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Abstract Since the discovery of Stimulator of Interferon Genes (STING) as an important pivot for cytosolic DNA sensation and interferon (IFN) induction, intensive efforts have been endeavored to clarify the molecular mechanism of its activation, its physiological function as a ubiquitously expressed protein, and to explore its potential as a therapeutic target in a wide range of immune-related diseases. With its orthodox ligand 2’3’-cyclic GMP–AMP (2’3’-cGAMP) and the upstream sensor 2’3’-cGAMP synthase (cGAS) to be found, STING acquires its central functionality in the best-studied signaling cascade, namely the cGAS–STING–IFN pathway. However, recently updated research through structural research, genetic screening, and biochemical assay greatly extends the current knowledge of STING biology. A second ligand pocket was recently discovered in the transmembrane domain for a synthetic agonist. On its downstream outputs, accumulating studies sketch primordial and multifaceted roles of STING beyond its cytokine-inducing function, such as autophagy, cell death, metabolic modulation, endoplasmic reticulum (ER) stress, and RNA virus restriction. Furthermore, with the expansion of the STING interactome, the details of STING trafficking also get clearer. After retrospecting the brief history of viral interference and the milestone events since the discovery of STING, we present a vivid panorama of STING biology taking into account the details of the biochemical assay and structural information, especially its versatile outputs and functions beyond IFN induction. We also summarize the roles of STING in the pathogenesis of various diseases and highlight the development of small-molecular compounds targeting STING for disease treatment in combination with the latest research. Finally, we discuss the open questions imperative to answer.

Abstract Background We evaluated the prognostic role of deficient mismatch repair (dMMR) systems in stage II and stage III colon cancer patients during different postoperative periods. We also assessed whether patients aged ≥75 could benefit from chemotherapy. Methods This retrospective study was conducted across three medical centers in China. Kaplan–Meier survival methods and Cox proportional hazards models were used to evaluate the differences in overall survival (OS) and disease-free survival (DFS) rates. Propensity score matching was performed to reduce imbalances in the baseline characteristics of the patients. Landmark analysis was performed to evaluate the role of dMMR during different postoperative periods. Results The median follow-up time for all patients was 45.0 months (25–75 IQR: 38.0–82.5). There was no significant OS (p = 0.350) or DFS (p = 0.752) benefit associated with dMMR for stage II and III patients during the first postoperative year. However, significant OS (p

Abstract Background Screening recommendations for colorectal cancer (CRC) are mainly based on family history rather than lifestyle risk factors. We aimed to assess and compare risk factors for colorectal neoplasm (CRN) and evaluate trends in neoplasm detection rates during the three rounds of screening from 2012 to 2020 in Tianjin, China. Methods This study was based on 89,535 first-recorded colonoscopies in Tianjin CRC screening program, 2012–2020. Of these, 45,380 individuals with complete family history and lifestyle factors were included for population attributable fraction (PAF) estimation. Results The overall detection rate of nonadvanced adenomas, advanced adenomas and CRC was 39.3%, 5.9% and 1.5%, respectively. The PAFs of current smoking, alcohol consumption, physical activity, higher BMI and family history of CRC, respectively, were 8.9%, 2.6%, 1.9%, 5.8%, and 1.1% for males with nonadvanced CRN; 12.3%, 7.3%, 4.9%, 7.2%, and 0.8% for males with advanced CRN; 3.4%, 0.4%, 2.1%, 7.8%, and 0.7% for females with nonadvanced CRN; and 4.3%, 0.2%, 8.2%, 8.5%, and -0.6% for females with advanced CRN. The PAFs of selected lifestyle factors were 19.9% for males with nonadvanced CRN, 29.0% for males with advanced CRN, 9.7% for females with nonadvanced CRN and 13.8% for females with advanced CRN. Conclusions Modifiable lifestyle factors, including smoking, alcohol consumption, physical activity and BMI, have a larger contribution to CRN than family history of CRC. Our findings will provide references for developing guidelines of CRC prevention and control in China.

Laser trackers are instruments used to measure the three-dimensional coordinates of objects with high precision. It is necessary to study calibration methods to identify geometric errors of laser trackers so as to compensate for errors and improve measurement accuracy. Among the offsets, tilts, and eccentricity errors in the system, the transit tilt and offset errors play important roles and have an essential effect on the measurement accuracy, which need to be calibrated and compensated for. Current methods for detecting geometric errors between the transit and standing axes are complex and time-consuming. In this paper, a fast and novel calibration method is proposed, which can be easily operated with high precision. Two test rods are coaxially mounted on the ends of the transit axis to extend the hidden transit axis. Then, a telecentric measurement system is used to record the projection images of the test rods and identify the position of the transit axis. The transit offset and tilt errors were thus obtained by comparing the central axis positions of the two test rods before and after the standing axis rotated 180 degrees. Moreover, a numerical analysis method was proposed based on the least square circle fitting method to calibrate the installation eccentricity errors of the test rods. Experimental results verify that the measurement accuracy of the laser tracker can be improved after error compensation using the calibrated transit offset and tilt errors. The measurement error of the laser tracker can be reduced from 161 µm to about 73 µm after compensation at a distance of 5 m, while the error can be decreased from 143 µm to about 108 µm at a distance of 10 m.

Senescence of activated hepatic stellate cells (aHSCs) is a stable growth arrest that is implicated in liver fibrosis regression. Senescent cells often accompanied by a multi-faceted senescence-associated secretory phenotype (SASP). But little is known about how alanine-serine-cysteine transporter type-2 (ASCT2), a high affinity glutamine transporter, affects HSC senescence and SASP during liver fibrosis. Here, we identified ASCT2 is mainly elevated in aHSCs and positively correlated with liver fibrosis in human and mouse fibrotic livers. We first discovered ASCT2 inhibition induced HSCs to senescence in vitro and in vivo. The proinflammatory SASP were restricted by ASCT2 inhibition at senescence initiation to prevent paracrine migration. Mechanically, ASCT2 was a direct target of glutaminolysis-dependent proinflammatory SASP, interfering IL-1α/NF-κB feedback loop via interacting with precursor IL-1α at Lys82. From a translational perspective, atractylenolide III is identified as ASCT2 inhibitor through directly bound to Asn230 of ASCT2. The presence of –OH group in atractylenolide III is suggested to be favorable for the inhibition of ASCT2. Importantly, atractylenolide III could be utilized to treat liver fibrosis mice. Taken together, ASCT2 controlled HSC senescence while modifying the proinflammatory SASP. Targeting ASCT2 by atractylenolide III could be a therapeutic candidate for liver fibrosis.

Thermochemical redox reactions of metal oxides are promising for CO2 capture, gas purification, air separation, and energy storage. Here, the authors report mixed metal oxides derived from layered double hydroxides precursors, and demonstrate their reversible and stable thermochemical oxygen storage.

Developing high-strain piezoelectric materials has been a long-term challenge. Here, the authors report one strategy to enhance the electrostrain via designing heterostrain through atomic-scale defect engineering and mesoscale domain engineering.

IntroductionRice-fish farming can play an important role in increasing food production in less developed countries. The Lao People’s Democratic Republic (Laos) is one of the least developed countries in the world, and rice is the most important crop in Laos.MethodsThe present study conducted field surveys in 2022 in order to get a better understanding of the status of rice-fish production systems in mountainous areas in Northern Laos. Rice-fish farming was defined as one form of aquaculture in the present study, characterized by seed stocking and feed input. Rice field fisheries is mainly the harvesting of wild fish with no seed and feed input.ResultsWe found that rice-fish production systems, including both integrated rice-fish farming and rice field fisheries, are still in resources poor status with low input levels of fertilizer and chemicals. Rice-fish farms generally have higher intensification levels in rice farming than that rice field fisheries farms. Rice-fish farms generate significantly more value output of rice than that rice field fisheries farms. Overall, rice-fish farms use land and labor more efficiently and generate higher land and labor productivity measured in value output than rice field fisheries farms.DiscussionWe suggest that more rice field fisheries farms can be intensified with extra seed and feed input to move to rice-fish farming to produce more aquatic products and more value output with the same rice field areas in Northern Laos.

Abstract Background The goal of this study was to determine the clinical outcomes of total arch replacement with frozen elephant trunk surgery and hybrid debranching surgery for acute type A aortic dissection patients. Methods From January 2017 to December 2019, the clinical data of acute type A aortic dissection patients were retrospectively collected and analyzed. There were 142 patients underwent total arch replacement with frozen elephant trunk surgery and 35 patients underwent hybrid debranching surgery. Result The age, the body mass index and the renal insufficiency of patients in the hybrid group were higher than those in the total arch replacement (TAR) group (all P

Abstract Compressed sensing has captured considerable attention of researchers in the past decades. In this paper, with the aid of the powerful null space property, some deterministic recovery conditions are established for the previous $$\ell _{1}$$ ℓ 1 – $$\ell _{1}$$ ℓ 1 method and the $$\ell _{1}$$ ℓ 1 – $$\ell _{2}$$ ℓ 2 method to guarantee the exact sparse recovery when the side information of the desired signal is available. These obtained results provide a useful and necessary complement to the previous investigation of the $$\ell _{1}$$ ℓ 1 – $$\ell _{1}$$ ℓ 1 and $$\ell _{1}$$ ℓ 1 – $$\ell _{2}$$ ℓ 2 methods that are based on the statistical analysis. Moreover, one of our theoretical findings also shows that the sharp conditions previously established for the classical $$\ell _{1}$$ ℓ 1 method remain suitable for the $$\ell _{1}$$ ℓ 1 – $$\ell _{1}$$ ℓ 1 method to guarantee the exact sparse recovery. Numerical experiments on both the synthetic signals and the real-world images are also carried out to further test the recovery performance of the above two methods.

Abstract Convertible bonds are an important segment of the corporate bond market, however, as hybrid instruments, convertible bonds are difficult to value because they depend on variables related to the underlying stock, the fixed-income part, and the interaction between these components. Besides, embedded options, such as conversion, call, and put provisions are often restricted to certain periods, may vary over time, and are subject to additional path-dependent features of the state variables. Moreover, the most challenging problem in convertible bond valuation is the underlying stock return process modeling as it retains various complex statistical properties. In this paper, we propose DeepPricing, a novel data-driven convertible bonds pricing model, which is inspired by the recent success of generative adversarial networks (GAN), to address the above challenges. The method introduces a new financial time-series generative adversarial networks (FinGAN), which is able to reproduce risk-neutral stock return process that retains the unique statistical properties such as the fat-tailed distributions, the long-range dependence, and the asymmetry structure etc., and then transit to its risk-neutral distribution. Thus it is more flexible and accurate to capture the dynamics of the underlying stock return process and keep the rich set of real-world convertible bond specifications compared with previous model-driven models. The experiments on the Chinese convertible bond market demonstrate the effectiveness of DeepPricing model. Compared with the convertible bond market prices, our model has a better convertible bonds pricing performance than both model-driven models, i.e. Black-Scholes, the constant elasticity of variance, GARCH, and the state-of-the-art GAN-based models, i.e. FinGAN-MLP, FinGAN-LSTM. Moreover, our model has a better fitting capacity for higher-volatility convertible bonds and the overall convertible bond market implied volatility smirk, especially for equity-liked convertible bonds, convertible bonds trading in the bull market, and out-of-the-money convertible bonds. Furthermore, the Long-Short and Long-Only investment strategies based on our model earn a significant annualized return with 41.16% and 31.06%, respectively, for the equally-weighted portfolio during the sample period.

High concentrations of ground-level ozone (O3) pose a significant threat to human health. Obtaining high-spatiotemporal-resolution information about ground-level O3 is of paramount importance for O3 pollution control. However, the current monitoring methods have a lot of limitations. Ground-based monitoring falls short in providing extensive coverage, and remote sensing based on satellites is constrained by specific spectral bands, lacking sensitivity to ground-level O3. To address this issue, we combined brightness temperature data from the Himawari-8 satellite with meteorological data and ground-based station data to train four machine learning models to obtain high-spatiotemporal-resolution information about ground-level O3, including Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Among these, the CatBoost model exhibited superior performance, achieving a ten-fold cross-validation R2 of 0.8534, an RMSE of 17.735 μg/m3, and an MAE of 12.6594 μg/m3. Furthermore, all the selected feature variables in our study positively influenced the model. Subsequently, we employed the CatBoost model to estimate averaged hourly ground-level O3 concentrations at a 2 km resolution. The estimation results indicate a close relationship between ground-level O3 concentrations and human activities and solar radiation.

Obtaining a suitable chemical composition for high-entropy alloys (HEAs) with superior mechanical properties and good biocompatibility is still a formidable challenge through conventional trial-and-error methods. Here, based on a large amount of experimental data, a machine learning technique may be used to establish the relationship between the composition and the mechanical properties of the biocompatible HEAs. Subsequently, first-principles calculations are performed to verify the accuracy of the prediction results from the machine learning model. The predicted Young’s modulus and yield strength of HEAs performed very well in the previous experiments. In addition, the effect on the mechanical properties of alloying an element is investigated in the selected Ti-Zr-Hf-Nb-Ta HEA with the high crystal symmetry. Finally, the Ti8-Zr20-Hf16-Nb35-Ta21 HEA predicted by the machine learning model exhibits a good combination of biocompatibility and mechanical performance, attributed to a significant electron flow and charge recombination. This work reveals the importance of these strategies, combined with machine learning and first-principles calculations, on the development of advanced biocompatible HEAs.