Demethylation of the miR-146a promoter by 5-Aza-2’-deoxycytidine correlates with delayed progression of castration-resistant prostate cancer

BACKGROUND: Androgen deprivation therapy is the primary strategy for the treatment of advanced prostate cancer; however, after an initial regression, most patients will inevitably develop a fatal androgen-independent tumor. Therefore, understanding the mechanisms of the transition to androgen independence prostate cancer is critical to identify new ways to treat older patients who are ineligible for conventional chemotherapy. METHODS: The effects of 5-Aza-2’-deoxycytidine (5-Aza-CdR) on the viability and the apoptosis of the androgen-dependent (LNCaP) and androgen-independent (PC3) cell lines were examined by MTS assay and western blot analysis for the activation of caspase-3. The subcutaneous LNCaP xenografts were established in a nude mice model. MiR-146a and DNMTs expressions were analyzed by qRT-PCR and DNA methylation rates of LINE-1 were measured by COBRA-IRS to determine the global DNA methylation levels. The methylation levels of miR-146a promoter region in the different groups were quantified by the bisulfite sequencing PCR (BSP) assay. RESULTS: We validated that 5-Aza-CdR induced cell death and increased miR-146a expression in both LNCaP and PC3 cells. Notably, the expression of miR-146a in LNCaP cells was much higher than in PC3 cells. MiR-146a inhibitor was shown to suppress apoptosis in 5-Aza-CdR-treated cells. In a castrate mouse LNCaP xenograft model, 5-Aza-CdR significantly suppressed the tumors growth and also inhibited prostate cancer progression. Meanwhile, miR-146a expression was significantly enhanced in the tumor xenografts of 5-Aza-CdR-treated mice and the androgen-dependent but not the androgen-independent stage of castrated mice. In particular, the expression of miR-146a was significantly augmented in both stages of the combined treatment (castration and 5-Aza-CdR). Additionally, the methylation percentage of the two CpG sites (−444 bp and −433 bp), which were around the NF-κB binding site at miR-146a promoter, showed the lowest methylation levels among all CpG sites in the combined treatment tumors of both stages. CONCLUSION: Up-regulating miR-146a expression via the hypomethylation of the miR-146a promoter by 5-Aza-CdR was correlated with delayed progression of castration-resistant prostate cancers. Moreover, site-specific DNA methylation may play an important role in miR-146a expression in androgen-dependent prostate cancer progression to androgen-independent prostate cancer and therefore provides a potentially useful biomarker for assessing drug efficacy in prostate cancer

Mirror: A Universal Framework for Various Information Extraction Tasks

Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations. Meanwhile, this divergence leads to information waste and increases difficulties in building complex applications in real scenarios. Recent studies often formulate IE tasks as a triplet extraction problem. However, such a paradigm does not support multi-span and n-ary extraction, leading to weak versatility. To this end, we reorganize IE problems into unified multi-slot tuples and propose a universal framework for various IE tasks, namely Mirror. Specifically, we recast existing IE tasks as a multi-span cyclic graph extraction problem and devise a non-autoregressive graph decoding algorithm to extract all spans in a single step. It is worth noting that this graph structure is incredibly versatile, and it supports not only complex IE tasks, but also machine reading comprehension and classification tasks. We manually construct a corpus containing 57 datasets for model pretraining, and conduct experiments on 30 datasets across 8 downstream tasks. The experimental results demonstrate that our model has decent compatibility and outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings. The code, model weights, and pretraining corpus are available at https://github.com/Spico197/Mirror .Comment: Accepted to EMNLP23 main conferenc

PriWhisper: Enabling Keyless Secure Acoustic Communication for Smartphones

Short-range wireless communication technologies have been used in many security-sensitive smartphone applications and services such as contactless micro payment and device pairing. Typically, the data confidentiality of the existing short-range communication systems relies on so-called key-exchange then encryption mechanism. Namely, both parties need to spend extra communication to establish a common key before transmitting their actual messages, which is inefficient, especially for short communication sessions. In this work, we present PriWhisper -- a keyless secure acoustic short-range communication system for smartphones. It is designed to provide a purely software-based solution to secure smartphone short-range communication without the key agreement phase. PriWhisper adopts the emerging friendly jamming technique from radio communication for data confidentiality. The system prototype is implemented and evaluated on several Android smartphone platforms for efficiency and usability. We theoretically and experimentally analyze the security of our proposed acoustic communication system against various passive and active adversaries. In particular, we also study the (in)separability of the data signal and jamming signal against Blind Signal Segmentation (BSS) attacks such as Independent Component Analysis (ICA). The result shows that PriWhisper provides sufficient security guarantees for commercial smartphone applications and yet strong compatibilities with most legacy smartphone platforms

A case study on the impacts of future climate change on soybean yield and countermeasures in Fujin city of Heilongjiang province, China

Global climate change poses a great impact on crop growth, development and yield. Soybean production in Northeast China, which is one of the traditional dominant soybean production areas in China, is of great significance for developing the domestic soybean industry and reducing dependence on imported soybeans. Therefore, it is crucial to evaluate the impacts of future climate change on soybean yield in Northeast China, and to propose reasonable adaptation measures. In this study, we took Fujin city of Heilongjiang province in Northeast China as an example, and used the CROPGRO-soybean model in DSSAT (Decision Support System for Agrotechnology Transfer) to simulate the impacts of future climate change on soybean yield in the four periods of the 2020s (2021-2030), 2030s (2031-2040), 2040s (2041-2050) and 2050s (2051-2060) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), and further determine the best agronomic management practices. The results showed that the calibrated and validated model is suitable for simulating soybean in the study area. By analyzing the meteorological data under future climate scenarios RCP4.5 and RCP8.5 from the PRECIS regional climate model, we found that the average temperature, cumulative precipitation and cumulative solar radiation would mostly increase during the growing season in Fujin city of Heilongjiang province. Combined with the model simulation results, it is shown that under the effect of CO2 fertilization, future climate change will have a positive impact on soybean yield. Compared to the baseline (1986-2005), the soybean yield would increase by 0.6% (7.4%), 3.3% (5.1%), 6.0% (16.8%) and 12.3% (20.6%) in the 2020s, 2030s, 2040s and 2050s under RCP4.5 (RCP8.5).Moreover, the optimal sowing dates and the optimal supplemental irrigation amount under RCP4.5 (RCP8.5) are May 10 (May 5) and 50 mm (40mm), respectively. Under future climate conditions, the agronomic management practices, such as advancing the sowing date and supplementary irrigation in the key stage of soybean growth would increase soybean yield and make soybean growth more adaptable to future climate change

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression

Abstract State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships between features, and tracking SOH attenuations effectively. However, traditional GPR methods based on explicit functions require multiple screenings of optimal mean and covariance functions, which results in data scarcity and increased time consumption. In this study, we propose a GPR-implicit function learning, which is a prior knowledge algorithm for calculating mean and covariance functions from a preliminary data set instead of screening. After introducing the implicit function, the average root mean square error (Average RMSE) is 0.0056 F and the average mean absolute percent error (Average MAPE) is 0.6%, where only the first 5% of the data are trained to predict the remaining 95% of the cycles, thereby decreasing the error by more than three times than previous studies. Furthermore, less cycles (i.e., 1%) are trained while still obtaining low prediction errors (i.e., Average RMSE is 0.0094 F and Average MAPE is 1.01%). This work highlights the strength of GPR-implicit function model for SOH prediction of energy storage devices with high precision and limited property data

Constitutive Modeling and Hot Deformation Behavior of Duplex Structured Mg-Li-Al-Sr Alloy

Hot deformation behavior of an as-extruded duplex structured Mg-9Li-3Al-2.5Sr alloy is investigated via hot compression tests conducted at 200-350°C with strain rate of 0.001-1 s-1. The flow behavior of Mg-9Li-3Al-2.5Sr alloy can be described accurately by hyperbolic sine constitutive equation and the average activation energy for deformation is calculated as 143.5 kJ/mol. Based on a dynamic materials model, the processing maps of Mg-9Li-3Al-2.5Sr alloy which describe the variation of power dissipation efficiency are constructed as a function of temperature and strain rate. The processing maps exhibit an area of discontinuous dynamic recrystallization occurring at 280-300°C with strain rate of 0.001-0.01 s-1, which corresponds to the optimum hot working conditions

Unraveling Morphology and Phase Control of NaLnF₄ Upconverting Nanocrystals

Near-infrared to visible upconversion NaLnF₄ (Ln = Pr to Lu and Y) nanocrystals hold great promise for use in biological labeling and imaging due to their intrinsic characteristics such as high chemical stability, nonblinking and sharp bandwidth luminescence, large Stokes shift, as well as appropriate and abundant energy states necessary for efficient energy transfer to achieve near-infrared to visible upconversion emission. However, there are still significant hurdles in the control of size, shape, and phase of NaLnF₄ nanocrystals due to limited understanding of their growth behavior and growth mechanism. Here we describe an approach to facilely control the shape and phase of upconversion NaLnF₄ nanocrystals. Besides the optimization of synthetic conditions such as organic surfactant and reaction temperature, we have developed a program, in which a correlation between the ionic radius of lanthanide ions and the shape/phase of NaLnF₄ nanocrystals is established, to precisely control the shape and phase of NaLnF₄ nanocrystals by precisely tuning the mean ionic radius of lanthanide ions through lanthanide doping. The availability of such upconverting nanocrystals with controlled size, shape, and phase provides a platform for applications ranging from biological imaging, biological sensing, and three-dimensional displays