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•TICs in cervical cancer associate with overall survival.•A novel prognostic model named aTMNs predicts overall survival with high accuracy.•TICs act as intrinsic features to ...distinguish cervical cancer from normal tissues.•Immunomodulators in cervical cancer have borderline predictive effect.
Tumor-infiltrating immune cells (TICs) are highly relevant to tumor development and are promising prognostic biomarkers. However, the precise assessment of TICs is limited by the deficiencies of traditional measurements, such as the lack of phenotypic markers. Here, we analyzed the composition of TICs in cervical cancer based on RNA expression data with a metagene approach called CIBERSORT and evaluated the prognostic value of TICs. The immune infiltration profiles functioned as intrinsic features to distinguish cervical cancer from normal tissue. According to the Cox regression analysis, higher levels of activated memory CD4+ T cells were independently associated with favorable overall survival (OS) (hazard ratio HR = 0.71, 95% confidence interval CI: 0.57–0.89; p = 0.003), whereas a higher fraction of activated mast cells was independently associated with adverse outcomes (HR = 1.53, 95% CI: 1.23–1.91; p < 0.001). Furthermore, a novel prognostic model named aTMNs (activated memory CD4+ T cells, activated mast cells and activated natural killer NK cells) was constructed to predict OS in cervical cancer with high accuracy (area under the curve AUC = 0.723, concordance index C-index = 0.738): risk score = −0.34508 × (proportion of activated memory CD4+ T cells) + 0.426841 × (proportion of activated mast cells) + 0.272202 × (proportion of activated NK cells). The aTMNs model outperformed the immunomodulator model (AUC = 0.673, C-index = 0.693). Overall, TICs are important prognostic determinants in cervical cancer and may be a useful resource for the development of effective immunotherapy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Deep learning models based on radar echo extrapolation have been widely used in precipitation nowcasting. However, they face the challenge of insufficient input information when extending the ...forecast lead time, requiring the incorporation of physics‐based numerical weather prediction (NWP). Given that the strengths of radar and NWP data vary depending on the forecast time, effectively fusing these two data sources in a unified deep learning model remains an open research problem. In this study, we propose a Time‐aware Adaptive Feature Fusion Network (TAFFNet) for very short‐term precipitation forecasts up to 12 hr. TAFFNet fuses features adaptively according to their relative contributions to forecast skill at different times. Experimental results demonstrate that TAFFNet performs the best for very short‐term precipitation forecasts. The case studies show that adaptively fusing NWP with radar can improve the accuracy of precipitation forecasts, especially for predicting the initiation and dissipation of storms at longer lead times.
Plain Language Summary
Very short‐term precipitation forecasting aims to predict precipitation for a couple of hours, which is crucial for public safety and emergency management in cities. Recently, deep learning models have been widely used for precipitation nowcasting (0–2 hr) based on observation extrapolation. However, as the forecast time increases, extrapolating historical observations alone is inadequate without incorporating physical constraints. On the contrary, traditional NWP can provide essential physical background, but the prediction accuracy still needs improvement for early forecasting periods. In this study, we proposed a novel deep learning model named TAFFNet to combine both advantages of radar and NWP for very short‐term precipitation forecasts up to 12 hr. The features of radar and NWP are first extracted, respectively. Their features are fused adaptively in the forecasting part through a time‐aware adaptive feature fusion (TAFF) module, according to their relative contributions to forecast skill at different lead times. The feature fusion approach can be optimized adaptively through model training. The proposed model is evaluated on the radar and NWP model data set covering southeast China. Results demonstrate that incorporating NWP into a radar‐based deep learning model can improve very short‐term precipitation forecasts. TAFFNet can better capture the storm's initiation and dissipation during forecasting.
Key Points
A novel deep learning model termed Time‐aware Adaptive Feature Fusion Network (TAFFNet) is proposed for very short‐term precipitation forecasts up to 12 hr
Features from radar and numerical weather prediction are fused adaptively in deep levels based on their relative contributions to forecast at different lead times
TAFFNet can improve very short‐term precipitation forecasts, especially for predicting storms initiation and dissipation at longer times
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background: Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy, with a high incidence and poor prognosis. In the past several decades, hundreds of proteins have been reported to be ...associated with the prognosis of ESCC, but none has been widely accepted to guide clinical care. This study aimed to identify proteins with great potential for predicting prognosis of ESCC. Methods: We conducted a systematic review on immunohistochemical (IHC) prognostic markers of ESCC according to the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines. Literature related to IHC prognostic markers of ESCC were searched from PubMed, Embase, Web of Science, and Cochrane Library until January 30th, 2017. The risk of bias of these original studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool. Results: We identified 11 emerging IHC markers with reproducible results, including eight markers epidermal growth factor receptor (EGFR), Cyclin D1, vascular endothelial growth factor (VEGF), Survivin, Podoplanin, Fascin, phosphorylated mammalian target of rapamycin (p-mTOR), and pyruvate kinase M2 (PKM2) indicating unfavorable prognosis and 3 markers (P27, P16, and E-cadherin) indicating favorable prognosis of ESCC. Conclusion: Strong evidence supports that these 11 emerging IHC markers or their combinations may be useful in predicting prognosis and aiding personalized therapy decision-making for ESCC patients.
The electrocatalytic nitrogen reduction reaction (NRR) for synthesizing ammonia holds promise as an alternative to the traditional high-energy-consuming Haber-Bosch method. Rational and accurate ...catalyst design is needed to overcome the challenge of activating N
and to suppress the competitive hydrogen evolution reaction (HER). Single-atom catalysts have garnered widespread attention due to their 100% atom utilization efficiency and unique catalytic performance. In this context, we constructed theoretical models of metal single-atom catalysts supported on titanate nanosheets (M-TiNS). Initially, density functional theory (DFT) was employed to screen 12 single-atom catalysts for NRR- and HER-related barriers, leading to the identification of the theoretically optimal NRR catalyst, Ru-TiNS. Subsequently, experimental synthesis of the Ru-TiNS single-atom catalyst was successfully achieved, exhibiting excellent performance in catalyzing NRR, with the highest NH
yield rate reaching 15.19 μmol mg
h
and a Faradaic efficiency (FE) of 15.3%. The combination of experimental results and theoretical calculations demonstrated the efficient catalytic ability of Ru sites, validating the effectiveness of the constructed theoretical screening process and providing a theoretical foundation for the design of efficient NRR catalysts.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Numerous studies have shown that long non-coding RNAs (lncRNAs) play key roles during multiple cancer processes, such as cell proliferation, apoptosis, migration and invasion. The previous studies ...found that NKILA interacted with and suppressed the nuclear translocation of NF-KappaB, which influenced metastasis and prognosis in breast cancer. However the clinical significance and biological role of NKILA in non-small cell lung cancer (NSCLC) remains unknown.
We examined expression levels of NKILA in 106 pairs of NSCLC tissues and cell lines. The expression level of NKILA after TGF-β1 stimulation also was examined by qRT-PCR and validated by Chromatin immunoprecipitation (ChIP). Gain-of-function and loss-of-function assays were performed to examine the effect of NKILA on proliferation, migration and invasion of NSCLC cells. RNA immunoprecipitation (RIP), western blot and rescue experiments were carried out to reveal the interrelation between NKILA, NF-κB and EMT signal pathway.
The expression of NKILA was down-regulated in NSCLC cancer tissues compared with matched adjacent noncancerous tissues, and lower NKILA expression in tumor tissues were significantly correlated with lymph node metastasis and advanced TNM stage. We found that the expression of NKILA was mainly regulated by classical TGF-β signal pathway in NSCLC cells rather than NF-κB pathway reported in breast cancer. Gain and loss of function assays found that NKILA inhibited migration, invasion and viability of NSCLC cells. Mechanistic study showed that NKILA attenuated Snail expression via inhibiting the phosphorylation of IκBα and NF-κB activation, subsequently suppressed the expression of markers of epithelial-mesenchymal transition process.
The present study found that the expression of NKILA was downregulated in tumor tissues of NSCLC, which improved the metastasis of NSCLC patients. In vitro studies further clarified that the expression of NKILA was regulated through classical TGF-β signal pathway, which subsequently inhibited migration and invasion of NSCLC cells through interfering NF-κB/Snail signal pathway in NSCLC cells.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Narrow band-gap semiconductor Bi2S3 is deposited on the surface of WO3 nanosheets for PEC water splitting. The photothermal effect of Bi2S3 induced by the absorption of infrared light leads to the ...temperature elevation, which accelerates charge carrier migration and finally increases the efficiency of solar energy conversion.
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Infrared light absorbed by semiconductors hardly contributes to the solar energy conversion due to its low photon energy. Herein, photothermal effect activated by infrared part of solar light is introduced to promote the photoelectrochemical (PEC) water splitting of photoanodes. Narrow band-gap semiconductor Bi2S3 is deposited on the surface of WO3 nanosheets, exhibiting a broad-spectral response. In addition to the enhanced density of photo-generated electrons, significant temperature elevation is observed for the Bi2S3/WO3 composite photoanode under the illumination of infrared part of solar light because of the photothermal conversion property of Bi2S3. The moderately enhanced temperature accelerates charge carrier migration and finally increases the efficiency of solar energy conversion. With the assistance of photothermal effect, a remarkable photocurrent density of 4.05 mA cm−2 at 1.23 V vs. reversible reference electrode (VRHE) is achieved by Bi2S3/WO3 composite photoanode, over 880% higher than that of the pristine WO3. The introduction of photothermal effect activated by infrared light provides general and robust strategy to promote the PEC performance of photoanodes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This article surveys the current state of security issues and available defense mechanisms regarding popular online social networks. It covers a wide variety of attacks and the corresponding defense ...mechanisms, if available. The authors organize these attacks into four categories - privacy breaches, viral marketing, network structural attacks, and malware attacks - and focus primarily on privacy concerns. They offer an in-depth discussion of each category and analyze the connections among the different security issues involved.
In the context of an increasingly interconnected global economy, deciphering the complex ripple effects of external financial disruptions on national economies is a task of utmost significance. This ...article dives deep into the intricate repercussions of such disturbances on the macroeconomic dynamics of China using the example of the potential insolvency of a Silicon Valley bank. Grounded in empirical scrutiny, we leverage data spanning from Q1 2000 to Q1 2022 and the analytical utility of the impulse response function to illuminate our findings. We find that external financial tumult triggers a global recession, adversely impacting China’s export-driven economy while simultaneously unsettling aggregate output, employment levels, and wage stability. Simultaneously, these disruptions induce variability in consumption tendencies, investment trajectories, and import volumes and inject instability into interest rate paradigms. We also acknowledge the potential for currency depreciation and bank insolvency incidents to induce inflationary stresses, primarily by escalating the costs of imports. However, these inflationary tendencies may be offset by the concomitant economic slowdown and diminished demand inherent to global recessions. Importantly, the tightening of global credit conditions, coupled with existing financial ambiguities, may obstruct investment initiatives, curtail imports, and exert influence on both risk-free and lending interest rates. Our investigation also probes into the response of the Chinese government’s monetary policy to these external financial shocks. Despite the vital role of monetary policy in alleviating the impacts of these shocks, the potential secondary effects on China’s domestic economy warrant attention. Our study underscores the imperative of proper policy design rooted in a profound understanding of the intricate economic interdependencies for effective management and mitigation of the potentially detrimental consequences of such financial upheavals on China’s macroeconomic resilience within the tapestry of a tightly knit global financial ecosystem.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
BACKGROUND Diabetic nephropathy (DN) is a disease characterized by oxidative stress and apoptosis of renal tubular epithelial cells driven by hyperglycemia. Apigenin is a flavonoid compound that ...possesses potent anti‑apoptotic properties. The present study aimed to explore the protective effects and underlying mechanisms of apigenin on renal tubular epithelial cells exposed to hyperglycemia. MATERIAL AND METHODS Human renal epithelial cell HK-2 were incubated to D-glucose to establish in vitro DN model. The cell viability, lactate dehydrogenase (LDH) release, apoptosis and oxidative stress were evaluated. qRT-PCR was performed to determine the mRNA levels of NF-E2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1). Western blot analysis was performed to measure the protein expressions of Nrf2. RESULTS In HK-2 cells, high glucose reduced cell viability in a concentration- and time-dependent manner. Apigenin suppressed the decrease in cell viability and increase in supernatant LDH release at 100 and 200 μM after 48-h treatment. Apigenin reduced apoptotic rate and pro-inflammatory cytokines production. Apigenin suppressed oxidative stress and increased mRNA expressions of Nrf2 and HO-1. Inhibition of Nrf2 using small interfering RNA (siRNA), or cotreatment with LY294002, an inhibitor of PI3K/Akt, abolished the protective effect on high glucose-induced injury, oxidative stress, and pro-inflammatory cytokines production by apigenin. LY294002 also attenuated the increase in Nrf2 protein by apigenin in high glucose-treated HK-2 cells. CONCLUSIONS Apigenin protects renal tubular epithelial cells against high glucose-induced injury through suppression of oxidative stress and inflammation via activation of the Nrf2 pathway.
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