring the computation of the Hessian matrix's inverse.

Abstract Lung cancer is the primary cause of cancer-related death worldwide, and its global incidence and mortality rates remain high. The differential expression of circular RNAs (circRNAs) can affect the development of cancer, but the mechanisms by which circRNAs regulate lung cancer progression remain unclear. In this study, we identified circSORBS1, a circRNA that has not been previously described in lung cancer and is significantly underexpressed in lung cancer tissues, blood and cell lines, and the low expression of circSORBS1 correlated with tumour grade and prognosis. In vitro and in vivo functional experiments revealed that circSORBS1 overexpression inhibited cell proliferation and migration while enhancing apoptosis. Mechanistically, circSORBS1 acts as a sponge for miR-6779-5p, indirectly inhibiting RUFY3 mRNA degradation. Simultaneously, it binds to RUFY3 mRNA to enhance its stability. This dual regulatory mechanism leads to an increase in RUFY3 protein levels, which ultimately activates the YWHAE/BAD/BCL2 apoptotic signalling pathway and suppresses lung cancer progression. Our findings not only increase the knowledge about the regulatory pattern of circRNA expression but also provide new insights into the mechanisms by which circRNAs regulate lung cancer development.

The expansion applications of semiconducting polymer dots (Pdots) among optical nanomaterial field have long posed a challenge for researchers, promoting their intelligent application in multifunctional nano-imaging systems and integrated nanomedicine carriers for diagnosis and treatment. Despite notable progress, several inadequacies still persist in the field of Pdots, including the development of simplified near-infrared (NIR) optical nanoprobes, elucidation of their inherent biological behavior, and integration of information processing and nanotechnology into biomedical applications. This review aims to comprehensively elucidate the current status of Pdots as a classical nanophotonic material by discussing its advantages and limitations in terms of biocompatibility, adaptability to microenvironments in vivo, etc. Multifunctional integration and surface chemistry play crucial roles in realizing the intelligent application of Pdots. Information visualization based on their optical and physicochemical properties is pivotal for achieving detection, sensing, and labeling probes. Therefore, we have refined the underlying mechanisms and constructed multiple comprehensive original mechanism summaries to establish a benchmark. Additionally, we have explored the cross-linking interactions between Pdots and nanomedicine, potential yet complete biological metabolic pathways, future research directions, and innovative solutions for integrating diagnosis and treatment strategies. This review presents the possible expectations and valuable insights for advancing Pdots, specifically from chemical, medical, and photophysical practitioners’ standpoints.

With integration of large-scale renewable energy, new controllable devices, and required reinforcement of power grids, modern power systems have typical characteristics such as uncertainty, vulnerability and openness, which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability, interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation, this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First, theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally, state-of-the-art and future development of HHI in power grid regulation are summarized, aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

To improve the delightful flavor of mulberry wine through semi-artificial inoculation fermentation with Saccharomyces cerevisiae, we studied the dynamics change of microbiota, along with the physicochemical properties and metabolite profiles and their interaction relationship during the fermentation process. The abundance of lactic acid bacteria (Weissella, Lactobacillus, Fructobacillus, and Pediococcus) increased significantly during fermentation, while yeasts gradually established dominance. The inter-kingdom network of the dominant genera analysis further identified the following as core microbiota: Alternaria, Botrytis, Kazachstania, Acremonium, Mycosphaerella, Pediococcus, Gardnerella, and Schizothecium. Additionally, pH, alcohol, and total acid were significantly affected by microbiota variation. Fourteen of all identified volatile compounds with key different aromas were screened using PCA, OPLS-DA, and rOAV. The network of interconnected core microbiota with key different aromas revealed that Kazachstania and Pediococcus had stronger correlations with 1-butanol, 3-methyl-, propanoic acid, and 2-methyl-ethyl ester.

Abstract Introduction Venous thromboembolism(VTE) is a leading cause of death in patients with lung cancer. Furthermore, hospitalization of patients with advanced lung cancer for VTE treatment represents a major economic burden on the national public health resources. Therefore, we performed this prospective study to identify clinical biomarkers for the early identification of VTE in lung cancer patients. Methods This prospective study enrolled 158 patients with confirmed lung cancer, including 27 who were diagnosed with VTE within six months of the follow-up after lung cancer diagnosis. Multivariate logistic regression analysis was used to evaluate the diagnostic performancese of all the relevant clinical features and laboratory indicators in identifying lung cancer patients with a higher risk of VTE. A novel risk prediction model was constructed consisting of five clinical variables with the best diagnostic performances and was validated using the receiver operation characteristic(ROC) curves. The diagnostic performances of the new risk prediction model was also compared with the Khorana risk score (KRS) and the Padua risk score (PRS). Results The VTE group of lung cancer patients (n = 27) showed significantly higher serum levels of fibrin degradation products (FDP), D-dimer, thrombomodulin (TM), thrombin-antithrombin-complex (TAT), α2-plasmin inhibitor-plasmin Complex (PIC), and tissue plasminogen activator-plasminogen activator inhibitor complex (t-PAIC) compared to those in the non-VTE group (n = 131). ROC curve analyses showed that the diagnostic efficacy of the new VTE risk prediction model with TM ≥ 9.75 TU/ml, TAT ≥ 2.25ng/ml, t-PAIC ≥ 7.35ng/ml, history of VTE, and ECOG PS score ≥ 2 was superior than the KRS and the PRS in the early identification of lung cancer patients with a higher risk of VTE. Conclusions The new risk prediction model showed significantly high diagnostic efficacy in the early identification of lung cancer patients with a high risk of VTE. The diagnostic efficacy of the new risk prediction model was higher than the KRS and the PRS in this cohort of lung cancer patients.

UAVs are increasingly prominent in the competition for space due to their multiple characteristics, such as strong maneuverability, long flight distance, and high survivability. A new online safe flight control method based on constrained reinforcement learning is proposed for the intelligent safety control of UAVs. This method adopts constrained policy optimization as the main reinforcement learning framework and develops a constrained policy optimization algorithm with extra safety budget, which introduces Lyapunov stability requirements and limits rudder deflection loss to ensure flight safety and improves the robustness of the controller. By efficiently interacting with the constructed simulation environment, a control law model for UAVs is trained. Subsequently, a condition-triggered meta-learning online learning method is used to adjust the control raw online ensuring successful attitude angle tracking. Simulation experimental results show that using online control laws to perform aircraft attitude angle control tasks has an overall score of 100 points. After introducing online learning, the adaptability of attitude control to comprehensive errors such as aerodynamic parameters and wind improved by 21% compared to offline learning. The control law can be learned online to adjust the control policy of UAVs, ensuring their safety and stability during flight.

Measurements of Very-Low-Frequency (VLF) transmitter signals have been widely used to investigate the effects of various space weather events on the D-region ionosphere, including nowcasting solar flares. Previous studies have established a method to nowcast solar flares using VLF measurements, but only using measurements from dayside propagation paths, and there remains limited focus on day–night mixed paths, which are important for method applicability. Between March and May of 2022, the Sun erupted a total of 56 M-class and 6 X-class solar flares, all of which were well captured by our VLF receiver in Antarctica. Using these VLF measurements, we reexamine the capability of the VLF technique to nowcast solar flares by including day–night mixed propagation paths and expanding the path coverage in longitude compared to that in previous studies. The amplitude and phase maximum changes are generally positively correlated with X-ray fluxes, whereas the time delay is negatively correlated. The curve-fitting parameters that we obtain for the X-ray fluxes and VLF signal maximum changes are consistent with those in previous studies for dayside paths, even though different instruments are used, supporting the flare-nowcasting method. Moreover, the present results show that, for day–night mixed paths, the amplitude and phase maximum changes also scale linearly with the logarithm of the flare X-ray fluxes, but the level of change is notably different from that for dayside paths. The coefficients used in the flare-nowcasting method need to be updated for mixed propagation paths.

In this work, a trilayer composite film of alginate-polyvinylpyrrolidone-alginate loaded with β-lactoglobulin nanoparticles was prepared based on the layer-by-layer assembly. The formation of β-lactoglobulin nanoparticles was induced by adjusting the ambient pH and temperature in combination with binding apigenin. The formation pattern and storage stability of β-lactoglobulin nanoparticles were investigated by particle size, polydispersity and Zeta potential. Then, the β-lactoglobulin nanoparticles were loaded into sodium alginate-polyvinylpyrrolidone-sodium alginate trilayer composite film to investigate the effects of nano-loading on the mechanical, transmission, optical and thermal properties of the film. The results showed that β-lactoglobulin nanoparticles with good stability could be obtained by adjusting the ambient pH to 7.1 and heating the temperature to 75 ℃ under the protein/ligand molar ratio of 1:8. The mechanical properties and water vapor barrier of the films were significantly improved when the nanoparticles were added to the trilayer composite films at concentrations of 0.2 and 0.3 mg/mL. Moreover, the addition of β-lactoglobulin nanoparticles improved the light transmittance and thermal stability of the trilayer composite film. In conclusion, the trilayer composite film made of alginate loaded with β-lactoglobulin nanoparticles exhibited good packaging properties and high application potential.

Nowadays, high mobility scenarios have become increasingly common. The widespread adoption of High-speed Rail (HSR) in China exemplifies this trend, while more promising use cases, such as vehicle-to-everything, continue to emerge. However, the Internet access provided in high mobility environments still struggles to achieve seamless connectivity. The next generation of wireless cellular technology 5 G further poses more requirements on the end-to-end evolution to fully utilize its ultra-high band-width, while existing network diagnostic tools focus on above-IP layers or below-IP layers only. We then propose HiMoDiag, which enables flexible online analysis of the network performance in a cross-layer manner, i.e., from the top (application layer) to the bottom (physical layer). We believe HiMoDiag could greatly simplify the process of pinpointing the deficiencies of the Internet access delivery on HSR, lead to more timely optimization and ultimately help to improve the network performance.

Abstract Background Many studies have shown that circular RNAs (circRNAs) are abnormally expressed in various tumor tissues and served as a key regulator in the occurrence and development of cancer. However, in hepatocellular carcinoma (HCC), the molecular mechanism of circRNAs in body fluids remains to be further explored. Methods The expression levels of genes and proteins were detected by quantitative real‐time polymerase chain reaction (qRT‐PCR) and western blotting, respectively. Cell counting Kit‐8 (CCK‐8), 5‐Ethynyl‐2'‐deoxyuridine (EdU), wound healing assay, Transwell assays, flow cytometry, and tumor formation models in nude mice were conducted to investigate the effects of circFAM114A2 on HCC cells both in vitro and in vivo. RNA antisense purification (RAP), dual luciferase reporter assays and rescue assays were carried out to verify the interaction between circFAM114A2, miR‐630 and HHIP. Results CircFAM114A2 was significantly downregulated in HCC tissues and was associated with microvascular invasion and lymph node metastasis of HCC patients. We also observed that circFAM114A2 was lowly expressed in HCC plasma, which may serve as an effective biomarker to screen HCC patients from healthy controls (area under curve (AUC)=0.922). In vitro, circFAM114A2 overexpression significantly blunted HCC cell proliferation, migration, invasion, and promoted apoptosis, whereas circFAM114A2 silencing posed opposite effects. In vivo, circFAM114A2 overexpression inhibited the growth of HCC cells. Mechanistically, circFAM114A2 could increase the expression of the tumor suppressor HHIP via acting as a sponge for miR‐630. Conclusions CircFAM114A2 exerts a tumor suppressor role in HCC through miR‐630/HHIP axis, and may be served as a potential diagnostic and therapeutic biomarker for HCC patients.

The exploration of optical and photonic phenomena, particularly the modulation of pulse signals and the ultrafast control of light fields at extreme temporal and spatial scales, substantially enhances our understanding of light–matter interactions and broadens the scope of potential applications inspired by metamaterials and metasurfaces. In this perspective, we highlight advancements in ultrafast metaphotonics by introducing ultrafast pulse shaping and control using metadevices. We begin with a detailed exposition of the principles of metasurfaces and evaluate their role in manipulating light fields in high-frequency and terahertz bands, emphasizing the importance of metasurfaces in ultrafast optics. We then present several methods for controlling the output response of metadevices using external physical fields or phase-change materials to achieve active metadevices. Finally, we anticipate the prospects of this field in terms of fundamental research and practical applications. The integration of these 2 disciplines will drive vibrant developments across multiple fields, including biology, chemistry, and materials science.

When agents need to collaborate without previous coordination, the multi-agent cooperation problem transforms into an ad hoc teamwork (AHT) problem. Mainstream research on AHT is divided into type-based and type-free methods. The former depends on known teammate types to infer the current teammate type, while the latter does not require them at all. However, in many real-world applications, the complete absence and sufficient knowledge of known types are both impractical. Thus, this research focuses on the challenge of AHT with limited known types. To this end, this paper proposes a method called a Few typE-based Ad hoc Teamwork via meta-reinforcement learning (FEAT), which effectively adapts to teammates using a small set of known types within a single episode. FEAT enables agents to develop a highly adaptive policy through meta-reinforcement learning by employing limited priors about known types. It also utilizes this policy to generate a diverse type repository automatically. During the ad hoc cooperation, the agent can autonomously identify known teammate types followed by directly utilizing the pre-trained optimal cooperative policy or swiftly updating the meta policy to respond to teammates of unknown types. Comprehensive experiments in the pursuit domain validate the effectiveness of the algorithm and its components.

Quasi-NPR (negative Poisson’s ratio) steel is a new type of super bolt material with high strength, high ductility, and a micro-negative Poisson’s effect. This material overcomes the contrasting characteristics of the high strength and high ductility of steel and it has significant energy-absorbing characteristics, which is of high value in deep rock and soil support engineering. However, research on the shear resistance of quasi-NPR steel has not been carried out. To study the shear performance of quasi-NPR steel bolted rock joints, indoor shear tests of bolted rock joints under different normal stress conditions were carried out. Q235 steel and #45 steel, two representative ordinary bolt steels, were set up as a control group for comparative tests to compare and analyze the shear strength, deformation and instability mode, shear energy absorption characteristics, and bolting contribution of different types of bolts. The results show that the jointed rock masses without bolt reinforcement undergo brittle failure under shear load, while the bolted jointed rock masses show obvious ductile failure characteristics. The shear deformation capacity of quasi-NPR steel is more than 3.5 times that of Q235 steel and #45 steel. No fracture occurs in the quasi-NPR steel during large shear deformation and it can provide stable shear resistance. However, the other two types of control bolts become fractured under the same conditions. Quasi-NPR steel has significant energy-absorbing characteristics under shear load and has obvious advantages in terms of absorbing the energy released by shear deformation of jointed rock masses as compared with ordinary steel. In particular, the shear force plays a major role in resisting the shear deformation of Q235 steel and #45 steel, therefore, fracture failure occurs under small bolt deformation. However, the axial force of quasi-NPR steel can be fully exerted when resisting joint shear deformation; the steel itself does not break when large shear deformation occurs, and the supporting effect of the jointed rock mass is effectively guaranteed.

Brain metastasis occurs in approximately 30% of patients with lung adenocarcinoma (LUAD) and is closely associated with poor prognosis, recurrence, and death. However, dynamic gene regulation and molecular mechanism driving LUAD progression remain poorly understood. In this study, we performed a comprehensive single-cell transcriptome analysis using data from normal, early stage, advanced stage, and brain metastasis LUAD. Our single-cell-level analysis reveals the cellular composition heterogeneity at different stages during LUAD progression. We identified stage-specific risk genes that could contribute to LUAD progression and metastasis by reprogramming immune-related and metabolic-related functions. We constructed an early advanced metastatic dysregulated network and revealed the dynamic changes in gene regulations during LUAD progression. We identified 6 early advanced (HLA-DRB1, HLA-DQB1, SFTPB, SFTPC, PLA2G1B, and FOLR1), 8 advanced metastasis (RPS15, RPS11, RPL13A, RPS24, HLA-DRB5, LYPLA1, KCNJ15, and PSMA3), and 2 common risk genes in different stages (SFTPD and HLA-DRA) as prognostic markers in LUAD. Particularly, decreased expression of HLA-DRA, HLA-DRB1, HLA-DQB1, and HLA-DRB5 refer poor prognosis in LUAD by controlling antigen processing and presentation and T cell activation. Increased expression of PSMA3 and LYPLA1 refer poor prognosis by reprogramming fatty acid metabolism and RNA catabolic process. Our findings will help further understanding the pathobiology of brain metastases in LUAD.

Multiple studies have indicated that circular RNAs (circRNAs) play a regulatory role in different stages of tumors by interacting with various molecules. With continuous in-depth research on the biological functions of circRNAs, increasing evidence has shown that circRNAs play important roles in carcinogenesis caused by environmental pollutants. However, the function and mechanism of circRNAs in arsenic exposure-induced lung cancer occurrence have not been reported. In this study, RNA sequencing and qPCR assays revealed that the expression of circBRWD1 was decreased in BEAS-2B-As cells and multiple lung cancer cell lines. Silencing circBRWD1 promoted cell viability and proliferation, inhibited cell apoptosis, and accelerated the G0/G1 phase transition in BEAS-2B-As cells; however, these functions were abrogated by circBRWD1 overexpression. Mechanistically, under arsenic exposure, expression of decreased circBRWD1 led to enhanced stability of the mRNA to which it directly binds (c-JUN, c-MYC, and CDK6 mRNA), increasing its expression. This mechanism promotes the malignant transformation of lung cells and ultimately leads to lung cancer. Our findings thus reveal the molecular mechanism of arsenic carcinogenesis.

The gamma-ray burst (GRB) event GRB 221009A was the brightest event that has ever been detected to date. Owing to its unexpected brightness, the temporal and/or spectral information of the prompt emission cannot be accurately measured by many satellites (with the only exception of GECAM-C), since they suffered from significant pulse pileup and data saturation effects. Similarly, the X45 solar flare event occurring on 2003 November 4 saturated space-borne X-ray detectors, and it was through ground-based measurements of very low-frequency (VLF) signals that the magnitude of this event was determined, since VLF signals are particularly sensitive to the disturbance on the D -region ionosphere caused by low-energy photons. Therefore, in this study, we first report measurements of VLF signals from the JJI and VTX transmitter as recorded in Shiyan, China, when GRB 221009A occurred. The amplitude change was ∼1.25 and ∼2.31 dB for the JJI and VTX transmitter, respectively. Using a suite of well-validated models, we have further simulated the influence on the D -region ionosphere induced by low-energy photons (

Abstract Differences in genetic molecular features including mutation, copy number alterations and DNA methylation, can explain interindividual variability in response to anti-cancer drugs in cancer patients. However, identifying genetic alteration-driven genes and characterizing their functional mechanisms in different cancer types are still major challenges for cancer studies. Here, we systematically identified functional regulations between genetic alteration-driven genes and drug target genes and their potential prognostic roles in breast cancer. We identified two mutation and copy number-driven gene pairs (PARP1-ACSL1 and PARP1-SRD5A3), three DNA methylation-driven gene pairs (PRLR-CDKN1C, PRLR-PODXL2 and PRLR-SRD5A3), six gene pairs between mutation-driven genes and drug target genes (SLC19A1-SLC47A2, SLC19A1-SRD5A3, AKR1C3-SLC19A1, ABCB1-SRD5A3, NR3C2-SRD5A3 and AKR1C3-SRD5A3), and four copy number-driven gene pairs (ADIPOR2-SRD5A3, CASP12-SRD5A3, SLC39A11-SRD5A3 and GALNT2-SRD5A3) that all served as prognostic biomarkers of breast cancer. In particular, RARP1 was found to be upregulated by simultaneous copy number amplification and gene mutation. Copy number deletion and downregulated expression of ACSL1 and upregulation of SRD5A3 both were observed in breast cancers. Moreover, copy number deletion of ACSL1 was associated with increased resistance to PARP inhibitors. PARP1-ACSL1 pair significantly correlated with poor overall survival in breast cancer owing to the suppression of the MAPK, mTOR and NF-kB signaling pathways, which induces apoptosis, autophagy and prevents inflammatory processes. Loss of SRD5A3 expression was also associated with increased sensitivity to PARP inhibitors. The PARP1-SRD5A3 pair significantly correlated with poor overall survival in breast cancer through regulating androgen receptors to induce cell proliferation. These results demonstrate that genetic alteration-driven gene pairs might serve as potential biomarkers for the prognosis of breast cancer and facilitate the identification of combination therapeutic targets for breast cancers.

BackgroundMetastasis is the major cause of high recurrence and mortality of hepatocellular carcinoma (HCC). Unfortunately, there are few reports on effective biomarkers of HCC metastasis. Previous studies have reported that SAA1 may be a predictor and prognostic biomarker for multiple malignant tumors. However, the role of SAA1 in HCC has not yet been investigated.MethodsWe applied RNA sequencing and proteomics analysis to investigate the expression landscape of HCC cell lines and patient serum, respectively. SAA1 is a common key gene and listed as a candidate biomarker of HCC metastasis. It was validated in two cell lines, 107 participants serum, and 63 matched HCC and adjacent non-tumorous liver tissues. Human Protein Atlas (HPA), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) datasets were integrated to explore SAA1 expression among various cell types and organs. The diagnostic and prognostic value of SAA1 in HCC were determined through receiver operating characteristic (ROC) and Kaplan–Meier curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network were constructed for SAA1, as well as for its co−expressed genes. We further analyzed the correlation between SAA1 and co-expression genes.ResultsWe found 7 differentially expressed genes (DEGs) and 14 differentially expressed proteins (DEPs) were related to HCC metastasis. SAA1, a key candidate biomarker, was highly enriched in hepatocytes and liver organ, and it was also highly expressed in HCC cells and the serum and tissues of HCC patients. The results of ROC curve analysis indicated that SAA1 had better predictive values for distinguishing HCC metastasis from non-metastasis. Kaplan-Meier curve analysis revealed that HCC patients with higher SAA1 expression had worse overall survival.ConclusionsOur findings provide new insights into HCC metastasis by identifying candidate gene prediction biomarkers for HCC metastasis.

For China, one of its most important commitments is to realize its “3060” targets of achieving a CO2 emission peak by 2030 and carbon neutrality by 2060. However, for a developing country with heavy carbon utilization, achieving carbon neutrality in a short period necessitates tough changes. This paper briefly introduces energy and electricity scenarios and analyzes the challenges based on the current power system in China. Moreover, it summarizes the six characteristics of China’s future power grid and highlights some partially representative projects in the country.

The circular RNA (circRNA) circLAMA3 is significantly downregulated in bladder cancer tissues and cell lines. However, its function in bladder cancer has not yet been explored, and further research is needed. In this study, functional experiments demonstrated that circLAMA3 significantly inhibited the proliferation, migration, and invasion of bladder cancer cells and inhibited bladder cancer growth in vivo. Mechanistically, circLAMA3 directly binds to and promotes the degradation of MYCN mRNA, thereby reducing the MYCN protein expression in bladder cancer cells. Decreased expression of the MYCN protein inhibits the promoter activity and expression of CDK6. Ultimately, circLAMA3 affects DNA replication by downregulating CDK6, resulting in G0/G1 phase arrest and inhibition of bladder cancer proliferation. In summary, we report a potential novel regulatory mechanism via which a circRNA directly binds an mRNA and thereby regulates its fate. Moreover, circLAMA3 significantly affects the progression of bladder cancer and has potential as a diagnostic biomarker and therapeutic target for bladder cancer.

Raw camel milk is generally contaminated with varied microbiota, including antibiotic–resistant bacteria (ARB), that can act as a potential pathway for the spread of antibiotic resistance genes (ARGs). In this study, high–throughput quantitative PCR and 16S rRNA gene–based Illumine sequencing data were used to establish a comprehensive understanding of the antibiotic resistome and its relationship with the bacterial community in Bactrian camel milk from Xinjiang. A total of 136 ARGs and up to 1.33 × 108 total ARG copies per gram were identified, which predominantly encode resistance to β–lactamas and multidrugs. The ARGs’ profiles were mainly explained by interactions between the bacteria community and physicochemical indicators (77.9%). Network analysis suggested that most ARGs exhibited co–occurrence with Corynebacterium, Leuconostoc and MGEs. Overall, raw camel milk serves as a reservoir for ARGs, which may aggravate the spread of ARGs through vertical and horizontal gene transfer in the food chain.

Breast cancer is a cancer of high complexity and heterogeneity, with differences in prognosis and survival among patients of different subtypes. Copy number variations (CNVs) within enhancers are crucial drivers of tumorigenesis by influencing expression of their targets. In this study, we performed an integrative approach to identify CNA-driven enhancers and their effect on expression of target genes in four breast cancer subtypes by integrating expression data, copy number data and H3K27ac data. We identified 672, 555, 531, 361 CNA-driven enhancer-gene pairs and 280, 189, 113 and 98 CNA-driven enhancer-lncRNA pairs in the Basal-like, Her2, LumA and LumB subtypes, respectively. We then reconstructed a CNV-driven enhancer-lncRNA-mRNA regulatory network in each subtype. Functional analysis showed CNA-driven enhancers play an important role in the progression of breast cancer subtypes by influencing P53 signaling pathway, PPAR signaling pathway, systemic lupus erythematosus and MAPK signaling pathway in the Basal-like, Her2, LumA and LumB subtypes, respectively. We characterized the potentially prognostic value of target genes of CNV-driven enhancer and lncRNA-mRNA pairs in the subtype-specific network. We identified MUM1 and AC016876.1 as prognostic biomarkers in LumA and Basal-like subtypes, respectively. Higher expression of MUM1 with an amplified enhancer exhibited poorer prognosis in LumA patients. Lower expression of AC016876.1 with a deleted enhancer exhibited poorer survival outcomes of Basal-like patients. We also identified enhancer-related lncRNA-mRNA pairs as prognostic biomarkers, including AC012313.2-MUM1 in the LumA, AC026471.4-PLK5 in the LumB, AC027307.2-OAZ1 in the Basal-like and AC022431.1-HCN2 in the Her2 subtypes. Finally, our results highlighted target genes of CNA-driven enhancers and enhancer-related lncRNA-mRNA pairs could act as prognostic markers and potential therapeutic targets in breast cancer subtypes.

The expansion of student international mobility has been discussed increasingly with respect to concerns about social inequality. The outbreak of the COVID pandemic has exacerbated the risks of studying abroad. Due to the differences in the ability of students from different social backgrounds to cope with risks, the inequality of opportunities for studying abroad may further expand. Based on the survey of 1256 college graduates between January and March 2021, this study analyses the impact of family background on the choice of studying abroad under the background of the epidemic. The research shows that the inequality in transnational education participation has been intensified by the pandemic. Findings of mediation analysis suggested that family background has powerful influences on students' willingness to continue with their overseas study plan after the pandemic. It shapes students' decision on whether go abroad or not directly, via their mentality of the pandemic, and through their past educational choices (field of study) and attainment (university type). Our study provides a more nuanced understanding of the way how COVID pandemic affects students from different backgrounds.

Aberrant expression of long non-coding RNAs (lncRNA) is associated with altered DNA methylation and histone modifications during carcinogenesis. However, identifying epigenetically dysregulated lncRNAs and characterizing their functional mechanisms in different cancer subtypes are still major challenges for cancer studies. In this study, we systematically analyzed the epigenetic alterations of lncRNAs at important regulatory elements in three breast cancer subtypes. We identified 87, 691, and 1,197 epigenetically dysregulated lncRNAs in luminal, basal, and claudin-low subtypes of breast cancer, respectively. The landscape of epigenetically dysregulated lncRNAs at enhancer elements revealed that epigenetic changes of the majority of lncRNAs occurred in a subtype-specific manner and contributed to subtype-specific biological functions. We identified six acetylation of lysine 27 on histone H3 (H3K27ac)-dysregulated lncRNAs and three DNA methylation-dysregulated lncRNAs (CTC-303L1.2, RP11-738B7.1, and SLC26A4-AS1) as prognostic biomarkers of basal subtype. These lncRNAs were involved in immune response-related biological functions. Treatment of the basal breast cancer cell line MDA-MB-468 with CREBBP/EP300 bromodomain inhibitors downregulated H3K27 acetylation levels and caused a decrease in the expression of five H3K27ac-dysregulated lncRNAs (LINC00393, KB-1836B5.1, RP1-140K8.5, AC005162.1, and AC020916.2) and inhibition of the growth of breast cancer cells. One epigenetically dysregulated lncRNA (LINC01983) and four lncRNA regulators (UCA1, RP11-221J22.2, RP11-221J22.1, and RP1-212P9.3) were identified as prognostic biomarkers of the luminal molecular subtype of breast cancer by controlling the tumor necrosis factor (TNF) signaling pathway, T helper (Th)17 cell differentiation, and T cell migration. Finally, our results highlighted a profound role of enhancer-related H3K27ac-dysregulated lncRNAs, DNA methylation-dysregulated lncRNAs, and lncRNA regulators in breast cancer subtype carcinogenesis and their potential prognostic value.

To overcome the fruit’s perishability, mulberry wine has been developed as a method of preservation. However, dynamic changes in metabolites during mulberry wine fermentation have not been reported yet. In the present investigation, UHPLC-QE-MS/MS coupled with multivariate statistical analyses was employed to scrutinize the metabolic profiles, particularly the flavonoid profiles, throughout the process of vinification. In general, the major differential metabolites encompassed organic heterocyclic compounds, amino acids, phenylpropanoids, aromatic compounds, and carbohydrates. The contents of total sugar and alcohol play a primary role that drove the composition of amino acids, polyphenol, aromatic compound, and organic acid metabolites based on the Mantel test. Importantly, among the flavonoids, abundant in mulberry fruit, luteolin, luteolin-7-O-glucoside, (−)-epiafzelechin, eriodictyol, kaempferol, and quercetin were identified as the differential metabolic markers during blackberry wine fermentation and ripening. Flavonoid, flavone and flavonol biosynthesis were also identified to be the major metabolic pathways of flavonoids in 96 metabolic pathways. These results will provide new information on the dynamic changes in flavonoid profiles during black mulberry winemaking.

With the emerging technologies for Energy Intent (EI) and data-driven applications, the conventional power grid network is undergoing a radical modernization. An efficient energy management and electricity price forecasting remains a challenging task. In this paper, a new Robust Data Predictive Control framework for Energy Management System (RDPC-EMS) is developed to overcome the uncertainty of the electricity retail price market and minimize the total operating costs for the multi-microgrids (MMG) system. The proposed framework solves the economic energy dispatch based on an accurate Electricity Price Forecasting (EPF) by an Outlier-Robust Extreme Learning Machine (OR-ELM) algorithm and a two layers cooperative Distributed Model Predictive Control (DMPC). The First level provides an optimal energy scheduling between the Distribution System Operator (DSO) and cooperative microgrids systems to minimize the operating cost based on the forecasted electricity price. In contrast, second level maintains the supply-demand balance by applying the optimal energy scheduling from the first layer through an adjustment of the distributed energy resources (DER). The electricity retail price prediction is assessed using real dataset from the Iso New England electricity market. The OR-ELM regression method shows a significant forecasting performance in terms of error metrics. For instance, the mean absolute error in the training stage 2.05% for OR-ELM with a comparison of 4.17% and 6.29% for Support Vector Regression (SVR), and Artificial Neural Network (ANN) models respectively. Finally, simulation results demonstrate the efficiency RDPC-EMS for daily operating cost reduction, with decrease of 15% for MG 1 and 16% for MG 2.

Abstract Background The aim of this study was to determine the risk factors and develop a nomogram for blood transfusions after posterior lumbar spinal fusion (PSL). Methods We conducted a retrospective, single-center study based on 885 patients receiving PSL, and data was obtained from May 2015 to September 2019. Univariable and multivariable logistics regression analysis were conducted to identify risk factors for blood transfusion, and a nomogram was constructed to individually evaluate the risk of blood transfusion. Discrimination, calibration, and clinical usefulness were validated by the receiver operating characteristics (ROC), C-index, calibration plot, and decision curve analysis, respectively. Bootstrapping validation was performed to assess the performance of the model. Results Of 885 patients, 885 were enrolled in the final study population, and 289 received blood transfusion. Statistical analyses showed that low preoperative hemoglobin (Hb), longer time to surgery, operative time, levels of fusion > 1, longer surgery duration, and higher total intraoperative blood loss (IBL) were the risk factors for transfusion. The C-index was 0.898 (95% CI 0.847–0.949) in this dataset and 0.895 in bootstrapping validation, respectively. Calibration curve showed satisfied discrimination and calibration of the nomogram. Decision curve analysis (DCA) shown that the nomogram was clinical utility. Conclusions In summary, we investigated the relationship between the blood transfusion requirement and predictors: levels of fusion, operative time, time to surgery, total intraoperative EBL, and preoperative Hb level. Our nomogram with a robust performance in the assessment of risk of transfusion can contribute to clinicians in making clinical decision. However, external validation is still needed in the further.

Purpose – The purpose of this paper is to investigate the design method of partial observer canonical form (POCF), which is one of the important research tools for industrial plants. Design/methodology/approach – Motivated by the two-steps method proposed in Xu et al. (2020), this paper extends this method to the case of Multi-Input Multi-Output (MIMO) nonlinear system. It decomposes the original system into two subsystems by observable decomposition theorem first and then transforms the observable subsystem into OCF. Furthermore, the necessary and sufficient conditions for the existing of POCF are proved. Findings – The proposed method has a wide range of applications including completely observable nonlinear system, noncompletely observable nonlinear system, autonomous nonlinear system and forced nonlinear system. Besides, comparing to the existing results (Saadi et al., 2016), the method requires less verified conditions. Originality/value – The new method concerning design POCF has better plants compatibility and less validation conditions.

Mobile Crowdsensing (MCS) is a new mode of sensing for the Internet of Things, which has become a research hotspot. In an MCS market, there are usually three parties, i.e. requesters, workers and the platform. Each party of the crowdsensing market wants to obtain more benefits, so different mechanisms of task assignment need to be provided respectively to meet the different needs of the three parties. Great efforts have been invested on task assignment mechanisms from the perspective of the platform or requesters, i.e. a user recruitment algorithm of profits-maximizing for the platform under budget constraint, an efficient and truthful pricing mechanism for team formation and so on. However, to the best of our knowledge, there is rare mechanism for the task scheduling or planning from the perspective of workers, without considering how to maximize the benefits of workers in the case of multitasking. In this paper, a theoretical analysis on the calculation model of workers' benefits is conducted to investigate the influence factors of workers' income and its relation. Consequently, a heuristic multi-task scheduling algorithm based on Ant Colony Optimization algorithm (ACO) is proposed to determine a task scheduling strategy to maximize the workers' benefits. Finally, extensive experiments are carried out by using the STSP dataset available online, and it is shown that the proposed algorithm significantly reduces the cost of completing multiple tasks, and substantially improves the workers' benefits.

The exploitation and utilization of clean energy such as wind and photovoltaic power plays an important role in the reduction in carbon emissions to achieve the goal of “emission peak and carbon neutral”, but such a quantity of clean energy accessing the electric system will foster the transition of the electric power system structure. The intelligentization of power equipment will be an inevitable trend of development. High breaking performance, remote control and a digital detection platform of miniature circuit breaker, a protective equipment of a power distribution system, have also been inevitable requirements of the power Iot system. Based on the above, this paper studies three aspects: high-performance AC and DC general switching technology, remote control technology and operation status’ digital monitoring. A new DC non-polar breaking technology is proposed, which improves the short circuit breaking ability. An experimental prototype using the above techniques was fabricated and passed the DC 1000 V/10 kA short-circuit breaking test. On the basis of the above, an intelligent circuit breaker is developed, which contains multiple functions: remote switching, real-time temperature detection, energy metering and fault warning. Moreover, a software for digital condition monitoring and remote control is developed. This work has certain theoretical and practical significance for the development of the power Internet of things.

One of the challenges in multi-agent systems comes from the environmental non-stationarity that policies of all agents are evolving individually over time. Many existing multi-agent reinforcement learning (MARL) methods have been proposed to address this problem. However, these methods rely on a large amount of training data and some of them require agents to intensely communicate, which is often impractical in real-world applications. To better tackle the non-stationarity problem, this article combines model-based reinforcement learning (MBRL) and meta-learning and proposes a method called Model-based Imagined Rollouts Augmentation (MIRA). Based on an environment dynamics model, distributed agents can independently perform multi-agent rollouts with opponent models during exploitation and learn to infer the environmental non-stationarity as a latent variable using the rollouts. Based on the world model and latent-variable inference module, we perform multi-agent soft actor-critic implementation for centralized training and decentralized decision making. Empirical results on the Multi-agent Particle Environment (MPE) have proved that the algorithm has a very considerable improvement in sample efficiency as well as better convergent rewards than state-of-the-art MARL methods, including COMA, MAAC, MADDPG, and VDN.

Abstract One of the goals of the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission is to constrain the interior structure of Mars. We present a hierarchical transdimensional Bayesian approach to extract phase velocity dispersion and interior shear‐wave velocity (VS) models from a single seismogram. This method was adapted to Mars from a technique recently developed for Earth (Xu & Beghein, 2019, https://doi.org/10.1093/gji/ggz133). Monte Carlo Markov Chains seek an ensemble of one dimensional (1‐D) VS models between a source and a receiver that can explain the observed waveform. The models obtained are used to calculate the phase velocities of fundamental and higher modes at selected periods, and a subsequent analysis is performed to assess which modes were reliably measured. An advantage of our approach is that it can also fit unknown data noise, which reduces the risk of overfitting the data. In addition, uncertainties in the source parameters can be propagated, yielding more accurate model parameter uncertainties. In this study, we first present our technique and discuss the challenges stemming from using a single station to characterize both structure and the source and from the absence of a Mars reference model. We then demonstrate the method feasibility using the Mars Structure Service blind test data and our own synthetic data, which included realistic noise levels based on the noise recorded by InSight.

Abstract SEIS, the seismometer of the InSight mission, which landed on Mars on 26 November 2018, is monitoring the seismic activity of the planet. The goal of the Mars Structure Service (MSS) is to provide, as a mission product, the first average 1‐D velocity model of Mars from the recorded InSight data. Prior to the mission, methodologies have been developed and tested to allow the location of the seismic events and estimation of the radial structure, using surface waves and body waves arrival times, and receiver functions. The paper describes these validation tests and compares the performance of the different algorithms to constrain the velocity model below the InSight station and estimate the 1‐D average model over the great circle path between source and receiver. These tests were performed in the frame of a blind test, during which synthetic data were inverted. In order to propagate the data uncertainties on the output model distribution, Bayesian inversion techniques are mainly used. The limitations and strengths of the methods are assessed. The results show the potential of the MSS approach to retrieve the structure of the crust and underlying mantle. However, at this time, large quakes with clear surface waves have not yet been recorded by SEIS, which makes the estimation of the 1‐D average seismic velocity model challenging. Additional locatable events, especially at large epicentral distances, and development of new techniques to fully investigate the data, will ultimately provide more constraints on the crust and mantle of Mars.

The problem of the terminal impact angle control guidance law, considering the target observability for passive guidance with bearing-only measurement, is investigated in this paper. Modified line-of-sight (LOS) angle error dynamics and their closed-loop analytical solution are developed to enhance the target observability, and then their characteristics are studied, which makes the LOS angular rate oscillate in the early stage. The terminal impact angle control guidance law with the global sliding mode is designed to eliminate the approaching stage of sliding mode control, which makes the system robust throughout the entire process of control. Finally, numerical simulations are presented to demonstrate the performance of the proposed guidance law under various conditions, which achieves the desired results.

Chitosan (CS) has been extensively studied as a natural polymer, in the field of wound repair, due to its useful properties, which include a lack of toxicity and stimulation, excellent biological affinity, degradability, and promotion of collagen deposition. However, inferior mechanical strength and moderate antibacterial properties are the drawbacks restricting its further clinical application. Many researchers have adopted the use of nanotechnology, in particular metallic nanoparticles (MNPs), in order to improve the mechanical strength and specific antibacterial properties of chitosan composites, with promising results. Furthermore, chitosan naturally functions as a reducing agent for MNPs, which can also reduce cytotoxicity. Thus, CS, in combination with MNPs, exhibits antibacterial activity, excellent mechanical strength, and anti-inflammatory properties, and it has great potential to accelerate the process of wound healing. This review discusses the current use of CS and MNPs in wound healing and emphasises the synergy and the advantages for various applications in wound healing.

In recent years, crowdsourcing has become a research hotspot. How to formulate a reasonable task allocation mechanism to recruit the most suitable participant for the current perceptual task, and maximize the benefits of the platform has become a problem that most researchers focus on. Great efforts have been invested on task assignment mechanisms from the perspective of the platform or requesters, i.e., quality-sensitive, budget-sensitive, time-sensitive, and location-sensitive. Especially for the location-sensitive task assignment mechanism, many studies motivate users to participate by some coverage estimation methods, i.e., minimizing the traveling distance. Most existing methods statically estimate the distance between the current location of the participant and the task destination, without giving any consideration about the movement track of the participant, which may result in the failure of task for the misallocation. In this paper, we propose a location-sensitive task assignment mechanism using predictable mobility based on Markov model for the vehicle-based crowdsourcing platform. Specially, we present a location transfer prediction model based on Markov model named Markov-TPM by analyzing the positional regularity of task participants during a period of time, to predict where the participant will appear in the next time period first. In addition, we propose a task assignment mechanism based on Markov-TPM that is helpful for the platform to select the most suitable participant to complete the task. Finally, experiments are carried out by using the data set about the taxi trajectory which is collected in Shanghai, and it is shown that the proposed algorithm can improve the accuracy of the task-delivered, which is evidently superior to two algorithms compared, i.e., random prediction algorithm and prediction algorithm based on neighbor relation.

Single-cell analysis is pivotal to deciphering complex phenomena like heterogeneity, bistability, and asynchronous oscillations, where a population ensemble cannot represent individual behaviors. Bulk cell-free systems, despite having unique advantages of manipulation and characterization of biochemical networks, lack the essential single-cell information to understand a class of out-of-steady-state dynamics including cell cycles. Here, by encapsulating Xenopus egg extracts in water-in-oil microemulsions, we developed artificial cells that are adjustable in sizes and periods, sustain mitotic oscillations for over 30 cycles, and function in forms from the simplest cytoplasmic-only to the more complicated ones involving nuclear dynamics, mimicking real cells. Such innate flexibility and robustness make it key to studying clock properties like tunability and stochasticity. Our results also highlight energy as an important regulator of cell cycles. We demonstrate a simple, powerful, and likely generalizable strategy of integrating strengths of single-cell approaches into conventional in vitro systems to study complex clock functions.