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As the standard treatments for cancer, chemotherapy and radiotherapy have been widely applied to clinical practice worldwide. However, the resistance to cancer therapies is a major challenge in clinics and scientific research, resulting in tumor recurrence and metastasis. The mechanisms of therapy resistance are complicated and result from multiple factors. Among them, non-coding RNAs (ncRNAs), along with their modifiers, have been investigated to play key roles in regulating tumor development and mediating therapy resistance within various cancers, such as hepatocellular carcinoma, breast cancer, lung cancer, gastric cancer, etc. In this review, we attempt to elucidate the mechanisms underlying ncRNA/modifier-modulated resistance to chemotherapy and radiotherapy, providing some therapeutic potential points for future cancer treatment.

Bacterial membrane vesicles (MVs) have attracted increasing attention due to their significant roles in bacterial physiology and pathogenic processes. In this review, we provide an overview of the importance and current research status of MVs in regulating bacterial physiology and pathogenic processes, as well as their crucial roles in environmental adaptation and pathogenic infections. We describe the formation mechanism, composition, structure, and functions of MVs, and discuss the various roles of MVs in bacterial environmental adaptation and pathogenic infections. Additionally, we analyze the limitations and challenges of MV-related research and prospect the potential applications of MVs in environmental adaptation, pathogenic mechanisms, and novel therapeutic strategies. This review emphasizes the significance of understanding and studying MVs for the development of new insights into bacterial environmental adaptation and pathogenic processes. Overall, this review contributes to our understanding of the intricate interplay between bacteria and their environment and provides valuable insights for the development of novel therapeutic strategies targeting bacterial pathogenicity.

Influenza A virus (IAV) polymerase protein PB2 has been shown to partially inhibit the host immune response by blocking the induction of interferons (IFNs). However, the IAV PB2 protein that regulates the downstream signaling pathway of IFNs is not well characterized. Here, we report that IAV PB2 protein reduces cellular sensitivity to IFNs, suppressing the activation of STAT1/STAT2 and ISGs. Furthermore, IAV PB2 protein targets mammalian JAK1 at lysine 859 and 860 for ubiquitination and degradation. Notably, the H5 subtype of highly pathogenic avian influenza virus with I283M/K526R mutations on PB2 increases the ability to degrade mammalian JAK1 and exhibits higher replicate efficiency in mammalian (but not avian) cells and mouse lung tissues, and causes greater mortality in infected mice. Altogether, these data describe a negative regulatory mechanism involving PB2-JAK1 and provide insights into an evasion strategy from host antiviral immunity employed by IAV. The PB2 subunit of the RNA polymerase of influenza virus antagonizes interferon signalling. Here, the authors biochemically characterise the molecular interactions that mediate this, and how this impacts viral replication in the context of different influenza subtypes.

Glacier snow line altitude (SLA) at the end of the ablation season is an indicator of the equilibrium line altitude (ELA), which is a key parameter for calculating and assessing glacier mass balance. Here, we present an automated algorithm to classify bare ice and snow cover on glaciers using Landsat series images and calculate the minimum annual glacier snow cover ratio (SCR) and maximum SLA for reference glaciers during the 1985–2020 period in Google Earth Engine. The calculated SCR and SLA values are verified using the observed glacier accumulation area ratio (AAR) and ELA. We select 14 reference glaciers from High Mountain Asia (HMA), the Caucasus, the Alps, and Western Canada, which represent four mountainous regions with extensive glacial development in the northern hemisphere. The SLA accuracy is ~73%, with a mean uncertainty of ±24 m, for 13 of the reference glaciers. Eight of these glaciers yield R2 > 0.5, and the other five glaciers yield R2 > 0.3 for their respective SCR–AAR relationships. Furthermore, 10 of these glaciers yield R2 > 0.5 and the other three glaciers yield R2 > 0.3 for their respective SLA–ELA relationships, which indicate that the calculated SLA from this algorithm provides a good fit to the ELA observations. However, Careser Glacier yields a poor fit between the SLA calculations and ELA observations owing to tremendous surface area changes during the analyzed time series; this indicates that glacier surface shape changes due to intense ablation will lead to a misclassification of the glacier surface, resulting in deviations between the SLA and ELA. Furthermore, cloud cover, shadows, and the Otsu method limitation will further affect the SLA calculation. The post-2000 SLA values are better than those obtained before 2000 because merging the Landsat series images reduces the temporal resolution, which allows the date of the calculated SLA to be closer to the date of the observed ELA. From a regional perspective, the glaciers in the Caucasus, HMA and the Alps yield better results than those in Western Canada. This algorithm can be applied to large regions, such as HMA, to obtain snow line estimates where manual approaches are exhaustive and/or unfeasible. Furthermore, new optical data, such as that from Sentinel-2, can be incorporated to further improve the algorithm results.

Background The development of chemotherapy resistance critically constrains the therapeutic efficacy of docetaxel (DTX) in prostate cancer (PCa). Platycodin D (PD) is isolated from the plant Platycodon grandiflorus , and has been found to possess anti-tumor effect. However, whether PD can enhance the therapeutic effect of DTX on PCa and its related mechanisms have not yet been reported. Methods The effects of PD on PCa cell proliferation, apoptosis, and ferroptosis were examined in vitro. The impact of PD on tumorigenesis was assessed in vivo by utilizing a PCa cell xenograft mouse model. PCR array was used to determine the key gene regulated by PD. Transcriptome sequencing and MeRIP-PCR were implemented to explore the molecular mechanism of METTL16 in ferroptosis. Results We found that PD suppressed cell proliferation and induced cell apoptosis and ferroptosis in PCa cells. In addition, PD enhanced the therapeutic effect of DTX through triggering ferroptosis. PCR array results indicated that METTL16 was a crucial molecule in the regulation of PCa cell ferroptosis by PD, and PD significantly decreased METTL16 expression in PCa cells. Overexpression of METTL16 repressed PD-treated PCa cell ferroptosis. Mechanistically, METTL16 promoted the expression of NUPR1, and overexpression of METTL16 increased the m6A modification level of NUPR1. Conclusions PD promotes PCa cell ferroptosis to enhance the therapeutic effect of DTX in PCa via the METTL16/m6A/NUPR1 axis. Our findings offer a novel strategy for improving the therapeutic efficacy of DTX in PCa.

The performance of the event-related potential (ERP)-based brain–computer interface (BCI) declines when applying it into the real environment, which limits the generality of the BCI. The sound is a common noise in daily life, and whether it has influence on this decline is unknown. This study designs a visual-auditory BCI task that requires the subject to focus on the visual interface to output commands and simultaneously count number according to an auditory story. The story is played at three speeds to cause different workloads. Data collected under the same or different workloads are used to train and test classifiers. The results show that when the speed of playing the story increases, the amplitudes of P300 and N200 potentials decrease by 0.86 μV (p = 0.0239) and 0.69 μV (p = 0.0158) in occipital-parietal area, leading to a 5.95% decline (p = 0.0101) of accuracy and 9.53 bits/min decline (p = 0.0416) of information transfer rate. The classifier that is trained by the high workload data achieves higher accuracy than the one trained by the low workload if using the high workload data to test the performance. The result indicates that the sound could affect the visual ERP-BCI by increasing the workload. The large similarity of the training data and testing data is as important as the amplitudes of the ERP on obtaining high performance, which gives us an insight on how make to the ERP-BCI generalized.

Accurate monitoring of surface temperature and melting on the Greenland Ice Sheet (GrIS) is important for tracking the ice sheet’s mass balance as well as global and Arctic climate change. Using a moderate-resolution-imaging-spectroradiometer (MODIS)-derived land-surface-temperature (LST) data product with a resolution of 1 km from 2000 to 2020, the temporal and spatial variations of annual and seasonal ‘clear-sky’ surface temperature were evaluated. We also monitored summer surface melting and studied the relationship between the mass balance of the ice sheet and changes in surface temperature and melting. The results show that the mean annual LST during the study period is −24.86 ± 5.46 °C, with the highest of −22.48 ± 5.61 °C in 2010 and the lowest temperature of −26.49 ± 5.30 °C in 2015. With the change of season, the spatial variation of the ice-sheet surface temperature changes greatly. 2012 and 2019 experienced the warmest summers (−5.92 ± 4.01 °C and −6.51 ± 3.93 °C), with extreme cumulative melting detected on the ice-sheet surface (89.9% and 89.7%, respectively), and 2002 also experienced a greater extent of melting. But short period of melt in 2002 and 2019 (30.6% and 31.4%, respectively), accounted for a larger proportion, with neither the duration nor intensity of the melt reaching that of 2012. There is a strong correlation between the GrIS surface temperature and its mass balance. By fitting the relationship between surface temperature and mass balance, it was found that 93.83% (6.17%) of the ice-sheet response to surface-temperature change was via surface-mass balance (discharge and basal-mass balance).

Pre-existing Ca 2+ handling abnormalities constitute the arrhythmogenic substrate in patients developing postoperative atrial fibrillation (POAF), a common complication after cardiac surgery. Postoperative interleukin (IL)-6 levels are associated with atrial fibrosis in several animal models of POAF, contributing to atrial arrhythmias. Here, we hypothesize that IL-6-mediated-Ca 2+ handling abnormalities contribute to atrial fibrillation (AF) in sterile pericarditis (SP) rats, an animal model of POAF. SP was induced in rats by dusting atria with sterile talcum powder. Anti-rat-IL-6 antibody (16.7 μg/kg) was administered intraperitoneally at 30 min after the recovery of anesthesia. In vivo electrophysiology, ex vivo optical mapping, western blots, and immunohistochemistry were performed to elucidate mechanisms of AF susceptibility. IL-6 neutralization ameliorated atrial inflammation and fibrosis, as well as AF susceptibility in vivo and the frequency of atrial ectopy and AF with a reentrant pattern in SP rats ex vivo . IL-6 neutralization reversed the prolongation and regional heterogeneity of Ca 2+ transient duration, relieved alternans, reduced the incidence of discordant alternans, and prevented the reduction and regional heterogeneity of the recovery ratio of Ca 2+ transient. In agreement, western blots showed that IL-6 neutralization reversed the reduction in the expression of ryanodine receptor 2 (RyR2) and phosphorylated phospholamban. Acute IL-6 administration to isolated rat hearts recapitulated partial Ca 2+ handling phenotype in SP rats. In addition, intraperitoneal IL-6 administration to rats increased AF susceptibility, independent of fibrosis. Our results reveal that IL-6-mediated-Ca 2+ handling abnormalities in SP rats, especially RyR2-dysfunction, independent of IL-6-induced-fibrosis, early contribute to the development of POAF by increasing propensity for arrhythmogenic alternans.

Chlorophyll is crucial for pear tree growth and fruit quality. In order to integrate the unmanned aerial vehicle (UAV) multispectral vegetation indices and textural features to realize the estimation of the SPAD value of pear leaves, this study used the UAV multispectral remote sensing images and ground measurements to extract the vegetation indices and textural features, and analyze their correlation with the SPAD value of leaves during the fruit expansion period of the pear tree. Finally, four machine learning methods, namely XGBoost, random forest (RF), back-propagation neural network (BPNN), and optimized integration algorithm (OIA), were used to construct inversion models of the SPAD value of pear trees, with different feature inputs based on vegetation indices, textural features, and their combinations, respectively. Moreover, the differences among these models were compared. The results showed the following: (1) both vegetation indices and textural features were significantly correlated with SPAD values, which were important indicators for estimating the SPAD values of pear leaves; (2) combining vegetation indices and textural features significantly improved the accuracy of SPAD value estimation compared with a single feature type; (3) the four machine learning algorithms demonstrated good predictive ability, and the OIA model outperformed the single model, with the model based on the OIA inversion model combining vegetation indices and textural features having the best accuracy, with R2 values of 0.931 and 0.877 for the training and validation sets, respectively. This study demonstrated the efficacy of integrating multiple models and features to accurately invert SPAD values, which, in turn, supported the refined management of pear orchards.

To extend the time span of the microwave (MW) land surface temperature (LST) dataset in China, this study proposed an optimized empirical algorithm for Advanced Microwave Scanning Radiometer 2 (AMSR2) LST retrieval based on the algorithm for its predecessor, the AMSR-Earth Observing System (AMSR-E). A modified comprehensive classification system of environmental variables (CCSEV) that considered the impact of landform, landcover, atmospheric conditions, and solar radiation on the variation of LST was first constructed, and the LST for each class in the CCSEV was then retrieved through stepwise regression using the brightness temperature in different AMSR2 channels. The results indicate that the annual RMSE of the AMSR2 LST, compared to the reference Moderate Resolution Imaging Spectroradiometer (MODIS) LST from 2012 to 2020, varies between 3.26 K and 3.61 K in the daytime and 2.76 K and 2.96 K in the nighttime, respectively. The RMSE of the AMSR2 LST compared to the field measurements at the sites of the Beidahe river basin and Naqu regions varies between 4.16 K and 5.26 K in the daytime and 2.4 K and 5.17 K in the nighttime. The accuracy is relatively low in the warmer months and daytime due to the stronger solar radiation, and is also relatively low in western China due to the dominate highly fluctuating topography and barren and arid landcover. Generally, the accuracy of the AMSR2 LST is comparable with that of the AMSR-E LST retrieved by the predecessor algorithm, which facilitates coherent long-term applications using AMSR series LST datasets.