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Triple-negative breast cancer (TNBC) has been considered a huge clinical unmet need due to its aggressive progression and highly frequent metastasis. mRNA therapeutics supply a potential and versatile immunotherapy of oncology treatment. Here, we developed α-lactalbumin mRNA-lipid nanoparticles (α-LNP) as a potential therapeutical strategy for TNBC. The α-LNP induced the specific IgG antibodies and activated IFN γ-secreting-T cells in vivo. Additionally, the safety of α-LNP also had been demonstrated in vivo. When vaccinated prior to tumor implantation, α-LNP showed a preventive effect against 4T1 tumor growth and extended the survival of the tumor model by activating the memory immune responses. Furthermore, α-LNP administration in combination with surgical removal of neoplasm effectively inhibited the progression and metastasis in the TNBC model. Taken together, our results indicate that the α-LNP vaccine is a promising novel treatment for both therapeutics and prophylactics in TNBC.

The nanocomposite (BC@Cu-BTC) composed of copper-based organic skeleton (Cu-BTC) and bamboo charcoal (BC) was successfully fabricated. The large specific surface area of Cu-BTC (1245.25 m2/g) and the synergistic effect with BC are beneficial for enhancing the current signal used to detect dopamine in aqueous solution. Additionally, cyclic voltammetry studies have shown that diffusion regulates the kinetic process of dopamine on the surface of the BC@Cu-BTC electrode, which is confirmed by the Warburg impedance in the low frequency region. The results show that the sensor has a very low detection limit (1.572 μmol/L at S/N = 3) and a wide detection range (1 ∼ 100 μmol/L). Based on excellent selectivity, stability, repeatability and reproducibility, the analytical performance of dopamine detection is evaluated. Finally, when BC@Cu-BTC is used as the electrode material, dopamine is detected in the urine, and the results are satisfactory.

Pennisetum alopecuroides (L.), one of the important exotic plants, gives great economic value to animal husbandry in China. In order to study the distribution of Pennisetum alopecuroides (L.) in China and its response to climate change, based on the distribution records of Pennisetum alopecuroides (L.), our study used the Maximum Entropy (MaxEnt) model and geographic information system (GIS) methods, combined with environmental factors such as climate and terrain, to predict the potential distribution areas suitable for Pennisetum alopecuroides (L.) under current and future climate scenarios. The results showed that annual precipitation was the most important factor affecting the distribution of Pennisetum alopecuroides (L.). In current climate scenario, the total area of suitable for Pennisetum alopecuroides (L.) growth was about 576.5 km2, accounting for about 60.5% of the total land area of China. Among all the suitable areas, the area of low, middle and high fitness areas accounted for 5.69%, 20.55% and 33.81% of the total area respectively. In future climate scenarios (RCP4.5), the suitable area of Pennisetum alopecuroides (L.) would decrease with climate change, showing a clear trend of northward expansion in China. A concentrated and contiguous distribution region for Pennisetum alopecuroides (L.) would appear in northeast China. The model was tested by the receiver operating characteristic curve (ROC), and the average area under the curve of ROC of the training set was 0.985, which was reliable. This work provided an important reference and theoretical basis for the efficient utilization and plant regionalization of Pennisetum alopecuroides (L.) in future.

Abstract Background The APETALA2/ethylene response factor (AP2/ERF) family are important regulatory factors involved in plants’ response to environmental stimuli. However, their roles in salt tolerance in Lotus corniculatus remain unclear. Results Here, the key salt-responsive transcription factor LcERF056 was cloned and characterised. LcERF056 belonging to the B3–1 (IX) subfamily of ERFs was considerably upregulated by salt treatment. LcERF056-fused GFP was exclusively localised to nuclei. Furthermore, LcERF056- overexpression (OE) transgenic Arabidopsis and L. corniculatus lines exhibited significantly high tolerance to salt treatment compared with wild-type (WT) or RNA interference expression (RNAi) transgenic lines at the phenotypic and physiological levels. Transcriptome analysis of OE, RNAi, and WT lines showed that LcERF056 regulated the downstream genes involved in several metabolic pathways. Chromatin immunoprecipitation-quantitative polymerase chain reaction (ChIP-qPCR) and yeast one-hybrid (Y1H) assay demonstrated that LcERF056 could bind to cis-element GCC box or DRE of reactive oxygen species (ROS)-related genes such as lipid-transfer protein, peroxidase and ribosomal protein. Conclusion Our results suggested that the key regulator LcERF056 plays important roles in salt tolerance in L. corniculatus by modulating ROS-related genes. Therefore, it may be a useful target for engineering salt-tolerant L. corniculatus or other crops.

In vitro preparation of mRNA is a key step for mRNA therapeutics. The widely used T7 RNA polymerase (RNAP) was shown to have many by-products during in vitro transcription (IVT) process, among which double-stranded RNA (dsRNA) is the major by-product to activate the intracellular immune response. Here, we describe the use of a new VSW-3 RNAP that reduced dsRNA production during IVT and the resulting mRNA exhibited low inflammatory stimulation in cells. Compared to T7 RNAP transcripts, these mRNA exhibited superior protein expression levels, with an average of 14-fold increase in Hela cells and 5-fold increase in mice. In addition, we found that VSW-3 RNAP did not require modified nucleotides to improve protein production of IVT products. Our data suggest that VSW-3 RNAP could be a useful tool for mRNA therapeutics.

The engine system is critical for a marine vehicle, and its performance significantly affects the efficiency and safety of the whole ship. Due to the harsh working environment and the complex system structure, a marine system is prone to have many kinds of novelties and faults. Timely detection of faults via effective condition monitoring is vital for such systems, avoiding serious damage and economic loss. However, it is difficult to realize online monitoring because of the limitations of measurement and health monitoring methods. In this paper, a marine engine system simulator is set up with enhanced sensory placement for static and dynamic data collection. The test rig and processing for static and dynamic data are described. Then, a physics-based multivariate modeling method is proposed for the health monitoring of the system. Case studies are carried out considering the misfire fault and the exhaust valve leakage fault. In the misfire fault test, the exhaust gas temperature of the misfired cylinder dropped from the confidence interval 100–150 °C to 70–80 °C and the head vibration features decreased from the confidence interval 900–1300 m/s2 to around 200–300 m/s2. For the exhaust valve leakage fault, the engine body vibration main bearing impact RMS increased nearly 10 times. Comparisons between the model-predicted confidence interval and measured data reveal that the proposed model based on the fault-related static and dynamic features successfully identified the two faults and their positions, proving the effectiveness of the proposed framework.

Background: Management of patients with prostate cancer and bone metastatic disease remains a major clinical challenge. Loss or mutation of p53 has been identified to be involved in the tumor progression and metastasis. Nevertheless, direct evidence of a specific role for wild-type p53 (wt-p53) in bone metastasis and the mechanism by which this function is mediated in prostate cancer remain obscure.Methods: The expression and protein levels of wt-53, AIP4, and CXCR4 in prostate cancer cells and clinical specimens were assessed by real-time PCR, immunohistochemistry and western blot analysis. The role of wt-p53 in suppressing aggressive and metastatic tumor phenotypes was assessed using in vitro transwell chemotaxis, wound healing, and competitive colocalization assays. Furthermore, whether p53 deletion facilitates prostate cancer bone-metastatic capacity was explored using an in vivo bone-metastatic model. The mechanistic model of wt-p53 in regulating gene expression was further explored by a luciferase reporter assay and chromatin immunoprecipitation (ChIP) assay.Results: Our findings revealed that wt-p53 suppressed the prostate cancer cell migration rate, chemotaxis and attachment toward the osteoblasts in vitro. The bone-metastatic model showed that deletion of wt-p53 remarkably increased prostate cancer bone-metastatic capacity in vivo. Mechanistically, wt-p53 could induce the ligand-induced degradation of the chemokine receptor CXCR4 by transcriptionally upregulating the expression of ubiquitin ligase AIP4. Treatment with the CXCR4 inhibitor AMD3100 or transduction of the AIP4 plasmid abrogated the pro-bone metastasis effects of TP53 deletion.Conclusion: Wt-p53 suppresses the metastasis of prostate cancer cells to bones by regulating the CXCR4/CXCL12 activity in the tumor cells/bone marrow microenvironment interactions. Our findings suggest that targeting the wt-p53/AIP4/CXCR4 axis might be a promising therapeutic strategy to manage prostate cancer bone metastasis.

Developing electroactive materials with superior sensitivity, selectivity, and so on is an important issue for target ions detection. Hierarchically heterostructured nanomaterials represent a kind of promising active materials for electrochemical sensors due to the synthetic effect between different components and the high surface area. Herein, heterostructured UIO-66-NH2/CNTs nanocomposites have been successfully synthesized via the solvothermal method and investigated for the detection of Pb2+ ions. The SEM, TEM, XRD and FTIR collectively confirmed that the UIO-66-NH2 was successfully loaded on the surface of the CNTs. The specific surface areas of the UIO-66-NH2/CNTs are 4–7 times higher than that of activated CNTs and more mesoporous structures appear in composite materials. The electrochemical impedance spectroscopy measurements showed that the concentration of CNTs could influence the electron mobility and the UIO-66-NH2/CNTs-15 exhibits the lowest charge transfer resistance and the highest capacitive property. The cyclic voltammetry study towards K3[Fe(CN)6] further demonstrated that the CNTs can improve the conductivity of the composites especially for the UIO-66-NH2/CNTs-15, which was conducive to the sensing behavior confirmed by follow-up performance experiments. Under the optimized condition, UIO-66-NH2/CNTs-15 exhibits the best sensing performance towards Pb2+. Correspondingly, a high sensitivity of 1.64 μA (μmol/L)−1 and a low detection limitation of 0.206 μmol/L (S/N = 3) are achieved. Meanwhile, a wide detection range, excellent selectivity, repeatability, and stability are also obtained. The recovery experiment by standard addition method is also carried out and a satisfactory result has been attained indicating a promising sensor for practical application.

Wilted (around 35% DM) or un-wilted (around 35% DM) Italian ryegrass treated with three additives (formic acid, FA; Lactobacillus plantarum, LP; Lactobacillus buchneri, LB) was utilized to evaluate the effects of the dry matter (DM) contents on the microbial community and fermentation characteristics, which was ensiled for 60 days in a laboratory-scale silo, followed by 3 days of aerobic exposure. Significantly lower pH and higher lactic acid (LA) contents were observed in the LP-treated group ensiled at both DM contents (differences were significant when p < 0.05). The contents of LA, acetic acid (AA), numbers of lactic acid bacteria (LAB) and ammonia nitrogen (NH3-N) in the FA-treated group were significantly lower than those in other treatments (p < 0.05). L. buchneri was the dominant bacteria after 60 days fermentation, while Enterobacteria became prevalent after 3 days of aerobic exposure. L. buchneri was found in the LB-treated group with higher acetic acid. Although the best fermentation quality was observed in the LP-treated silages, the aerobic stability was lowest compared to other groups (p < 0.05). In conclusion, our findings suggest that the DM content of Italian ryegrass affected its epiphytic microbial community and the effectiveness of the different type of additives. Formic acid was more suitable for un-wilted Italian ryegrass silage, L. plantarum had a better effect in wilted Italian ryegrass silage, and L. buchneri prolonged the aerobic stability of Italian ryegrass. DM content and purpose of ensiling should be the key factors for choosing different types of additives for Italian ryegrass silage.

The wide use of different alternative fuels (AL) has led to challenges to the internal combustion (IC) engine tribology. To avoid any unpredicted damages to lubrication joints by using AL fuels, this study aims to accurately evaluate the influences of alternative fuels on the tribological behavior of IC engines. Recent achievements of the acoustic emission (AE) mechanism in sliding friction provide an opportunity to explain the tribological AE responses on engines. The asperity–asperity–collision (AAC) and fluid–asperity–shearing (FAS) mechanisms were applied to explain the AE responses from the piston ring and cylinder liner system. A new adaptive threshold–wavelet packets transform (WPT) method was developed to extract tribological AE features. Experimental tests were conducted by fueling three fuels: pure diesel (PD), biodiesel (BD), and Fischer–Tropsch (F–T) diesel. The FAS–AE indicators of biodiesel and F–T diesel show a tiny difference compared to the baseline diesel using two types of lubricants. Biodiesel produces more AAC impacts with higher AAC–AE responses than F–T diesel, which occurs at high speeds due to high temperatures and more particles after combustion than diesel. This new algorithm demonstrated the high performance of using AE signals in monitoring the tribological impacts of alternative fuels on engines.

The problem of timely detecting the engine faults that make engine operating parameters exceed their control limits has been well-solved. However, in practice, a fault of a diesel engine can be present with weak signatures, with the parameters fluctuating within their control limits when the fault occurs. The weak signatures of engine faults bring considerable difficulties to the effective condition monitoring of diesel engines. In this paper, a multivariate statistics-based fault detection approach is proposed to monitor engine faults with weak signatures by taking the correlation of various parameters into consideration. This approach firstly uses principal component analysis (PCA) to project the engine observations into a principal component subspace (PCS) and a residual subspace (RS). Two statistics, i.e., Hotelling’s T 2 and Q statistics, are then introduced to detect deviations in the PCS and the RS, respectively. The Hotelling’s T 2 and Q statistics are constructed by taking the correlation of various parameters into consideration, so that faults with weak signatures can be effectively detected via these two statistics. In order to reasonably determine the control limits of the statistics, adaptive kernel density estimation (KDE) is utilized to estimate the probability density functions (PDFs) of Hotelling’s T 2 and Q statistics. The control limits are accordingly derived from the PDFs by giving a desired confidence level. The proposed approach is demonstrated by using a marine diesel engine. Experimental results show that the proposed approach can effectively detect engine faults with weak signatures.

Internal combustion (IC) engine based powertrains are one of the most commonly used transmission systems in various industries such as train, ship and power generation industries. The powertrains, acting as the cores of machinery, dominate the performance of the systems; however, the powertrain systems are inevitably degraded in service. Consequently, it is essential to monitor the health of the powertrains, which can secure the high efficiency and pronounced reliability of the machines. Conventional vibration based monitoring approaches often require a considerable number of transducers due to large layout of the systems, which results in a cost-intensive, difficultly-deployed and not-robust monitoring scheme. This study aims to develop an efficient and cost-effective approach for monitoring large engine powertrains. Our model based investigation showed that a single measurement at the position of coupling is optimal for monitoring deployment. By using the instantaneous angular speed (IAS) obtained at the coupling, a novel fault indicator and polar representation showed the effective and efficient fault diagnosis for the misfire faults in different cylinders under wide working conditions of engines; we also verified that by experimental studies. Based on the simulation and experimental investigation, it can be seen that single IAS channel is effective and efficient at monitoring the misfire faults in large powertrain systems.

To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively.

Colorectal cancer represents a lethal disease that has raised concern and has attracted significant attention. Adenocarcinoma is the most common type of colorectal cancer (CRC). MicroRNAs are thought to be potential biomarkers of CRC. Many researchers have focused on the expression pattern of miRNAs in CRC. However, previous studies did not pay particular attention to the effects of the degree of differentiation of the cancer with respect to the miRNA expression profile. First, this study compared the expression level of 1547 miRNAs by qRT-PCR in Colorectal adenocarcinoma tissues to that in paired normal tissues. In all, 93 miRNAs were identified that were significantly dysregulated in Colorectal adenocarcinoma relative to normal tissues (P