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Based on the rapid development of network technology and the arrival of the digital era, big data algorithms have gradually penetrated various fields, providing great convenience for users. The research focus of this paper is to analyze deeply how big data algorithms are widely used in personalized services. It will also take the music service platform NetEase Cloud as an example to explore its effects on improving user satisfaction, customized services, and optimization of recommendation systems. By analyzing the specific practices of NetEase Cloud, this article reveals the application value of big data algorithms in building user profiles, providing personalized services, optimizing recommendation systems, and providing competitive advantages in the music industry. In addition, the platform accurately meets user needs. It optimizes intelligent recommendations dynamically by integrating users' historical behavioral data and user feedback, improving NetEase Cloud's competitive advantage in music platforms. The significance of this research is to explore the strong competitive advantages that big data algorithms have brought to NetEase Cloud Platform in the field of personalized services and to look forward to the impact that big data algorithms will have on the music industry in the future.

Directly harvesting solar energy for battery charging represents an ultimate solution toward low-cost, green, efficient and sustainable electrochemical energy storage. Here, we design a sunlight promotion strategy into rechargeable zinc–air battery with significantly reduced charging potential below the theoretical cell voltage of zinc–air batteries. The sunlight-promoted zinc–air battery using BiVO 4 or α-Fe 2 O 3 air photoelectrode achieves a record-low charge potential of ~1.20 and ~1.43 V, respectively, under illumination, which is lowered by ~0.5–0.8 V compared to the typical charge voltage of ~2 V in conventional zinc–air battery. The band structure and photoelectrochemical stability of photoelectrodes are found to be key factors determining the charging performance of sunlight-promoted zinc–air batteries. The introduction of photoelectrode as an air electrode opens a facile way for developing integrated single-unit zinc–air batteries that can efficiently use solar energy to overcome the high charging overpotential of conventional zinc–air batteries. The authors here report a sunlight-promoted rechargeable zinc–air battery in which photoelectrode is used as the air electrode to substantially lower the charge potential under illumination. Notably, the battery can be initially charged with an extremely low voltage of ~1.20 V.

ObjectiveTo characterise the oral microbiome, gut microbiome and serum lipid profiles in patients with active COVID-19 and recovered patients; evaluate the potential of the microbiome as a non-invasive biomarker for COVID-19; and explore correlations between the microbiome and lipid profile.DesignWe collected and sequenced 392 tongue-coating samples, 172 faecal samples and 155 serum samples from Central China and East China. We characterised microbiome and lipid molecules, constructed microbial classifiers in discovery cohort and verified their diagnostic potential in 74 confirmed patients (CPs) from East China and 37 suspected patients (SPs) with IgG positivity.ResultsOral and faecal microbial diversity was significantly decreased in CPs versus healthy controls (HCs). Compared with HCs, butyric acid-producing bacteria were decreased and lipopolysaccharide-producing bacteria were increased in CPs in oral cavity. The classifiers based on 8 optimal oral microbial markers (7 faecal microbial markers) achieved good diagnostic efficiency in different cohorts. Importantly, diagnostic efficacy reached 87.24% in the cross-regional cohort. Moreover, the classifiers successfully diagnosed SPs with IgG antibody positivity as CPs, and diagnostic efficacy reached 92.11% (98.01% of faecal microbiome). Compared with CPs, 47 lipid molecules, including sphingomyelin (SM)(d40:4), SM(d38:5) and monoglyceride(33:5), were depleted, and 122 lipid molecules, including phosphatidylcholine(36:4p), phosphatidylethanolamine (PE)(16:0p/20:5) and diglyceride(20:1/18:2), were enriched in confirmed patients recovery.ConclusionThis study is the first to characterise the oral microbiome in COVID-19, and oral microbiomes and lipid alterations in recovered patients, to explore their correlations and to report the successful establishment and validation of a diagnostic model for COVID-19.

Hydrogels, possessing high biocompatibility and adaptability to biological tissue, show great usability in medical applications. In this research, a series of novel cross-linked chitosan quaternary ammonium salt loading with gentamicin sulfate (CTMCSG) hydrogel films with different cross-linking degrees were successfully obtained by the reaction of chitosan quaternary ammonium salt (TMCS) and epichlorohydrin. Fourier transform infrared spectroscopy (FTIR), thermal analysis, and scanning electron microscope (SEM) were used to characterize the chemical structure and surface morphology of CTMCSG hydrogel films. The physicochemical property, gentamicin sulphate release behavior, cytotoxicity, and antibacterial activity of the CTMCSG against Escherichia coli and Staphylococcus aureus were determined. Experimental results demonstrated that CTMCSG hydrogel films exhibited good water stability, thermal stability, drug release capacity, as well as antibacterial property. The inhibition zone of CTMCSG hydrogel films against Escherichia coli and Staphylococcus aureus could be up to about 30 mm. Specifically, the increases in maximum decomposition temperature, mechanical property, water content, swelling degree, and a reduction in water vapor permeability of the hydrogel films were observed as the amount of the cross-linking agent increased. The results indicated that the CTMCSG-4 hydrogel film with an interesting physicochemical property, admirable antibacterial activity, and slight cytotoxicity showed the potential value as excellent antibacterial wound dressing.

The rural energy transition is critical in China’s efforts to achieve carbon neutrality and improve air quality. However, the costs and health benefits associated with the transition to carbon neutrality remain unclear. Here we explore the cost-effective transition pathways and air quality-related health impacts using an integrated energy-air quality-health modeling framework. We find that decarbonizing rural cooking and heating would triple contemporary energy consumption from 2014 to 2060, considerably reducing energy poverty nationwide. By 2060, electric cooking ranges and air-to-air heat pumps should be widely integrated, costing an additional 13 billion USD nationally in transformation costs, with ~40% concentrated in Shandong, Heilongjiang, Shanxi and Hebei provinces. Rural residential decarbonization would remarkably improve air quality in northern China, yielding substantial health co-benefits. Notably, monetized health benefits in most provinces are projected to offset transformation costs, except for certain relatively lower-development southwestern provinces, implying more financial support for rural residents in these areas will be needed. Electric cooking and air-to-air heat pump adoption in China advances carbon neutrality and the rural energy transition, with the transformation costs offset by monetized health benefits in most provinces.

Aqueous battery systems feature high safety, but they usually suffer from low voltage and low energy density, restricting their applications in large-scale storage. Here, we propose an electrolyte-decoupling strategy to maximize the full potential of Zn–MnO 2 batteries by simultaneously enabling the optimal redox chemistry of both the Zn and MnO 2 electrodes. The decoupled Zn–MnO 2 battery exhibits an open-circuit voltage of 2.83 V (in contrast to the typical voltage of 1.5 V in conventional Zn–MnO 2 batteries), as well as cyclability with only 2% capacity fading after deep cycling for 200 h. Benefiting from the full utilization of MnO 2 , the Zn–MnO 2 battery is also able to maintain approximately 100% of its capacity at various discharge current densities. We also demonstrate the feasibility of integrating the Zn–MnO 2 battery with a wind and photovoltaic hybrid power generating system. This electrolyte-decoupling strategy is shown to be applicable for other high-performance zinc-based aqueous batteries such as Zn–Cu and Zn–Ag batteries. Low energy density and limited cyclability are preventing the commercialization of aqueous Zn–MnO 2 batteries. Here, the authors combine the merits of operating Zn anodes in alkaline conditions and MnO 2 cathodes in acidic conditions, via an electrolyte-decoupling strategy, to realize high-performance batteries.

Background Hepatocellular carcinoma (HCC) is one of the most common malignancies with a high morbidity and mortality worldwide. MicroRNAs are key regulators of HCC genesis. However, the regulatory role and underlying mechanisms of microRNA in HCC is still limited. Methods Cyclin B1 (CCNB1) mRNA levels were examined in non-tumor and liver cancer of The Cancer Genome Atlas (TCGA) cohort. CCNB1 was knockdown to evaluate the HCC cell proliferation, migration and invasion. MicroRNA-144 targeting CCNB1 was identified with TargetScan analysis and confirmed with reporter assay. Overexpression of MicroRNA-144 was achieved using microRNA mimics and function of microRNA-144 was tested in vitro HCC cell line proliferation and in vivo tumor formation experiments. Results Here, we found that the high level expression of CCNB1 was closely associated with poor prognosis in HCC patients. Knockdown of CCNB1 by RNA interference significantly inhibited cell proliferation, migration and invasion in HCC. Furthermore, we found that miR-144 directly targeted CCNB1 and inhibited CCNB1 expression. Moreover, in vivo experiments of subcutaneous tumor formation further demonstrated that miR-144 delayed tumor formation by negative regulation of CCNB1. Conclusion Therefore, we conclude that microRNA-144/CCNB1 axis plays an important role in human HCC. Therapies targeting microRNA-144 could potentially improve HCC treatment.

With regard to human–machine interaction, accurate emotion recognition is a challenging problem. In this paper, efforts were taken to explore the possibility to complete the feature abstraction and fusion by the homogeneous network component, and propose a dual-modal emotion recognition framework that is composed of a parallel convolution (Pconv) module and attention-based bidirectional long short-term memory (BLSTM) module. The Pconv module employs parallel methods to extract multidimensional social features and provides more effective representation capacity. Attention-based BLSTM module is utilized to strengthen key information extraction and maintain the relevance between information. Experiments conducted on the CH-SIMS dataset indicate that the recognition accuracy reaches 74.70% on audio data and 77.13% on text, while the accuracy of the dual-modal fusion model reaches 90.02%. Through experiments it proves the feasibility to process heterogeneous information within homogeneous network component, and demonstrates that attention-based BLSTM module would achieve best coordination with the feature fusion realized by Pconv module. This can give great flexibility for the modality expansion and architecture design.

Preterm labor, a condition associated with various risk factors such as a history of prior preterm birth (PTB) and multiple pregnancies, has recently seen an increasing focus on its potential link with dyslipidemia. This study aims to investigate the relationship between dyslipidemia in expectant mothers and the risks of PTB. We studied 6963 mothers who gave birth at the International Peace Maternal and Child Health Hospital of Shanghai Jiaotong University School of Medicine in 2020, among which, 437 women had PTB. We extracted clinical and lipid data from electronic records, using multivariable logistic regression and restricted cubic spline models to explore the link between lipid concentrations (by quartiles) in pregnancy stages and PTB risk. The PTB rate was 6.3%. Early pregnancy in the PTB group showed elevated ApoA, ApoB, CHOL, LDL, and TG levels compared to controls (all P < 0.05). Late pregnancy showed no notable lipid differences. Multivariable analysis revealed elevated ApoA, TG, higher age, BMI ≥ 28 kg/m 2 , hypertension, assisted reproductive technology and gestational diabetes as PTB risk factors (all P < 0.05). After adjustments, higher ApoA, ApoB, CHOL and TG levels correlated with increased PTB risk. Using the lowest quartile, the adjusted ORs for early pregnancy's highest quartile of ApoA, ApoB, CHOL and TG were 1.348, 1.442, 1.442 and 2.156, respectively. Our findings indicate that dyslipemia in early pregnancy, including elevated levels of ApoA, ApoB, CHOL and TG, are associated with PTB. Managing lipid abnormalities during pregnancy may help reduce the risk of PTB.

Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber-attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs to manipulate the power system state estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the energy management system towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include bad data detection algorithms to eliminate errors from the acquired measurements, e.g. in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. To overcome the limitations of traditional residual-based BDD approaches, data-driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This study provides a comprehensive review of the most up-to-date machine learning methods for detecting FDIAs against power system SE algorithms.